SYSTEMS AND METHODS FOR EXPERT SYSTEMS FOR UNDERBALANCED DRILLING OPERATIONS USING BAYESIAN DECISION NETWORKS
Systems and methods are provided for an underbalanced drilling (UBD) expert system that provides underbalanced drilling recommendations, such as best practices. The UBD expert system may include one or more Bayesian decision network (BDN) model that receive inputs and output recommendations based on Bayesian probability determinations. The BDN models may include: a general UBD BDN model, a flow UBD BDN model, a gaseated (i.e., aerated) UBD BDN model, a foam UBD BDN model, a gas (e.g., air or other gases) UBD BDN model, a mud cap UBD BDN model, an underbalanced liner drilling (UBLD) BDN model, an underbalanced coil tube (UBCT) BDN model, and a snubbing and stripping BDN model.
This application claims priority to U.S. Provisional Patent Application No. 61/722,027 filed on Nov. 2, 2012, entitled “Systems and Methods for Expert Systems for Underbalanced Drilling Operations Using Bayesian Decision Networks,” the disclosure of which is hereby incorporated by reference in its entirety.
BACKGROUND OF THE INVENTION1. Field of the Invention
This invention relates generally to the drilling and extraction of oil, natural gas, and other resources, and more particularly to evaluation and selection of underbalanced drilling systems.
2. Description of the Related Art
Oil, gas, and other natural resources are used for numerous energy and material purposes. The search for extraction of oil, natural gas, and other subterranean resources from the earth may cost significant amounts of time and money. Once a resource is located, drilling systems may be used to access the resources, such as by drilling into various geological formations to access deposits of such resources. The drilling systems rely on numerous components and operational techniques to reduce cost and time and maximize effectiveness. For example, drill strings, drill bits, drilling fluids, and other components may be selected to achieve maximum effectiveness for a formation and other parameters that affect the drilling system. Typically, many years of field experience and laboratory work are used to develop and select the appropriate components and operational practices for a drilling system. However, these techniques may be time-consuming and expensive. Moreover, such techniques may produce inconsistent results and may not incorporate recent changes in practices and opinions regarding the drilling systems.
SUMMARY OF THE INVENTIONVarious embodiments of methods and systems for expert systems for determining underbalanced drilling operations using Bayesian decision networks are provided herein. In some embodiments, a system is provided that includes one or more processors and a non-transitory tangible computer-readable memory. The non-transitory tangible computer-readable memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes an underbalanced drilling Bayesian decision network (BDN) model. The underbalanced drilling BDN model includes a first section having a formation indicators uncertainty node configured to receive one or more formation indicators from the one or more inputs, a formation considerations decision node configured to receive one or more formation considerations from the one or more inputs, and a first consequences node dependent on the formation indicators uncertainty node and the formation considerations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from one or more formation indicators and the one or more formation considerations. The underbalanced BDN model includes a second section having a planning phases uncertainty node configured to receive one or more planning phases from the one or more inputs, a planning phases recommendations decision node configured to receive one or more planning phases recommendations from the one or more inputs, and a second consequences node dependent on the planning phases uncertainty node and the planning phases recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from one or more planning phases and the one or more planning phases recommendations. Finally, the underbalanced drilling BDN model also includes a third section having a an equipment requirements uncertainty node configured to receive one or more equipment requirements from the one or more inputs, an equipment recommendations decision node configured to receive one or more equipment recommendations from the one or more inputs, and a third consequences node dependent on the equipment requirements uncertainty node and the equipment recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more equipment requirements and the one or more equipment recommendations.
In some embodiments, a computer-implemented method for an underbalanced drilling expert system having an underbalanced drilling Bayesian decision network (BDN) model is provided. The method includes receiving one or more inputs and providing the one or more nodes of a first section of the underbalanced drilling BDN model. The one or more nodes include a formation indicators uncertainty node and a formation considerations decision node. Additionally, the method further includes determining one or more underbalanced drilling recommendations at a consequences node of the first section of the underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs and providing the one or more underbalanced drilling recommendations to a user.
Additionally, in some embodiments, a system having one or more processors and a non-transitory tangible computer-readable memory is provided. The memory the memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes a flow underbalanced drilling Bayesian decision network (BDN) model. The flow underbalanced drilling BDN model includes a first section having a tripping types uncertainty node configured to receive one or more tripping types from the one or more inputs, a permeability level uncertainty node configured to receive one or more permeability levels from the one or more inputs, a tripping options decision node configured to receive one or more tripping options from the one or more inputs, and a first consequences node dependent on the tripping uncertainty node, the permeability level uncertainty node, and the tripping options decision node and configured to output the one or more flow underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more tripping types, the one or more permeability levels, and the one or more tripping options. The flow underbalanced drilling BDN model also includes a second section having a connection types uncertainty node configured to receive one or more connection types from the one or more inputs, a connection options decision node configured to receive one or more connection options from the one or more inputs, and a second consequences node dependent on the connection uncertainty node and the connection options decision node and configured to output the one or more flow underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more connection types and the one or more connection options. Finally, the foam underbalanced drilling BDN model includes a third section having a flow drilling types uncertainty node configured to receive one or more flow drilling types from the one or more inputs, a flow drilling options decision node configured to receive one or more flow drilling options from the one or more inputs, and a third consequences node dependent on the flow drilling uncertainty node and the flow drilling options decision node and configured to output the one or more flow underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more flow drilling types and the one or more flow drilling options.
Further, in some embodiments a computer-implemented method for an underbalanced drilling expert system having a flow underbalanced drilling (UBD) Bayesian decision network (BDN) model is provided. The method includes receiving one or more inputs and providing the one or more inputs to one or more nodes of a first section of the flow underbalanced drilling BDN model. The one or more nodes include a tripping uncertainty node configured to receive one or more tripping types, a permeability level uncertainty node configured to receive one or more permeability levels, and a tripping options decision node a tripping options decision node configured to receive one or more tripping options. The method further includes determining one or more underbalanced drilling recommendations at a consequences node of the first section of the flow underbalanced drilling BDN model by calculating one or more Bayesian probabilities based on the one or more inputs and providing the one or more underbalanced drilling recommendations to a user.
Additionally, in some embodiments a system is provided having one or more processors and a non-transitory tangible computer-readable memory accessible by the one or more processors. The memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes a gaseated underbalanced drilling Bayesian decision network (BDN) model. The gaseated underbalanced drilling BDN model includes a first section having a gas injection process uncertainty node configured to receive one or more gas injection process types from the one or more inputs, a gas injection processes characteristics decision node configured to receive one or more gas injection process characteristics from the one or more inputs, and a first consequences node dependent on the gas injection process uncertainty node and the gas infection processes considerations decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas injection process types and the one or more gas injection process characteristics. The gaseated underbalanced drilling BDN model also includes a second section having a fluid volume limits uncertainty node configured to receive one or more fluid volume limits from the one or more inputs, a fluid volume limits requirements decision node configured to receive one or more fluid volume limits requirements from the one or more inputs, and a second consequences node dependent on the fluid volume limits uncertainty node and the fluid volume limits requirements decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more fluid volume limits and the one or more fluid volume limits requirements. Additionally, the gaseated underbalanced drilling BDN model includes a third section having a kick type uncertainty node configured to receive one or more kick types from the one or more inputs, a kicks recommendations decision node configured to receive one or more kicks recommendations from the one or more inputs, and a third consequences node dependent on the kick type uncertainty node and the kicks recommendations decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more kick types and the one or more kicks recommendations. Finally, the gaseated underbalanced drilling BDN model includes a fourth section having an operational considerations uncertainty node configured to receive one or more operational considerations from the one or more inputs, an operational recommendations decision node configured to receive one or more operational recommendations from the one or more inputs, and a fourth consequences node dependent on the operational considerations uncertainty node and the operational recommendations decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more operational recommendations and the one or more operational recommendations.
In some embodiments, a computer-implemented method for an underbalanced drilling expert system having a gaseated underbalanced drilling Bayesian decision network (BDN) model is provided. The method includes receiving one or more inputs and providing the one or more inputs to one or more nodes of a first section of the gaseated underbalanced drilling (UBD) BDN model. The one or more nodes include a gas injection process uncertainty node configured to receive one or more gas injection process types, and a gas injection processes characteristics decision node configured to receive one or more gas injection process characteristics. Additionally, the method includes determining one or more underbalanced drilling recommendations at a consequences node of the first section of the gaseated UBD BDN model by calculating one or more Bayesian probabilities based on the one or more inputs and providing the one or more underbalanced drilling recommendations to a user.
Moreover, in some embodiments a system is provided having one or more processors and a non-transitory tangible computer-readable memory accessible by the one or more processors. The memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes a foam underbalanced drilling (UBD) Bayesian decision network (BDN) model. The foam underbalanced drilling BDN model includes a first section having a foam systems considerations uncertainty node configured to receive one or more foam systems considerations from the one or more inputs, a foam systems recommendations decision node configured to receive one or more foam systems recommendations from the one or more inputs and a first consequences node dependent on the foam systems considerations uncertainty node and the foam systems recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more foam systems considerations and the one or more foam systems recommendations. The foam underbalanced drilling BDN model also includes a second section having a foam systems designs uncertainty node configured to receive one or more foam system designs from the one or more inputs, a foam system designs recommendations decision node configured to receive one or more foam system designs recommendations from the one or more inputs, and a second consequences node dependent on the foam systems designs uncertainty node and the foam system designs recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more foam system designs and the one or more foam system designs recommendations.
In some embodiments, a computer-implemented method for an underbalanced drilling expert system having a foam underbalanced drilling (UBD) Bayesian decision network (BDN) model is provided. The method includes receiving one or more inputs and providing the one or more inputs to one or more nodes of a first section of the foam UBD BDN model. The one or more nodes include a foam systems considerations uncertainty node configured to receive one or more foam systems considerations and a foam systems recommendations decision node configured to receive one or more foam systems recommendations. Additionally, the method includes determining one or more underbalanced drilling recommendations at a consequences node of the first section of the foam UBD BDN model, by calculating one or more Bayesian probabilities based on the one or more inputs and providing the one or more underbalanced drilling recommendations to a user.
Additionally, in some embodiments a system is provided having one or more processors and a non-transitory tangible computer-readable memory accessible by the one or more processors. The memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes a gas underbalanced drilling (UBD) Bayesian decision network (BDN) model. The gas underbalanced drilling BDN model includes a first section having a rotary and hammer drilling uncertainty node configured to receive one or more rotary and hammer drilling types from the one or more inputs, a rotary and hammer drilling recommendations decision node configured to receive one or more rotary and hammer drilling recommendations from the one or more inputs, and a first consequences node dependent on the rotary and hammer drilling uncertainty node and the rotary and hammer drilling recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more rotary and hammer drilling types and the one or more rotary and hammer drilling recommendations. The gas underbalanced drilling BDN model includes a second section a gas drilling considerations uncertainty node configured to receive one or more gas drilling considerations from the one or more inputs, a gas drilling considerations recommendations decision node configured to receive gas drilling considerations recommendations from the one or more inputs, and a second consequences node dependent on the gas drilling considerations uncertainty node and the gas drilling considerations recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas drilling considerations and the one or more gas drilling considerations recommendations. Additionally, the gas underbalanced drilling BDN model includes a third section having a gas drilling operations uncertainty node configured to receive one or more gas drilling operations from the one or more inputs, a gas drilling operations recommendations decision node configured to receive gas drilling operations recommendations from the one or more inputs, and a third consequences node dependent on the gas drilling operations uncertainty node and the gas drilling operations recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas drilling operations and the one or more gas drilling operations recommendations. Finally, the gas underbalanced drilling BDN model includes a fourth section having a gas drilling rig equipment uncertainty node configured to receive one or more gas drilling rig equipment from the one or more inputs, a gas drilling rig equipment recommendations decision node configured to receive gas drilling rig equipment recommendations from the one or more inputs, and a fourth consequences node dependent on the gas drilling rig equipment uncertainty node and the gas drilling rig equipment recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas drilling rig equipment and the one or more gas drilling rig equipment recommendations.
