SLUG INDUCEMENT IN WELLBORE CLEAN-UP OPTIMIZATION

Abstract

Systems and methods of the present disclosure provide for a process that includes receiving, at one or more processors, an indication to send a slug through a wellbore. The process also includes selecting, by the one or more processors, one or more choke templates indicating choke sizes of a choke for the wellbore. Moreover, the process includes controlling, using the one or more processors, the choke to the choke sizes based on the one or more templates as part of a wellbore clean-up process.

Description

FIELD OF THE INVENTION

This disclosure relates generally to hydrocarbon production and exploration and, more particularly, to methods and apparatuses to induce slugs in wellbore clean-up operations.

BACKGROUND INFORMATION

Wellbores may be drilled into subsurface rocks to create wells to access subterranean fluids, such as hydrocarbons, stored in subterranean formations. When these subterranean fluids are produced from the wells, it may be desirable to obtain certain characteristics of the produced fluids to facilitate efficient and economic exploration and production. For example, it may be desirable to obtain flow rates and/or other characteristics of the produced fluids. These produced fluids are often multiphase fluids (e.g., having some combination of water, oil, and gas).

Well clean-up is an initial phase of a well test and begins with opening the well. During this phase, solids and/or non-reservoir fluids, such as completion, drilling, and stimulation fluids, are produced to the surface together with reservoir fluids. At this stage, the effluent composition may be at least partially unknown, and the flow can be unstable and characterized by a slug flow. Slugs may include slugs of unwanted fluids, slugs of hydrocarbons, and the like. Slugs may be efficient at cleaning out the wellbore, but slugs may be difficult to induce in the wellbore due to the complex nature of the distribution of the multiple fluid and/or slurry phases dispersed throughout the wellbore.

SUMMARY

A summary of certain embodiments described herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure.

In one embodiment, a method includes receiving, at one or more processors, an indication to send a slug through a wellbore. The method also includes selecting, by the one or more processors, one or more choke templates indicating choke sizes of a choke for the wellbore. Furthermore, the method includes controlling, using the one or more processors, the choke to the choke sizes based on the one or more choke templates as part of a wellbore clean-up process.

In another embodiment, a system includes one or more sensors configured to take measurements of a plurality of parameters related to a wellbore. The system also includes one or more memory devices storing instructions and one or more processors configured to execute the instructions to cause the one or more processors to record one or more parameters of the plurality of parameters, wherein the one or more parameters are related to pressure or flow rates related to the wellbore. The instructions also cause the one or more processors to compute a superficial velocity based on the one or more parameters and to compute density and viscosity of a liquid mixture in the wellbore. The instructions also cause the one or more processors to identify a flow regime based on the superficial velocity, density, and viscosity and to determine whether the flow regime is in a slug flow region of a flow map. If the flow regime is in the slug flow region, the instructions cause the one or more processors to perform a first adjustment type, and if the flow regime is not in the slug flow region, the instructions cause the one or more processors to perform a second adjustment type.

In a further embodiment, a system includes one or more sensors configured to take measurements of a plurality of parameters related to a wellbore and one or more chokes of the wellbore configured to restrict flow through the wellbore. The system also includes one or more memory devices storing instructions and one or more processors configured to execute the instructions to cause the one or more processors to receive an indication to perform a wellbore clean-up process. The instructions also cause the one or more processors to select one or more choke templates for controlling respective choke sizes of the one or more chokes in a sequence over time. The selection of the one or more choke templates is based at least in part on at least one of the plurality of parameters. The instructions also cause the one or more processors to control the respective choke sizes based on the one or more choke templates to cause one or more slugs to traverse the wellbore as part of the wellbore clean-up process.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings, in which:

FIG. 1 illustrates a diagram of a data capturing system used to capture data in and/or around an oilfield, such as in a wellbore, in accordance with embodiments of the present disclosure;

FIG. 2 illustrates a computing system used to process data from the data capturing system of FIG. 1, in accordance with embodiments of the present disclosure;

FIG. 3 illustrates a process for performing a deterministic optimization for a clean-up operation of the wellbore, in accordance with embodiments of the present disclosure;

FIG. 4 illustrates a process for performing an optimization for a clean-up operation of the wellbore with at least one uncertainty, in accordance with embodiments of the present disclosure;

FIG. 5 illustrates a graphical illustration of a CHOKE keyword of an optimizer/simulator with template CVs for use in the process of FIG. 3 or FIG. 4, in accordance with embodiments of the present disclosure;

FIG. 6 illustrates an example well showing a height, volume inside tubing of the well, and length, in accordance with embodiments of the present disclosure;

FIG. 7 illustrates a graphical illustration of a CHOKE keyword of an optimizer/simulator with library CVs for use in the process of FIG. 3 or FIG. 4, in accordance with embodiments of the present disclosure;

FIG. 8 illustrates a flow map that indicates what type of flow occurs in a wellbore, in accordance with embodiments of the present disclosure;

FIG. 9 illustrates a flow chart of a process for using a slug model to control operation of a well clean-up operation, in accordance with embodiments of the present disclosure; and

FIG. 10 illustrates a flow chart of a process for using templates in a wellbore clean-up operation, in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following, reference is made to embodiments of the disclosure. It should be understood, however, that the disclosure is not limited to specific described embodiments. Instead, any combination of the following features and elements, whether related to different embodiments or not, is contemplated to implement and practice the disclosure. Furthermore, although embodiments of the disclosure may achieve advantages over other possible solutions and/or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the disclosure. Thus, the following aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the claims except where explicitly recited in a claim. Likewise, reference to “the disclosure” shall not be construed as a generalization of inventive subject matter disclosed herein and should not be considered to be an element or limitation of the claims except where explicitly recited in a claim.

Although the terms first, second, third, etc., may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region, layer or section from another region, layer or section. Terms such as “first”, “second” and other numerical terms, when used herein, do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed herein could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.

When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Additionally, it should be understood that references to “one embodiment” or “an embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.

Some embodiments will now be described with reference to the figures. Like elements in the various figures will be referenced with like numbers for consistency. In the following description, numerous details are set forth to provide an understanding of various embodiments and/or features. It will be understood, however, by those skilled in the art, that some embodiments may be practiced without many of these details, and that numerous variations or modifications from the described embodiments are possible. As used herein, the terms “above” and “below”, “up” and “down”, “upper” and “lower”, “upwardly” and “downwardly”, and other like terms indicating relative positions above or below a given point are used in this description to more clearly describe certain embodiments. Furthermore, “optimize” as used herein is intended to cover scenarios where certain objectives/parameters are enhanced or improved even if there may be further improvement available. In other words, an operation may be optimized without being the most optimized possible solution.

With the foregoing in mind, FIG. 1 illustrates a data capturing system 10 to capture and produce data output 12 in an oilfield that is captured as part of a clean-up operation, wireline operation, pumping operation, drilling operation, extraction operation, or any other operation being performed. In the illustrated embodiment, the data capture is being at least partially performed by a wireline tool 14 suspended by a rig 15 and into a wellbore 16 during drilling. During production/clean-out data may be acquired using other tools (e.g., surface measurements). The wireline tool 14 is adapted for deployment into wellbore 16 for generating well logs, performing downhole tests, collecting samples, and/or collecting any other data. For instance, the wireline tool 14 may assist in performing a logging while drilling (LWD) operation. Additionally or alternatively, the wireline tool 14 may, for example, have an explosive, radioactive, electrical, or acoustic energy source 18 that sends and/or receives electrical signals to surrounding subterranean formations 20 and/or fluids therein. Return signals may be detected using the wireline tool 14 and/or other tools located at other locations at/near the oilfield.

