METHOD FOR DETERMINING AND ADJUSTING A POWER STATE PARAMETER OF A TURBINE IN REAL TIME

Abstract

A method for determining power state parameters related to a turbine when performing coiled tubing drilling within a wellbore at a wellsite. The method may include receiving data related to a drill string within the wellbore and a media being pumped into the wellbore, determining a downhole density and a downhole flowrate of a gas portion and a liquid portion of a media being pumped to a turbine within the wellbore based on the received data. The method also includes combining the determined downhole density and flowrates of the gas and liquid portions of the media to provide a downhole density and flowrate of the media, determining a power state parameter related to the turbine, and then adjusting an operation of the drill string in real time in response to the determined power state parameter. The power state parameter may be displayed on a screen.

Description

BACKGROUND

When performing coiled tubing drilling (CTD) at a wellsite, it is helpful to know the turbine power state, including stall torque, RPM, bearing thrust load, etc. Knowing the turbine power state can in turn guide the field operations at the wellsite so as to apply appropriate drilling parameters in order to achieve high drilling efficiency and avoid tool failure.

What is needed is a new method to determine and then adjust a power state of a turbine being used within a wellbore by determining a turbine pressure drop that may be based on a flowrate and density of a mixed media that is being delivered to the turbine.

SUMMARY

Disclosed are methods, systems, and computer programs for determining power state parameters related to a turbine when performing coiled tubing drilling within a wellbore at a wellsite. The method may include receiving data related to a drill string within the wellbore and a media being pumped into the wellbore, determining a downhole density of a gas and a liquid portion of a media being pumped to a turbine within the wellbore based on the received data, and determining a downhole flowrate of the gas and liquid portions of the media being pumped to the turbine based on the received data. The method may also include combining the determined downhole density and flowrates of the gas and liquid portions of the media to provide a downhole density and flowrate of the media, determining a power state parameter related to the turbine based on the downhole density and flowrate of the media, and then adjusting an operation of the drill string in real time in response to the determined power state parameter. The power state parameter may be displayed on a screen.

In certain embodiments, a computing system may be provided, the computing system having one or more processors and a memory system including one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. In certain embodiments, the operations include receiving data related to a drill string within the wellbore and a media being pumped into the wellbore, determining a downhole density of gas and liquid portions of a media being pumped to a turbine within the wellbore based on the received data, and determining a downhole flowrate of the gas and liquid portions of the media being pumped to the turbine based on the received data. The operations may also include combining the determined downhole density and flowrates of the gas and liquid portions to provide a downhole density and flowrate of the media, determining a power state parameter related to the turbine based on the downhole density and flowrate of the media, and then adjusting an operation of the drill string in real time in response to the determined power state parameter. The power state parameter may be displayed on a screen and a site action may be performed that is based on the power state parameter.

In certain embodiments, a non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations may be provided. The operations may include receiving data related to a drill string including a turbine. The received data may include a plurality of parameters related to a media being pumped into a wellbore including the drill string, an internal pressure within a coil coupled to the turbine, a pressure of an annulus disposed above the turbine within the drill string, and at least one parameter related to the turbine. The media may include a mixture of a gas and a liquid. In certain embodiments, the parameters related to the media may include a surface temperature of the media, a surface fluid density of the media, a surface flowrate of the liquid portion of the media, and a surface flowrate of the gas portion of the media. The at least one parameter related to the turbine may include a blade type, a number of blades, a number of thrust bearings, a number of mechanical bearings, or a combination thereof. The operations may also include determining a downhole density of the gas and liquid portions of the media being pumped into the wellbore of the based on the surface temperature of the media and the pressure annulus above the turbine, determining a downhole flowrate of the gas and liquid portions of the media being pumped into the wellbore based on the surface flowrate of the gas and liquid portions of the media, and determining a downhole density and flowrate of the media based on a turbine pressure drop. The turbine pressure drop may be defined as a difference between the pressure within the coil and the pressure of the annulus disposed above the turbine. The downhole density of the media may be further based on a downhole gas fraction volume and the downhole density of the gas and liquid portions of the media. The downhole flowrate of the media may be further based on downhole flowrate of the gas and liquid portions of the media. The operations may also include determining a power state parameter related to the turbine based on the downhole density and flowrate of the media. The power state parameter may include a turbine stall torque, a RPM of the turbine, a thrust load of the turbine, a free spin speed of the turbine, a peak power of the turbine, or a combination thereof. The operations may also include adjusting an operation of the drill string in real time in response to the determined power state parameter. Adjusting the operation of the drill string may include varying the surface flowrate of the gas and liquid portions of the media, varying the surface density of the media, varying a weight or torque applied to a drill bit within the drill string, or a combination thereof. The operations may also include displaying the power state parameter on a screen and then performing a site action based on the power state parameter. Performing the site action may include generating or transmitting a signal that instructs or causes an action to occur. The action may include a physical action, the physical action including selecting where or how to drill a wellbore in a subsurface formation, drilling the wellbore, varying a trajectory of the wellbore, deciding to stop drilling and pull the drill string up, or a combination thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements. It is appreciated that various features may not be drawn to scale, and the dimensions of various features may be arbitrarily increased or reduced for clarity of discussion. Further, it is contemplated that features of one or more embodiments may be incorporated in other embodiments without additional recitation.

