WELL CHEMICAL INJECTION CONTROL

Abstract

A method can include receiving data for a well; determining a gas flow rate and a liquid level for the well; determining an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and issuing a signal to chemical injection equipment to inject the amount of chemical into the well.

Description

RELATED APPLICATION

This application claims priority to and the benefit of a US Provisional Application having Ser. No. 63/436,442, filed 30 Dec. 2022, which is incorporated by reference herein in its entirety.

BACKGROUND

A reservoir can be a subsurface formation that can be characterized at least in part by its porosity and fluid permeability. As an example, a reservoir may be part of a basin such as a sedimentary basin. A basin can be a depression (e.g., caused by plate tectonic activity, subsidence, etc.) in which sediments accumulate. As an example, where hydrocarbon source rocks occur in combination with appropriate depth and duration of burial, a petroleum system may develop within a basin, which may form a reservoir that includes hydrocarbon fluids (e.g., oil, gas, etc.).

In oil and gas exploration, interpretation is a process that involves analysis of data to identify and locate various subsurface structures (e.g., horizons, faults, geobodies, etc.) in a geologic environment. Various types of structures (e.g., stratigraphic formations) may be indicative of hydrocarbon traps or flow channels, as may be associated with one or more reservoirs (e.g., fluid reservoirs). In the field of resource extraction, enhancements to interpretation can allow for construction of a more accurate model of a subsurface region, which, in turn, may improve characterization of the subsurface region for purposes of resource extraction. Characterization of one or more subsurface regions in a geologic environment can guide, for example, performance of one or more operations (e.g., field operations, etc.). As an example, a more accurate model of a subsurface region may make a drilling operation more accurate as to a borehole's trajectory where the borehole is to have a trajectory that penetrates a reservoir, etc., where fluid may be produced via the borehole (e.g., as a completed well, etc.). As an example, one or more workflows may be performed using one or more computational frameworks and/or one or more pieces of equipment that include features for one or more of analysis, acquisition, model building, control, etc., for exploration, interpretation, drilling, fracturing, production, etc.

SUMMARY

A method can include receiving data for a well; determining a gas flow rate and a liquid level for the well; determining an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and issuing a signal to chemical injection equipment to inject the amount of chemical into the well. A system can include one or more processors; memory accessible to the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: receive data for a well; determine a gas flow rate and a liquid level for the well; determine an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and issue a signal to chemical injection equipment to inject the amount of chemical into the well. One or more computer-readable media can include computer-executable instructions executable by a system to instruct the system to: receive data for a well; determine a gas flow rate and a liquid level for the well; determine an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and issue a signal to chemical injection equipment to inject the amount of chemical into the well. Various other apparatuses, systems, methods, etc., are also disclosed.

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the described implementations can be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.

FIG. 1 illustrates an example system that includes various framework components associated with one or more geologic environments;

FIG. 2 illustrates examples of systems;

FIG. 3 illustrates an example of a system;

FIG. 4 illustrates an example of a system and an example plot;

FIG. 5 illustrates an example of a system;

FIG. 6 illustrates example plots;

FIG. 7 illustrates an example of a table;

FIG. 8 illustrates an example of a framework;

FIG. 9 illustrates an example of a method and an example of a system; and

FIG. 10 illustrates examples of computer and network equipment.

DETAILED DESCRIPTION

This description is not to be taken in a limiting sense, but rather is made merely for the purpose of describing the general principles of the implementations. The scope of the described implementations should be ascertained with reference to the issued claims.

FIG. 1 shows an example of a system 100 that includes a workspace framework 110 that can provide for instantiation of, rendering of, interactions with, etc., a graphical user interface (GUI) 120. In the example of FIG. 1, the GUI 120 can include graphical controls for computational frameworks (e.g., applications) 121, projects 122, visualization 123, one or more other features 124, data access 125, and data storage 126. As an example, the system 100 can provide for instantiation of one or more computational frameworks for a process operations environment that includes equipment at one or more sites that can perform process operations.

In the example of FIG. 1, the workspace framework 110 may be tailored to a particular geologic environment such as an example geologic environment 150. For example, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and that may be intersected by a fault 153. As an example, the geologic environment 150 may be outfitted with a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a wellsite and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, FIG. 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

In the example of FIG. 1, the GUI 120 shows some examples of computational frameworks, including the DRILLPLAN, PETREL, TECHLOG, PETROMOD, ECLIPSE, PIPESIM, and INTERSECT frameworks (SLB, Houston, Texas).

The DRILLPLAN framework provides for digital well construction planning and includes features for automation of repetitive tasks and validation workflows, enabling improved quality drilling programs (e.g., digital drilling plans, etc.) to be produced quickly with assured coherency.

The PETREL framework can be part of the DELFI cognitive E&P environment (SLB, Houston, Texas) for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir.

The TECHLOG framework can handle and process field and laboratory data for a variety of geologic environments (e.g., deepwater exploration, shale, etc.). The TECHLOG framework can structure wellbore data for analyses, planning, etc.

The PETROMOD framework provides petroleum systems modeling capabilities that can combine one or more of seismic, well, and geological information to model the evolution of a sedimentary basin. The PETROMOD framework can predict if, and how, a reservoir has been charged with hydrocarbons, including the source and timing of hydrocarbon generation, migration routes, quantities, and hydrocarbon type in the subsurface or at surface conditions.

The ECLIPSE framework provides a reservoir simulator (e.g., as a computational framework) with numerical solutions for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes.

The INTERSECT framework provides a high-resolution reservoir simulator for simulation of detailed geological features and quantification of uncertainties, for example, by creating accurate production scenarios and, with the integration of precise models of the surface facilities and field operations, the INTERSECT framework can produce reliable results, which may be continuously updated by real-time data exchanges (e.g., from one or more types of data acquisition equipment in the field that can acquire data during one or more types of field operations, etc.). The INTERSECT framework can provide completion configurations for complex wells where such configurations can be built in the field, can provide detailed enhanced-oil-recovery (EOR) formulations where such formulations can be implemented in the field, can analyze application of steam injection and other thermal EOR techniques for implementation in the field, advanced production controls in terms of reservoir coupling and flexible field management, and flexibility to script customized solutions for improved modeling and field management control. The INTERSECT framework, as with the other example frameworks, may be utilized as part of the DELFI cognitive E&P environment, for example, for rapid simulation of multiple concurrent cases. For example, a workflow may utilize one or more of the DELFI environment on demand reservoir simulation features.

The aforementioned DELFI environment provides various features for workflows as to subsurface analysis, planning, construction and production, for example, as illustrated in the workspace framework 110. Such an environment may be referred to as a process operations environment that can include a variety of frameworks (e.g., applications, etc.). As shown in FIG. 1, outputs from the workspace framework 110 can be utilized for directing, controlling, etc., one or more processes in the geologic environment 150 and, feedback 160, can be received via one or more interfaces in one or more forms (e.g., acquired data as to operational conditions, equipment conditions, environment conditions, etc.).

In the example of FIG. 1, the visualization features 123 may be implemented via the workspace framework 110, for example, to perform tasks as associated with one or more of subsurface regions, planning operations, constructing wells and/or surface fluid networks, and producing from a reservoir.

As an example, a visualization process can implement one or more of various features that can be suitable for one or more web applications. For example, a template may involve use of the JAVASCRIPT object notation format (JSON) and/or one or more other languages/formats. As an example, a framework may include one or more converters. For example, consider a JSON to PYTHON converter and/or a PYTHON to JSON converter. Such an approach can provide for compatibility of devices, frameworks, etc., with respect to one or more sets of instructions.

As an example, visualization features can provide for visualization of various earth models, properties, etc., in one or more dimensions. As an example, visualization features can provide for rendering of information in multiple dimensions, which may optionally include multiple resolution rendering. In such an example, information being rendered may be associated with one or more frameworks and/or one or more data stores. As an example, visualization features may include one or more control features for control of equipment, which can include, for example, field equipment that can perform one or more field operations. As an example, a workflow may utilize one or more frameworks to generate information that can be utilized to control one or more types of field equipment (e.g., drilling equipment, wireline equipment, fracturing equipment, etc.).

As to a reservoir model that may be suitable for utilization by a simulator, consider acquisition of seismic data as acquired via reflection seismology, which finds use in geophysics, for example, to estimate properties of subsurface formations and to determine locations of various subsurface features. As an example, reflection seismology may provide seismic data representing waves of elastic energy (e.g., as transmitted by P-waves and S-waves, in a frequency range of approximately 1 Hz to approximately 100 Hz). Seismic data may be processed and interpreted, for example, to understand better composition, fluid content, extent and geometry of subsurface rocks. Such interpretation results can be utilized to plan, simulate, perform, etc., one or more operations for production of fluid from a reservoir (e.g., reservoir rock, etc.).