Computer facilities may be positioned at various locations about the oilfield (e.g., the surface unit 22) and/or at remote locations. The surface unit 22 may be used to communicate with the wireline tool 14 and/or offsite operations, as well as with other surface or downhole sensors. The surface unit 22 is capable of communicating with the wireline tool 14, pumps, a choke 23, and/or other equipment. For instance, the choke 23 may be an adjustable choke that controls fluid flow out of the wellbore. The surface unit 22 may also collect data generated during the drilling operation, clean-out operation, production operation, and/or logging and produces data output 12, which may then be stored or transmitted. In other words, the surface unit 22 may collect data generated during the clean-out operation and may produce data output 12 that may be stored or transmitted.

The surface unit 22 may include one or more various sensors and/or gauges that may additionally or alternatively be located at other locations in the oilfield. These sensors and/or gauges may be positioned about the oilfield (e.g., in/at the rig 15) to collect data relating to various field operations. As shown, at least one downhole sensor 24 is positioned in the wireline tool 14 to measure downhole parameters which relate to, for example porosity, permeability, fluid composition and/or other parameters of the field operation. During drilling, different or more parameters, such as weight on bit, torque on bit, pressures, temperatures, flow rates, compositions, rotary speed, and/or other parameters of the field operation, may be measured.

The surface unit 22 may include a transceiver 33 to enable communications between the surface unit 22 and various portions of the oilfield or other locations. The surface unit 22 may also be provided with or functionally connected to one or more controllers for actuating mechanisms at the oilfield. The surface unit 22 may then send command signals to the oilfield in response to data received. The surface unit 22 may receive commands via the transceiver 33 or may itself execute commands to the controller. A computing system including a processor may be included to analyze the data (locally or remotely), make decisions, control operations, and/or actuate the controller. In this manner, the oilfield may be selectively adjusted based on the data collected. This technique may be used to enhance portions of the field operation, such as controlling drilling, weight on bit, pump rates, and/or other parameters. These adjustments may be made automatically based on an executing application with or without user input.

A mud pit 26 is used to draw drilling mud into the drilling tools via flow line 28 for circulating drilling mud down through the drilling tools, then up wellbore 16 and back to the surface. The drilling mud may be filtered and returned to the mud pit 26. A circulating system may be used for storing, controlling, or filtering the flowing drilling muds. The drilling tools are advanced into subterranean formations 20 to reach a reservoir 30. Each well may target one or more reservoirs.

Generally, the wellbore 16 is drilled according to a drilling plan that is established prior to drilling. The drilling plan sets forth equipment, pressures, trajectories and/or other parameters that define the drilling process for the wellsite. The drilling operation may then be performed according to the drilling plan. However, as information is gathered, the drilling operation may need to deviate from the drilling plan. Additionally, as drilling or other operations are performed, the subsurface conditions may change. The earth model may also be adjusted as new information is collected.

After the drilling operation is completed, at least some drilling mud and/or other materials other than the desired subterranean fluid may remain in the wellbore. To remove these unwanted materials, a clean-up operation may be performed. As effluent travel upwards through the wellbore 16, it travels through the choke 23. As previously noted, this effluent may be multiphase consisting of multiple fluids (e.g., oil, gas, and water). This multiphase fluid traverses the choke 23 and enters into a separation and analysis system 32. The separation and analysis system 32 may be at least partially included in the surface unit 22. The separation and analysis system 32 may include a horizontal separator, a vertical separator, and/or any other mechanisms that may facilitate separation of the incoming effluent. For instance, the separator may include a 3-phase gravity separator that separates the effluent into its separate gas, oil, and water sub-elements. The analysis portion of the separation and analysis system 32 may test for how successful the separation of the sub-elements has been. Additionally or alternatively, the analysis portion of the separation and analysis system 32 may determine flow rates of water and other liquids to determine whether the clean-up has been completed. Additionally, if the effluent contains solids, analysis portion of the separation and analysis system 32 may determine the value of basic sediments and water (BSW) in the effluent to determine whether the clean-up operation has been completed.

The data gathered by sensors 24 may be collected by the surface unit 22 and/or other data collection sources for analysis or other processing. The data collected by the sensors 24 may be used alone or in combination with other data. The data may be collected in one or more databases and/or transmitted to another location on-site or offsite. The data may be historical data, real time data, or combinations thereof. The real time data may be used in real time or stored for later use. The data may also be combined with historical data and/or other inputs for further analysis. The data may be stored in separate databases and/or combined into a single database.

FIG. 2 is a block diagram of a system 250 that may be used for analyzing/utilizing the data output 12 from the data capturing system 10, as described in FIG. 1. The data output 12, as described in FIG. 1, is received as input data 252 at a computing device 254. The computing device 254 may be implemented in the surface unit 22 and/or may be implemented at other locations within the oilfield or remotely from the oilfield where the remote locations are able to receive the data via the transceiver 33. The various functional blocks shown in FIG. 2 may include hardware elements (including circuitry), software elements (including computer code stored on a tangible computer-readable medium), or a combination of both hardware and software elements. It should be noted that FIG. 2 is merely one example of a particular implementation and is intended to illustrate the types of components that may be present in the computing device 254.

As illustrated, the computing device 254 includes one or more processor(s) 256, a memory 258, a display 260, input devices 262, one or more neural networks(s) 264, and one or more interface(s) 266. In the computing device 254, the processor(s) 256 may be operably coupled with the memory 258 to facilitate the use of the processors(s) 256 to implement various stored programs. Such programs or instructions executed by the processor(s) 256 may be stored in any suitable article of manufacture that includes one or more tangible, computer-readable media at least collectively storing the instructions or routines, such as the memory 258. The memory 258 may include any suitable articles of manufacture for storing data and executable instructions, such as random-access memory, read-only memory, rewritable flash memory, hard drives, and optical discs. In addition, programs (e.g., an operating system) encoded on such a computer program product may also include instructions that may be executed by the processor(s) 256 to enable the computing device 254 to provide various functionalities.

The input devices 262 of the computing device 254 may enable a user to interact with the computing device 254 (e.g., pressing a button to increase or decrease a volume level). The interface(s) 266 may enable the computing device 254 to interface with various other electronic devices. The interface(s) 266 may include, for example, one or more network interfaces for a personal area network (PAN), such as a Bluetooth network, for a local area network (LAN) or wireless local area network (WLAN), such as an IEEE 802.11× Wi-Fi network or an IEEE 802.15.4 wireless network, and/or for a wide area network (WAN), such as a cellular network. The interface(s) 266 may additionally or alternatively include one or more interfaces for, for example, broadband fixed wireless access networks (WiMAX), mobile broadband Wireless networks (mobile WiMAX), and so forth.