Further, in some embodiments a computer-implemented method is provided for an underbalanced drilling expert system having a gas underbalanced drilling (UBD) Bayesian decision network (BDN) model. The method includes receiving one or more inputs and providing the one or more inputs to one or more nodes of a first section of the gas underbalanced drilling BDN model. The one or more nodes include a rotary and hammer drilling uncertainty node and a rotary and hammer recommendations decision node. Additionally, the method includes determining one or more underbalanced drilling recommendations at a consequences node of the first section of the gas underbalanced drilling BDN model, by calculating one or more Bayesian probabilities based on the one or more inputs and providing the one or more underbalanced drilling recommendations to a user.
In some embodiments, a system is provided having one or more processors and a non-transitory tangible computer-readable memory accessible by the one or more processors. The memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes a mud cap underbalanced drilling (UBD) Bayesian decision network (BDN) model. The mud cap underbalanced drilling BDN model includes a first section having a mud cap drilling types uncertainty node configured to receive one or more mud cap drilling types from the one or more inputs, a mud cap drilling types recommendations decision node configured to receive one or more mud cap drilling types recommendations from the one or more inputs, and a first consequences node dependent on the mud cap drilling types uncertainty node and the mud cap drilling types recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more mud cap drilling types and the one or more mud cap drilling types recommendations.
In some embodiments a computer-implemented method is provided for an underbalanced drilling expert system having a mud cap underbalanced drilling (UBD) Bayesian decision network (BDN) model. The method includes receiving one or more inputs and providing the one or more inputs to one or more nodes of a first section of the mud cap UBD BDN model. The one or more nodes include mud cap drilling types uncertainty node configured to receive one or more mud cap drilling types a mud cap drilling types recommendations decision node configured to receive one or more mud cap drilling types recommendations. The method further includes determining one or more underbalanced drilling recommendations at a consequences node of the first section of the mud cap UBD BDN model by calculating one or more Bayesian probabilities based on the one or more inputs and providing the one or more underbalanced drilling recommendations to a user.
Moreover, in some embodiments a system is provided having one or more processors and a non-transitory tangible computer-readable memory accessible by the one or more processors. The memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes an underbalanced liner drilling (UBLD) Bayesian decision network (BDN) model. The UBLD BDN model includes a first section having a UBLD plans uncertainty node configured to receive one or more UBLD plans from the one or more inputs, a UBLD plans recommendations decision node configured to receive one or more UBLD plans recommendations from the one or more inputs, and a first consequences node dependent on the UBLD planning uncertainty node and the UBLD planning recommendations decision node and configured to output the one or more underbalanced liner drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD plans and the one or more UBLD plans recommendations. The UBLD BDN model also includes a second section having a UBLD solvable problems uncertainty node configured to receive one or more UBLD solvable problems from the one or more inputs, a UBLD advantages decision node configured to receive one or more UBLD advantages from the one or more inputs, and a second consequences node dependent on the UBLD problems uncertainty node and the UBLD advantages decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD solvable problems and the one or more UBLD advantages. Additionally, the UBLD BDN model includes a third section having a UBLD considerations uncertainty node configured to receive one or more UBLD considerations from the one or more inputs, a UBLD considerations recommendations decision node configured to receive one or more UBLD considerations recommendations from the one or more inputs, and a third consequences node dependent on the UBLD considerations uncertainty node and the UBLD recommendations decision node and configured to output the one or more underbalanced liner drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD considerations and the one or more UBLD considerations recommendations.
In some embodiments a computer-implemented method is provided for an underbalanced drilling expert system having an underbalanced drilling liner (UBLD) Bayesian decision network (BDN) model. The method includes receiving one or more inputs and providing the one or more inputs to one or more nodes of a first section of the UBLD BDN model. The one or more nodes include a UBLD plans uncertainty node configured to receive one or more UBLD plans and a UBLD plans recommendations decision node configured to receive one or more UBLD plans recommendations. The method also includes determining one or more underbalanced drilling recommendations at a consequences node of the first section of the UBLD BDN model by calculating one or more Bayesian probabilities based on the one or more inputs and providing the one or more underbalanced drilling recommendations to a user.
Moreover, in some embodiments a system is provided having one or more processors and a non-transitory tangible computer-readable memory accessible by the one or more processors. The memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes an underbalanced coil tube (UBCT) Bayesian decision network (BDN) model. The UBCT BDN model includes a first section having a UBCT preplanning uncertainty node configured to receive one or more UBCT preplans from the one or more inputs, a UBCT preplanning requirements decision node configured to receive one or more UBCT preplan requirements from the one or more inputs, a first consequences node dependent on the UBCT preplanning uncertainty node and the UBCT preplanning recommendations decision node and configured to output the one or more UBCT drilling requirements based on one or more Bayesian probabilities calculated from the one or more UBCT preplans and the one or more UBCT preplan requirements. The UBCT BDN model also includes a second section having a UBCT considerations uncertainty node configured to receive one or more UBCT considerations from the one or more inputs, a UBCT recommendations decision node configured to receive one or more UBCT recommendations from the one or more inputs, and a second consequences node dependent on the UBCT considerations uncertainty node and the UBCT recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBCT considerations and the one or more UBCT recommendations.
In some embodiments a computer-implemented method is provided for an underbalanced drilling expert system having an underbalanced coil tube (UBCT) Bayesian decision network (BDN) model. The method includes receiving one or more inputs and providing the one or more inputs to one or more nodes of a first section of the UBCT BDN model. The one or more nodes include a UBCT preplanning uncertainty node configured to receive one or more UBCT preplans and a UBCT preplanning requirements decision node configured to receive one or more UBCT preplan requirements. The method also includes determining one or more underbalanced drilling recommendations at a consequences node of the first section of the UBCT BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs and providing the one or more underbalanced drilling recommendations to a user.
Further, in some embodiments another system is provided having one or more processors and a non-transitory tangible computer-readable memory accessible by the one or more processors. The memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes a snubbing and stripping Bayesian decision network (BDN) model. The snubbing and stripping BDN model includes a first section having a snubbing types uncertainty node configured to receive one or more snubbing types from the one or more inputs and a snubbing types recommendations decision node configured to receive one or more snubbing types recommendations from the one or more inputs, and a first consequences node dependent on the snubbing types uncertainty node and the snubbing types recommendations decision node and configured to output the one or more underbalanced recommendations based on one or more Bayesian probabilities calculated from the one or more snubbing types and the one or more snubbing types recommendations. The snubbing and stripping BDN model also includes a second section having a snubbing units uncertainty node configured to receive one or more snubbing units from the one or more inputs, a snubbing units recommendations decision node configured to receive one or more snubbing units recommendations from the one or more inputs, and a second consequences node dependent on the snubbing units uncertainty node and the snubbing units recommendations decision node and configured to output the one or more stripping and snubbing recommendations based on one or more Bayesian probabilities calculated from the one or more snubbing units types and the one or more snubbing units recommendations. Additionally, the snubbing and stripping BDN model includes a third section having a snubbing operations uncertainty node configured to receive one or more snubbing operations from the one or more inputs, a snubbing operations recommendations decision node configured to receive one or more snubbing operations recommendations from the one or more inputs, and a third consequences node dependent on the snubbing operations uncertainty node and the snubbing operations recommendations decision node and configured to output the one or more stripping and snubbing recommendations based on one or more Bayesian probabilities calculated from the one or more snubbing operations and the one or more snubbing operations recommendations. Finally, the snubbing and stripping BDN model also includes a fourth section having a stripping procedures uncertainty node configured to receive one or more stripping procedures from the one or more inputs, a stripping procedures recommendations decision node configured to receive one or more stripping procedures recommendations from the one or more inputs, and a fourth consequences node dependent on the stripping procedures uncertainty node and the stripping procedures recommendations decision node and configured to output the one or more stripping and snubbing recommendations based on one or more Bayesian probabilities calculated from the one or more stripping procedures and the one or more stripping procedures recommendations.
Finally, in some embodiments another computer-implemented method is provided for an underbalanced drilling expert system having a snubbing and stripping Bayesian decision network (BDN) model. The method includes receiving one or more inputs and providing the one or more inputs to one or more nodes of a first section of the snubbing and stripping BDN model. The one or more nodes include snubbing types uncertainty node configured to receive one or more snubbing types and a snubbing types recommendations decision node configured to receive one or more snubbing types recommendations. The method also includes determining one or more underbalanced drilling recommendations at a consequences node of the first section of the snubbing and stripping BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs and providing the one or more underbalanced drilling recommendations to a user.
While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. The drawings may not be to scale. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but to the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims.
DETAILED DESCRIPTIONAs discussed in more detail below, provided in some embodiments are systems, methods, and computer-readable media for an underbalanced drilling (UBD) expert system based on Bayesian decision network (BDN) models. In some embodiments, the UBD expert system includes a user interface and incorporates probability data based on expert opinions. The UBD expert system may include multiple BDN models, such as a general UBD model, a flow UBD drilling model, a gaseated (i.e., aerated) UBD drilling model, a foam UBD model, a gas (e.g., air or other gases) UBD model, a mud cap UBD model, an underbalanced liner drilling (UBLD) model, an underbalanced coil tube (UBCT) drilling model, and a snubbing and stripping model. Each model may include multiple sections and may receive inputs and provide outputs, such as recommendations, based on the inputs. The inputs to an uncertainty node of a BDN model may include probabilities associated with each input, or a user may select a specific input for the uncertainty node. Based on these inputs, and the inputs to a decision node, a model may put recommendations from a consequences node.
The underbalanced drilling system 106 may develop the well 104 by drilling a hole into the formation 102 using a drill bit, e.g., a roller cone bits, drag bits, etc. The underbalanced drilling system 106 may generally include, for example, a wellhead, pipes, bodies, valves, seals and so on that enable drilling of the well 104, provide for regulating pressure in the well 16, and provide for the injection of chemicals into the well 104. As used herein, the term underbalanced drilling refers to a drilling operation in which the wellbore pressure is purposely maintained at a lower pressure than the fluid pressure in the formation 102. Accordingly, the UBD drilling system 106 may include, for example, dry air systems, mist systems, aerated mud systems, gaseated systems, foam systems (e.g., stable foam systems) and other suitable systems. During operation, various UBD-specific scenarios may occur that require adjustments to different parameters of the UDB drilling system 106, such as different equipment, different operations, different tripping, different flow, different connections, different gas injections, different gas and fluid volumes, well kicks, different foams, different air and gas systems, different mud caps, different underbalanced liners, different underbalanced coil tubes, and snubbing and stripping. In some embodiments, the well 104, underbalanced drilling system 106 and other components may include sensors, such as temperature sensors, pressure sensors, and the like, to monitor the drilling process and enable a user to gather information about well conditions.
The underbalanced drilling system 106, well 104, and formation 102 may provide a basis for various inputs 112 to the underbalanced drilling expert system 108. For example, as described below, temperature ranges, the formation 102, and potential hole problems may be provided as inputs 112 to the underbalanced drilling expert system 108. The underbalanced drilling expert system 108 may access an expert data repository 114 that includes expert data, such as probability data used by the underbalanced drilling expert system 108. The expert data may be derived from best practices, expert opinions, research papers, and the like. As described further below, based on the inputs 112, the underbalanced drilling expert system 108 may output recommendations for the underbalanced drilling system 106. For example, the underbalanced drilling expert system 108 may provide the optimal equipment, UBD operations, tripping, connections, flow drilling operations, gas injection processes, air and gas operations, and so on as described further below. Based on these recommendations, different practices may be selected and used in the UBD drilling system 106
In some embodiments, the underbalanced drilling expert system 202 may include a user interface 206 and an expert data repository 208. The user interface 206 may be implemented using any suitable elements, such as windows, menus, buttons, web pages, and so on. As described in detail below, the underbalanced drilling expert system 202 may include one or more Bayesian decision network (BDN) models 210 that implemented Bayesian probability logic 212. The BDN models 210 may evaluate selections of inputs and associated probabilities 214 and output a decision 216 from the BDN model. In the embodiments described herein, the BDN model 210 may include nine different BDN models related to UDB drilling: a general approach to UBD model, a flow UBD drilling model, a gaseated (i.e., aerated) UBD drilling model, a foam UBD model, a gas (e.g., air or other gases) UBD model, a mud cap UBD model, an underbalanced liner drilling (UBLD) model, an underbalanced coil tube (UBCT) drilling model, and a snubbing and stripping model. Each model may include multiple sections and is described in further detail below. The UBD expert system 202 may include any one or combination of the models mentioned above. The BDN models 210 may then calculate Bayesian probabilities for the consequences resulting from the selected inputs, and then output recommended operations. For each BDN model, the output may include a table of probabilities for various recommendations, a single recommendation based on the highest Bayesian probability, or expected utility values for each BDN model to enable to user to evaluate and select the operation having the optimal expected utility for the selected inputs.