Field acquisition equipment may be utilized to acquire seismic data, which may be in the form of traces where a trace can include values organized with respect to time and/or depth (e.g., consider 1D, 2D, 3D or 4D seismic data). For example, consider acquisition equipment that acquires digital samples at a rate of one sample per approximately 4 ms. Given a speed of sound in a medium or media, a sample rate may be converted to an approximate distance. For example, the speed of sound in rock may be on the order of around 5 km per second. Thus, a sample time spacing of approximately 4 ms would correspond to a sample “depth” spacing of about 10 meters (e.g., assuming a path length from source to boundary and boundary to sensor). As an example, a trace may be about 4 seconds in duration; thus, for a sampling rate of one sample at about 4 ms intervals, such a trace would include about 1000 samples where latter acquired samples correspond to deeper reflection boundaries. If the 4 second trace duration of the foregoing example is divided by two (e.g., to account for reflection), for a vertically aligned source and sensor, a deepest boundary depth may be estimated to be about 10 km (e.g., assuming a speed of sound of about 5 km per second).

As an example, a model may be a simulated version of a geologic environment. As an example, a simulator may include features for simulating physical phenomena in a geologic environment based at least in part on a model or models. A simulator, such as a reservoir simulator, can simulate fluid flow in a geologic environment based at least in part on a model that can be generated via a framework that receives seismic data. A simulator can be a computerized system (e.g., a computing system) that can execute instructions using one or more processors to solve a system of equations that describe physical phenomena subject to various constraints. In such an example, the system of equations may be spatially defined (e.g., numerically discretized) according to a spatial model that includes layers of rock, geobodies, etc., that have corresponding positions that can be based on interpretation of seismic and/or other data. A spatial model may be a cell-based model where cells are defined by a grid (e.g., a mesh). A cell in a cell-based model can represent a physical area or volume in a geologic environment where the cell can be assigned physical properties (e.g., permeability, fluid properties, etc.) that may be germane to one or more physical phenomena (e.g., fluid volume, fluid flow, pressure, etc.). A reservoir simulation model can be a spatial model that may be cell-based.

A simulator can be utilized to simulate the exploitation of a real reservoir, for example, to examine different productions scenarios to find an optimal one before production or further production occurs. A reservoir simulator does not provide an exact replica of flow in and production from a reservoir at least in part because the description of the reservoir and the boundary conditions for the equations for flow in a porous rock are generally known with an amount of uncertainty. Certain types of physical phenomena occur at a spatial scale that can be relatively small compared to size of a field. A balance can be struck between model scale and computational resources that results in model cell sizes being of the order of meters; rather than a lesser size (e.g., a level of detail of pores). A modeling and simulation workflow for multiphase flow in porous media (e.g., reservoir rock, etc.) can include generalizing real micro-scale data from macro scale observations (e.g., seismic data and well data) and upscaling to a manageable scale and problem size. Uncertainties can exist in input data and solution procedure such that simulation results too are to some extent uncertain. A process known as history matching can involve comparing simulation results to actual field data acquired during production of fluid from a field. Information gleaned from history matching, can provide for adjustments to a model, data, etc., which can help to increase accuracy of simulation.

As an example, a simulator may utilize an object-based software framework, which may include entities based on pre-defined classes to facilitate modeling and simulation. As an example, an object class can encapsulate reusable code and associated data structures. Object classes can be used to instantiate object instances for use by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data. A model of a basin, a reservoir, etc. may include one or more boreholes where a borehole may be, for example, for measurements, injection, production, etc. As an example, a borehole may be a wellbore of a well, which may be a completed well (e.g., for production of a resource from a reservoir, for injection of material, etc.).

While several simulators are illustrated in the example of FIG. 1, one or more other simulators may be utilized, additionally or alternatively. For example, consider the KINETIX/VISAGE geomechanics simulator (SLB, Houston Texas). The KINETIX/VISAGE simulator can include finite element numerical solvers that may provide simulation results such as, for example, results as to compaction and subsidence of a geologic environment, well and completion integrity in a geologic environment, cap-rock and fault-seal integrity in a geologic environment, fracture behavior in a geologic environment, thermal recovery in a geologic environment, CO2 disposal, etc. The aforementioned PIPESIM simulator includes solvers that may provide simulation results such as, for example, multiphase flow results (e.g., from a reservoir to a wellhead and beyond, etc.), flowline and surface facility performance, etc. The PIPESIM simulator may be integrated, for example, with the AVOCET production operations framework (SLB, Houston Texas). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as steam-assisted gravity drainage (SAGD), etc.). As an example, the PIPESIM simulator may be an optimizer that can optimize one or more operational scenarios at least in part via simulation of physical phenomena. The MANGROVE simulator (SLB, Houston, Texas) provides for optimization of stimulation design (e.g., stimulation treatment operations such as hydraulic fracturing) in a reservoir-centric environment. The MANGROVE framework can combine scientific and experimental work to predict geomechanical propagation of hydraulic fractures, reactivation of natural fractures, etc., along with production forecasts within 3D reservoir models (e.g., production from a drainage area of a reservoir where fluid moves via one or more types of fractures to a well and/or from a well). The MANGROVE framework can provide results pertaining to heterogeneous interactions between hydraulic and natural fracture networks, which may assist with optimization of the number and location of fracture treatment stages (e.g., stimulation treatment(s)), for example, to increased perforation efficiency and recovery.

The aforementioned PETREL framework provides components that allow for optimization of exploration and development operations. The PETREL framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes (e.g., with respect to one or more geologic environments, etc.). Such a framework may be considered an application (e.g., executable using one or more devices) and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

As mentioned, a framework may be implemented within or in a manner operatively coupled to the DELFI environment, which is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence and machine learning. As an example, such an environment can provide for operations that involve one or more frameworks. The DELFI environment may be referred to as the DELFI framework, which may be a framework of frameworks. As an example, the DELFI framework can include various other frameworks, which can include, for example, one or more types of models (e.g., simulation models, etc.). In some embodiments, coupled to generally refers to a connection between components, which can be an indirect connection (e.g., with intervening components) or direct connection (e.g., without intervening components).

As an example, a workflow may utilize one or more types of data for one or more processes (e.g., stratigraphic modeling, basin modeling, completion designs, drilling, production, injection, etc.). As an example, one or more tools may provide data that can be used in a workflow or workflows that may implement one or more frameworks (e.g., PETREL, TECHLOG, PETROMOD, ECLIPSE, etc.). As explained, a framework or frameworks may be operable in a computational environment, which may be a distributed environment (e.g., cloud-based, etc.). As an example, a computational environment such as the DELFI environment can provide for coordination between projects and individuals where workflows can depend on one or more types of data and utilize one or more frameworks.

FIG. 2 shows an example of a geologic environment 210 that includes reservoirs 211-1 and 211-2, which may be faulted by faults 212-1 and 212-2, an example of a network of equipment 230, an enlarged view of a portion of the network of equipment 230, referred to as network 240, and an example of a system 250. FIG. 2 shows some examples of offshore equipment 214 for oil and gas operations related to the reservoir 211-2 and onshore equipment 216 for oil and gas operations related to the reservoir 211-1.

In the example of FIG. 2, the various equipment 214 and 216 can include drilling equipment, wireline equipment, production equipment, etc. For example, consider the equipment 214 as including a drilling rig that can drill into a formation to reach a reservoir target where a well can be completed for production of hydrocarbons. In such an example, one or more features of the system 100 of FIG. 1 may be utilized. For example, consider utilizing a drilling or well plan framework, a drilling execution framework, etc., to plan, execute, etc., one or more drilling operations.

In FIG. 2, the network 240 can be an example of a relatively small production system network. As shown, the network 240 forms somewhat of a tree like structure where flowlines represent branches (e.g., segments) and junctions represent nodes. As shown in FIG. 2, the network 240 provides for transportation of oil and gas fluids from well locations along flowlines interconnected at junctions with final delivery at a central processing facility.

In the example of FIG. 2, various portions of the network 240 may include conduit. For example, consider a perspective view of a geologic environment that includes two conduits which may be a conduit to Man1 and a conduit to Man3 in the network 240.