In certain embodiments, to enable the computing device 254 to communicate over the aforementioned wireless networks (e.g., Wi-Fi, WiMAX, mobile WiMAX, 4G, LTE, and so forth), the computing device 254 may include a transceiver (Tx/Rx) 267. The transceiver 267 may include any circuitry that may be useful in both wirelessly receiving and wirelessly transmitting signals (e.g., data signals). The transceiver 267 may include a transmitter and a receiver combined into a single unit.

The input devices 262, in combination with the display 260, may allow a user to control the computing device 254. For example, the input devices 262 may be used to control/initiate operation of the neural network(s) 264. Some input devices 262 may include a keyboard and/or mouse, a microphone that may obtain a user's voice for various voice-related features, and/or a speaker that may enable audio playback. The input devices 262 may also include a headphone input that may provide a connection to external speakers and/or headphones.

The neural network(s) 264 may include hardware and/or software logic that may be arranged in one or more network layers. In some embodiments, the neural network(s) 264 may be used to implement machine learning and may include one or more suitable neural network types. For instance, the neural network(s) 264 may include a perceptron, a feed-forward neural network, a multi-layer perceptron, a convolutional neural network, a long short-term memory (LSTM) network, a sequence-to-sequence model, and/or a modular neural network. In some embodiments, the neural network(s) 264 may include at least one deep learning neural network.

The output of the neural network(s) 264 may be based on the input data 252, such as flow rates or other data captured during drilling, clean-out, and/or other operations. This output may be used by the computing device 254. Additionally or alternatively, the output from the neural network(s) 264 may be transmitted using a communication path 268 from the computing device 254 to a gateway 270. The communication path 268 may use any of the communication techniques previously discussed as available via the interface(s) 266. For instance, the interface(s) 266 may connect to the gateway 270 using wired (e.g., Ethernet) or wireless (e.g., IEEE 802.11) connections. The gateway 270 couples the computing device 254 to a wide-area network (WAN) connection 272, such as the Internet. The WAN connection 272 may couple the computing device 254 to a cloud network 274. The cloud network 274 may include one or more computing devices 254 grouped into one or more locations (e.g., data centers). The cloud network 274 includes one or more databases 276 that may be used to store the output of the neural network(s) 264. In some embodiments, the cloud network 274 may perform additional transformations on the data using its own processor(s) 256 and/or neural network(s) 264.

The computing device 254 may be used to perform an optimization process to optimize for the objective function, F. For instance, FIG. 3 is a block diagram of a process 300 for performing a deterministic optimization that may be performed using the computing device 254. As illustrated, the computing device 254 receives F and bounds of control variables (CVs) (block 302). As discussed herein, the objective function F may pertain to inducing slugs in the wellbore 16 and/or any other suitable function applicable to wellbore clean-up operations. For instance, F may be received as defined by a user via the input devices 262, pulled from the memory 258, received as a selection from presented possible functions, and/or other mechanisms suitable for defining the objective function, F. Similarly, the bounds for the CVs may be defined and/or received by the computing device 254. Furthermore, the computing device 254 receives a convergence tolerance and max count (block 304). The convergence tolerance and/or the max count may be defined similarly to how the CVs and/or F is received. The convergence tolerance and/or the max count control how many iterations are to be performed. A larger convergence tolerance deems the optimization converged or optimized at an earlier time while the max count defines a maximum number of iterations before the optimization using the process 300 is halted. In other words, a higher threshold condition will be met earlier than a lower one.

The computing device 254 initializes the CVs and a count (block 306). For instance, the count may set an index (e.g., n) for values (e.g., x) to a first value (e.g., x_n=1). Using these CV values, the computing device 254 computes a corresponding F for the count (e.g., F_n) (block 308). As is described below in further detail, the computation of the corresponding F using the CV values may be calculated using coupled emulators that perform a “trial” using the CVs in tandem. The computing device 254 may determine a maximum change from a maximum of the previous iteration (e.g., initially set to some default value, such as undefined) (block 310). For instance, the computing device 254 may determine a maximum change of the corresponding F and a previously computed F (or undefined value).

The computing device 254 may then determine whether the change is less than or equal to the convergence tolerance (block 312). For instance, the computing device 254 may subtract the corresponding function from the max of the previous iteration. If the absolute value of the difference is less than or equal to the convergence tolerance, the corresponding function may be deemed “optimized,” and the process 300 may end.

If the difference is less than the convergence tolerance, the computing device 254 may determine whether the maximum count, as previously defined, has been reached (block 314). If the max count has been reached, the computing device 254 may end the process 300. If the count is less than the maximum count, the computing device 254 increments the count (block 316). The computing device 254 then updates the CVs (e.g., x_n) based on the incremented count (block 318). With the updated CVs, the computing device 254 may re-compute the new corresponding function F, and the process 300 starts over from block 308.

FIG. 4 is a flow chart of a process 330 that is an optimization with one or more uncertainties using the computing device 254. Such uncertainty may be related to static reservoir 30 properties, depth and severity of fluid invasion in the near wellbore region, and/or layer-specific productivity indexes. As illustrated, the computing device 254 receives F, bounds of control variables (CVs), a risk aversion factor, and one or more uncertainties (e.g., U₁, U₂, . . . . U_K) (block 332). Furthermore, the computing device 254 receives a convergence tolerance and max counts (block 334). The function F, bounds of CVs, the risk aversion, the one or more uncertainties, the convergence tolerance, and/or the max count may be received/determined in any of the methods discussed above in relation to the function F, the bounds of the CV, the convergence tolerance, or the max count in relation to FIG. 3. Specifically, in some embodiments, the risk aversion may be a user-defined risk aversion used to indicate how much risk to take during in the optimization. As previously described, the convergence tolerance and/or the max count control how many iterations are to be performed. As previously noted, a larger convergence tolerance deems the optimization converged or optimized at an earlier time while the max count defines a maximum number of iterations before the optimization using the process 330 is halted. The block 334 may be similar to the block 302 except that there are additional counts. A count (e.g., count j) may be used to index risk aversion factors to enable computing F for different risk aversion factors. Another count (e.g., count k) may be used to index uncertainties to enable computing F for multiple uncertainties. Each of the counts may be used with their own max counts.

The computing device 254 initializes the CVs and counts (block 336) similar to block 306. The computing device 254 selects one uncertain value (U_k) from the uncertainties using the count (block 338). Using these CV values, the risk aversion factor, and the selected uncertainty, the computing device 254 computes a corresponding F for the count (e.g., F_n) (block 340). As previously noted and as is described below in further detail below, the computation of the corresponding F using the CV values, risk aversion factor, and selected uncertainty may be calculated using coupled emulators that perform a “trial” using the CVs in tandem. The computing device 254 then determines whether there are more uncertainties to be used for the set of CVs (block 342). If more uncertainties are to be used, the computing device 254 selects another uncertainty (e.g., U₁) and computes an alternative corresponding function.

Using the CV values and corresponding uncertainties, the computing device 254 may determine a maximum change from a maximum of the previous iterations (e.g., initially set to some default value, such as undefined) (block 344). Furthermore, this computation may be based on the risk aversion factor with F(x_n|λ_j, U)=μ(x_n|U)−λ_j σ(x_n|U), wherein x_n are the CVs, λ_j is the risk aversion factor indexed with count j. F=μ−λσ is a generic optimization under uncertainty algorithm where μ is mean and σ is the standard deviation.