FIG. 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

FIG. 2 illustrates a relationship between the thermal expansion coefficient of water and temperature, according to an embodiment.

FIG. 3 illustrates a graphical interface listing exemplary inputs and a set of corresponding results related to a media used to actuate a turbine, according to an embodiment.

FIG. 4 illustrates a graphical interface showing a number of user implemented inputs and a series of predicted results from a computation fluid dynamics (CFD) simulation, according to an embodiment.

FIG. 5A illustrates a relationship between torque and RPM for a turbine, according to an embodiment.

FIG. 5B illustrates a relationship between thrust force and normalized RPM for a turbine, according to an embodiment.

FIG. 5C illustrates a graphical interface showing coefficients for the fitted functions.

FIG. 6 illustrates a relationship between torque and RPM of a turbine over two different media flows, according to an embodiment.

FIG. 7 illustrates a relationship between power and RPM for a turbine over two different media flows, according to an embodiment.

FIG. 8 illustrates a relationship between thrust load and RPM for a turbine over two different media flows, according to an embodiment.

FIG. 9 illustrates a relationship between thrust bearing and RPM for a turbine over two different media flows, according to an embodiment.

FIG. 10 illustrates a relationship between pressure drop and RPM for a turbine over two different media flows, according to an embodiment.

FIG. 11 illustrates a graphical interface for a user to input a number of parameters in order to determine the power being output by the turbine in real time, according to an embodiment.

FIG. 12 illustrates a graphical interface displaying a data log for a wellsite incorporating a number of real-time calculations of power states related to the turbine as provided by the workflow, according to an embodiment.

FIG. 13 illustrates a flowchart of a method for determining power state parameters related to a turbine when performing coiled tubing drilling within a wellbore at a wellsite, according to an embodiment.

FIG. 14 illustrates a schematic view of a computing system for performing at least a portion of the method(s) described herein, according to an embodiment.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the disclosed technology. However, it will be apparent to one of ordinary skill in the art that the disclosed embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

It will also be understood that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the disclosure. The first object or step, and the second object or step, are both objects or steps, respectively, but they are not to be considered the same object or step.

The terminology used in the description of the disclosed techniques is for the purpose of describing particular embodiments and is not intended to be limiting. As used in the description of this disclosure and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any combination of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and/or “comprising,” when used in this disclosure, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

Those with skill in the art will appreciate that while some terms in this disclosure may refer to absolutes, e.g., all of the components of a wavefield, all source receiver traces, each of a plurality of objects, etc., the methods and techniques disclosed herein may also be performed on fewer than all of a given thing, e.g., performed on one or more components and/or performed on one or more source receiver traces. Accordingly, in instances in the disclosure where an absolute is used, the disclosure may also be interpreted to be referring to a subset.

System Overview

FIG. 1 illustrates an example of a system 100 that includes various management components 110 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc.). For example, the management components 110 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

In the example of FIG. 1, the management components 110 include a seismic data component 112, an additional information component 114 (e.g., well/logging data), a processing component 116, a simulation component 120, an attribute component 130, an analysis/visualization component 142 and a workflow component 144. In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120.