As shown in FIG. 2, the example system 250 includes one or more information storage devices 252, one or more computers 254, one or more networks 260 and instructions 270 (e.g., organized as one or more sets of instructions). As to the one or more computers 254, each computer may include one or more processors (e.g., or processing cores) 256 and memory 258 for storing the instructions 270 (e.g., one or more sets of instructions), for example, executable by at least one of the one or more processors. As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc. As an example, imagery such as subsurface imagery and/or surface imagery (e.g., satellite, geological, geophysical, etc.) may be stored, processed, communicated, etc. As an example, data may include SAR data, GPS data, etc. and may be stored, for example, in one or more of the storage devices 252. As an example, information that may be stored in one or more of the storage devices 252 may include information about equipment, location of equipment, orientation of equipment, fluid characteristics, etc.

As an example, the instructions 270 can include instructions (e.g., stored in the memory 258) executable by at least one of the one or more processors 256 to instruct the system 250 to perform various actions. As an example, the system 250 may be configured such that the instructions 270 provide for establishing a framework, for example, that can perform network modeling (see, e.g., the PIPESIM framework of the example of FIG. 1, etc.). As an example, one or more methods, techniques, etc. may be performed using one or more sets of instructions, which may be, for example, the instructions 270 of FIG. 2.

FIG. 3 shows an example of a wellsite system 300 (e.g., at a wellsite that may be onshore or offshore). As shown, the wellsite system 300 can include a mud tank 301 for holding mud and other material (e.g., where mud can be a drilling fluid), a suction line 303 that serves as an inlet to a mud pump 304 for pumping mud from the mud tank 301 such that mud flows to a vibrating hose 306, a drawworks 307 for winching drill line or drill lines 312, a standpipe 308 that receives mud from the vibrating hose 306, a kelly hose 309 that receives mud from the standpipe 308, a gooseneck or goosenecks 310, a traveling block 311, a crown block 313 for carrying the traveling block 311 via the drill line or drill lines 312, a derrick 314, a kelly 318 or a top drive 340, a kelly drive bushing 319, a rotary table 320, a drill floor 321, a bell nipple 322, one or more blowout preventors (BOPs) 323, a drillstring 325, a drill bit 326, a casing head 327 and a flow pipe 328 that carries mud and other material to, for example, the mud tank 301.

In the example system of FIG. 3, a borehole 332 is formed in subsurface formations 330 by rotary drilling; noting that various example embodiments may also use one or more directional drilling techniques, equipment, etc.

As shown in the example of FIG. 3, the drillstring 325 is suspended within the borehole 332 and has a drillstring assembly 350 that includes the drill bit 326 at its lower end. As an example, the drillstring assembly 350 may be a bottom hole assembly (BHA).

The wellsite system 300 can provide for operation of the drillstring 325 and other operations. As shown, the wellsite system 300 includes the traveling block 311 and the derrick 314 positioned over the borehole 332. As mentioned, the wellsite system 300 can include the rotary table 320 where the drillstring 325 pass through an opening in the rotary table 320.

As shown in the example of FIG. 3, the wellsite system 300 can include the kelly 318 and associated components, etc., or the top drive 340 and associated components. As to a kelly example, the kelly 318 may be a square or hexagonal metal/alloy bar with a hole drilled therein that serves as a mud flow path. The kelly 318 can be used to transmit rotary motion from the rotary table 320 via the kelly drive bushing 319 to the drillstring 325, while allowing the drillstring 325 to be lowered or raised during rotation. The kelly 318 can pass through the kelly drive bushing 319, which can be driven by the rotary table 320. As an example, the rotary table 320 can include a master bushing that operatively couples to the kelly drive bushing 319 such that rotation of the rotary table 320 can turn the kelly drive bushing 319 and hence the kelly 318. The kelly drive bushing 319 can include an inside profile matching an outside profile (e.g., square, hexagonal, etc.) of the kelly 318; however, with slightly larger dimensions so that the kelly 318 can freely move up and down inside the kelly drive bushing 319.

As to a top drive example, the top drive 340 can provide functions performed by a kelly and a rotary table. The top drive 340 can turn the drillstring 325. As an example, the top drive 340 can include one or more motors (e.g., electric and/or hydraulic) connected with appropriate gearing to a short section of pipe called a quill, that in turn may be screwed into a saver sub or the drillstring 325 itself. The top drive 340 can be suspended from the traveling block 311, so the rotary mechanism is free to travel up and down the derrick 314. As an example, a top drive 340 may allow for drilling to be performed with more joint stands than a kelly/rotary table approach.

In the example of FIG. 3, the mud tank 301 can hold mud, which can be one or more types of drilling fluids. As an example, a wellbore may be drilled to produce fluid, inject fluid or both (e.g., hydrocarbons, minerals, water, etc.).

In the example of FIG. 3, the drillstring 325 (e.g., including one or more downhole tools) may be composed of a series of pipes threadably connected together to form a long tube with the drill bit 326 at the lower end thereof. As the drillstring 325 is advanced into a wellbore for drilling, at some point in time prior to or coincident with drilling, the mud may be pumped by the pump 304 from the mud tank 301 (e.g., or other source) via a the lines 306, 308 and 309 to a port of the kelly 318 or, for example, to a port of the top drive 340. The mud can then flow via a passage (e.g., or passages) in the drillstring 325 and out of ports located on the drill bit 326 (see, e.g., a directional arrow). As the mud exits the drillstring 325 via ports in the drill bit 326, it can then circulate upwardly through an annular region between an outer surface(s) of the drillstring 325 and surrounding wall(s) (e.g., open borehole, casing, etc.), as indicated by directional arrows. In such a manner, the mud lubricates the drill bit 326 and carries heat energy (e.g., frictional or other energy) and formation cuttings to the surface where the mud (e.g., and cuttings) may be returned to the mud tank 301, for example, for recirculation (e.g., with processing to remove cuttings, etc.).

The mud pumped by the pump 304 into the drillstring 325 may, after exiting the drillstring 325, form a mudcake that lines the wellbore which, among other functions, may reduce friction between the drillstring 325 and surrounding wall(s) (e.g., borehole, casing, etc.). A reduction in friction may facilitate advancing or retracting the drillstring 325. During a drilling operation, the entire drillstring 325 may be pulled from a wellbore and optionally replaced, for example, with a new or sharpened drill bit, a smaller diameter drillstring, etc. As mentioned, the act of pulling a drillstring out of a hole or replacing it in a hole is referred to as tripping. A trip may be referred to as an upward trip or an outward trip or as a downward trip or an inward trip depending on trip direction.

As an example, consider a downward trip where upon arrival of the drill bit 326 of the drillstring 325 at a bottom of a wellbore, pumping of the mud commences to lubricate the drill bit 326 for purposes of drilling to enlarge the wellbore. As mentioned, the mud can be pumped by the pump 304 into a passage of the drillstring 325 and, upon filling of the passage, the mud may be used as a transmission medium to transmit energy, for example, energy that may encode information as in mud-pulse telemetry.

As an example, mud-pulse telemetry equipment may include a downhole device configured to effect changes in pressure in the mud to create an acoustic wave or waves upon which information may modulated. In such an example, information from downhole equipment (e.g., one or more modules of the drillstring 325) may be transmitted uphole to an uphole device, which may relay such information to other equipment for processing, control, etc.

As an example, telemetry equipment may operate via transmission of energy via the drillstring 325 itself. For example, consider a signal generator that imparts coded energy signals to the drillstring 325 and repeaters that may receive such energy and repeat it to further transmit the coded energy signals (e.g., information, etc.).

As an example, the drillstring 325 may be fitted with telemetry equipment 352 that includes a rotatable drive shaft, a turbine impeller mechanically coupled to the drive shaft such that the mud can cause the turbine impeller to rotate, a modulator rotor mechanically coupled to the drive shaft such that rotation of the turbine impeller causes said modulator rotor to rotate, a modulator stator mounted adjacent to or proximate to the modulator rotor such that rotation of the modulator rotor relative to the modulator stator creates pressure pulses in the mud, and a controllable brake for selectively braking rotation of the modulator rotor to modulate pressure pulses. In such example, an alternator may be coupled to the aforementioned drive shaft where the alternator includes at least one stator winding electrically coupled to a control circuit to selectively short the at least one stator winding to electromagnetically brake the alternator and thereby selectively brake rotation of the modulator rotor to modulate the pressure pulses in the mud.

In the example of FIG. 3, an uphole control and/or data acquisition system 362 may include circuitry to sense pressure pulses generated by telemetry equipment 352 and, for example, communicate sensed pressure pulses or information derived therefrom for process, control, etc.