The computing device 254 may then determine whether the change is less than or equal to the convergence tolerance (block 346). For instance, the computing device 254 may subtract the corresponding function from the max of the previous iteration. If the absolute value of the difference is less than or equal to the convergence tolerance, the corresponding function may be deemed “optimized.” Once the convergence tolerance is reached, the computing device 254 determines whether the current count j is greater than a count j max (block 348). If the count j has reached its maximum number, the process 330 ends. If the count j has not reached its max, computing device 254 increments count j (block 350) and proceeds to repeat computations for a different risk aversion factor. In some embodiments, the CVs and/or at least some of the counts may be initialized to a starting point for the new computations.

If the difference is less than the convergence tolerance or the count j has been incremented, the computing device 254 determines whether the maximum count, as previously defined, has been reached (block 352). If the max count has been reached, the computing device 254 may proceed the process 330 to block 348 to check whether the count j max has been reached. If the count is less than the maximum count, the computing device 254 increments the count (block 354). The computing device 254 then updates the CVs (e.g., x_n) based on the incremented count (block 356). With the updated CVs, the computing device 254 may re-compute the new corresponding function F, and the process 330 starts over from block 338.

As previously discussed, slugs may be desirable in the optimization of wellbore clean-up operations to ensure that the well is efficiently cleared of unwanted materials. In other words, the presence of slugs traveling through the wellbore 16 evacuating unwanted materials (e.g., drilling mud) from the wellbore to maximize quality of a wellbore clean-out operation. In other words, slug flow may be preferable to other multiphase flow types in wellbore clean-up as slug flow may have the smallest friction drop per unit length when compared to other multi-phase flow types. One mechanism to control whether slugs occur may be to change a choke size, A_c, over time. Choke adjustment may have a relatively large impact on slug flow when compared to other multi-phase flow types. In other words, the higher friction losses of the other phase types would mean that the choke effectively has to overcome both the friction losses in addition to encouraging evacuation of the unwanted fluids.

However, as previously noted, inducement of these slugs is non-trivial due to the complex nature of the multi-phase flow in the wellbore 16. Even using wellbore clean-up simulators using well-posed drift-flux models for four-phase, five-phase, six-phase, seven-phase, or n-phase flows may be unable to easily induce such slugs. Although wellbore clean-up simulators/optimizers may be used to realize choke schedules that control wellbore clean-up, the nature of this optimization may not directly induce slugs. Instead, it may control operations that may incidentally result in slugs, but it may not guarantee or prioritize them. Directly inducing slugs from prior analysis of multiphase fluid flow modeling may be absent from such simulations/optimization. Accordingly, a modified optimization of inducing slugs may be based on: 1) setting a choke schedule identified from prior analysis and computation and/or 2) through real-time slug-flow modeling directed by in situ conditions within the wellbore clean-up optimization. In some embodiments, the results of the prior analysis and/or real-time slug-flow modeling may be used to create a library of predefined templates that can be applied in the existing wellbore clean-up optimization framework to determine how to induce slugs in the wellbore for an improved clean-up.

For instance, the wellbore clean-up simulator may include paired control variables (CVs) that correspond to a choke size (A_c) and a duration for which the corresponding choke size is to be used for the choke. In some embodiments, the choke size may be any increment (e.g., 1, ¼, ⅛, 1/16, 1/32, 1/64 (as illustrated), etc.) in any suitable unit (e.g., mm, cm, inches). The paired CVs may be part of a CHOKE keyword that includes one or more paired CVs. In some embodiments, the durations (e.g., minutes, hours, etc.) in the keyword may be measured for only the corresponding duration. Alternatively, the durations may be indicated from an initial point with their corresponding durations being a difference between the indication and a previous indication. For instance, a first duration may be indicated with a first value (e.g., 0.1) that indicates the duration while a second duration may be indicated with a second value (e.g., 1.0) that shows that the duration of the choke corresponding to the second duration that is used for a difference (e.g., 0.9) between the two indications.

Additionally, in the wellbore clean-up simulator, a special control variable may be defined as a template. For instance, FIG. 5 shows a graphical illustration of a CHOKE keyword 400 with paired CVs in columns 402 and 404. The column 402 may correspond to the durations CVs previously discussed, and the column 404 may correspond to the choke size (A_c) CVs previously discussed. Furthermore, the CHOKE keyword 400 includes a template CV 406 (“$TMPL1$”). Note that the template CV 406 appears just once in a row that requires two arguments when the duration and Ac CVs are included. In some embodiments, this single-instance CV tells the simulator/optimizer that this template CV 406 is a template. The simulator/optimizer substitutes specific values for the choke size CV and the duration CV in place of an instance of the template CV 406 from one of one or more stored templates 408. After substitution of the contents of a template 408 into the CHOKE keyword, the simulator/optimizer may then run using the choke size CV and the duration CV. The number of templates 408 available for substitution for the template CV 406 may be any suitable number and/or may vary based on application. Specifically, the available ensemble of suitable templates 408 may be designated up-front, may be dynamic, and/or may be limited to a specific number (e.g., due to user definition of maximum number). As each template 408 represents a predefined CHOKE operation, each template 408 is unique with a unique index. To select between the templates 408, the simulator/optimizer may use mixed integer nonlinear programming (MINLP) to solve which templates 408 are available, to prioritize templates 408, and/or to select a template 408 from available templates. Since the durations of the templates 408 may be independent of a start time of template CV 408, in embodiments where the duration CV is a cumulative time, the corresponding durations of the duration CVs of the templates 408 may be added to the start time of the template CV 408 when substituted into the CHOKE keyword 400. In some embodiments, the optimizer/simulator may select which template 408 to use based on random selection from the set of templates 408. Additionally or alternatively, the optimizer/simulator may select which template 408 to use based on the templates 408 being conditioned on certain dimensionless values as is discussed below. Additionally or alternatively, the optimizer/simulator may select which template 408 to use based on a predefined library that groups templates 408 with a common facet that may be selected from in situ or operating characteristics of a flowing system. This determination of the common facet may be made by the optimizer/simulator based on measurements from the wellbore 16 and/or from input from a user. Additionally or alternatively, machine learning (ML) may be used to identify candidates based on characteristics.

As previously discussed, the templates 408 may be classified to aid in identifying a suitable slug-inducement choke schedule. As previously noted, the complexity of downhole multiphase flow distributions and phase slippages plus turbulence may make defining a definitive and universal mechanistic model to induce slugs impossible or impractical. Instead, different templates 408 may be classified using empirical data. For instance, different usages along with different wellbore 16 characteristics may be tracked. Additionally or alternative numerical studies may be applied using different choke schedules and used to define different templates. One way of classification for a quantitative framework may include using dimensionless terms that may be also used in labeling and indexing the slug-inducement using the templates 408.