In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT®.NET® framework (Redmond, Washington), which provides a set of extensible object classes. In the .NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

In the example of FIG. 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of FIG. 1, the analysis/visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (SLB, Houston Texas), the INTERSECT™ reservoir simulator (SLB, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). 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 SAGD, etc.).

In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (SLB, Houston, Texas). The 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. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (SLB, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

FIG. 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175. The framework 170 may include the commercially available OCEAN® framework where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software can include a framework for model building and visualization.

As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

In the example of FIG. 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.

As an example, the domain objects 182 can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

In the example of FIG. 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.

In the example of FIG. 1, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc. As an example, the geologic environment 150 may be outfitted with any of 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 well site 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 instead 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.

As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).

Method for Determining and Adjusting a Power State Parameter of a Turbine in Real Time

According to certain embodiments, when performing coiled tubing drilling, a mixed fluid and gas media may be pumped down a tubing to drive a downhole turbine portion of a drill string. In certain embodiments, the fluid portion of the mixed media may be water, while the gas portion of the mixed media may be nitrogen. In certain embodiments, the drill string and the turbine may be part of or included within the downhole equipment 154 as seen in FIG. 1. The density and flowrate of the media is dependent on the downhole pressure and temperature condition. A power state of the turbine, including a turbine pressure drop may affected by the flowrate and density of the mixed media. Thus, an iteration calculation process may be used to determine the downhole flowrate and turbine pressure drop, which in turn may then provide the turbine power state which allows a user to adjust the operation, maintenance, or overall performance of the downhole turbine.

According to certain embodiments, the process may start with determining a density and flowrate for the mixed media being pumped downhole in order to drive the turbine of the drill string. Additional inputs such as surface temperature, surface fluid density, surface fluid flowrate, surface gas flowrate, reservoir pressure as measured by a downhole sensor and sent to the surface in real-time, and the known specifications related to the type of turbine used in the drill sting may be used to determine the downhole density and flowrate of the media. In certain embodiments, given a measured temperature and pressure, gas and fluid density downhole can be determined. Using the law of conservation of mass, the flowrate of gas and fluid can be calculated, hence the media flowrate and density downhole can be derived.

In certain embodiments, a turbine pressure drop for a given media density and volumetric flowrate may be pre-calculated using a computational fluid dynamics (CFD) simulation. For a different volumetric flowrate and density, the pressure drop can be determined by using the CFD simulation results and a set of rules based on turbine physics. Once a volumetric flowrate and density of the media through the turbine are known, other power state parameters such as stall torque, RPM, thrust load can then be calculated.

According to certain embodiments, the water thermal expansion coefficient versus temperature may be fitted with a polynomial function as shown in FIG. 2. Using a number of exemplary inputs 302 within a graphical interface 300 seen in FIG. 3, for example, liquid weight 304, pressure 306, and temperature 308, a set of results 310 using the polynomial function of FIG. 2 may be provided. In certain embodiments, the set of results 310 may include but is not limited to an initial density of the fluid 312, an initial pressure 314, a further pressure 316, and a further temperature 318.

According to certain embodiments, a downhole fluid density may be given by Equation 1:

$\begin{array}{cc}\rho 1=\frac{\rho 0}{\left[1+a·T1\right]\left[1-\frac{P_1-P_0}{Eb}\right]}& \left(1\right)\end{array}$

where ρ0 is the initial fluid density 312, α is the expansion coefficient of the fluid, P0 is the initial pressure 314, P1 is the further pressure 316, and Eb is the bulk modulus of the fluid. The determined downhole fluid density 320 may then be incorporated into the set of results 310.

According to certain embodiments, a downhole gas density of the mixed media may be determined by Equation 2:

$\begin{array}{cc}{\rho }_{n\_\mathrm{downhole}}=\frac{\mathrm{PM}}{\mathrm{cRT}}& \left(2\right)\end{array}$

where P is a downhole pressure of the gas, R is the ideal gas constant, T is a downhole temperature of the gas, and M is a number of moles of gas being pumped into the turbine. In certain embodiments, c may be given of a constant value of 1.082.