The assembly 350 of the illustrated example includes a logging-while-drilling (LWD) module 354, a measurement-while-drilling (MWD) module 356, an optional module 358, a rotary-steerable system (RSS) and/or motor 360, and the drill bit 326. Such components or modules may be referred to as tools where a drillstring can include a plurality of tools.

As to a RSS, it involves technology utilized for directional drilling. Directional drilling involves drilling into the Earth to form a deviated bore such that the trajectory of the bore is not vertical; rather, the trajectory deviates from vertical along one or more portions of the bore. As an example, consider a target that is located at a lateral distance from a surface location where a rig may be stationed. In such an example, drilling can commence with a vertical portion and then deviate from vertical such that the bore is aimed at the target and, eventually, reaches the target. Directional drilling may be implemented where a target may be inaccessible from a vertical location at the surface of the Earth, where material exists in the Earth that may impede drilling or otherwise be detrimental (e.g., consider a salt dome, etc.), where a formation is laterally extensive (e.g., consider a relatively thin yet laterally extensive reservoir), where multiple bores are to be drilled from a single surface bore, where a relief well is desired, etc.

One approach to directional drilling involves a mud motor; however, a mud motor can present some challenges depending on factors such as rate of penetration (ROP), transferring weight to a bit (e.g., weight on bit, WOB) due to friction, etc. A mud motor can be a positive displacement motor (PDM) that operates to drive a bit (e.g., during directional drilling, etc.). A PDM operates as drilling fluid is pumped through it where the PDM converts hydraulic power of the drilling fluid into mechanical power to cause the bit to rotate.

As an example, a PDM may operate in a combined rotating mode where surface equipment is utilized to rotate a bit of a drillstring (e.g., a rotary table, a top drive, etc.) by rotating the entire drillstring and where drilling fluid is utilized to rotate the bit of the drillstring. In such an example, a surface RPM (SRPM) may be determined by use of the surface equipment and a downhole RPM of the mud motor may be determined using various factors related to flow of drilling fluid, mud motor type, etc. As an example, in the combined rotating mode, bit RPM can be determined or estimated as a sum of the SRPM and the mud motor RPM, assuming the SRPM and the mud motor RPM are in the same direction.

As an example, a PDM mud motor can operate in a so-called sliding mode, when the drillstring is not rotated from the surface. In such an example, a bit RPM can be determined or estimated based on the RPM of the mud motor.

A RSS can drill directionally where there is continuous rotation from surface equipment, which can alleviate the sliding of a steerable motor (e.g., a PDM). A RSS may be deployed when drilling directionally (e.g., deviated, horizontal, or extended-reach wells). A RSS can aim to minimize interaction with a borehole wall, which can help to preserve borehole quality. A RSS can aim to exert a relatively consistent side force akin to stabilizers that rotate with the drillstring or orient the bit in the desired direction while continuously rotating at the same number of rotations per minute as the drillstring.

The LWD module 354 may be housed in a suitable type of drill collar and can contain one or a plurality of selected types of logging tools. It will also be understood that more than one LWD and/or MWD module can be employed. Where the position of an LWD module is mentioned, as an example, it may refer to a module at the position of the LWD module 354, the MWD module 356, etc. An LWD module can include capabilities for measuring, processing, and storing information, as well as for communicating with the surface equipment. In the illustrated example, the LWD module 354 may include a seismic measuring device.

The MWD module 356 may be housed in a suitable type of drill collar and can contain one or more devices for measuring characteristics of the drillstring 325 and the drill bit 326. As an example, the MWD module 356 may include equipment for generating electrical power, for example, to power various components of the drillstring 325. As an example, the MWD module 356 may include the telemetry equipment 352, for example, where the turbine impeller can generate power by flow of the mud; it being understood that other power and/or battery systems may be employed for purposes of powering various components. As an example, the MWD module 356 may include one or more of the following types of measuring devices: a weight-on-bit measuring device, a torque measuring device, a vibration measuring device, a shock measuring device, a stick slip measuring device, a direction measuring device, and an inclination measuring device.

FIG. 3 also shows some examples of types of holes that may be drilled. For example, consider a slant hole 372, an S-shaped hole 374, a deep inclined hole 376 and a horizontal hole 378.

As an example, a drilling operation can include directional drilling where, for example, at least a portion of a well includes a curved axis. For example, consider a radius that defines curvature where an inclination with regard to the vertical may vary until reaching an angle between about 30 degrees and about 60 degrees or, for example, an angle to about 90 degrees or possibly greater than about 90 degrees.

As an example, a directional well can include several shapes where each of the shapes may aim to meet particular operational demands. As an example, a drilling process may be performed on the basis of information as and when it is relayed to a drilling engineer. As an example, inclination and/or direction may be modified based on information received during a drilling process.

As an example, deviation of a bore may be accomplished in part by use of a downhole motor and/or a turbine. As to a motor, for example, a drillstring can include a positive displacement motor (PDM).

As an example, a system may be a steerable system and include equipment to perform method such as geosteering. As mentioned, a steerable system can be or include an RSS. As an example, a steerable system can include a PDM or of a turbine on a lower part of a drillstring which, just above a drill bit, a bent sub can be mounted. As an example, above a PDM, MWD equipment that provides real time or near real time data of interest (e.g., inclination, direction, pressure, temperature, real weight on the drill bit, torque stress, etc.) and/or LWD equipment may be installed. As to the latter, LWD equipment can make it possible to send to the surface various types of data of interest, including for example, geological data (e.g., gamma ray log, resistivity, density and sonic logs, etc.).

The coupling of sensors providing information on the course of a well trajectory, in real time or near real time, with, for example, one or more logs characterizing the formations from a geological viewpoint, can allow for implementing a geosteering method. Such a method can include navigating a subsurface environment, for example, to follow a desired route to reach a desired target or targets.

As an example, a drillstring can include an azimuthal density neutron (ADN) tool for measuring density and porosity; a MWD tool for measuring inclination, azimuth and shocks; a compensated dual resistivity (CDR) tool for measuring resistivity and gamma ray related phenomena; one or more variable gauge stabilizers; one or more bend joints; and a geosteering tool, which may include a motor and optionally equipment for measuring and/or responding to one or more of inclination, resistivity and gamma ray related phenomena.

As an example, geosteering can include intentional directional control of a wellbore based on results of downhole geological logging measurements in a manner that aims to keep a directional wellbore within a desired region, zone (e.g., a pay zone), etc. As an example, geosteering may include directing a wellbore to keep the wellbore in a particular section of a reservoir, for example, to minimize gas and/or water breakthrough and, for example, to maximize economic production from a well that includes the wellbore.

Referring again to FIG. 3, the wellsite system 300 can include one or more sensors 364 that are operatively coupled to the control and/or data acquisition system 362. As an example, a sensor or sensors may be at surface locations. As an example, a sensor or sensors may be at downhole locations. As an example, a sensor or sensors may be at one or more remote locations that are not within a distance of the order of about one hundred meters from the wellsite system 300. As an example, a sensor or sensor may be at an offset wellsite where the wellsite system 300 and the offset wellsite are in a common field (e.g., oil and/or gas field).

As an example, one or more of the sensors 364 can be provided for tracking pipe, tracking movement of at least a portion of a drillstring, etc. As an example, the system 300 can include one or more sensors 366 that can sense and/or transmit signals to a fluid conduit such as a drilling fluid conduit (e.g., a drilling mud conduit). For example, in the system 300, the one or more sensors 366 can be operatively coupled to portions of the standpipe 308 through which mud flows. As an example, a downhole tool can generate pulses that can travel through the mud and be sensed by one or more of the one or more sensors 366. In such an example, the downhole tool can include associated circuitry such as, for example, encoding circuitry that can encode signals, for example, to reduce demands as to transmission. As an example, circuitry at the surface may include decoding circuitry to decode encoded information transmitted at least in part via mud-pulse telemetry. As an example, circuitry at the surface may include encoder circuitry and/or decoder circuitry and circuitry downhole may include encoder circuitry and/or decoder circuitry. As an example, the system 300 can include a transmitter that can generate signals that can be transmitted downhole via mud (e.g., drilling fluid) as a transmission medium.

As an example, one or more portions of a drillstring may become stuck. The term stuck can refer to one or more of varying degrees of inability to move or remove a drillstring from a bore. As an example, in a stuck condition, it might be possible to rotate pipe or lower it back into a bore or, for example, in a stuck condition, there may be an inability to move the drillstring axially in the bore, though some amount of rotation may be possible. As an example, in a stuck condition, there may be an inability to move at least a portion of the drillstring axially and rotationally.