For instance, the following discusses dimensionless terms that may be used as examples that may be supplemented and/or replaced without varying from the intended scope of the present disclosure. As noted herein, the subscript x denotes a specific fluid phase that is one of multiple different phases that may be present in the wellbore 16 at any point in time. An illustrative set may include the following:

$\begin{array}{cc}{h}_x^{*}=\frac{h_x}{h_{TVD}}& \left(\mathrm{Equation}1\right)\end{array}$ $\begin{array}{cc}{L}_x^{*}=\frac{L_x}{L_{MD}}& \left(\mathrm{Equation}2\right)\end{array}$ $\begin{array}{cc}{V}_x^{*}=\frac{V_x}{V_{well}}& \left(\mathrm{Equation}3\right)\end{array}$

where h_x* is a dimensionless height of phase x, L_x* is a dimensionless length of phase x, V_x* is a dimensionless volume of phase x, h_{TV D} is the total depth of the well, L_MD is the total length through the well, and V_well is the total well volume. For instance, FIG. 6 shows an example well 420 with a total height 422, a total well volume 424, and a total length 426.

Furthermore, Equations 1, 2, and 3 may be subject to the following conditions:

$\begin{array}{cc}{h}_{TVD}={\sum }_{\forall x}{h}_x& \left(\mathrm{Equation}4\right)\end{array}$ $\begin{array}{cc}{L}_{MD}={\sum }_{\forall x}{L}_x& \left(\mathrm{Equation}5\right)\end{array}$ $\begin{array}{cc}{V}_{well}={\sum }_{\forall x}{V}_x& \left(\mathrm{Equation}6\right)\end{array}$

Dimensionless tubing-head pressure (THP) and bottom-hole pressure (BHP) may be defined by:

$\begin{array}{cc}{P}_{THP}^{*}=\frac{P_{THP}}{P_{\mathrm{ref}.}}& \left(\mathrm{Equation}7\right)\end{array}$ $\begin{array}{cc}{P}_{BHP}^{*}=\frac{P_{BHP}}{P_{\mathrm{ref}.}}& \left(\mathrm{Equation}8\right)\end{array}$

where P_ref. is a predefined reference pressure that is to be constant (e.g., 100 Bars) for all tests. Fluid phase saturation, S_x, is dimensionless by definition and the sum of phase saturations for all values of x is normalized to 1.0. Moreover, fluid phase saturation applies over any specified length of the well even if very small or over the entire well. Conversely, a fluid phase void fraction, a_x, refers to the cross-sectional area of the well. Thus,

$\begin{array}{cc}{S}_x={\int }_{L_1}^{L_2}{\alpha }_{x}dL& \left(\mathrm{Equation}9\right)\end{array}$

where L1 and L2 are any points along in the well. Thus, for the entire well:

$\begin{array}{cc}{S}_{x❘\mathrm{well}}={\int }_0^{L_{MD}}{\alpha }_x\mathrm{dL}.& \left(\mathrm{Equation}10\right)\end{array}$

Consider a well with four in situ phases: oil, water, mud, and cushion. At initiation, the simulator/optimizer has a description, and location, of each phase of these fluids as they are declared in a keyword in the optimizer/simulator data deck. As previously noted, S_x|well is known for the entire well. The optimizer/simulator and/or other computing device may be used to translate S_x into its equivalent V_x. A different choke schedule is also defined with the efficacy of slug inducement determined using the optimizer/simulator. V_x expelled from the wellbore 16 may be tracked and assigned a qualitative metric (e.g., 0 to 1) showing expulsion efficacy. The choke schedule may be revised and repeated until slug behavior is detected when an appropriate dimensionless variable is assigned to the unique choke schedule. As previously noted, at this time, choke may be stored as relative time and/or cumulative time. The optimizer/simulator and/or other computing device 254 may define L_x* for either the entire well or a portion of the well and establish whether slugs were present. This determination may be repeated until periods of slug-inducement associated with the dimensionless values occur. If the well is vertical, L_x may be replaced.

If only a portion of the well is to be considered, the previous steps may also be applied except that it is applied to only sections of the well. Regardless of whether the entire well or only a portion of the well are considered, the choke schedule is adjusted and quantified to derive empirical data for generating and/or classifying the templates 408.

As previously noted, a library of templates 408 may be a collection of two or more preexisting templates that may have a common characteristic. For instance, the common characteristic may include h_x*, L_x*, V_x*, P_BHP*, P_THP*, and/or any other quantitative characteristic that is unique to the library. In other words, the library may be an ensemble of templates 408 assembled based on similarities in fluid density, viscosity, saturation, and/or other similar characteristics between wellbores and/or operations. In some embodiments, there may be no limit to the number of templates 408 that may be included in a library. However, more templates 408 defined in a library may cause the optimizer/simulator to take more time to converge. Accordingly, in some embodiments, the number of templates 408 includable in a library may be limited.

FIG. 7 is a CHOKE keyword 440 that includes paired CVs 442 and 444. Similar to the template CVs, the CHOKE keyword 440 may include library CVs ($LIB1$, $LIB2$, $LIB3$, and $LIB4$) that may be used to substitute in corresponding choke sizes and durations in the CHOKE keyword 440. However, each of the library CVs points to a corresponding library that is an ensemble of two or more templates 408. For instance, the library CV $LIB1$ corresponds to a library 446 of templates 408, the library CV $LIB2$ corresponds to a library 448 of templates 408, the library CV $LIB3$ corresponds to a library 450, and the library CV $LIB4$ corresponds to a library 452. The libraries 446, 448, 450, and 452 may have the same number or different numbers of templates 408. Furthermore, as illustrated, at least some of the templates 408 may be included in more than one library. In some embodiments, the CHOKE keyword 440 may include both template CVs and library CVs.

As previously noted, templates 408 are derived from observation and/or numerical analysis. It may be assumed that these constructed templates 408 may result in slugs. However, there may be an element of uncertainty in slug inducement due to the complexity of the wellbores. In other words, there may be no guarantee that a template will induce slug flow for a situation under consideration. To reduce this uncertainty, a large number of templates (and libraries) may be made available with indexed unique identifiers. For instance, the index and/or identifiers may be based on dimensionless terms per Equations 1-3, 7, and 8.

In addition to the identifiers, further analysis may be made to determine how to induce slug flow through modeling based on current in situ saturation and pressures. For instance, a determination of how to induce slug flow may be performed in real-time during the optimization itself. As may be understood, modeling slug flow may be exceptionally challenging to model under any circumstance much less for instances of complex saturations that may be present in a well before clean-up.

Any model-induced slug may use real-time solutions at any point in the well that may utilize intermediate results. Moreover, slug flow models may rely on gas being present. Accordingly, the model presented here assumes gas is present and an averaged ‘liquid’ mixture that includes all in situ liquid phases. This liquid mixture may also assume that there is no slippage between each liquid phase present. This assumption is reasonable for many liquids with like viscosity. However this assumption breaks-down as liquid viscosity differs between adjacent phases.