According to certain embodiments, a downhole gas flow rate may be determined by Equation 3:

$\begin{array}{cc}{\mathrm{GFl}}_{\mathrm{downhole}}={\mathrm{GFl}}_{\mathrm{surface}}·\frac{{\rho }_{n\_\mathrm{surface}}}{{\rho }_{n\_\mathrm{downhole}}}& \left(3\right)\end{array}$

where GFl_surface is a surface gas flowrate, ρ_{n_surface} is a surface gas density, and p_{n_downhole} is the downhole gas density determined by Equation 2.

According to certain embodiments, a downhole gas volume fraction may be determined by Equation 4:

$\begin{array}{cc}{\mathrm{GVF}}_{\mathrm{downhole}}=\frac{{\mathrm{GFl}}_{\mathrm{downhole}}}{{\mathrm{GFl}}_{\mathrm{downhole}}+{\mathrm{WFl}}_{\mathrm{downhole}}}& \left(4\right)\end{array}$

where GFl_downhole is a downhole gas flowrate and WFl_downhole is a downhole fluid flowrate.

According to certain embodiments, a downhole density of the media may be determined by Equation 5:

ρ_mixture=GVF_downhole·ρ_{n_downhole}+(1−GVF_downhole)·ρ_{w_downhole}  (5)

where GVF_downhole is the downhole gas volume fraction determined by Equation 4, ρ_{n_downhole} is the downhole gas density determined by Equation 2, and ρ_{w_downhole} is the downhole fluid density determined by Equation 1.

According to certain embodiments, a downhole flowrate of the media may be determined by Equation 6:

Mfl_downhole=Gfl_downhole+Wfl_downhole  (6)

where Gfl_downhole is the downhole gas flowrate determined by Equation 3 and Wfl_downhole is the downhole fluid flowrate.

In certain embodiments, the CFD simulation may be done for the specific type of turbine within the drill string in order to characterize a current or real-time power state for the turbine. The results of the CFD simulation may provide a matrix of parameters including but not limited to fluid density and flowrate extracted for different turbine blade designs. For example, FIG. 4 shows a graphical interface 400 showing a number of user implemented inputs 402 and a number of different predicted results 404 from a CFD simulation performed specifically for a turbine including blades, the predicted results 404 including but not limited to torque 406, power 408, free spin 410, and a turbine thrust load 412.

According to certain embodiments, the relationship between a torque and a RPM of the turbine may be calculated for 3 points and fitted to a linear curve as seen in FIG. 5A. In certain embodiments, a stall torque and a free spin speed of the turbine may be extracted from the linear curve seen in FIG. 5A. A thrust force related to the turbine versus normalized RPM may be fitted to a quadratic function as seen in FIG. 5B. The coefficients for the fitted functions are listed in FIG. 5C below for a turbine having blades. The same approach may be conducted for alternative turbines which include fewer, more, or different types of turbine blades, according to an embodiment.

According to certain embodiments, based on known turbine designs, a turbine power state that may be affected by the downhole flowrate and density of the media may be determined. For example, turbine power states that may be determined include but are not limited to a stall torque, torque, a peak power, a total rotor pressure drop, and a free spinning speed. The respective relationships between the turbine power states may be used to determine further power states or parameters of the active turbine which in turn inform the user of the current operational state of the turbine as it being used within the wellbore. For example, torque related to the turbine may be dependent upon the determined stall torque and free spin speed. From that, it follows that the rotating speed of the turbine may be linearly related to the flow rate ratio of the media, and that torque may be proportional to a flow rate of the media squared, thereby providing a final or downhole torque of the turbine that is proportional to the density ratio of the media

FIG. 6 presents the determined relationship between the torque of the turbine versus the RPM of the turbine for two different media flows, where each media flow has a different flowrate and density.

According to certain embodiments, a current power of the turbine may be determined at least in part by the torque of the turbine and the free spin speed of the turbine. FIG. 7 provides a power curve for a turbine having blades for two different media flows, where each media flow has a different flowrate and density.