As to the term “stuck pipe”, this can refer to a portion of a drillstring that cannot be rotated or moved axially. As an example, a condition referred to as “differential sticking” can be a condition whereby the drillstring cannot be moved (e.g., rotated or reciprocated) along the axis of the bore. Differential sticking may occur when high-contact forces caused by low reservoir pressures, high wellbore pressures, or both, are exerted over a sufficiently large area of the drillstring. Differential sticking can have time and financial cost.

As an example, a sticking force can be a product of the differential pressure between the wellbore and the reservoir and the area that the differential pressure is acting upon. This means that a relatively low differential pressure (delta p) applied over a large working area can be just as effective in sticking pipe as can a high differential pressure applied over a small area.

As an example, a condition referred to as “mechanical sticking” can be a condition where limiting or prevention of motion of the drillstring by a mechanism other than differential pressure sticking occurs. Mechanical sticking can be caused, for example, by one or more of junk in the hole, wellbore geometry anomalies, cement, keyseats or a buildup of cuttings in the annulus. One or more techniques, control algorithms, etc., may be implemented during drilling to reduce risks of issues that may result in non-productive time (NPT). As an example, a system such as the system 100 of FIG. 1 may be utilized before, during or after drilling for purposes of reducing risks (e.g., via simulation, control, etc.).

FIG. 4 shows an example of a system 400 that includes one or more wells 411, 412, 413 and 414, noting that fewer than four wells or more than four wells may be included in the system 400. As shown, the system 400 can include various sensors 420 operatively coupled to equipment 430 and/or a gateway 440, which can include various features for execution of one or more applications (e.g., edge applications, etc.).

FIG. 4 also shows an example plot 490 of pressure versus time that includes a region of stable conditions for a well (see, e.g., dashed oval) such as, for example, the well 411. As indicated, conditions can include casing head pressure (CHP), tubing head pressure (THP), and a pressure differential (Delta P); noting that one or more temperatures may be measured (e.g., consider tubing head temperature (THT), etc.). The plot 490 also shows a cutoff value for setting a liquid level cutoff that can depend on pressure. As an example, stable conditions may be observed and analyzed for one or more purposes such as, for example, calibration and/or operation of a system such as the system 400.

As an example, the system 400 may be utilized for foam assisted lift, which may be referred to as foam injection. Such an operation may be considered to be a type of EOR operation and/or artificial lift operation. In the example of FIG. 4, the system 400 can aim to optimize well production by introducing a foam chemical (e.g., or chemicals, etc.). In such an example, injection of the chemical can affect productivity of gas wells directly where fine tuning of the injection can improve the productivity of the well. Without foam injection (e.g., injection of a foaming agent), water coming from a reservoir can make a flow stream denser until water droplets from the flow stream drop to the bottom of the well, which can create a plug and backpressure the reservoir formation thereby reducing gas production progressively until the well stops producing.

In various instances, the natural energy of a hydrocarbon reservoir falls over time such that it drops to a level that is sufficient to only produce a fraction of the initial hydrocarbon in place at the end of a natural depletion stage. As explained, injection of water or other fluids into an underground formation can be performed in an effort to extract hydrocarbon contained in small pores, which can be an EOR technique. In various instances, carbon dioxide, nitrogen, air and/or hydrocarbon gases (e.g., mainly methane) can be used in as part of gas-based EOR. Gas injection can provide for improved microscopic sweeping, which can lead to lower residual oil saturation in pores compared to waterflooding. A challenge associated with gas injection can be poor volumetric sweep efficiency, as a result of which gas does not contact a large fraction of oil and, thus, the overall recovery remains low. Such inefficiency can occur due to channeling (e.g., flow of gas in high permeability streaks in heterogeneous reservoirs), viscous fingering that occurs because of the viscosity difference between the oil and gas, and gravity override due to the large density contrast between the gas and oil. The continued use of gas injection to improve oil recovery and the prospects for its increased use throughout the world provides an impetus to improve sweep efficiency of injected gas. As an example, foam injection may be an EOR technique (e.g., technology) or help to improve an EOR technique involving miscible or immiscible gas displacement (CO2, hydrocarbon gases etc.).

Foaming of injected gas can help to overcome various challenges in gas EOR. Foam can also be used to support thermal (e.g., steam) or chemical (e.g. alkaline-surfactant-polymer) EOR. Foam in a porous media can exist as a gas-liquid mixture, for example, with a continuous liquid phase (e.g., containing the surfactant or foaming agent) wetting rock; whereas, a part or all of the gas may be made discontinuous by thin liquid films called lamellae. Foam can be made by adding a foaming agent (e.g., a surfactant solution) to gas injection. Such an approach can involve a colloidal dispersion in which a gas is dispersed in a continuous liquid phase. Various surfactants can be added to a solution to stabilize foam by reducing interfacial tension. Surfactant stabilized foam can reduce gas mobility in a porous media, consequently improving volumetric sweep efficiency and oil recovery. As an example, a surfactant may be a surface-active agent.

From a reservoir perspective, foams can provide a means to counteract a displacing agent's naturally high mobility and low density and therefore can reduce fingering (channeling) and gravity override. Foams can also be applied near-well to reduce gas coning. In foam flooding the reduction of the interfacial tension (between oil and water) may not be a predominant effect. Rather, a predominant beneficial effect of the foam can be to reduce the mobility of gas. The reduction of gas mobility can depend on a range of factors including pressure and shear rate.

Foam has been used in improved and enhanced oil recovery (IOR/EOR) processes in the petroleum industry. Foam has been used to control the gas mobility and improving the sweep efficiency by increasing the effective viscosity and decreasing the relative permeability of the gas. Foam has also been used for gas shut off to reduce gas/oil ratio (GOR) at production wells.

As explained with respect to the system 400 of FIG. 4, foam injection (e.g., injection of a foaming agent) can help to reduce water related issues where water coming from a reservoir can make a stream flow denser until water droplets from the flow stream drop to the bottom of the well, which can create a plug and backpressure the reservoir formation thereby reducing gas production progressively until the well stops producing. In such an example, one or more foam injection parameters can be tailored to provide for improved results. As an example, the system 400 may operate in an autonomous manner to control one or more foam injection parameters to improve results.

As an example, a framework can provide for improved control of a foam injection operation. In such an example, the framework may operate using well trending parameters such as, for example, tubing head pressure (THP, which may also be known as well head pressure (WHP)), casing head pressure (CHP), and Qg (gas flowrate). As an example, a framework may operate to estimate water inflow from a reservoir into a well and accumulated water in tubing (e.g., liquid loaded level, liquid loading, or column of water).

As explained, accumulated water can backpressure a well and reduce productivity to the point that the well stops producing gas and condensates. To mitigate this effect of liquid loading, while various approaches exist, as explained, foam injection may be utilized that injects a foaming agent (e.g., surfactant or detergent). Foam generated by a foaming agent (e.g., and/or foam stability) can help to alleviate bottomhole pressure and allows a well to produce.

As an example, a framework can compute real-time or near real-time estimates of (1) water inflow from a reservoir into a well and (2) accumulated water in tubing of the well. Based on these estimates, the framework can dosify the volume of foaming agent needed to treat volumes of water (e.g., 1 plus 2) thereby reducing downtime due to liquid loading. As an example, a framework can include one or more features for calibration, for example, to calibrate the framework to a particular well or particular wells and/or to a particular chemical injector or particular chemical injectors. As an example, a framework may provide output for chemical injection into one or more wells. As an example, a framework may provide for output that can call for breaking of foam, for example, using a foam inhibitor (e.g., methanol, etc.).

As an example, a framework can be operatively coupled to a chemical injection equipment package that has the ability to inject on demand a specified volume of chemical (e.g., foaming agent). For example, consider inject X liters of foam per day in a continuous operation or inject X liters in a sporadic operation. As explained, the system 400 can include the gateway 440, which can be operatively coupled to equipment where the gateway 440 can execute one or more applications, which can include one or more applications for determining estimates and, for example, determining appropriate dosing of a foaming agent.

In the example of FIG. 4, the system 400 may perform computations locally (e.g., at the edge) and/or remotely (e.g., using a cloud platform). While real-time and near real-time implementations are mentioned, a system may operate periodically, for example, every minute, every hour, every number of hours, every day, etc. In various instances, periodic operation may be less than a two day interval as a longer interval may lead into too much accumulation of liquid and halting well production; however, such a limit may be determined on a case-by-case basis (e.g., or for a class of wells, a type of reservoir, etc.).