As described below, a user 204 may use the user interface 206 to enter selections 210 of inputs for the BDN model 210. The associated probabilities for the inputs may be obtained from the expert data repository 208. Based on the inputs 210, a user 204 may receive the outputs 212 from the BDN model 210, such as recommended UBD operations and expected utility values. The output 212 may be provided for viewing in the user interface 206. Further, as explained below, a user may return to the underbalanced drilling expert system 202 to add or change the inputs 214. The BDN model 210 may recalculate the outputs 216 based on the added or changed inputs 214 and the Bayesian probability logic 212. The recalculated outputs 216 may then provide additional or changed recommended underbalanced drilling practices and expected utility values. Here again, the outputs 216 may be provided to the user in the user interface 206. The user 204 may use a single BDN model of the UBD expert system 202, or may use multiple models of the UBD expert system 202, such as two, three, four, five, six, seven, eight, or nine models of the UDB expert system 202.
Next, the received selections may be provided as inputs to uncertainty nodes of a general UBD BDN model of the UBD expert system (block 310), and the selected inputs may include associated probability states, as determined from expert data 312. Next, the data from the uncertainly nodes may be combined (i.e., propagated to) a consequence node of the general UBD BDN model based on the expert systems data (block 312). The propagation and determination of consequences is based on the Bayesian logic described below in
Next, the received selections may be provided as inputs to uncertainty nodes of a flow UBD BDN model of the UBD expert system (block 317), and the selected inputs may include associated probability states, as determined from expert data 318. Next, the data from the uncertainly nodes may be combined (i.e., propagated to) a consequence node of the flow UBD BDN model based on the expert systems data (block 319), as based on the Bayesian logic described below in
Next, the received selections may be provided as inputs to uncertainty nodes of a gaseated UBD BDN model of the underbalanced drilling expert system (block 330), and the selected inputs may include associated probability states, as determined from expert data 332. Next, the data from the uncertainly nodes may be combined (i.e., propagated to) a consequence node of the gaseated UBD BDN model (block 333) based on the Bayesian logic described below in
Next, the received selections may be provided as inputs to uncertainty nodes of a general UBD BDN model of the UBD expert system (block 342), and the selected inputs may include associated probability states, as determined from expert data 343. Next, the data from the uncertainly nodes may be combined (i.e., propagated to) a consequence node of the general UBD BDN model based on the expert systems data (block 344) based on the Bayesian logic described below in
Next, the received selections may be provided as inputs to uncertainty nodes of the air and gas UBD BDN model of the underbalanced drilling expert system (block 354), and the selected inputs may include associated probability states, as determined from expert data 355. Next, the data from the uncertainly nodes may be combined (i.e., propagated to) a consequence node of the air and gas UBD BDN model (block 356) based on the Bayesian logic described below in
Next, the received selections may be provided as inputs to uncertainty nodes of a flow UBD BDN model of the UBD expert system (block 365), and the selected inputs may include associated probability states, as determined from expert data 366. Next, the data from the uncertainly nodes may be combined (i.e., propagated to) a consequence node of the flow UBD BDN model based on the expert systems data (block 367), as based on the Bayesian logic described below in
Next, the received selections may be provided as inputs to uncertainty nodes of a UBLD BDN model of the UBD expert system (block 375), and the selected inputs may include associated probability states, as determined from expert data 376. The data from the uncertainly nodes may then be combined (i.e., propagated to) a consequence node of the UBLD BDN model based on the expert systems data (block 378), as based on the Bayesian logic described below in
Next, the received selections may be provided as inputs to uncertainty nodes of a UBCT BDN model of the UBD expert system (block 386), and the selected inputs may include associated probability states, as determined from expert data 387. Next, the data from the uncertainly nodes may be combined (i.e., propagated to) a consequence node of the UBCT UBD BDN model based on the expert systems data (block 388) based on the Bayesian logic described below in
Finally,
Next, the received selections may be provided as inputs to uncertainty nodes of a scrubbing and stripping BDN model of the UBD expert system (block 398), and the selected inputs may include associated probability states, as determined from expert data 399. Next, the data from the uncertainly nodes may be combined (i.e., propagated to) a consequence node of the scrubbing and stripping BDN model based on the expert systems data (block 400). The propagation and determination of consequences is based on the Bayesian logic described below in
After defining the BDN model 400, the probability states associated with each node may be defined.
In the BDN model 400, the consequences associated with the consequences utility node 408 may be assigned input utility values.
Using the model and probabilities described above, the functionality of the BDN model 400 will be described. After receiving inputs to the model 400, the model 400 may simulate the uncertainty propagation based on the evidence, e.g., the probability states, at each node, using Bayesian probability determinations. A Bayesian probability may be determined according to Equation 1:
p(hypothesis|evidence) is the probability of a hypothesis conditioned upon evidence;
p(evidence|hypothesis) is the probability the evidence is plausible based on the hypothesis;
p(hypothesis) is the degree of certainty of the hypothesis; and
p(evidence) is the degree of certainty of the evidence.
Referring again to the BDN model 400 discussed above, the model 400 illustrates that a selection of drilling fluid affects the treating fluid and the swelling packer, as illustrated by the dependencies in the model 400. First, the total probability for a drilling fluid type may be calculated based on the evidence from the uncertainty nodes by Equation 2:
P(B|Ai) is the probability based on B in view of Ai;
P(Ai) is the probability of Ai; and
m is the total number of evidence items.
Using Equation 2, the total probability for a drilling fluid type and lactic acid treating fluid may be calculated according to Equation 3:
For example, using the probability data illustrated in
The results of the total probability calculations for drilling fluid types are illustrated in table 1000 depicted in
Using the total probabilities determined above, the Bayesian probability determination of Equation 1 may be used to calculate the Bayesian probability of a treating fluid used with a specific drilling fluid type and a particular swelling packer. Accordingly, a Bayesian probability may be derived by combining the Bayesian probability of Equation 1 with the total probability calculation of Equation 2, resulting in Equation 4:
Thus, based on Equation 4, the Bayesian probability determination for a lactic acid treating fluid and a formate drilling fluid type for a water swelling packer may be determined according to Equation 5, using the total probabilities depicted in the table 700 of
As depicted above in
As noted above, the values for the probabilities depicted in Equation 6 may be obtained from the probability states depicted in tables 600 and 700 of
The Bayesian probability determinations may also be performed for an oil swelling packer for the various combinations of treating fluid and drilling fluid types. Using the probability states depicted in tables 600 and 700 of
The results of the calculations shown above in Equations 5-12 are depicted in table 1100 in
After determining the Bayesian probabilities described above, the BDN model 400 may be used to select a swelling packer based on the inputs provided to the uncertainty nodes of the model 400. For example, the BDN model 400 may be used with two different interpretations of the output to provide the optimal swelling packer for the inputs provided to the model 400. In one interpretation, the model 400 may receive a user selection of an input for one uncertainty node, and an optimal swelling packer may be determined based on the possible inputs to the other uncertainty node. Thus, as shown table 1100 and
As mentioned above, table 900 of
Expectedutility is the expected utility value;
Consequence result is the Bayesian probability value associated with a consequence;
Inpututilityvalue is the input utility value associated with a consequence; and
n is the total number of consequences.
Accordingly, based on the input utility values depicted in
The calculation the expected utility of the expected utility for an oil swelling packer and a user selection of a CaCO3 drilling fluid type is illustrated below in Equation 15:
The results of the calculations performed in Equations 14 and 15 are summarized in
In other interpretations, a user may input values for all of the uncertainty nodes of the BDN model 400 to determine the optimal selection of a swelling packer. In such instances, the consequences may be determined directly from the consequences node 408 of the BDN model 400, as depicted above in table 800 of
Based on the consequences described above, the expected utility for the different swelling packers may be determined using Equation 13 described above. For example, based on table 1400 of
Similarly, the calculation of the expected utility for an oil swelling packer, using the values for consequences shown in table 1400 of
With the above concepts in mind, the BDN modeling techniques described above may be applied to more complicated models for an a UBD system. Such models may serve as a training tool or a guide to aid engineers, scientists, or other users in selecting and executing operations of an UBD system.
The planning phase section 1604 of the general UBD BDN model 1600 may include a planning phases uncertainty node 1616, a planning phases recommendations decision node 1618, and a planning phases consequences node 1620. As shown in the general UBD BDN model 1600, the planning phases consequences node 1620 is dependent on the inputs to the planning phases uncertainty node 1616 and the planning phases recommendations decision node 1618. The equipment section 1606 of the general UBD BDN model 1600 may include an equipment requirements uncertainty node 1622, an equipment recommendations decision node 1624, and an equipment consequences node 1626. As shown in
Finally, the operation planning section 1608 of the general UBD BDN model 1600 includes an operations types uncertainty node 1628, an operations decision node 1630, and an operations consequences node 1632 that is dependent on the inputs to the nodes 1628 and 1630. The output from each section 1602, 1604, 1606, and 1608 of the general UBD BDN model 1600 is propagated to a final consequences node 1634 and a general UBD expert node 1636. Thus, the final consequences node 1634 is dependent on the consequences nodes 1614, 1620, 1626, and 1632 of each section of the general UBD BDN model 1600.
In some embodiments, the BDN model 1600 may be implemented in a user interface similar to the depiction of the model 1600 in
Additionally,
Next,
After selecting one or more inputs for the nodes of the different sections 1602, 1604, 1606, and 1610 of the UBD BDN model 1600, the inputs may be propagated to the various consequence nodes 1614, 1620, 1626, and 1632 of each section, and then to the final consequences node 1634. The UBD BDN model 1600 may propagate the inputs using the Bayesian probability determinations described above in Equations 1, 2, and 4. By using the probabilities associated with the inputs, the UBD BDN model 1600 may then provide recommendations or expected utilities at each consequence node 1614, 1620, 1626, and 1632 for each section. Additionally, the UBD BDN model 1600 may provide recommendations or expected utilities at the final consequence node 1632 based on the propagated outputs from the consequence nodes 1614, 1620, 1626, and 1632. In some embodiments, the uncertainty nodes of the UBD BDN model 1600 may have inputs with associated probabilities. A user may select an input for one or more uncertainty nodes and view the recommendations based on the propagation of the selected input. For example, a user may select an input for the formation indicators node 1610 and receive a recommended formation consideration at the consequences node 1614. For example, a user may also select an input for the planning phases uncertainty node 1616 and receive a recommended planning phase recommendation (based on the inputs to the planning phases recommendations decision node 1618) from the consequences node 1620. One or more of the sections 1602, 1604, 1606, and 1608 of the UBD BDN model 1600 may be used; thus, a user may use one or more sections of the UBD BDN model 1600 but not use the remaining sections of the UDB BDN model 1600.
As mentioned above, in some embodiments a UBD expert system may also include a flow UBD BDN model.