According to certain embodiments, a current thrust force of the turbine may be determined at least in part by a normalized free spin speed of the turbine and a downhole density of the media. FIG. 8 provides a thrust load versus turbine speed curve for a turbine having blades for two different media flows, where each media flow has a different flowrate and density.

According to certain embodiments, a current thrust bearing of the turbine may be determined. FIG. 9 provides a thrust bearing versus turbine speed for a turbine having blades for two different media flows, where each media flow has a different flowrate and density.

According to certain embodiments, a pressure drop related to the turbine may be determined at least in part by a downhole density of the media. FIG. 10 provides a pressure drop versus turbine speed curve for a turbine having blades for two different media flows, where each media flow has a different flowrate and density. For a fixed media flowrate with a given density, the pressure drop across the working RPM range is relatively stable. The max pressure drop may be chosen to indicate the pressure drop for a given flowrate and density, according to certain embodiments.

In certain embodiments, the determined power state of the turbine may be further incorporated into or implemented with real-time data logs that are collected at the wellsite. For example, FIG. 11 provides a graphical interface 1100 for user to input a number of parameters in order to determine the power being output by the turbine in real time. The graphical interface 1100 provides the user the ability to select a specific well 1102 at the wellsite in addition to the specific type or model of turbine 1104 being used in the selected well. In certain embodiments, selected channels of data 1106 obtained from the corresponding well logs may be used as real-time measurement values or inputs. The selected channels of data 1106 may include a gas flowrate 1108, a reservoir pressure 1110, a downhole temperature 1112, and a fluid flowrate 1114.

In certain embodiments, after inputs such as those seen in FIG. 11 are entered, a number of calculated outputs may be determined and then added to the data logs so as to be monitored in real-time along with the other real-time measurement values received from the well. FIG. 12 illustrates a data log 1200 which includes both a number of real-time measurement readings normally obtained during the operation of a well at a wellsite, as well as a number of real-time calculations of power states related to the turbine as provided by the workflow. In certain embodiments, the calculated real-time power states may include but are not limited to a turbine max power 1202, a turbine pressure drop 1204, a turbine stall torque 1206, a turbine maximum allowable weight on bit (WOB) 1208, a turbine RPM 1210, and a turbine thrust force 1212. The determined real-time power states can be used to monitor turbine efficiency and loading conditions in real-time during the operation of the well.

Exemplary Workflow

FIG. 13 provides an exemplary detailed workflow 1300 for determining power state parameters related to a turbine when performing coiled tubing drilling within a wellbore at a wellsite. It is appreciated that a signal processing engine stored in a memory device (e.g., transitory or non-transitory memory) may cause a computer processor to assist or otherwise facilitate execution of one or more of the various processing stages of workflow 1300.

According to certain embodiments, the workflow 1300 may include receiving data related to a drill string having a turbine, as at 1302. The received data may include a plurality of parameters related to a media being pumped into a wellbore including the drill string, an internal pressure within a coil coupled to the turbine, a pressure of an annulus disposed above the turbine within the drill string, and at least one parameter related to the turbine. The media may include a mixture of a gas and a fluid. In certain embodiments, the parameters related to the media may include a surface temperature of the media, a surface fluid density of the media, a surface fluid flowrate of the media, and a surface gas flowrate of the media. The at least one parameter related to the turbine may include a blade type, a number of blades, a number of thrust bearings, a number of mechanical bearings, or a combination thereof.

According to certain embodiments, the workflow 1300 may also include determining a downhole gas density and a downhole fluid density of the media being pumped into the wellbore based on the surface temperature of the media and the pressure annulus above the turbine, as at 1304.

In certain embodiments, the workflow 1300 may also include determining a downhole gas flowrate and a downhole fluid flowrate of the media being pumped into the wellbore based on the surface gas flowrate of the media and surface fluid flowrate media, as at 1306.

In certain embodiments, the workflow 1300 may also include determining a downhole density and flowrate of the media based on a turbine pressure drop, as at 1308. The turbine pressure drop may be defined as a difference between the pressure within the coil and the pressure of the annulus disposed above the turbine. The downhole density of the media may be further based on a downhole gas fraction volume, the downhole gas density, and the downhole fluid density. The downhole flowrate of the media may be further based on downhole gas flowrate and the downhole fluid flowrate.