As an example, a gateway can include features of an AGORA gateway (e.g., consider one or more processors, memory, etc., which may be deployed as a “box” that can be locally powered and that can communicate locally with other equipment via one or more interfaces). As an example, one or more pieces of equipment may include computational resources that can be akin to those of an AGORA gateway or more or less than those of an AGORA gateway. As an example, an AGORA gateway may be a network device.

As an example, a gateway can include one or more features of an AGORA gateway (e.g., v.202, v.402, etc.) and/or another gateway. For example, consider an INTEL ATOM E3930 or E3950 Dual Core with DRAM and an eMMC and/or SSD. Such a gateway may include a trusted platform module (TPM), which can provide for secure and measured boot support (e.g., via hashes, etc.). A gateway may include one or more interfaces (e.g., Ethernet, RS485/422, RS232, etc.). As to power, a gateway may consume less than about 100 W (e.g., consider less than 10 W or less than 20 W). As an example, a gateway may include an operating system (e.g., consider LINUX DEBIAN LTS). As an example, a gateway may include a cellular interface (e.g., 4G LTE with Global Modem/GPS, etc.). As an example, a gateway may include a WIFI interface (e.g., 802.11 a/b/g/n). As an example, a gateway may be operable using AC 100-240 V, 50/60 Hz or 24 VDC. As to dimensions, consider a gateway that has a protective box with dimensions of approximately 10 in×8 in×4 in (e.g., 25 cm×20.3 cm×10.1 cm).

As an example, a framework can provide for control of liquid unloading using an optimization scheme that generates an optimal set point, which, for example, can be transmitted to a controller that actuates equipment to inject a foaming agent. In such an example, the controller may be a set point type of controller that may utilize one or more of proportion, integral and derivative control (PID) and/or one or more other types of control schemes. In such an example, the framework can generate an appropriate set point based on sensor data such that the set point can be put into action via appropriate foaming agent injection equipment.

As an example, a framework may operate in a data-driven and/or physics-driven approach. For example, one or more physics-based models, empirical models, and/or machine learning models may be employed. As explained, a framework may operate to provide for autonomous control of a foam injection operation by providing a set point value to be implemented by appropriate foam injection equipment. Without such a framework, an individual may be tasked with determining a set point where the individual may change the set point according to a visitation schedule where the individual visits a wellsite. For example, if the individual visits the wellsite once per day, then the most frequent set point change can be once per day. In such an example, if the set point is too high, that may waste foam and wear foam injection equipment, both of which may increase costs; whereas, if the set point is too low, then production may suffer until the next day where the individual increases the set point. In such an approach, even though foam injection equipment may include features for PID control, those features merely hold a set point and do not determine a proper set point. As an example, a framework may be integrated into foam injection equipment or may be separate from foam injection equipment. As an example, a controller (e.g., PID, etc.) may be integrated into a gateway or may be integrated into foam injection equipment. As explained, one or more approaches may be taken for implementation of a framework that can at least generate set point values for foam injection.

As an example, a system can include a gateway that can be operatively coupled to a flow meter and one or more well sensors, chemical injection equipment (e.g., skid-mounted and solar powered via solar panels), a dashboard component (e.g., for remote visualization and control), and a chemical injection application that can operate autonomously to adjust one or more set points such as, for example, a foaming agent dosing set point and a methanol dosing set point (e.g., an inhibitor, etc.). In such an example, the chemical injection application can autonomously adjust outputs based on well behavior and one or more trending parameters.

As an example, a framework may operate using Qg, THP, CHP and THT as inputs to generate one or more of Qfoam (e.g., foam flow rate) and Qmethanol (e.g., methanol flow rate) as outputs (e.g., a foaming agent flow rate and a foaming inhibitor flow rate); noting that through use of a two-way valve and/or one or more other pieces of fluid control equipment, a framework may operate to deliver one or the either, for example, to either form foam or to break foam. In such an example, the inputs and/or one or more proxies for one or more inputs may be received by the framework (e.g., via an interface, etc.) and the outputs can be transmitted via one or more mechanisms (e.g., wired, wireless, etc.) to chemical injection equipment that can control a set point or other suitable controller that can operate according to one or more of the outputs. As an example, methanol and/or another chemical may be a foam breaker that can disrupt foam structure, for example, if foaming exceeds a desired level and/or if foam breaking is desired.

A framework may be implemented to increase gas production, reduce field visits (e.g., as to set points, loading chemical, manually offload liquid, etc.), and improve chemical inventory consumption, which can reduce emissions (e.g., carbon dioxide, etc., as may be due to transportation, etc.).

FIG. 5 shows an example of a system 500 that includes a two-way valve coupled to a foaming agent source (e.g., a tank) and an inhibitor agent source (e.g., a tank). As explained, a foaming agent may be or include one or more surfactants, which may be referred to as surface active agents; whereas, an inhibitor agent may be foam inhibitor, which may, depending on characteristics, function as a foam breaker (e.g., to break existing foam cells, etc.). As shown, a two-way valve may be utilized as, for example, the foam agent and the inhibitor agent may have opposite effects with respect to foam and/or foaming where the two-way valve is utilized in either one state for delivery of the foam agent or in another state for delivery of the inhibitor agent. As an example, the system 500 of FIG. 5 may include one or more features of the system of FIG. 4, which may, for example, provide for control of delivery of the foam agent and/or the inhibitor agent. For example, the 440 may be included as part of a foam injection unit (e.g., a unit that can inject one or more types of agents for foam formation and/or foam inhibition).

As shown in the example of FIG. 5, the valve can be fluidly coupled to a pump that can be driven by a DC motor as powered by energy stored in a battery, which may be solar energy generated from one or more solar panels and/or one or more other sources of energy. The pump can be controlled using a remote control unit that can be operatively coupled to a remote terminal unit (RTU) and wireless communications unit. In the example of FIG. 5, the system 500 can be operatively coupled to a number of wells (see, e.g., wells 1 to 6). As explained, a system may be skid-mounted for delivery to a wellsite as an assembled unit (e.g., consider the system 500 as including a skid foam injection unit). While FIG. 5 shows six wells, fewer or more wells may be operatively coupled to one or more foam injection units, etc.

FIG. 6 shows an example plot 600 of data over a period of months for gas rate, THP, CHP and temperature. In the plot 600, a first period of time involved no foam injection, a second period of time involved manual injection (e.g., periodically), and a third period of time involved automatic injection using a system such as the system 500 of FIG. 5. As shown, the performance of the well became more stable with the automatic injection, particularly with respect to CHP and temperature; noting that gas rate spikes can occur with or without automatic injection.

FIG. 6 also shows example plots 612 and 614 where, prior to control, gas rate 612 is seen as decreasing with an increase in CHP 614 due to water loading; whereas, after control, gas rate and CHP are relatively stable and constant.

Without automated control, well behavior can be controlled to some extent via intermittent foam lift (e.g., manual dosing); however, casing pressure buildup can occur gradually as indicated while still having substantial water loading in a wellbore. With automated control, a process can have relatively continuous foam lift according to a set point, which may be manually determined, where casing pressure can be maintained to be relatively low and stable, which means no substantial water loading in the wellbore. With automated control, there can be a substantial increase in gas production per well (e.g., with continuous foam injection compared to manual intermittent operation). As explained, an approach that utilizes automated control can help to reduce human costs, risks and transportation and can increase gas production efficiency.

FIG. 7 shows an example of a table 700 for a period of manual injection and for a period of automatic injection. As shown, the manual injection period utilized a fixed chemical injection rate where injection was sporadic (e.g., human dosing), demanded human presence at the wellsite, resulted in intermittent production, caused an increased risk of hydrates (e.g., solids formation) due to discontinued periods of production, and relatively constant liquid loading. In the manual injection period, a person visited the wellsite (e.g., once per week) and injected chemical and then left until a next visit. In some instances, chemical injection for a foaming agent can involve manually dropping (e.g., injecting) a bar of foaming agent material into a well where the mass of the bar is known. With a system such as the system 500 of FIG. 5, automatic injection can provide for changes in chemical injection rate, which may still be set manually (e.g., manual set point) that result in a more appropriate amount of chemical injection, remote control and visualization and stability of production and other parameters. In the example table 700, the automatic injection involved manual set point adjustments that occurred on a daily basis. For example, at a set point value of 25, someone decided that it was not sufficient so that person changed the set point value to 30, which again proved insufficient such that the person upped it again to 100. As a set point value of 100 was too high, the person then readjusted back to 30. Such an approach, while improved compared to manual, still involves human decision making.