The connection section 1904 of the flow UBD BDN model 1900 includes a connection types uncertainty node 1916, a connection options decision node 1918, and a connection recommendations consequence node 1920. The connection recommendations consequence node 1920 is dependent on inputs to the uncertainty node 1916 and the decision node 1918. Finally, the flow drilling section 1906 includes a flow drilling types uncertainty node 1922, a flow drilling options decision node 1924, and a flow drilling recommendations consequence node 1926. As shown in
In some embodiments, the flow BDN model 1900 may be implemented in a user interface similar to the depiction of the model 1600 in
Next,
Finally
After selecting one or more inputs for the nodes of the different sections 1902, 1904, and 1906 of the flow UBD BDN model 1900, the inputs may be propagated to the various consequence nodes 1914, 1920, and 1926 of each section, and then to the final consequences node 1928. The flow UBD BDN model 1900 may propagate the inputs using the Bayesian probability determinations described above in Equations 1, 2, and 4. By using the probably states associate with the inputs, the flow UBD BDN model 1900 may then provide recommendations or expected utilities at each consequence node 1914, 1920, and 1926. Additionally, the flow UBD BDN model 1900 may provide recommendations or expected utilities at the final consequence node 1928 based on the propagated outputs from the consequence nodes 1914, 1920, and 1926. In some embodiments, the uncertainty nodes of the UBD BDN model 1900 may have inputs with associated probability distributions. In some embodiments, a user may select an input for one or more uncertainty nodes and view the recommendations based on the propagation of the selected input. For example, a user may select an input for the tripping types uncertainty node 1908, the permeability level uncertainty node 1910, or both, and receive a recommendation (based on the inputs to the tripping options decision node 1912) at the consequences node 1914. Similarly, a user may select an input for the connection types uncertainty node 1916 and receive a planning phase recommendation (based on the inputs to the connection options decision node 1918) from the consequences node 1920. One or more sections 1902, 1904, and 1906 of the flow UBD BDN model 1900 may be used; consequently, a user may use one or more sections of the flow UBD BDN model 1900 and not use the remaining sections of the UDB BDN model 1900.
After entering inputs for the tripping section 1902 of the flow UBD BDN model 1900, a user may select the tripping recommendation consequences node 1914 to view the recommendations determined by the flow UBD BDN model 1900. As shown in
In some embodiments, a UBD expert system may also include a gaseated UBD BDN model.
Next, the gas and fluid volume section 2204 includes a gas and fluid volume limits uncertainty node 2216, a requirements for gas and fluid volume limits decision node 2218, and a consequences node 2220 that is dependent on the uncertainty node 2216 and the decision node 2218. Additionally, the kicks section 1606 includes a kick type uncertainty node 2222, a well kicks recommendation decision node 2224, and a consequences node 2226 dependent on the uncertainty node 2222 and the well kicks recommendation node 2224. Finally, the operational section 1618 includes an operational considerations uncertainty node 2228, an operational recommendations decision node 2230, and a consequences node 2232. The consequences node 2232 is dependent on the uncertainty node 2228 and the operational recommendations node 2230. The output from each consequence node 2214, 2220, 2226, and 2232 of each section 2202, 2204, 2206, and 2208 may be propagated to a final consequences node 2234 and a flow UBD expert node 2234. Thus, the final consequences node 2234 is dependent on the consequences nodes 2214, 2220, 2226, and 2232.
As described above with regard to the other BDN models, in some embodiments the flow BDN model 2200 may be implemented in a user interface similar to the depiction of the model 2200 in
Finally,
Here again, after selecting one or more inputs for the nodes of the different sections 2202, 2204, 2206, and 2208 of the gaseated UBD BDN model 1900, the inputs may be propagated to the various consequence nodes 2214, 2220, 2226, and 2232 of each section, and then to the final consequences node 2234, using the Bayesian probability determinations described above in Equations 1, 2, and 4. By using the probably states associate with the inputs, the gaseated UBD BDN model 2200 may then provide recommendations or expected utilities at each consequence node 2214, 2220, 2226, and 2232. Additionally, the gaseated UBD BDN model 2200 may provide recommendations or expected utilities at the final consequence node 2234 based on the propagated outputs from the consequence nodes 2214, 2220, 2226, and 2232. In some embodiments, the uncertainty nodes of the UBD BDN model 2200 may have inputs with associated probability distributions. In some embodiments, a user may select an input for one or more uncertainty nodes and view the recommendations based on the propagation of the selected input. For example, a user may select an input for the gas injection process uncertainty node 2210 and receive a recommendation (based on the inputs to the gas injection processes characteristics decision node 2212) at the consequences node 2214. Similarly, a user may select an input for the kick type uncertainty node 2222 and receive a recommendation (based on the inputs to the well kicks recommendations decision node 2224) from the consequences node 2226. One or more sections 2202, 2204, 2206, and 2208 of the gaseated UBD BDN model 2200 may be used; consequently, a user may use one or more sections of the gaseated UBD BDN model 2200 and not use the remaining sections of the gaseated UDB BDN model 2200.
After entering inputs for the gas injection section 2202 of the gaseated UBD BDN model 2200, a user may select the consequences node 2214 to view the recommendations determined by the gaseated UBD BDN model 2200.
Additionally, in some embodiments, a UBD expert system may also include a foam UBD BDN model for use in determining optimal operations for a foam UBD system.
The foam systems considerations section 2402 includes a foam systems considerations uncertainty node 2406, a foam systems considerations decision node 2408, and a consequences node 2410 that is dependent on the uncertainty node 2406 and the decision node 2408. The foam system design section 2404 includes a foam system designs uncertainty node 2412, a foam systems designs recommendations decision node 2414, and a consequences node 2416 that is dependent on the uncertainty node 2412 and the decision node 2414. The output from each consequence node 2410 and 2416 may be propagated to a final consequences node 2418 and a UBD expert node 2420.
As described above with regard to the other BDN models, in some embodiments, the foam UBD BDN model 2400 may be implemented in a user interface similar to the depiction of the model 2400 in
Next,
As described above, after selecting one or more inputs for the nodes of the different sections 2402 and 2402 of the flow UBD BDN model 2400, the inputs may be propagated to the consequence nodes 2410 and 2416 of each section, and then to the final consequences node 2418, using the Bayesian probability determinations described above in Equations 1, 2, and 4. By using the probably states associate with the inputs, the flow UBD BDN model 2400 may then provide recommendations or expected utilities at each consequence node 2410 and 2416\. Additionally, the flow UBD BDN model 2400 may provide recommendations or expected utilities at the final consequence node 2418 based on the propagated outputs from the consequence nodes 2410 and 2416. In some embodiments, the uncertainty nodes of the flow UBD BDN model 2400 may have inputs with associated probability distributions. In some embodiments, a user may select an input for one or more uncertainty nodes and view the recommendations based on the propagation of the selected input. For example, a user may select an input for the foam systems considerations uncertainty node 2406 and receive a recommendation (based on the inputs to the foam systems considerations decision node 2408) at the consequences node 2410. Similarly, a user may select an input for the foam systems design uncertainty node 2412 and receive a recommendation (based on the inputs to the foam systems decision node 2414) from the consequences node 2416. One or both sections 2402 and 2404 of the flow UBD BDN model 2400 may be used; consequently, a user may use one or both sections of the flow UBD BDN model 2400 and not use the remaining sections of the flow UDB BDN model 2400.
As mentioned above, the UBD expert system may also include an air and gas UBD BDN model for providing optimal operations for an air and gas UBD system.
The rotary and hammer drilling section 2602 of the air and gas UBD BDN model 2600 may include a rotary and hammer drilling uncertainty node 2610, a rotary and hammer drilling recommendations decision node 2612, and a consequences node 2614 that is dependent on the uncertainty node 2610 and the decision node 2612. The considerations section 2604 may include a gas drilling considerations uncertainty node 2616, a gas drilling considerations decision node 2618, and a consequences node 2620 that is dependent on the uncertainty node 2616 and the decision node 2618. Additionally, the gas drilling operations section 2606 includes a gas drilling operations uncertainty node 2622, a gas drilling recommendations decision node 2624, and a consequences note 2626 that is dependent on the uncertainty node 2622 and the decision node 2624. Finally, as also shown in
In some embodiments, as described with regard to the other BDN models, the air and gas BDN model 2600 may be implemented in a user interface similar to the depiction of the model 2200 in
Next,
Next,
Finally,
As described above, after selecting one or more inputs for the nodes of the different sections 2602, 2604, 2606, and 2608 of the air and gas UBD BDN model 2600, the inputs may be propagated to the consequence nodes 2614, 2620, 2626, and 2632 of each section, and then to the final consequences node 2634, using the Bayesian probability determinations described above in Equations 1, 2, and 4. By using the Bayesian probabilities associate with the inputs, the air and gas UBD BDN model 2600 may provide recommendations or expected utilities at each consequence node. Additionally, the air and gas UBD BDN model 2600 may provide recommendations or expected utilities at the final consequence node 2634 based on the propagated outputs from the consequence nodes 2614, 2620, 2626, and 2632. In some embodiments, the uncertainty nodes of the air and gas UBD BDN model 2600 may have inputs with associated probability distributions. In some embodiments, a user may select an input for one or more uncertainty nodes and view the recommendations based on the propagation of the selected input. For example, a user may select an input for the rotary and hammer drilling uncertainty node 2610 and receive a recommendation (based on the inputs to the rotary and hammer drilling recommendations decision node 2612) at the consequences node 2614. Similarly, a user may select an input for the gas drilling operations uncertainty node 2622 and receive a recommendation (based on the inputs to the gas drilling operations recommendations decision node 2624) from the consequences node 2626. One or multiple sections 2602, 2604, 2606, and 2608 of the air and gas UBD BDN model 2600 may be used; consequently, a user may use one or both sections of the air and gas UBD BDN model 2600 and not use the remaining sections of the air and gas UBD BDN model 2600.
Similarly,
As described above, an output from the model 2600 may be provided from a consequences node of the model 2600.
As described above, the UBD expert system may also include a mud cap drilling BDN model for use in determining optimal operations for a mud cap UBD system.
The mud cap drilling types section 2602 includes a mud cap drilling types uncertainty node 2610, a mud cap drilling types recommendations decision node 2612, and a consequences node 2914 that is dependent on the uncertainty node 2610 and the decision node 2612. The drilling problems section 2904 includes a drilling problems uncertainty node 2916, a drilling problems recommendations decision node 2918, and a consequences node 2920 that is dependent on the uncertainty node 2916 and the decision node 2918. Finally, the floating mud cap drilling section 2906 includes a floating mud cap drilling considerations uncertainty node 2922, a floating mud cap drilling recommendations decision node 2924, and a consequences node 2926 that is dependent on the uncertainty node 2922 and the decision node 2924. The output from each of the consequences nodes 2914, 2920, and 2926 may be propagated to a final consequences node 2928 and a UBD expert node 2630. Thus, the final consequences node is dependent on the consequences nodes 2914, 2920, and 2926 of each section of the mud cap UDB BDN model 2900.
In some embodiments, as described above with regard to the other BDN models discussed herein, the mud cap UDB BDN model 2900 may be implemented in a user interface similar to the depiction of the model 2900 in
Next,
As described above, after selecting one or more inputs for the nodes of the different sections 2902, 2904, and 2906 of the mud cap UBD BDN model 2900, the inputs may be propagated to the consequence nodes 2914, 2920, and 2926 of each section, and then to the final consequences node 2928, using the Bayesian probability determinations described above in Equations 1, 2, and 4. By using the probability associate with the inputs, the mud cap UDB BDN model 2900 may then provide recommendations or expected utilities at each consequence node 2914, 2920, and 2926. Additionally, the mud cap UDB BDN model 2900 may provide recommendations or expected utilities at the final consequence node 2928 based on the propagated outputs from the consequence nodes 2914, 2920, and 2926. In some embodiments, the uncertainty nodes of the mud cap UDB BDN model 2900 may have inputs with associated probability distributions. In some embodiments, a user may select an input for one or more uncertainty nodes and view the recommendations based on the propagation of the selected input. For example, a user may select an input for the mud cap background uncertainty node 2910 and receive a recommendation (based on the inputs to the mud cap background recommendations decision node 2912) at the consequences node 2914. Similarly, a user may select an input for the drilling problems uncertainty node 2916 and receive a recommendation (based on the inputs to the drilling problems recommendations decision node 2918) from the consequences node 2920. Here again, one or multiple sections 2902, 2904, and 2906 of the mud cap UDB BDN model 2900 may be used; consequently, a user may use one or both sections of the mud cap UDB BDN model 2900 and not use the remaining sections of the mud cap UDB BDN model 2900.