In certain embodiments, the workflow 1300 may also include determining a power state parameter related to the turbine based on the downhole density and flowrate of the media, as at 1310. The power state parameter may include a turbine stall torque, a RPM of the turbine, a thrust load of the turbine, a free spin speed of the turbine, a peak power of the turbine, or a combination thereof.

In certain embodiments, the workflow 1300 may also include adjusting an operation of the drill string in real time in response to the determined power state parameter, as at 1312. Adjusting the operation of the drill string may include varying the surface gas or fluid flowrate of the media, varying the surface density of the media, varying a weight or torque applied to a drill bit within the drill string, or a combination thereof.

In certain embodiments, the workflow 1300 may also include displaying the power state parameter on a screen as at 1314 and then performing a site action based on the power state parameter, as at 1316. Performing the site action may include generating or transmitting a signal that instructs or causes an action to occur. The action may include a physical action, the physical action including selecting where or how to drill a wellbore in a subsurface formation, drilling the wellbore, varying a trajectory of the wellbore, compensating for changing conditions within the wellbore, deciding to stop drilling and pull the drill string up, or a combination thereof.

Exemplary Computing System

In some embodiments, the methods of the present disclosure may be executed by a computing system. FIG. 14 illustrates an example of such a computing system 1400, in accordance with some embodiments. The computing system 1400 may include a computer or computer system 1401A, which may be an individual computer system 1401A or an arrangement of distributed computer systems. The computer system 1401A includes one or more analysis modules 1402 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 1402 executes independently, or in coordination with, one or more processors 1404, which is (or are) connected to one or more storage media 1406. The processor(s) 1404 is (or are) also connected to a network interface 1407 to allow the computer system 1401A to communicate over a data network 1409 with one or more additional computer systems and/or computing systems, such as 1401B, 1401C, and/or 1401D (note that computer systems 1401B, 1401C and/or 1401D may or may not share the same architecture as computer system 1401A, and may be located in different physical locations, e.g., computer systems 1401A and 1401B may be located in a processing facility, while in communication with one or more computer systems such as 1401C and/or 1401D that are located in one or more data centers, and/or located in varying countries on different continents).

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

The storage media 1406 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of FIG. 14 storage media 1406 is depicted as within computer system 1401A, in some embodiments, storage media 1406 may be distributed within and/or across multiple internal and/or external enclosures of computing system 1401A and/or additional computing systems. Storage media 1406 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), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

It should be appreciated that computing system 1400 is merely one example of a computing system, and that computing system 1400 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of FIG. 14, and/or computing system 1400 may have a different configuration or arrangement of the components depicted in FIG. 14. The various components shown in FIG. 14 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits.

Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are included within the scope of the present disclosure.

Computational interpretations, models, and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 1400, FIG. 14), and/or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the risk index.

The steps in the processing methods described above may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are included within the scope of this disclosure.

Of course, many processing techniques for collected data, including one or more of the techniques and methods disclosed herein, may also be used successfully with collected data types other than seismic or other subsurface modeling data. While certain implementations have been disclosed in the context of seismic or other subsurface data collection and processing, those with skill in the art will recognize that one or more of the methods, techniques, and computing systems disclosed herein can be applied in many fields and contexts where data involving structures arrayed in a multi-dimensional space and/or subsurface region of interest may be collected and processed, e.g., medical imaging techniques such as tomography, ultrasound, MRI and the like for human tissue; radar, sonar, and LIDAR imaging techniques; mining area surveying and monitoring, oceanographic surveying and monitoring, and other appropriate multi-dimensional imaging problems.

Some examples of equations and mathematical expressions may have been provided in this disclosure. But those with skill in the art will appreciate that variations of these expressions and equations, alternative forms of these expressions and equations, and related expressions and equations that can be derived from the example equations and expressions provided herein may also be successfully used to perform the methods, techniques, and workflows related to the embodiments disclosed herein.