As an example, a framework can provide for automated set point adjustments such that an automated system (see, e.g., the system 500 of FIG. 5) can operate autonomously. An autonomous approach can save time and resources by generating and implementing set point values in a robust manner, optionally without human involvement. As an example, the system 400 can provide for autonomous set point adjustments.

As an example, a framework can operate by determining a liquid level in a well. For example, a framework may employ one or more techniques for determining liquid level in a well. In such an example, determination of liquid level may or may not involve using THP and/or CHP. As an example, a framework can operate using gas rate and liquid level as two inputs. As explained, one or more techniques may be utilized to determine liquid level. As an example, a downhole gauge may be available to provide information to determine liquid level. As an example, one or more machine learning models may be utilized to determine liquid level based on one or more inputs (e.g., one or more of THP, CHP, etc.). As to gas rate, it may be determined using one or more techniques.

As an example, a framework can determine how much foaming agent is needed in real-time to provide for desirable flow from a well. In such an example, the framework can utilize gas rate and liquid level to make such a determination. As explained, a determination as to how much foaming agent is needed can be in the form of a set point that can be implemented. As an example, a framework can provide for adaptive set point generation for control of foaming agent injection.

As an example, framework can be implemented locally and/or remotely with respect to a wellsite and/or chemical injection equipment. As explained, a framework can include features to determine liquid level in one or more manners, for example, from CHP and THP, from a downhole gauge, from a trained machine learning model, etc. As to gas rate, it can be determined using an orifice meter or may be determined via one or more other measurements. In general, a gas meter can be installed at a wellsite that can measure gas rate, which can be communicated to a framework.

As an example, chemical injection equipment may provide for chemical injection according to a schedule, which may call for continuous and/or periodic injection. In either instance, a framework can determine an appropriate amount of chemical to injection and whether to do so in a continuous and/or a periodic manner.

FIG. 8 shows an example of a framework 810 that includes various components for inputs 812, gas rate and liquid level 814 and injection amount 816 where output (e.g., injection amount 816) can be transmitted to chemical injection equipment 820.

As an example, a method can include identifying a well condition. For example, consider a method that includes identifying a stable period of production (e.g., stable CHP and THP). In such an example, if a stable period is identified, then the method can proceed to define a baseline for the stable well. However, if not identified, the method can define a baseline for the non-stable well. For example, if a well is not in a stable condition, a method can include adjusting (e.g., manually or automatically) chemical injection of the well until it becomes stable (e.g., stable conditions are achieved). In such an example, based on the latest well test for the well, the method can compute the well water rate (e.g., how much water the well may be producing) along with computing foam concentration:

$f_{\mathrm{conc}}=\frac{Q_{\mathrm{foam}}*1,000,000*158.987*\mathrm{f\_active}}{Q_w*3.78541*42}$

where f_cone is in ppm, Q_foam is in liters per day, Q_w is in liters per day and f_active is in percent.

In such an example, a method can proceed to define a baseline for the non-stable well. Where a well is stable, a method can include identifying the stable period of production (e.g., stable CHP and THP), defining a baseline delta pressure (CHP-THP) at the stable condition, and setting a liquid level cutoff value (e.g., as a pressure) as Max_DeltaP@stable condition*1.2. For example, consider the plot 490 of FIG. 4 as showing various pressure data and other values.

As an example, a method can include providing various inputs such as, for example, the following inputs:

	Density liquid (rhol)	1000	kg/m3
	Density gas (rhog)	0.9	kg/m3
	Tubing diameter (tbg_d_in)	2.441	in
	Water-gas ratio (WGR_m3per10 km3)	0.05	m3/10 km3
	Foam conc. (f_conc)	13741	ppm
	Foam active (f_active)	0.2%
	Cutoff (II_cutoff)	0.4	Mpa

As explained, a baseline may be established in one or more of various manners, which can depend on a well exhibiting stable conditions or not. As explained, for non-stable conditions of a well, a method may involve adjusting chemical injection until stable conditions are achieved. As an example, a method may involve letting a well remain in stable conditions for a number of days (e.g., as may be appropriate) followed by acquiring a latest stable gas flow rate. In such an example, based on a latest well test, a well water flow rate may be computed. In such an example, a foam concentration may be computed, for example, using the foregoing formula for f_conc.

As an example, one or more values may be generated via a well test and/or via one or more other techniques. As an example, one or more methods may be utilized to calibrate a framework and/or to provide for output from a framework such as output suitable for instructing chemical injection equipment. As explained, one or more physics-based approaches may be utilized and/or one or more machine learning model approaches may be utilized. As an example, a workflow can include identifying liquid loading conditions, providing real-time autonomous control of chemical injection, and providing chemical injection closed loop control.

As to types of machine learning models, consider one or more of a support vector machine (SVM) model, a k-nearest neighbors (KNN) model, an ensemble classifier model, a neural network (NN) model, etc. As an example, a machine learning model can be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.), an ensemble model (e.g., random forest, gradient boosting machine, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosted regression tree, etc.), a neural network model (e.g., radial basis function network, perceptron, back-propagation, Hopfield network, etc.), a regularization model (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least angle regression), a rule system model (e.g., cubist, one rule, zero rule, repeated incremental pruning to produce error reduction), a regression model (e.g., linear regression, ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, logistic regression, etc.), a Bayesian model (e.g., naïve Bayes, average on-dependence estimators, Bayesian belief network, Gaussian naïve Bayes, multinomial naïve Bayes, Bayesian network), a decision tree model (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, C5.0, chi-squared automatic interaction detection, decision stump, conditional decision tree, M5), a dimensionality reduction model (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, principal component regression, partial least squares discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, flexible discriminant analysis, linear discriminant analysis, etc.), an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.

As an example, a machine model may be built using a computational framework with a library, a toolbox, etc., such as, for example, those of the MATLAB framework (MathWorks, Inc., Natick, Massachusetts). The MATLAB framework includes a toolbox that provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor (KNN), k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Another MATLAB framework toolbox is the Deep Learning Toolbox (DLT), which provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. The DLT provides convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. The DLT includes features to build network architectures such as generative adversarial networks (GANs) and Siamese networks using custom training loops, shared weights, and automatic differentiation. The DLT provides for model exchange various other frameworks.

As an example, the TENSORFLOW framework (Google LLC, Mountain View, CA) may be implemented, which is an open source software library for dataflow programming that includes a symbolic math library, which can be implemented for machine learning applications that can include neural networks. As an example, the CAFFE framework may be implemented, which is a DL framework developed by Berkeley AI Research (BAIR) (University of California, Berkeley, California). As another example, consider the SCIKIT platform (e.g., scikit-learn), which utilizes the PYTHON programming language. As an example, a framework such as the APOLLO AI framework may be utilized (APOLLO.AI GmbH, Germany). As an example, a framework such as the PYTORCH framework may be utilized (Facebook AI Research Lab (FAIR), Facebook, Inc., Menlo Park, California).

As an example, a training method can include various actions that can operate on a dataset to train a ML model. As an example, a dataset can be split into training data and test data where test data can provide for evaluation. A method can include cross-validation of parameters and best parameters, which can be provided for model training.

The TENSORFLOW framework can run on multiple CPUs and GPUs (with optional CUDA (NVIDIA Corp., Santa Clara, California) and SYCL (The Khronos Group Inc., Beaverton, Oregon) extensions for general-purpose computing on graphics processing units (GPUS)). TENSORFLOW is available on 64-bit LINUX, MACOS (Apple Inc., Cupertino, California), WINDOWS (Microsoft Corp., Redmond, Washington), and mobile computing platforms including ANDROID (Google LLC, Mountain View, California) and IOS (Apple Inc.) operating system based platforms.

TENSORFLOW computations can be expressed as stateful dataflow graphs; noting that the name TENSORFLOW derives from the operations that such neural networks perform on multidimensional data arrays. Such arrays can be referred to as “tensors”.

As an example, a device may utilize TENSORFLOW LITE (TFL) or another type of lightweight framework. TFL is a set of tools that enables on-device machine learning where models may run on mobile, embedded, and loT devices. TFL is optimized for on-device machine learning, by addressing latency (no round-trip to a server), privacy (no personal data leaves the device), connectivity (Internet connectivity is demanded), size (reduced model and binary size) and power consumption (e.g., efficient inference and a lack of network connections). TFL provides multiple platform support, covering ANDROID and iOS devices, embedded LINUX, and microcontrollers. TFL provides diverse language support, which includes JAVA, SWIFT, Objective-C, C++, and PYTHON. TFL may provide high performance, with hardware acceleration and model optimization. TFL may provide for various machine learning tasks, which may include, for example, prediction, regression, classification, image classification, object detection, pose estimation, question answering, text classification, etc., on one or more of multiple platforms.