Additionally,
Further, the UBD expert system may also include an underbalanced liner drilling (UBLD) BDN model for use in determining optimal operations in a UBLD system.
Each section of the UBLD BDN model 3200 is described further below. The UBLD plans section 3202 includes a UBLD plans uncertainty node 3210, a UBLD plans recommendations decision node 3212, and a consequences node 3214 that is dependent on the uncertainty node 3210 and the decision node 3212. The UBLD problems and advantages section 3204 includes a UBLD solvable problems uncertainty node 3216, a UBLD advantages decision node 3218, and a consequences node 3220 that is dependent on the uncertainty node 3216 and the decision node 3218. Additionally, the considerations section 3206 includes a UBLD considerations uncertainty node 3222, a UBLD considerations recommendations decision node 3224, and a consequences node 3226 that is dependent on the uncertainty node 3222 and the decision node 3224. The output from each of the consequences nodes 3214, 3220, and 3226 may be propagated to a final consequences node 3228 and a UBD expert system node 3220. Accordingly, the final consequences node 3228 is dependent on the consequences nodes 3214, 3220, and 3226 for each section model 3200.
In some embodiments, as described above in the other BDN models discussed herein, the UBLD BDN model 3200 may be implemented in a user interface similar to the depiction of the model 3200 in
Additionally,
Finally,
As described above, after selecting one or more inputs for the nodes of the different sections 3202, 3204, and 3206 of the UBLD BDN model 3200, the inputs may be propagated to the consequence nodes 3214, 3220, and 3226 of each section, and then to the final consequences node 3228, using the Bayesian probability determinations described above in Equations 1, 2, and 4. By using the Bayesian probabilities associate with the inputs, the UBLD BDN model 3200 may provide recommendations or expected utilities at each consequence node. Additionally, the UBLD BDN model 3200 may provide recommendations or expected utilities at the final consequence node 3228 based on the propagated outputs from the consequence nodes 3214, 3220, and 3226. In some embodiments, the uncertainty nodes of the UBLD BDN model 3200 may have inputs with associated probability distributions. In some embodiments, a user may select an input for one or more uncertainty nodes and view the recommendations based on the propagation of the selected input. For example, a user may select an input for the UBLD plans uncertainty node 3210 and receive a recommendation (based on the inputs to the UBLD plans recommendations decision node 3212) at the consequences node 3214. Similarly, a user may select an input for the UBLD problems uncertainty node 3216 and receive a recommendation (based on the inputs to the UBLD advantages decision node 3218) from the consequences node 3220. One or multiple sections 3202, 3204, and 3206 of the UBLD BDN model 3200 may be used; thus, a user may use one or multiple sections of the UBLD BDN model 3200 and not use the remaining sections of the UBLD BDN model 3200.
In some embodiments, a UBD expert system may include a BDN model for an underbalanced coil tube (UBCT) system for use in determining optimal operations for a UBCT drilling system.
In some embodiments, as described above in the other BDN models discussed herein, the UBCT drilling BDN model 3500 may be implemented in a user interface similar to the depiction of the model 3500 in
The preplanning section 3502 includes a preplanning uncertainty node 3506, preplanning requirements decision node 3508, and a consequences node 3510 dependent on the uncertainty node 3506 and the decision node 3508. Additionally, the drilling considerations section 3504 includes a UBCT drilling considerations uncertainty node 3512, a UBCT drilling considerations solutions decision node 3514, and a consequences node 3516 dependent on the uncertainty node 3512 and the decision node 3514. The output from the consequences nodes 3510 and 3516 may be propagated to a final consequences node 3518 and a UBD expert system node 3520. Thus, the final consequences node 3518 is dependent on the consequences nodes 3510 and 3516.
As described above, after selecting one or more inputs for the nodes of the different sections 3502 and 3504 of the UBCTD BDN model 3500, the inputs may be propagated to the consequence nodes 3510 and 3516 of each section, and then to the final consequences node 3518, using the Bayesian probability determinations described above in Equations 1, 2, and 4. By using the probably states associate with the inputs, the UBCTD BDN model 3500 may then provide recommendations or expected utilities at each consequence node 3510 and 3516. Additionally, the UBCTD BDN model 3500 may provide recommendations or expected utilities at the final consequence node 3518 based on the propagated outputs from the consequence nodes 3510 and 3516. In some embodiments, the uncertainty nodes of the UBCTD BDN model 3500 may have inputs with associated probability distributions. In some embodiments, a user may select an input for one or more uncertainty nodes and view the recommendations based on the propagation of the selected input. For example, a user may select an input for the preplanning uncertainty node 3506 and receive a recommendation (based on the inputs to the preplanning requirements decision node 3508) at the consequences node 3510. Similarly, a user may select an input for the foam systems design uncertainty node 3512 and receive a recommendation (based on the inputs to the UBCT drilling considerations decision node 3514) from the consequences node 3516. One or both sections 3502 and 3504 of the UBCTD BDN model 3500 may be used; consequently, a user may use one or both sections of the UBCTD BDN model 3500 and not use the remaining sections of the flow UDB BDN model 2400
Finally, in some embodiments the UBD expert system may include a snubbing and stripping BDN model for use in determining optimal snubbing and stripping operations for a UBD system.
Each section of the snubbing and stripping BDN model 3700 is described in detail below. The snubbing section 3702 includes a snubbing types uncertainty node 3710, a snubbing types recommendations decision node 3712, and a consequences node 3714 that depends on the uncertainty node 3710 and the decision node 3712. Additionally, the snubbing units section 3704 includes a snubbing units uncertainty node 3716, a snubbing units recommendations decision node 3718, and a consequences node 3720 that depends on the uncertainty node 3716 and the decision node 3718. The snubbing operations section 3706 includes a snubbing operations uncertainty node 3722, a snubbing operations recommendations decision node 3724, and a consequences node 3726 that depends on the uncertainty node 3722 and the decision node 3724. Finally, the stripping procedures section 3708 includes a general stripping procedures uncertainty node 3728, a general stripping procedures recommendations decision node 3730, and a consequences node 3732 that depends on the uncertainty node 3728 and the decision node 3730. The output from each of the consequences nodes 3714, 3720, 3726, and 3732 may be propagated to a final consequences node 3734 and a UBD expert system node 3736. Accordingly, the final consequences node 3734 is dependent on the consequences nodes 3714, 3720, 3726, and 3732 for each section of the model 3700.
In some embodiments, as described above in the other BDN models discussed herein, the snubbing and stripping BDN model 3700 may be implemented in a user interface similar to the depiction of the model 3700 in
Next,
Further,
Finally,
As described above, after selecting one or more inputs for the nodes of the different sections 3702, 3704, 3706, and 3708 of the snubbing and stripping BDN model 3700, the inputs may be propagated to the consequence nodes 3714, 3720, 3726, and 2632 of each section, and then to the final consequences node 3734, using the Bayesian probability determinations described above in Equations 1, 2, and 4. By using the Bayesian probabilities associate with the inputs, the snubbing and stripping BDN model 3700 may provide recommendations or expected utilities at each consequence node. Additionally, the snubbing and stripping BDN model 3700 may provide recommendations or expected utilities at the final consequence node 3734 based on the propagated outputs from the consequence nodes 3714, 3720, 3726, and 2632. In some embodiments, the uncertainty nodes of the snubbing and stripping BDN model 3700 may have inputs with associated probability distributions. In some embodiments, a user may select an input for one or more uncertainty nodes and view the recommendations based on the propagation of the selected input. For example, a user may select an input for the rotary and hammer drilling uncertainty node 3710 and receive a recommendation (based on the inputs to the rotary and hammer drilling recommendations decision node 3712) at the consequences node 3714. Similarly, a user may select an input for the gas drilling operations uncertainty node 3722 and receive a recommendation (based on the inputs to the gas drilling operations recommendations decision node 3724) from the consequences node 3726. One or multiple sections 3702, 3704, 3706, and 3708 of the snubbing and stripping BDN model 3700 may be used; consequently, a user may use one or both sections of the snubbing and stripping BDN model 3700 and not use the remaining sections of the snubbing and stripping BDN model 3700.
Similarly,
As described above, an output from the model 3700 may be provided from a consequences node of the model 3700.
The various BDN models described above may be constructed based on the inputs for the uncertainty nodes, decision nodes, and the associated probabilities. The construction of a section of the various BDN models is illustrated in
Additionally, inputs for a decision node of a section of a specific BDN model may be determined (block 4008). Here again, the inputs may be determined from the expert data 4004. As described above, in some embodiments, the expert data 4004 may be used to generate probability data stored in a database. The determined inputs and associated probability states may then be entered into a decision node of the appropriate BDN model. (block 4010).
Finally the consequence probabilities may be determined based on the Bayesian logic described above in Equations 1, 2, and 4 (block 4012). Here again, the determination of various probabilities may be determined from expert data 4004. For example, various combinations of inputs to the uncertainty node and decision node may result in different probability states as determined from the expert data 4004. The consequence probabilities may then be entered into the consequences node of the appropriate BDN model (block 4014). Next the section of the BDN model may be completed and additional sections may be constructed in the manner described above.
In some embodiments, after completing a section of a BDN model or all sections of a BDN model, the BDN model may be tested (block 4016). For example, inputs to the uncertainty nodes of the BDN model may be selected and the outputs may be tested against manual determinations based on the expert data 4004. Finally, if the model is complete and tested, the UBD expert system incorporating the BDN model may be provided (block 4018).
Advantageously, in the case of new and changed practices, expert opinions, and the like, a BDN model may be updated by changing the probability states for the appropriate nodes. For example, practices, expert opinions, and the like may be reviewed to determine if there are changes (decision block 4020). If there are new or changed practices, expert opinions, or other sources of expert data (line 4022), then additional expert data may be obtained (block 4024) and used to determine inputs to the uncertainty node and decision node of the appropriate section of a BDN model. Any new and changed determinations may be entered into the appropriate nodes and an updated BDN model may be completed (block 4026).
As will be understood by those skilled in the art, the computer 4100 may include various internal and external components that contribute to the function of the device and which may allow the computer 4100 to function in accordance with the techniques discussed herein. As will be appreciated, various components of computer 4100 may be provided as internal or integral components of the computer 4100 or may be provided as external or connectable components. It should further be noted that
Computer 4100 may include any combination of devices or software that may perform or otherwise provide for the performance of the techniques described herein. For example, the computer 4100 may be representative of the client computer 200 or a server implementing some or all portions of the UBD expert system 108 or other components of the systems described above. Accordingly, the computer 4100 may include or be a combination of a cloud-computing system, a data center, a server rack or other server enclosure, a server, a virtual server, a desktop computer, a laptop computer, a tablet computer, a mobile telephone, a personal digital assistant (PDA), a media player, a game console, a vehicle-mounted computer, or the like. The computer 4100 may be a unified device providing any one of or a combination of the functionality of a media player, a cellular phone, a personal data organizer, a game console, and so forth. Computer 4100 may also be connected to other devices that are not illustrated, or may operate as a stand-alone system. In addition, the functionality provided by the illustrated components may in some embodiments be combined in fewer components or distributed in additional components. Similarly, in some embodiments, the functionality of some of the illustrated components may not be provided or other additional functionality may be available.
In addition, the computer 4100 may allow a user to connect to and communicate through a network 4116 (e.g., the Internet, a local area network, a wide area network, etc.) and to acquire data from a satellite-based positioning system (e.g., GPS). For example, the computer 4100 may allow a user to communicate using the World Wide Web (WWW), e-mail, text messaging, instant messaging, or using other forms of electronic communication, and may allow a user to obtain the location of the device from the satellite-based positioning system, such as the location on an interactive map.
In one embodiment, the display 4106 may include a liquid crystal display (LCD) or an organic light emitting diode (OLED) display, although other display technologies may be used in other embodiments. The display 4106 may display a user interface (e.g., a graphical user interface), such a user interface for a Bayesian decision network. In accordance with some embodiments, the display 4106 may include or be provided in conjunction with touch sensitive elements through which a user may interact with the user interface. Such a touch-sensitive display may be referred to as a “touch screen” and may also be known as or called a touch-sensitive display system.