Average liquid mixture density is defined as follows:

$\begin{array}{cc}\langle {\rho }_{Lmix}\rangle =\left(\langle {\alpha }_{\mathrm{wat}}\rangle {\rho }_{\mathrm{wat}}\right)+\left(\langle {\alpha }_{mud}\rangle {\rho }_{mud}\right)+\left(\langle {\alpha }_{cus}\rangle {\rho }_{cus}\right)+\left(\langle {\alpha }_{oil}\rangle {\rho }_{oil}\right),& \left(\mathrm{Equation}11\right)\end{array}$

where ρ is density and a is the average void fraction each for specific phase or of the mix based on the corresponding subscript, and the subscripts wat, mud, cus, and oil each relate to water, mud, cushion, and oil respectively. Although four phases are shown here, any suitable number (e.g., 4, 5, 6, 7, or 8) of phases may be used. Similarly, the average liquid mixture viscosity is defined as follows:

$\begin{array}{cc}\langle {\mu }_{Lmix}\rangle =\left(\langle {\alpha }_{\mathrm{wat}}\rangle {\mu }_{\mathrm{wat}}\right)+\left(\langle {\alpha }_{mud}\rangle {\mu }_{mud}\right)+\left(\langle {\alpha }_{cus}\rangle {\mu }_{cus}\right)+\left(\langle {\alpha }_{oil}\rangle {\mu }_{oil}\right),& \left(\mathrm{Equation}12\right)\end{array}$

where μ is viscosity for specific phase, region, or of the mix based on the corresponding subscript. Moreover,

$\begin{array}{cc}{\sum }_{\forall x}{\alpha }_x=1.,& \left(\mathrm{Equation}13\right)\end{array}$

where x applies to all phases: liquid and gaseous and/or slurry phases (containing solids). The sum may pertain to the entire well or only a section of the well under examination.

There are multiple flow maps that may be used. For instance, FIG. 8 shows a flow map 460 that may be based on Mukherjee and Brill. As illustrated, the flow map 460 contains dimensionless coordinates _Lmix 462 and _gas 464, where _Lmix is a dimensionless superficial liquid mixture number and _gas is a dimensionless superficial gas number. The flow map 460 applies to vertical flow, but similar principles may be applied to inclined or horizontal flow maps. The dimensionless superficial gas number, _gas, is defined using the following:

$\begin{array}{cc}{N}_{gas}={u_{gas}^s\left(\frac{\langle {\rho }_{Lmix}\rangle }{g{\sigma }_{gas-Lmix}}\right)}^{1/4},& \left(\mathrm{Equation}14\right)\end{array}$

where g is the constant acceleration due to gravity, σ_gas-Lmix is surface tension between the interface of the liquid mixture and gas phase, and u_gas^s is the superficial gas velocity. In some embodiments, the surface tension between the interface of the liquid mixture and gas phase may be an approximate value that is a function of an in situ temperature.

The dimensionless superficial liquid mixture number, _Lmix, is defined using the following:

$\begin{array}{cc}{𝒩}_{Lmix}={u_{liq}^s\left(\frac{\langle {\rho }_{Lmix}\rangle }{g{\sigma }_{gas-Lmix}}\right)}^{1/4}& \left(\mathrm{Equation}15\right)\end{array}$

where u_liq^s is the superficial liquid velocity. In the flow map 460, a slug region may be the region where more slugs are induced. For instance, a target coordinate with _gas=46 and _Lmix=30 denotes a point located in the slug flow region.

As previously noted, no single slug flow model may be universally acceptable for the various different possible conditions. Furthermore, there are considerable differences in the underlying physics of horizontal slug and vertical slug flows and everything in between. The following model approach applies to pure vertical flow. Similar techniques may be applied to horizontal and/or inclined flows using similar techniques.

The flow model may be indicated using a single implicit function:

$\begin{array}{cc}\left(9.916\sqrt{\mathrm{gD}}\right){\left(1-{\alpha }_{\mathrm{gas},\mathrm{Tb}}\right)\left[1-\sqrt{{\alpha }_{\mathrm{gas},\mathrm{Tb}}}\right]}^{1/2}-& \left(\mathrm{Equation}16\right)\end{array}$ $\left[u_{\mathrm{Tb}}{\alpha }_{\mathrm{gas},\mathrm{Tb}}\right]+\tilde{A}=0,$

where D is the diameter of the well, u_Tb is the velocity of fluid of the Taylor bubble, and α_gas,Tb is the gas void fraction of the Taylor bubble. Ã is defined using the following

$\begin{array}{cc}\tilde{A}=\left({\alpha }_{\mathrm{gas},\mathrm{Ls}}{u}_{\mathrm{Tb}}\right)+\left(1-{\alpha }_{\mathrm{gas},\mathrm{Ls}}\right)\times \left[\left(u_{\mathrm{gas}}^s+\langle u_{\mathrm{Lmix}}^s\rangle \right)-{\alpha }_{\mathrm{gas},\mathrm{Ls}}\left\{1.53{\left(\frac{{\sigma }_{\mathrm{gas}-\mathrm{Lmix}}g\left(\langle {\rho }_{\mathrm{Lmix}}\rangle -{\rho }_{\mathrm{gas}}\right)}{{\langle {\rho }_{\mathrm{Lmix}}\rangle }^2}\right)}^{1/4}\sqrt{1-{\alpha }_{\mathrm{gas},\mathrm{Ls}}}\right\}\right].& \left(\mathrm{Equation}17\right)\end{array}$

α_gas,Ls may be approximated using the following equation:

$\begin{array}{cc}{\alpha }_{\mathrm{gas},\mathrm{Ls}}\approx \frac{u_{\mathrm{gas}}^s}{0.425+2.65\left(u_{\mathrm{gas}}^s+\langle u_{\mathrm{Lmix}}^s\rangle \right)}.& \left(\mathrm{Equation}18\right)\end{array}$

The approximate for α_gas,Ls may be substituted into Equation 16 and 17 leaving α_gas,Tb. as the only unknown enabling a determination of the α_gas,Tb. from the substitution of Equation 18 into Equation 17 that is, in turn, substituted into Equation 16. Once α_gas,Tb has been located using the substitutions into Equation 16, the Taylor bubble bulk velocity may be obtained as follows:

$\begin{array}{cc}{u}_{\mathrm{Tb}}=1.2\left(\langle u_{\mathrm{Lmix}}^s\rangle +u_{\mathrm{gas}}^s\right)+0.35\sqrt{\mathrm{gD}}.& \left(\mathrm{Equation}19\right)\end{array}$

Likewise, the velocity of the liquid phase in the Taylor bubble is approximated as follows:

$\begin{array}{cc}{\langle u_{\mathrm{Lmix}}\rangle }_{\mathrm{Tb}}=9.916\sqrt{\mathrm{gD}\left(1-\sqrt{{\alpha }_{\mathrm{gas},\mathrm{Tb}}}\right)}.& \left(\mathrm{Equation}20\right)\end{array}$

The average liquid velocity in the liquid slug units may be determined from the following:

$\begin{array}{cc}{\langle u_{\mathrm{Lmix}}\rangle }_{\mathrm{Ls}}=\frac{U_{\mathrm{Tb}}\left({\alpha }_{\mathrm{gas},\mathrm{Tb}}-{\alpha }_{\mathrm{gas},\mathrm{Ls}}\right)-\left(1-{\alpha }_{\mathrm{gas},\mathrm{Tb}}\right){\langle U_{\mathrm{Lmix}}\rangle }_{\mathrm{Tb}}}{\left(1-{\alpha }_{\mathrm{gas},\mathrm{Ls}}\right)}.& \left(\mathrm{Equation}21\right)\end{array}$