FIG. 9 shows an example of a method 900 that includes a reception block 910 for receiving data for a well; a determination block 920 for determining a gas flow rate and a liquid level for the well; a determination block 930 for determining an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and an issuance block 940 for issuing a signal to chemical injection equipment to inject the amount of chemical into the well.

In the example of FIG. 9, a system 990 includes one or more information storage devices 991, one or more computers 992, one or more networks 995 and instructions 996. As to the one or more computers 992, each computer may include one or more processors (e.g., or processing cores) 993 and memory 994 for storing the instructions 996, for example, executable by at least one of the one or more processors. As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc.

The method 900 is shown along with various computer-readable media blocks 911, 921, 931 and 941 (e.g., CRM blocks). Such blocks may be utilized to perform one or more actions of the method 900. For example, consider the system 990 of FIG. 9 and the instructions 996, which may include instructions of one or more of the CRM blocks 911, 921, 931 and 941.

As an example, a method can include receiving data for a well; determining a gas flow rate and a liquid level for the well; determining an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and issuing a signal to chemical injection equipment to inject the amount of chemical into the well. In such an example, the chemical can include a surfactant and/or another chemical that generates foam.

As an example, an amount of chemical can facilitate gas production from a well.

As an example, a higher liquid level can correspond to a demand for a greater amount of chemical.

As an example, data can include gas flow meter measurement data where, for example, the gas flow meter measurement data are indicative of the gas flow rate.

As an example, data can include downhole gauge data where, for example, the downhole gauge data are indicative of the liquid level.

As an example, data can include one or more of tubing head pressure and casing head pressure where, for example, the data are indicative of liquid level.

As an example, a method can include determining a liquid level by implementing a trained machine learning model. In such an example, the method can include generating the trained machine learning model using data from one or more wells. As an example, a trained machine learning model can operate on input that includes one or more of tubing head pressure and casing head pressure.

As an example, chemical injection equipment can include a manifold for injection of chemical into one or more wells. In such an example, the manifold may be controllable to inject a specific amount of chemical into a specific well. As an example, a method can include determining one or more amounts of chemical to inject into one or more wells.

As an example, a method can be performed using a computational framework operatively coupled to chemical injection equipment. In such an example, the computational framework can be local and/or remote from a site of a well or wells.

As an example, a system can include one or more processors; memory accessible to the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: receive data for a well; determine a gas flow rate and a liquid level for the well; determine an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and issue a signal to chemical injection equipment to inject the amount of chemical into the well.

As an example, one or more computer-readable media can include computer-executable instructions executable by a system to instruct the system to: receive data for a well; determine a gas flow rate and a liquid level for the well; determine an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and issue a signal to chemical injection equipment to inject the amount of chemical into the well.

As an example, a computer program product can include one or more computer-readable storage media that can include processor-executable instructions to instruct a computing system to perform one or more methods and/or one or more portions of a method.

In some embodiments, a method or methods may be executed by a computing system. FIG. 10 shows an example of a system 1000 that can include one or more computing systems 1001-1, 1001-2, 1001-3 and 1001-4, which may be operatively coupled via one or more networks 1009, which may include wired and/or wireless networks. As shown, the system 1000 can include one or more other components 1008.

As an example, a system can include an individual computer system or an arrangement of distributed computer systems. In the example of FIG. 10, the computer system 1001-1 can include one or more modules 1002, which may be or include processor-executable instructions, for example, executable to perform various tasks (e.g., receiving information, requesting information, processing information, simulation, outputting information, etc.).

As an example, a module may be executed independently, or in coordination with, one or more processors 1004, which is (or are) operatively coupled to one or more storage media 1006 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 1004 can be operatively coupled to at least one of one or more network interface 1007. In such an example, the computer system 1001-1 can transmit and/or receive information, for example, via the one or more networks 1009 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.).

As an example, the computer system 1001-1 may receive from and/or transmit information to one or more other devices, which may be or include, for example, one or more of the computer systems 1001-2, etc. A device may be located in a physical location that differs from that of the computer system 1001-1. As an example, a location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.

As an example, a processor may be or include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

As an example, the storage media 1006 may be implemented as one or more computer-readable or machine-readable storage media. As an example, storage may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems.

As an example, a storage medium or storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLUERAY disks, or other types of optical storage, or other types of storage devices.

As an example, a storage medium or media may be located in a machine running machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

As an example, various components of a system such as, for example, a computer system, may be implemented in hardware, software, or a combination of both hardware and software (e.g., including firmware), including one or more signal processing and/or application specific integrated circuits.

As an example, a system may include a processing apparatus that may be or include a general purpose processors or application specific chips (e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriate devices.

As an example, a device may be a mobile device that includes one or more network interfaces for communication of information. For example, a mobile device may include a wireless network interface (e.g., operable via IEEE 802.11, ETSI GSM, BLUETOOTH, satellite, etc.). As an example, a mobile device may include components such as a main processor, memory, a display, display graphics circuitry (e.g., optionally including touch and gesture circuitry), a SIM slot, audio/video circuitry, motion processing circuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and a battery. As an example, a mobile device may be configured as a cell phone, a tablet, etc. As an example, a method may be implemented (e.g., wholly or in part) using a mobile device. As an example, a system may include one or more mobile devices.

As an example, a system may be a distributed environment, for example, a so-called “cloud” environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc. As an example, a device or a system may include one or more components for communication of information via one or more of the Internet (e.g., where communication occurs via one or more Internet protocols), a cellular network, a satellite network, etc. As an example, a method may be implemented in a distributed environment (e.g., wholly or in part as a cloud-based service).

As an example, information may be input from a display (e.g., consider a touchscreen), output to a display or both. As an example, information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed. As an example, information may be output stereographically or holographically. As to a printer, consider a 2D or a 3D printer. As an example, a 3D printer may include one or more substances that can be output to construct a 3D object. For example, data may be provided to a 3D printer to construct a 3D representation of a subterranean formation. As an example, layers may be constructed in 3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example, holes, fractures, etc., may be constructed in 3D (e.g., as positive structures, as negative structures, etc.).

Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures.

Claims

1. A method comprising:

receiving data for a well;

determining a gas flow rate and a liquid level for the well;

determining an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and

issuing a signal to chemical injection equipment to inject the amount of chemical into the well.

2. The method of claim 1, wherein the chemical comprises a surfactant.

3. The method of claim 1, wherein the chemical generates foam.

4. The method of claim 1, wherein the amount of chemical facilitates gas production from the well.

5. The method of claim 1, wherein a higher liquid level corresponds to a greater amount of the chemical.

6. The method of claim 1, wherein the data comprise gas flow meter measurement data.

7. The method of claim 6, wherein the gas flow meter measurement data are indicative of the gas flow rate.

8. The method of claim 1, wherein the data comprise downhole gauge data.

9. The method of claim 8, wherein the downhole gauge data are indicative of the liquid level.

10. The method of claim 1, wherein the data comprise one or more of tubing head pressure and casing head pressure.

11. The method of claim 10, wherein the data are indicative of liquid level.

12. The method of claim 1, wherein determining the liquid level comprises implementing a trained machine learning model.

13. The method of claim 12, comprising generating the trained machine learning model using data from one or more wells.

14. The method of claim 12, wherein the trained machine learning model operates on input that comprises one or more of tubing head pressure and casing head pressure.

15. The method of claim 1, wherein the chemical injection equipment comprises a manifold for injection of the chemical into one or more wells.

16. The method of claim 1, comprising determining an amount of chemical to inject into another well.

17. The method of claim 1, wherein the method is performed using a computational framework operatively coupled to the chemical injection equipment.

18. The method of claim 17, wherein the computational framework is local and/or remote from a site of the well.

19. A system comprising:

one or more processors;

memory accessible to the one or more processors;

processor-executable instructions stored in the memory and executable to instruct the system to: receive data for a well; determine a gas flow rate and a liquid level for the well; determine an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and issue a signal to chemical injection equipment to inject the amount of chemical into the well.

20. One or more computer-readable media comprising computer-executable instructions executable by a system to instruct the system to:

receive data for a well;

determine a gas flow rate and a liquid level for the well;

determine an amount of chemical to inject into the well based on the gas flow rate and the liquid level; and

issue a signal to chemical injection equipment to inject the amount of chemical into the well.