The processor 4102 may provide the processing capability required to execute the operating system, programs, user interface, and any functions of the computer 4100. The processor 4102 may receive instructions and data from a memory (e.g., system memory 4104). The processor 4102 may include one or more processors, such as “general-purpose” microprocessors, and special purpose microprocessors, such as ASICs. For example, the processor 4102 may include one or more reduced instruction set (RISC) processors, such as those implementing the Advanced RISC Machine (ARM) instruction set. Additionally, the processor 4102 may include single-core processors and multicore processors and may include graphics processors, video processors, and related chip sets. Accordingly, computer 4100 may be a uni-processor system including one processor (e.g., processor 4102a), or a multi-processor system including any number of suitable processors (e.g., 4102a-4102n). Multiple processors may be employed to provide for parallel or sequential execution of one or more sections of the techniques described herein. Processes, such as logic flows, described herein may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating corresponding output.
As will be understood by those skilled in the art, the memory 4104 (which may include one or more tangible non-transitory computer readable storage medium) may include volatile memory, such as random access memory (RAM), and non-volatile memory, such as ROM, flash memory, a hard drive, any other suitable optical, magnetic, or solid-state storage medium, or a combination thereof. The memory 4104 may be accessible by the processor 4102 and other components of the computer 4100. The memory 4104 may store a variety of information and may be used for a variety of purposes. The memory 4104 may store executable computer code, such as the firmware for the computer 4100, an operating system for the computer 4100, and any other programs or other executable code necessary for the computer 4100 to function. The executable computer code may include program instructions 4118 executable by a processor (e.g., one or more of processors 4102a-4102n) to implement one or more embodiments of the present invention. Instructions 4118 may include modules of computer program instructions for implementing one or more techniques described. Program instructions 4118 may define a computer program (which in certain forms is known as a program, software, software application, script, or code). A computer program may be written in a programming language, including compiled or interpreted languages, or declarative or procedural languages. A computer program may include a unit suitable for use in a computing environment, including as a stand-alone program, a module, a component, a subroutine. A computer program may or may not correspond to a file in a file system. A program may be stored in a section of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or sections of code). A computer program may be deployed to be executed on one or more computer processors located locally at one site or distributed across multiple remote sites and interconnected by a communication network. In addition, the memory 4104 may be used for buffering or caching during operation of the computer 4100. The memory 4104 may also store data files such as media (e.g., music and video files), software (e.g., for implementing functions on computer 4100), preference information (e.g., media playback preferences), wireless connection information (e.g., information that may enable media device to establish a wireless connection), telephone information (e.g., telephone numbers), and any other suitable data.
As mentioned above, the memory 4104 may include volatile memory, such as random access memory (RAM). The memory 4104 may also include non-volatile memory, such as ROM, flash memory, a hard drive, any other suitable optical, magnetic, or solid-state storage medium, or a combination thereof. The interface 4114 may include multiple interfaces and may couple various components of the computer 4100 to the processor 4102 and memory 4104. In some embodiments, the interface 4114, the processor 4102, memory 4104, and one or more other components of the computer 4100 may be implemented on a single chip, such as a system-on-a-chip (SOC). In other embodiments, these components, their functionalities, or both may be implemented on separate chips. The interface 4114 may be configured to coordinate I/O traffic between processors 4102a-4102n, system memory 4104, network interface 1410, I/O devices 1412, other peripheral devices, or a combination thereof. The interface 4114 may perform protocol, timing or other data transformations to convert data signals from one component (e.g., system memory 4104) into a format suitable for use by another component (e.g., processors 4102a-4102n). The interface 4114 may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard.
The computer 4100 may also include an input and output port 4108 to allow connection of additional devices, such as I/O devices 4112. Embodiments of the present invention may include any number of input and output ports 4108, including headphone and headset jacks, universal serial bus (USB) ports, Firewire or IEEE-1394 ports, and AC and DC power connectors. Further, the computer 4100 may use the input and output ports to connect to and send or receive data with any other device, such as other portable computers, personal computers, printers, etc.
The computer 4100 depicted in
Various embodiments may further include receiving, sending or storing instructions and/or data implemented in accordance with the foregoing description upon a computer-accessible medium. Generally speaking, a computer-accessible/readable storage medium may include a non-transitory storage media such as magnetic or optical media, (e.g., disk or DVD/CD-ROM), volatile or non-volatile media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.), ROM, etc., as well as transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as network and/or a wireless link.
Further modifications and alternative embodiments of various aspects of the invention will be apparent to those skilled in the art in view of this description. Accordingly, this description is to be construed as illustrative only and is for the purpose of teaching those skilled in the art the general manner of carrying out the invention. It is to be understood that the forms of the invention shown and described herein are to be taken as examples of embodiments. Elements and materials may be substituted for those illustrated and described herein, parts and processes may be reversed or omitted, and certain features of the invention may be utilized independently, all as would be apparent to one skilled in the art after having the benefit of this description of the invention. Changes may be made in the elements described herein without departing from the spirit and scope of the invention as described in the following claims. Headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description.
As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). The words “include”, “including”, and “includes” mean including, but not limited to. As used throughout this application, the singular forms “a”, “an” and “the” include plural referents unless the content clearly indicates otherwise. Thus, for example, reference to “an element” includes a combination of two or more elements. Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that throughout this specification discussions utilizing terms such as “processing”, “computing”, “calculating”, “determining” or the like refer to actions or processes of a specific apparatus, such as a special purpose computer or a similar special purpose electronic processing/computing device. In the context of this specification, a special purpose computer or a similar special purpose electronic processing/computing device is capable of manipulating or transforming signals, typically represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the special purpose computer or similar special purpose electronic processing/computing device.
Claims
1. A system, comprising:
- one or more processors;
- a non-transitory tangible computer-readable memory, the memory comprising:
- an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced drilling expert system comprising an underbalanced drilling Bayesian decision network (BDN) model, the underbalanced drilling BDN model comprising: a first section, comprising: a formation indicators uncertainty node configured to receive one or more formation indicators from the one or more inputs; a formation considerations decision node configured to receive one or more formation considerations from the one or more inputs; and a first consequences node dependent on the formation indicators uncertainty node and the formation considerations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from one or more formation indicators and the one or more formation considerations; a second section, comprising: a planning phases uncertainty node configured to receive one or more planning phases from the one or more inputs; a planning phases recommendations decision node configured to receive one or more planning phases recommendations from the one or more inputs; and a second consequences node dependent on the planning phases uncertainty node and the planning phases recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from one or more planning phases and the one or more planning phases recommendations; and a third section, comprising: an equipment requirements uncertainty node configured to receive one or more equipment requirements from the one or more inputs; an equipment recommendations decision node configured to receive one or more equipment recommendations from the one or more inputs; and a third consequences node dependent on the equipment requirements uncertainty node and the equipment recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more equipment requirements and the one or more equipment recommendations.
2. The system of claim 1, wherein the UBD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences, and the third consequences node.
3. The system of claim 1, comprising a user interface configured to display the UBD BDN model and receive user selections of the one or more inputs.
4. The system of claim 1, wherein the one or more formation indicators, the one or more planning phases, and the one or more equipment requirements are each associated with a respective plurality of probabilities.
5. A computer-implemented method for an underbalanced drilling expert system having an underbalanced drilling Bayesian decision network (BDN) model, the method comprising:
- receiving one or more inputs;
- providing the one or more nodes of a first section of the underbalanced drilling BDN model, the one or more nodes comprising: a formation indicators uncertainty node; a formation considerations decision node;
- determining one or more underbalanced drilling recommendations at a consequences node of the first section of the underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;
- providing the one or more underbalanced drilling recommendations to a user.
6. The method of claim 5, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the UBD BDN model.
7. The computer-implemented method of claim 5, comprising:
- providing the one or more inputs to one or more nodes of a second section of the underbalanced drilling BDN model, the one or more nodes comprising: a planning phases uncertainty node configured to receive one or more planning phases; a planning phases recommendations decision node configured to receive one or more planning phases recommendations;
- determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
8. The computer-implemented method of claim 7, comprising:
- providing the one or more inputs to one or more nodes of a third section of the underbalanced drilling BDN model, the one or more nodes comprising: an equipment requirements uncertainty node configured to receive one or more equipment requirements; an equipment recommendations decision node configured to receive one or more equipment recommendations;
- determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
9. A system, comprising:
- one or more processors;
- a non-transitory tangible computer-readable memory, the memory comprising: an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more flow underbalanced drilling recommendations based on one or more inputs, the flow underbalanced drilling expert system comprising a flow underbalanced drilling Bayesian decision network (BDN) model, the flow underbalanced drilling BDN model comprising: a first section, comprising: a tripping types uncertainty node configured to receive one or more tripping types from the one or more inputs; a permeability level uncertainty node configured to receive one or more permeability levels from the one or more inputs; a tripping options decision node configured to receive one or more tripping options from the one or more inputs; a first consequences node dependent on the tripping uncertainty node, the permeability level uncertainty node, and the tripping options decision node and configured to output the one or more flow underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more tripping types, the one or more permeability levels, and the one or more tripping options; a second section, comprising: a connection types uncertainty node configured to receive one or more connection types from the one or more inputs; a connection options decision node configured to receive one or more connection options from the one or more inputs; a second consequences node dependent on the connection uncertainty node and the connection options decision node and configured to output the one or more flow underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more connection types and the one or more connection options; a third section, comprising: a flow drilling types uncertainty node configured to receive one or more flow drilling types from the one or more inputs; a flow drilling options decision node configured to receive one or more flow drilling options from the one or more inputs; a third consequences node dependent on the flow drilling uncertainty node and the flow drilling options decision node and configured to output the one or more flow underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more flow drilling types and the one or more flow drilling options.
10. The system of claim 9, wherein the flow UBD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences, and the third consequences node.
11. The system of claim 9, comprising a user interface configured to display the flow UBD BDN model and receive user selections of the one or more inputs.
12. The system of claim 9, wherein the one or more tripping types, the one or more permeability levels, the one or connection types, and the one or more flow drilling types are each associated with a respective plurality of probabilities.
13. A computer-implemented method for an underbalanced drilling expert system having a flow underbalanced drilling (UBD) Bayesian decision network (BDN) model, the method comprising:
- receiving one or more inputs;
- providing the one or more inputs to one or more nodes of a first section of the flow underbalanced drilling BDN model, the one or more nodes comprising: a tripping uncertainty node configured to receive one or more tripping types; a permeability level uncertainty node configured to receive one or more permeability levels; and a tripping options decision node a tripping options decision node configured to receive one or more tripping options;
- determining one or more underbalanced drilling recommendations at a consequences node of the first section of the flow underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;
- providing the one or more underbalanced drilling recommendations to a user.
14. The computer-implemented method of claim 13, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the flow UBD BDN model.
15. The computer-implemented method of claim 13, comprising:
- providing the one or more inputs to one or more nodes of a second section of the flow UBD BDN model, the one or more nodes comprising: a connection types uncertainty node configured to receive one or more connection types; a connection options decision node configured to receive one or more connection options;
- determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
16. The computer-implemented method of claim 15, comprising:
- providing the one or more inputs to one or more nodes of a third section of the flow UBD BDN model, the one or more nodes comprising: a flow drilling types uncertainty node configured to receive one or more flow drilling types; a flow drilling options decision node configured to receive one or more flow drilling options;
- determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the flow UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
17. A system, comprising:
- one or more processors;
- a non-transitory tangible computer-readable memory accessible by the one or more processors, the memory comprising:
- an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced drilling expert system comprising a gaseated underbalanced drilling Bayesian decision network (BDN) model, the gaseated underbalanced drilling BDN model comprising: a first section, comprising: a gas injection process uncertainty node configured to receive one or more gas injection process types from the one or more inputs; a gas injection processes characteristics decision node configured to receive one or more gas injection process characteristics from the one or more inputs; a first consequences node dependent on the gas injection process uncertainty node and the gas infection processes considerations decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas injection process types and the one or more gas injection process characteristics; a second section, comprising: a fluid volume limits uncertainty node configured to receive one or more fluid volume limits from the one or more inputs; a fluid volume limits requirements decision node configured to receive one or more fluid volume limits requirements from the one or more inputs; a second consequences node dependent on the fluid volume limits uncertainty node and the fluid volume limits requirements decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more fluid volume limits and the one or more fluid volume limits requirements; a third section, comprising: a kick type uncertainty node configured to receive one or more kick types from the one or more inputs; a kicks recommendations decision node configured to receive one or more kicks recommendations from the one or more inputs; a third consequences node dependent on the kick type uncertainty node and the kicks recommendations decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more kick types and the one or more kicks recommendations; and a fourth section, comprising: an operational considerations uncertainty node configured to receive one or more operational considerations from the one or more inputs; an operational recommendations decision node configured to receive one or more operational recommendations from the one or more inputs; and a fourth consequences node dependent on the operational considerations uncertainty node and the operational recommendations decision node and configured to output the one or more gaseated underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more operational recommendations and the one or more operational recommendations.