The foregoing description, for purposes of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to explain the principles of this disclosure and its practical applications, to thereby enable others skilled in the art to use the disclosed approach and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

1. A method for determining power state parameters related to a turbine when performing coiled tubing drilling within a wellbore at a wellsite, the method comprising:

receiving data related to a drill string within the wellbore and a media being pumped into the wellbore, wherein the media flows through a turbine that is coupled to the drill string in the wellbore, and wherein the media comprises a gas portion and a liquid portion;

determining a downhole density of the gas and liquid portions in the turbine based on the received data;

determining a downhole flowrate of the gas and liquid portions in the turbine based on the received data;

combining the determined downhole density and flowrates of the gas and liquid portions to provide a downhole density and flowrate of the media; and

determining a power state parameter related to the turbine based on the downhole density and flowrate of the media.

2. The method of claim 1, wherein the received data related to the media being pumped into the wellbore comprises:

a surface temperature of the media;

a surface density of the liquid portion of the media;

a surface flowrate of the liquid portion of the media; and

a surface flowrate of the gas portion of the media.

3. The method of claim 1, further comprising displaying the power state parameter on a screen

4. The method of claim 2, wherein the received data related to the drill string comprises:

an internal pressure measured above the turbine;

an annulus pressure measured above the turbine; and

at least one parameter related to the turbine, wherein the at least one parameter related to the turbine comprises a blade type, a number of blades, a number of thrust bearings, a number of mechanical bearings, or a combination thereof.

5. The method of claim 4, wherein the received data related to the drill string further comprises a turbine pressure drop, wherein the turbine pressure drop is defined as a difference between the internal pressure and the annulus pressure measured above the turbine.

6. The method of claim 5, wherein the downhole density and flowrate of the media being pumped into the wellbore are based on the turbine pressure drop, the surface flowrate of the gas portion of the media, and the surface flowrate of the liquid portion of the media.

7. The method of claim 1, further comprising adjusting an operation of the drill string in real time in response to the determined power state parameter.

8. The method of claim 6, wherein the downhole density of the media is further based on a downhole gas volume fraction, the downhole density of the gas portion of the media, and the downhole density of the liquid portion of the media.

9. The method of claim 6, wherein the downhole flowrate of the media is further based on downhole flowrate of the gas portion of the media and the downhole flowrate of the liquid portion of the media.

10. The method of claim 1, wherein the power state parameter comprises a turbine stall torque, a RPM of the turbine, a thrust load of the turbine, a free spin speed of the turbine, a peak power of the turbine, or a combination thereof.

11. A computing system, comprising:

one or more processors;

a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising: receiving data related to a drill string within a wellbore and a media being pumped into the wellbore; determining a downhole density of gas and liquid portions of the media being pumped to a turbine within the wellbore based on the received data; determining a downhole flowrate of the gas and liquid portions of the media being pumped to the turbine based on the received data; combining the determined downhole density and flowrates of the gas and liquid portions of the media to provide a downhole density and flowrate of the media; determining a power state parameter related to the turbine based on the downhole density and flowrate of the media; adjusting an operation of the drill string in real time in response to the determined power state parameter; displaying the power state parameter on a screen; and performing a site action based on the power state parameter.

12. The computing system of claim 11, wherein adjusting the operation of the drill string comprises varying a surface flowrate of the gas or liquid portions of the media, varying a surface density of the media, varying a weight or torque applied to a drill bit within the drill string, or a combination thereof.

13. The computing system of claim 11, wherein the received data related to the media being pumped into the wellbore comprises a surface density, wherein the surface density of the media is less than water.

14. The computing system of claim 11, wherein determining the downhole density of the gas portion of the media is defined by: ρ n ⁢ _ ⁢ downhole = PM cRT wherein ρn_downhole is the downhole density of the gas portion of the media, P is a downhole pressure of the gas portion of the media, M is a number of moles gas being pumped into the turbine, R is a ideal gas constant, T is a downhole temperature of the gas portion of the media, and c is a constant value of 1.082.

15. The computing system of claim 14, wherein determining the downhole flowrate of the gas portion of the media is defined by: GFl downhole = GFl surface · ρ n ⁢ _ ⁢ surface ρ n ⁢ _ ⁢ downhole wherein GFldownhole is the downhole flowate of the gas portion of the media, GFlsurface is a surface flowrate of the gas portion of the media, ρn_surface is a surface density of the gas portion of the media, and ρn_downhole is a downhole density of the gas portion of the media.