The average gas phase velocity in the liquid slug may be determined as follows:

$\begin{array}{cc}{\langle u_{\mathrm{gas}}\rangle }_{\mathrm{Ls}}=& \left(\mathrm{Equation}22\right)\end{array}$ ${\langle u_{\mathrm{Lmix}}\rangle }_{\mathrm{Ls}}+{1.53\left[\frac{{\sigma }_{\mathrm{gas}-\mathrm{Lmix}}g\left(\langle {\rho }_{\mathrm{Lmix}}\rangle -{\rho }_{\mathrm{gas}}\right)}{{\langle {\rho }_{\mathrm{Lmix}}\rangle }^2}\right]}^{1/4}\sqrt{1-{\alpha }_{\mathrm{gas},\mathrm{Ls}}}.$

The gas phase velocity of the Taylor bubble may be found using the following:

$\begin{array}{cc}{\langle u_{\mathrm{gas}}\rangle }_{\mathrm{Tb}}=\frac{U_{\mathrm{su}}\left({\alpha }_{\mathrm{gas},\mathrm{Tb}}-{\alpha }_{\mathrm{gas},\mathrm{Ls}}\right)+{\alpha }_{\mathrm{gas},\mathrm{Ls}}{\langle U_{\mathrm{gas}}\rangle }_{\mathrm{Ls}}}{{\alpha }_{\mathrm{gas},\mathrm{Tb}}}.& \left(\mathrm{Equation}23\right)\end{array}$

The average liquid mixture hold-up of the slug unit may be defined using the following:

$\begin{array}{cc}{\langle {\alpha }_{\mathrm{Lmix}}\rangle }_{\mathrm{su}}\cong \frac{U_{\mathrm{su}}\left({\langle {\alpha }_{\mathrm{gas},}\rangle }_{\mathrm{Ls}}\right)+U_{\mathrm{gas},\mathrm{Ls}}(1-{\langle U_{\mathrm{gas}}\rangle }_{\mathrm{Ls}}-u_{\mathrm{gas}}^s}{U_{\mathrm{Tb}}},& \left(\mathrm{Equation}24\right)\end{array}$

where α_LmixLs=(1−α_gasLS) yielding an average slug unit gas void fraction:

$\begin{array}{cc}{\langle {\alpha }_{\mathrm{gas}}\rangle }_{\mathrm{su}}=1-{\langle {\alpha }_{\mathrm{Lmix}}\rangle }_{\mathrm{su}},& \left(\mathrm{Equation}25\right)\end{array}$

where u_su is the slug unit velocity and may be approximated using the following:

$\begin{array}{cc}{u}_{\mathrm{su}}\cong \frac{Q_{\mathrm{Lmix}}-Q_{\mathrm{gas}}}{A_c}={\langle U_{\mathrm{Lmix}}\rangle }_{\mathrm{su}}.& \left(\mathrm{Equation}26\right)\end{array}$

Furthermore, frictional pressure drop over the slug unit may be determined using the following:

$\begin{array}{cc}{\left({\Delta }_{\mathrm{Pf}}\right)}_{\mathrm{su}}=f_{\mathrm{su}}\frac{2{\langle {\rho }_{\mathrm{Lmix}}\rangle }_{\mathrm{su}}{u}_{\mathrm{su}}^{2}L}{D},& \left(\mathrm{Equation}27\right)\end{array}$

where L_su is approximately L and the slug unit void-fraction-averaged properties are obtained using the following equations:

$\begin{array}{cc}{\langle {\rho }_{\mathrm{Lmix}}\rangle }_{\mathrm{su}}=\left[{\langle {\alpha }_{\mathrm{gas}}\rangle }_{\mathrm{su}}\times {\rho }_{\mathrm{gas}}+\left(1-{\langle {\alpha }_{\mathrm{gas}}\rangle }_{\mathrm{su}}\right)\times \langle {\rho }_{\mathrm{Lmix}}\rangle \right],\mathrm{and}& \left(\mathrm{Equation}28\right)\end{array}$ $\begin{array}{cc}{\langle {\mu }_{\mathrm{Lmix}}\rangle }_{\mathrm{su}}=\left[{\langle {\alpha }_{\mathrm{gas}}\rangle }_{\mathrm{su}}\times {\mu }_{\mathrm{gas}}+\left(1-{\langle {\alpha }_{\mathrm{gas}}\rangle }_{\mathrm{su}}\right)\times \langle {\mu }_{\mathrm{Lmix}}\rangle \right].& \left(\mathrm{Equation}29\right)\end{array}$

The void-fraction-averaged ‘slug unit Reynolds number,’ _Resu, is then determined using the following:

$\begin{array}{cc}{{\mathrm{Re}}_{}\rangle }_{\mathrm{su}}=\frac{{\mathrm{Du}}_{\mathrm{su}}{\langle {\rho }_{\mathrm{Lmix}}\rangle }_{\mathrm{su}}}{{\langle {\mu }_{\mathrm{Lmix}}\rangle }_{\mathrm{su}}}.& \left(\mathrm{Equation}30\right)\end{array}$

Moreover, f_su from Equation 27 may be determined using the following:

$\begin{array}{cc}\frac{1}{\sqrt{f_{\mathrm{su}}}}=1.74-2{\log }_{10}\left(\frac{2\in }{D}+\frac{18.7}{{\langle {\mathrm{Re}}_{}\rangle }_{\mathrm{su}}\sqrt{f_{\mathrm{su}}}}\right).& \left(\mathrm{Equation}31\right)\end{array}$

Numerous investigators have proposed alternative methods for estimating friction loss, gas- and liquid-slug void fractions, estimates for known vortex shedding phenomena in the liquid slug unit, slug-tail mixing relationships and (not insignificantly) slug lengths and frequencies. However, the aforementioned material should form a reasonable basis upon which to establish an ‘average’ slug unit pressure drop suitably distinct from other flow type/flow regime-specific pressure drops.

Using the foregoing slug model (or another suitable slug model), the wellhead choke may be adjusted so that some of the wellbore (e.g., the vertical portion) is located in the slug flow region 466. For instance, the used slug model may be used to establish an approximate pressure drop over a region of interest in the wellbore 16 (e.g., vertical well region). FIG. 9 is a flow chart of an embodiment of a process 500 for using the slug model to control operation of a well clean-up operation. The process 500 may be implemented at least in part using instructions stored in the memory 258 and performed by the one or more processor(s) 256. For instance, the process 500 may be performed as part of a wellbore clean-up simulator/optimizer implemented using the one or more processor(s) 256.

The process 500 includes recording one or more parameters related to pressure or flow rates in the wellbore 16 (block 502). For instance, the sensors in the data capturing system 10 may measure P_THP, Q_Lmix, and/or Q_gas. Additionally or alternatively, the sensors may capture data used to estimate one or more pressure or flow rates. For instance, the sensors may measure a temperature that is used to map to a P_BHP and/or other pressures or flow rates.

The process 500 includes computing a superficial velocity based at least in part on the one or more parameters (block 504). The process 500 also includes computing viscosity and/or density of the liquid mixture (block 506). For instance, this computation may be made using Equations 11 and 12. In some embodiments, the viscosity and/or density of the liquid mixture may be calculated for an average value as phase saturation may be unlikely to be uniformly distributed through the region of interest in the wellbore 16.