18. The system of claim 17, wherein the gaseated UBD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences, the third consequences node, and the fourth consequences node.
19. The system of claim 17, comprising a user interface configured to display the gaseated UBD BDN model and receive user selections of the one or more inputs.
20. The system of claim 17, wherein the one or more gas injection process types, the one or more fluid volume limits, the one or kick types, and the one or more operational considerations are each associated with a respective plurality of probabilities
21. A computer-implemented method for an underbalanced drilling expert system having a gaseated underbalanced drilling Bayesian decision network (BDN) model, the method comprising:
- receiving one or more inputs;
- providing the one or more inputs to one or more nodes of a first section of the gaseated underbalanced drilling (UBD) BDN model, the one or more nodes comprising: a gas injection process uncertainty node configured to receive one or more gas injection process types; a gas injection processes characteristics decision node configured to receive one or more gas injection process characteristics;
- determining one or more underbalanced drilling recommendations at a consequences node of the first section of the gaseated UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;
- providing the one or more underbalanced drilling recommendations to a user.
22. The computer-implemented method of claim 21, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the gaseated UBD BDN model.
23. The computer-implemented method of claim 21, comprising:
- providing the one or more inputs to one or more nodes of a second section of the gaseated UBD BDN model, the one or more nodes comprising: a fluid volume limits uncertainty node configured to receive one or more fluid volume limits from the one or more inputs; a fluid volume limits requirements decision node configured to receive one or more fluid volume limits requirements;
- determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the gaseated UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
24. The computer-implemented method of claim 23, comprising:
- providing the one or more inputs to one or more nodes of a third section of the gaseated UBD BDN model, the one or more nodes comprising: a kick type uncertainty node configured to receive one or more kick types; a kicks recommendations decision node configured to receive one or more kicks recommendations;
- determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the gaseated UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
25. The computer-implemented method of claim 24, comprising:
- providing the one or more inputs to one or more nodes of a fourth section of the gaseated UBD BDN model, the one or more nodes comprising: an operational considerations uncertainty node configured to receive one or more operational considerations; an operational recommendations decision node configured to receive one or more operational recommendations;
- determining the one or more underbalanced drilling recommendations at a fourth consequences node of the third section of the gaseated UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
26. A system, comprising:
- one or more processors;
- a non-transitory tangible computer-readable memory accessible by the one or more processors, the memory comprising:
- an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced drilling expert system comprising a foam underbalanced drilling (UBD) Bayesian decision network (BDN) model, the foam UBD BDN model comprising:
- a first section, comprising: a foam systems considerations uncertainty node configured to receive one or more foam systems considerations from the one or more inputs; a foam systems recommendations decision node configured to receive one or more foam systems recommendations from the one or more inputs; and a first consequences node dependent on the foam systems considerations uncertainty node and the foam systems recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more foam systems considerations and the one or more foam systems recommendations; and
- a second section, comprising: a foam systems designs uncertainty node configured to receive one or more foam system designs from the one or more inputs; a foam system designs recommendations decision node configured to receive one or more foam system designs recommendations from the one or more inputs; and a second consequences node dependent on the foam systems designs uncertainty node and the foam system designs recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more foam system designs and the one or more foam system designs recommendations.
27. The system of claim 26, wherein the foam UBD BDN model comprises a final consequences node dependent on the first consequences node and the second consequences node.
28. The system of claim 26, comprising a user interface configured to display the foam UBD BDN model and receive user selections of the one or more inputs.
29. The system of claim 26, wherein the one or more foam systems considerations and the one or more foam systems designs are each associated with a respective plurality of probabilities.
30. A computer-implemented method for an underbalanced drilling expert system having a foam underbalanced drilling (UBD) Bayesian decision network (BDN) model, the method comprising:
- receiving one or more inputs;
- providing the one or more inputs to one or more nodes of a first section of the foam UBD BDN model, the one or more nodes comprising: a foam systems considerations uncertainty node configured to receive one or more foam systems considerations; and a foam systems recommendations decision node configured to receive one or more foam systems recommendations;
- determining one or more underbalanced drilling recommendations at a consequences node of the first section of the foam UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs; and
- providing the one or more underbalanced drilling recommendations to a user.
31. The computer-implemented method of claim 30, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the foam UBD BDN model.
32. The computer-implemented method of claim 30, comprising:
- providing the one or more inputs to one or more nodes of a second section of the foam UBD BDN model, the one or more nodes comprising: a foam systems designs uncertainty node configured to receive one or more foam system designs; a foam system designs recommendations decision node configured to receive one or more foam system designs recommendations;
- determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the foam UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
33. A system, comprising: an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced drilling expert system comprising a gas underbalanced drilling (UBD) Bayesian decision network (BDN) model, the gas underbalanced drilling BDN model comprising:
- one or more processors;
- a non-transitory tangible computer-readable memory, the memory comprising:
- a first section, comprising: a rotary and hammer drilling uncertainty node configured to receive one or more rotary and hammer drilling types from the one or more inputs; a rotary and hammer drilling recommendations decision node configured to receive one or more rotary and hammer drilling recommendations from the one or more inputs; and a first consequences node dependent on the rotary and hammer drilling uncertainty node and the rotary and hammer drilling recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more rotary and hammer drilling types and the one or more rotary and hammer drilling recommendations;
- a second section, comprising: a gas drilling considerations uncertainty node configured to receive one or more gas drilling considerations from the one or more inputs; a gas drilling considerations recommendations decision node configured to receive gas drilling considerations recommendations from the one or more inputs; and a second consequences node dependent on the gas drilling considerations uncertainty node and the gas drilling considerations recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas drilling considerations and the one or more gas drilling considerations recommendations;
- a third section, comprising: a gas drilling operations uncertainty node configured to receive one or more gas drilling operations from the one or more inputs; a gas drilling operations recommendations decision node configured to receive gas drilling operations recommendations from the one or more inputs; and a third consequences node dependent on the gas drilling operations uncertainty node and the gas drilling operations recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas drilling operations and the one or more gas drilling operations recommendations; and
- a fourth section, comprising: a gas drilling rig equipment uncertainty node configured to receive one or more gas drilling rig equipment from the one or more inputs; a gas drilling rig equipment recommendations decision node configured to receive gas drilling rig equipment recommendations from the one or more inputs; and a fourth consequences node dependent on the gas drilling rig equipment uncertainty node and the gas drilling rig equipment recommendations decision node and configured to output the one or more air and gas underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more gas drilling rig equipment and the one or more gas drilling rig equipment recommendations.
34. The system of claim 33, wherein the gas UBD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences node, the third consequences node, and the fourth consequences node.
35. The system of claim 33, comprising a user interface configured to display the gas UBD BDN model and receive user selections of the one or more inputs.
36. The system of claim 33, wherein the one or more rotary and hammer drilling types, the one or more gas drilling considerations, the one or more gas drilling operations, and the one or more gas drilling rig equipment are each associated with a respective plurality of probabilities.
37. A computer-implemented method for an underbalanced drilling expert system having a gas underbalanced drilling (UBD) Bayesian decision network (BDN) model, the method comprising:
- receiving one or more inputs;
- providing the one or more inputs to one or more nodes of a first section of the gas underbalanced drilling BDN model, the one or more nodes comprising: a rotary and hammer drilling uncertainty node; a rotary and hammer recommendations decision node;
- determining one or more underbalanced drilling recommendations at a consequences node of the first section of the gas underbalanced drilling BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;
- providing the one or more underbalanced drilling recommendations to a user.
38. The computer-implemented method of claim 37, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the gas UBD BDN model.
39. The computer-implemented method of claim 37, comprising:
- providing the one or more inputs to one or more nodes of a second section of the gas UBD BDN model, the one or more nodes comprising: a gas drilling considerations uncertainty node configured to receive one or more gas drilling considerations; a gas drilling considerations recommendations decision node configured to receive gas drilling considerations recommendations;
- determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the gas UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
40. The computer-implemented method of claim 39, comprising:
- providing the one or more inputs to one or more nodes of a third section of the gas UBD BDN model, the one or more nodes comprising: a gas drilling operations uncertainty node configured to receive one or more gas drilling operations; a gas drilling operations recommendations decision node configured to receive gas drilling operations recommendations;
- determining the one or more underbalanced drilling recommendations at a second consequences node of the third section of the gas UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
41. The computer-implemented method of claim 40, comprising:
- providing the one or more inputs to one or more nodes of a fourth section of the gas UBD BDN model, the one or more nodes comprising: a gas drilling rig equipment uncertainty node configured to receive one or more gas drilling rig equipment from the one or more inputs; a gas drilling rig equipment recommendations decision node configured to receive gas drilling rig equipment recommendations;
- determining the one or more underbalanced drilling recommendations at a fourth consequences node of the fourth section of the gas UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
42. A system, comprising,
- one or more processors;
- a non-transitory tangible computer-readable memory, the memory comprising:
- a mud cap underbalanced drilling expert system executable by the one or more processors and configured to provide one or more mud cap underbalanced drilling recommendations based on one or more inputs, the mud cap underbalanced drilling expert system comprising a mud cap underbalanced drilling Bayesian decision network (BDN) model, the mud cap underbalanced drilling BDN model comprising: a first section, comprising: a mud cap drilling types uncertainty node configured to receive one or more mud cap drilling types from the one or more inputs; a mud cap drilling types recommendations decision node configured to receive one or more mud cap drilling types recommendations from the one or more inputs; and a first consequences node dependent on the mud cap drilling types uncertainty node and the mud cap drilling types recommendations decision node and configured to output the one or more mud cap underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more mud cap drilling types and the one or more mud cap drilling types recommendations; a second section, comprising: a mud cap drilling problems uncertainty node configured to receive one or more mud cap drilling problems from the one or more inputs; a mud cap drilling problems recommendations decision node configured to receive one or more mud cap drilling problems recommendations from the one or more inputs; and a second consequences node dependent on the mud cap drilling problems uncertainty node and the mud cap drilling problems recommendations decision node and configured to output the one or more mud cap underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more mud cap drilling problems and the one or more mud cap drilling problems recommendations; and a third section, comprising: a floating mud cap drilling considerations uncertainty node configured to receive one or more floating mud cap drilling considerations types from the one or more inputs; a floating mud cap drilling recommendations decision node configured to receive one or more floating mud cap drilling recommendations from the one or more inputs; and a third consequences node dependent on the floating mud cap drilling considerations uncertainty node and the floating mud cap drilling recommendations decision node and configured to output the one or more mud cap underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more floating mud cap drilling considerations types and the one or more floating mud cap drilling recommendations.
43. The system of claim 42, wherein the mud cap UBD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences node, and the third consequences node.
44. The system of claim 42, comprising a user interface configured to display the mud cap UBD BDN model and receive user selections of the one or more inputs.
45. The system of claim 42, wherein the one or more mud cap drilling types, the one or more mud cap drilling problems, and the one or more floating mud cap considerations are each associated with a respective plurality of probabilities.