16. The computing system of claim 15, further comprising determining a downhole gas volume fraction, wherein the downhole gas volume fraction is defined by: GVF downhole = GFl downhole GFl downhole + WFl downhole wherein GVFdownhole is the downhole gas volume fraction, GFldownhole is the downhole flowrate of the gas portion of the media, and WFldownhole is a downhole flowrate of the liquid portion of the media.

17. The computing system of claim 16, wherein the downhole density of the media is defined by: wherein ρmixture is the downhole density of the media, GVFdownhole is the downhole gas volume fraction, ρn_downhole is the downhole density of the gas portion of the media, and ρw_downhole is the downhole density of the liquid portion of the media.

ρmixture=GVFdownhole·ρn_downhole+(1−GVFdownhole)·ρw_downhole

18. The computing system of claim 15, wherein the downhole flowrate of the media is defined by: wherein Mfldownhole is the downhole flowrate of the media, Gfldownhole is the downhole flowrate of the gas portion of the media, and Wfldownhole is the downhole flowrate of the liquid portion of the media.

Mfldownhole=Gfldownhole+Wfldownhole

19. The computing system of claim 11, wherein performing the site action comprises generating or transmitting a signal that instructs or causes an action to occur, wherein the action comprises a physical action, and wherein the physical action comprises selecting where or how to drill a wellbore in a subsurface formation, drilling the wellbore, varying a trajectory of the wellbore, compensating for changing conditions within the wellbore, deciding to stop drilling and pull the drill string up, or a combination thereof.

20. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising:

receiving data related to a drill string comprising a turbine, wherein the received data comprises a plurality of parameters related to a media being pumped into a wellbore comprising the drill string, an internal pressure measured above the turbine, an annulus pressure measured above the turbine, and at least one parameter related to the turbine, wherein the media comprises a gas portion and a liquid portion, wherein the parameters related to the media comprises: a surface temperature of the media; a surface density of the liquid portion of the media; a surface flowrate of the liquid portion of the media; and a surface flowrate of the gas portion of the media, and wherein the at least one parameter related to the turbine comprises a blade type, a number of blades, a number of thrust bearings, a number of mechanical bearings, or a combination thereof;

determining a downhole density of the gas and liquid portions of the media being pumped into the wellbore of the based on the surface temperature of the media and the pressure annulus above the turbine;

determining a downhole flowrate of the gas and liquid portions of the media being pumped into the wellbore based on the surface flowrate of the gas portion of the media and surface flowrate of the liquid portion of the media;

combining the determined downhole density and flowrates of the gas and liquid portions to provide a downhole density and flowrate of the media based on a turbine pressure drop, wherein the turbine pressure drop is defined as a difference between the pressure within a coil and the pressure of the annulus disposed above the turbine, wherein the downhole density of the media is further based on a downhole gas fraction volume, the downhole density of the gas and liquid portions of the media, and wherein the downhole flowrate of the media is further based on the downhole flowrate of the gas and liquid portions of the media;

determining a power state parameter related to the turbine based on the downhole density and flowrate of the media, wherein the power state parameter comprises a turbine stall torque, a RPM of the turbine, a thrust load of the turbine, a free spin speed of the turbine, a peak power of the turbine, or a combination thereof;

adjusting an operation of the drill string in real time in response to the determined power state parameter, wherein adjusting the operation of the drill string comprises varying the surface flowrate of the gas or liquid portions of the media, varying the surface density of the media, varying a weight or torque applied to a drill bit within the drill string, or a combination thereof;

displaying the power state parameter on a screen; and

performing a site action based on the power state parameter, wherein performing the site action comprises generating or transmitting a signal that instructs or causes an action to occur, wherein the action comprises a physical action, and wherein the physical action comprises selecting where or how to drill a wellbore in a subsurface formation, drilling the wellbore, varying a trajectory of the wellbore, compensating for changing conditions within the wellbore, deciding to stop drilling and pull the drill string up, or a combination thereof.