The process 500 further includes identifying a flow regime (block 508). For instance, the process 500 may include using a flow map (e.g., flow map 460) to determine which region the well is operating in using _Lmix and _gas. The process 500 determines whether the flow region is the slug flow region 466 (block 510). If the well is operating in the slug flow region 466, the controller (e.g., optimizer/simulator/control software executed by the one or more processor(s) 256) performs a first adjustment type to control operation of the well clean-up (block 512). For instance, the controller may perform a gradual adjustment of the choke size to ensure that slug flow is maintained. For instance, this adjustment may include selecting one or more templates or libraries that correspond to a gentle adjustment from the current condition to a target condition.

If the well is operating outside of the slug flow region 466, the controller may perform a second adjustment type (block 514). For instance, the second adjustment type may be based on the slug flow model. For example, using the foregoing model, the one or more processor(s) 256 may compute a friction drop and the associated head loss to determine how to adjust the choke size at the surface to match a predicted tubing head pressure. In other words, this adjustment may be more aggressive than the first adjustment type. Furthermore, this adjustment may also be performed using templates and/or libraries. For instance, the templates and/or libraries may be indexed by friction drop and associated head loss to map how to adjust the choke size. Moreover, although the foregoing discusses using a single surface-based choke, some embodiments may include using one or more sub-surface chokes in addition to the surface choke. Regardless of adjustment types, the process 500 returns to block 502 until the wellbore clean-up operation has been completed.

FIG. 10 is a flow chart of a process 520 that may be implemented using the controller. As illustrated, the process 520 begins with receiving an indication to send a slug through the wellbore 16 (block 522). The indication may be an indication that a drilling process has completed, a clean-up operation is to be initiated, receipt of a manual selection through the input devices 262, and/or any other suitable indication that a slug should be induced into the wellbore 16.

To induce a slug, one or more processor(s) 256 select one or more choke templates (block 524). As previously noted, these one or more choke templates may be used to control choke sizes and durations of each size in one or more chokes. For instance, the choke templates may be used to control a choke size of a surface choke and to control a choke size of one or more sub-surface chokes. The choke sizes may be the same or may be different between the surface and sub-surface chokes. The one or more processor(s) 256 may select the one or more choke templates directly as templates 408 and/or by selecting libraries of templates 408. The selection may be based on one or more conditions of the wellbore, such as slug flow region operation, tubing head pressure, flow rates, viscosities, temperatures, and/or other conditions that may impact which template 408 may be appropriate for use. Using the one or more selected templates 408, the controller may control choke size as part of a wellbore clean-up operation (block 526).

The techniques presented and claimed herein are referenced and applied to material objects and concrete examples of a practical nature that demonstrably improve the present technical field and, as such, are not abstract, intangible or purely theoretical. Further, if any claims appended to the end of this specification contain one or more elements designated as “means for [perform]ing [a function] . . . ” or “step for [perform]ing [a function] . . . ”, it is intended that such elements are to be interpreted under 35 U.S.C. § 112(f). However, for any claims containing elements designated in any other manner, it is intended that such elements are not to be interpreted under 35 U.S.C. § 112(f).

Claims

1. A method, comprising: receiving, at one or more processors, an indication to send a slug through a wellbore;selecting, by the one or more processors, one or more choke templates indicating choke sizes of a choke for the wellbore; andcontrolling, using the one or more processors, the choke to the choke sizes based on the one or more choke templates as part of a wellbore clean-up process.

2. The method of claim 1, wherein the one or more choke templates indicate a duration for each respective choke size of the choke sizes.

3. The method of claim 2, wherein each choke template of the one or more choke templates is a sequence of choke sizes with corresponding durations and choke sizes.

4. The method of claim 3, wherein the one or more choke templates are organized into libraries of choke templates that each comprise a sequence of choke templates.

5. The method of claim 4, wherein the libraries are grouped according to applicability due to a characteristic that is common among the choke templates in a respective library.

6. The method of claim 5, wherein the common characteristic comprises a height of a phase, a length of a slug unit, a volume of the phase, a tubing head pressure, a bottom hole pressure, fluid density, fluid viscosity, saturation, temperature, or a combination thereof.

7. The method of claim 1, wherein the indication comprises an indication to initiate the wellbore clean-up process.

8. The method of claim 7, wherein the indication to initiate the wellbore clean-up process comprises an indication of completion of drilling process or receiving a manual selection via an input device.

9. The method of claim 1, wherein the one or more choke templates are applicable to two or more chokes comprising the choke.

10. The method of claim 9, wherein the two or more chokes comprises at least one surface choke and at least one sub-surface choke.

11. The method of claim 1, wherein selecting the one or more choke templates is based on one or more conditions of the wellbore.

12. The method of claim 11, wherein the one or more conditions of the wellbore comprise slug flow region of operation, tubing head pressure, flow rates, viscosities, temperatures, or a combination thereof.

13. A system, comprising: one or more sensors configured to take measurements of a plurality of parameters related to a wellbore;one or more memory devices storing instructions; andone or more processors configured to execute the instructions to cause the one or more processors to: record one or more parameters of the plurality of parameters, wherein the one or more parameters are related to pressure or flow rates related to the wellbore;compute a superficial velocity based on the one or more parameters;compute density and viscosity of a liquid mixture in the wellbore;identify a flow regime based on the superficial velocity, density, and viscosity;determine whether the flow regime is in a slug flow region of a flow map;if the flow regime is in the slug flow region, perform a first adjustment type; andif the flow regime is not in the slug flow region, perform a second adjustment type.

14. The system of claim 13, comprising one or more chokes of the wellbore configured to restrict flow through the wellbore, wherein the first adjustment type comprises a more gradual change to respective choke size of the one or more chokes of the wellbore, and the second adjustment type comprises a less gradual change to the respective choke sizes of the one or more chokes.

15. The system of claim 14, wherein the one or more chokes comprise at least one surface choke and at least one sub-surface choke.

16. The system of claim 14, wherein the second adjustment type comprises using a slug model to determine how to adjust the respective choke sizes by computing a friction drop and associated head loss to match a predicted tubing head pressure.

17. The system of claim 14, wherein the change to the respective choke sizes is different among the one or more chokes.

18. A system, comprising: one or more sensors configured to take measurements of a plurality of parameters related to a wellbore;one or more chokes of the wellbore configured to restrict flow through the wellbore;one or more memory devices storing instructions; andone or more processors configured to execute the instructions to cause the one or more processors to: receive an indication to perform a wellbore clean-up process;select one or more choke templates for controlling respective choke sizes of the one or more chokes in a sequence over time, wherein the selection of the one or more choke templates is based at least in part on at least one of the plurality of parameters; andcontrol the respective choke sizes based on the one or more choke templates to cause one or more slugs to traverse the wellbore as part of the wellbore clean-up process.

19. The system of claim 18, wherein the one or more chokes comprises at least one surface choke and at least one sub-surface choke.

20. The system of claim 19, wherein the one or more choke templates comprise a first set of templates for the at least one surface choke and a second set of templates for the at least one sub-surface choke.