46. A computer-implemented method for an underbalanced drilling expert system having a mud cap underbalanced drilling (UBD) Bayesian decision network (BDN) model, the method comprising:
- receiving one or more inputs;
- providing the one or more inputs to one or more nodes of a first section of the mud cap UBD BDN model, the one or more nodes comprising: a mud cap drilling types uncertainty node configured to receive one or more mud cap drilling types; a mud cap drilling types recommendations decision node configured to receive one or more mud cap drilling types recommendations;
- determining one or more underbalanced drilling recommendations at a consequences node of the first section of the mud cap UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs; and
- providing the one or more underbalanced drilling recommendations to a user.
47. The computer-implemented method of claim 46, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the mud cap UBD BDN model.
48. The computer-implemented method of claim 46, comprising:
- providing the one or more inputs to one or more nodes of a second section of the mud cap UBD BDN model, the one or more nodes comprising: a mud cap drilling problems uncertainty node configured to receive one or more mud cap drilling problems; a mud cap drilling problems recommendations decision node configured to receive one or more mud cap drilling problems recommendations;
- determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the gas UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
49. The computer-implemented method of claim 48, comprising:
- providing the one or more inputs to one or more nodes of a third section of the mud cap UBD BDN model, the one or more nodes comprising: a floating mud cap drilling considerations uncertainty node configured to receive one or more floating mud cap drilling considerations; a floating mud cap drilling recommendations decision node configured to receive one or more floating mud cap drilling recommendations;
- determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the mud cap UBD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
50. A system, comprising, a underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced expert system comprising an underbalanced liner drilling (UBLD) Bayesian decision network (BDN) model, the UBLD BDN model comprising:
- one or more processors;
- a non-transitory tangible computer-readable memory, the memory comprising:
- a first section, comprising: a UBLD plans uncertainty node configured to receive one or more UBLD plans from the one or more inputs; a UBLD plans recommendations decision node configured to receive one or more UBLD plans recommendations from the one or more inputs; and a first consequences node dependent on the UBLD planning uncertainty node and the UBLD planning recommendations decision node and configured to output the one or more underbalanced liner drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD plans and the one or more UBLD plans recommendations;
- a second section, comprising: a UBLD solvable problems uncertainty node configured to receive one or more UBLD solvable problems from the one or more inputs; a UBLD advantages decision node configured to receive one or more UBLD advantages from the one or more inputs; and a second consequences node dependent on the UBLD problems uncertainty node and the UBLD advantages decision node and configured to output the one or more underbalanced liner drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD solvable problems and the one or more UBLD advantages; and
- a third section, comprising: a UBLD considerations uncertainty node configured to receive one or more UBLD considerations from the one or more inputs; a UBLD considerations recommendations decision node configured to receive one or more UBLD considerations recommendations from the one or more inputs; and a third consequences node dependent on the UBLD considerations uncertainty node and the UBLD recommendations decision node and configured to output the one or more underbalanced liner drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD considerations and the one or more UBLD considerations recommendations.
51. The system of claim 50, wherein the UBLD BDN model comprises a final consequences node dependent on the first consequences node, the second consequences node, and the third consequences node.
52. The system of claim 50, comprising a user interface configured to display the UBLD BDN model and receive user selections of the one or more inputs.
53. The system of claim 50, wherein the one or more UBLD plans, the one or more UBLD solvable problems, and the one or more UBLD considerations are each associated with a respective plurality of probabilities.
54. A computer-implemented method for an underbalanced drilling expert system having an underbalanced liner drilling (UBLD) Bayesian decision network (BDN) model, the method comprising:
- receiving one or more inputs;
- providing the one or more inputs to one or more nodes of a first section of the UBLD BDN model, the one or more nodes comprising: a UBLD plans uncertainty node configured to receive one or more UBLD plans; a UBLD plans recommendations decision node configured to receive one or more UBLD plans recommendations;
- determining one or more underbalanced drilling recommendations at a consequences node of the first section of the UBLD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;
- providing the one or more underbalanced drilling recommendations to a user.
55. The computer-implemented method of claim 54, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the UBLD BDN model.
56. The computer-implemented method of claim 54, comprising:
- providing the one or more inputs to one or more nodes of a second section of the UBLD BDN model, the one or more nodes comprising: a UBLD solvable problems uncertainty node configured to receive one or more UBLD solvable problems; a UBLD advantages decision node configured to receive one or more UBLD advantages;
- determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the UBLD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
57. The computer-implemented method of claim 56, comprising:
- providing the one or more inputs to one or more nodes of a third section of the UBLD BDN model, the one or more nodes comprising: a UBLD considerations uncertainty node configured to receive one or more UBLD considerations; a UBLD considerations recommendations decision node configured to receive one or more UBLD considerations recommendations;
- determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the UBLD BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
58. A system, comprising: an underbalanced drilling (UBD) expert system executable by the one or more processors and configured to provide one or more UBD recommendations based on one or more inputs, the UBD expert system comprising an underbalanced coil tube (UBCT) Bayesian decision network (BDN) model, the UBCT BDN model comprising:
- one or more processors;
- a non-transitory tangible computer-readable memory, the memory comprising:
- a first section, comprising: a UBCT preplanning uncertainty node configured to receive one or more UBCT preplans from the one or more inputs; a UBCT preplanning requirements decision node configured to receive one or more UBCT preplan requirements from the one or more inputs; and a first consequences node dependent on the UBCT preplanning uncertainty node and the UBCT preplanning recommendations decision node and configured to output the one or more UBCT drilling requirements based on one or more Bayesian probabilities calculated from the one or more UBCT preplans and the one or more UBCT preplan requirements; and
- a second section, comprising: a UBCT considerations uncertainty node configured to receive one or more UBCT considerations from the one or more inputs; a UBCT recommendations decision node configured to receive one or more UBCT recommendations from the one or more inputs; and a second consequences node dependent on the UBCT considerations uncertainty node and the UBCT recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBCT considerations and the one or more UBCT recommendations.
59. The system of claim 58, wherein the UBCT BDN model comprises a final consequences node dependent on the first consequences node and the second consequences node.
60. The system of claim 58, comprising a user interface configured to display the UBCT BDN model and receive user selections of the one or more inputs.
61. The system of claim 58, wherein the one or more UBCT preplans and the one or more UBCT considerations are each associated with a respective plurality of probabilities.
62. A computer-implemented method for an underbalanced drilling expert system having an underbalanced coil tube (UBCT) drilling Bayesian decision network (BDN) model, the method comprising:
- receiving one or more inputs;
- providing the one or more inputs to one or more nodes of a first section of the UBCT BDN model, the one or more nodes comprising: a UBCT preplanning uncertainty node configured to receive one or more UBCT preplans; a UBCT preplanning requirements decision node configured to receive one or more UBCT preplan requirements;
- determining one or more underbalanced drilling recommendations at a consequences node of the first section of the UBCT BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs;
- providing the one or more underbalanced drilling recommendations to a user.
63. The computer-implemented method of claim 62, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the UBCT BDN model.
64. The computer-implemented method of claim 62, comprising:
- providing the one or more inputs to one or more nodes of a second section of the UBCT BDN model, the one or more nodes comprising: a UBCT considerations uncertainty node configured to receive one or more UBCT considerations; a UBCT recommendations decision node configured to receive one or more UBCT recommendations;
- determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the UBCT BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
65. A system, comprising: an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs, the underbalanced drilling expert system comprising a snubbing and stripping Bayesian decision network (BDN) model, the snubbing and stripping BDN model comprising
- one or more processors;
- a non-transitory tangible computer-readable memory, the memory comprising:
- a first section, comprising: a snubbing types uncertainty node configured to receive one or more snubbing types from the one or more inputs; a snubbing types recommendations decision node configured to receive one or more snubbing types recommendations from the one or more inputs; and a first consequences node dependent on the snubbing types uncertainty node and the snubbing types recommendations decision node and configured to output the one or more underbalanced recommendations based on one or more Bayesian probabilities calculated from the one or more snubbing types and the one or more snubbing types recommendations; and
- a second section, comprising: a snubbing units uncertainty node configured to receive one or more snubbing units from the one or more inputs; a snubbing units recommendations decision node configured to receive one or more snubbing units recommendations from the one or more inputs; and a second consequences node dependent on the snubbing units uncertainty node and the snubbing units recommendations decision node and configured to output the one or more stripping and snubbing recommendations based on one or more Bayesian probabilities calculated from the one or more snubbing units types and the one or more snubbing units recommendations; and
- a third section, comprising: a snubbing operations uncertainty node configured to receive one or more snubbing operations from the one or more inputs; a snubbing operations recommendations decision node configured to receive one or more snubbing operations recommendations from the one or more inputs; and a third consequences node dependent on the snubbing operations uncertainty node and the snubbing operations recommendations decision node and configured to output the one or more stripping and snubbing recommendations based on one or more Bayesian probabilities calculated from the one or more snubbing operations and the one or more snubbing operations recommendations; and
- a fourth section, comprising: a stripping procedures uncertainty node configured to receive one or more stripping procedures from the one or more inputs; a stripping procedures recommendations decision node configured to receive one or more stripping procedures recommendations from the one or more inputs; and a fourth consequences node dependent on the stripping procedures uncertainty node and the stripping procedures recommendations decision node and configured to output the one or more stripping and snubbing recommendations based on one or more Bayesian probabilities calculated from the one or more stripping procedures and the one or more stripping procedures recommendations.
66. The system of claim 65, wherein the snubbing and stripping BDN model comprises a final consequences node dependent on the first consequences node and the second consequences node.
67. The system of claim 65, comprising a user interface configured to display the snubbing and stripping BDN model and receive user selections of the one or more inputs.
68. The system of claim 65, wherein the one or more snubbing types, the one or more snubbing units, the one or more snubbing operations, and the one or more stripping procedures are each associated with a respective plurality of probabilities.
69. A computer-implemented method for an underbalanced drilling expert system having a snubbing and stripping Bayesian decision network (BDN) model, the method comprising:
- receiving one or more inputs;
- providing the one or more inputs to one or more nodes of a first section of the snubbing and stripping BDN model, the one or more nodes comprising: a snubbing types uncertainty node configured to receive one or more snubbing types; a snubbing types recommendations decision node configured to receive one or more snubbing types recommendations;
- determining one or more underbalanced drilling recommendations at a consequences node of the first section of the snubbing and stripping BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs; and
- providing the one or more underbalanced drilling recommendations to a user.
70. The computer-implemented method of claim 69, wherein providing the one or more underbalanced drilling recommendations to a user comprises displaying the one or more underbalanced drilling recommendations in a user interface element of a user interface configured to display the snubbing and stripping BDN model.
71. The computer-implemented method of claim 69, comprising:
- providing the one or more inputs to one or more nodes of a second section of the snubbing and stripping BDN model, the one or more nodes comprising: a snubbing units uncertainty node configured to receive one or more snubbing units; a snubbing units recommendations decision node configured to receive one or more snubbing units recommendations;
- determining the one or more underbalanced drilling recommendations at a second consequences node of the second section of the snubbing and stripping BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
72. The computer-implemented method of claim 71, comprising:
- providing the one or more inputs to one or more nodes of a third section of the snubbing and stripping BDN model, the one or more nodes comprising: a snubbing operations uncertainty node configured to receive one or more snubbing operations; a snubbing operations recommendations decision node configured to receive one or more snubbing operations recommendations;
- determining the one or more underbalanced drilling recommendations at a third consequences node of the third section of the snubbing and stripping BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
73. The computer-implemented method of claim 72, comprising:
- providing the one or more inputs to one or more nodes of a fourth section of the snubbing and stripping BDN model, the one or more nodes comprising: a stripping procedures uncertainty node configured to receive one or more stripping procedures; a stripping procedures recommendations decision node configured to receive one or more stripping procedures recommendations;
- determining the one or more underbalanced drilling recommendations at a fourth consequences node of the fourth section of the snubbing and stripping BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs.
Type: Application
Filed: Mar 14, 2013
Publication Date: May 8, 2014
Inventors: Abdullah Saleh Hussain Al-Yami (Dhahran), Jerome Schubert (College Station, TX)
Application Number: 13/827,746