Patent Application
20250163777
This disclosure relates generally to hydrocarbon production and exploration and, more particularly, to methods and apparatuses to monitor wellbore clean-up operations.
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, 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. The clean-up phase may vary from scenario to scenario and facility to facility and for different objective functions. Thus, a single clean-up flow plan may not be suitable for all scenarios, facilities, and objective functions.
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 one or more sets of parameters related to an operation corresponding to a wellbore, and at a first time, simulating the operation using the one or more sets of parameters. The pertinent results from the simulation are recorded and stored in a table. The pertinent results include at least one constraint. At a second time, the constraint is retrieved from the table for performance of the operation, and the constraint is used to control the operation.
In another embodiment, a system 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 one or more sets of parameters related to an operation corresponding to a commercial process facility. The instructions also cause the one or more processors to simulate the operation using a plurality of simulators and the one or more sets of parameters before performing the operation. The plurality of simulators include a commercial process facility simulator and a wellbore clean-up simulator. The instructions further cause the one or more processors to record pertinent results from the simulation in one or more tables. The pertinent results include control parameters that determine whether a constraint is satisfied. Furthermore, the instructions cause the one or more processors to control the operation using the control parameters based at least in part on the constraint during the operation.
In a further embodiment, a system 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 one or more sets of parameters related to an operation corresponding to a commercial process facility and to simulate the operation using a plurality of simulators and the one or more sets of parameters before performing the operation. The plurality of simulators includes a commercial process facility simulator and a wellbore clean-up simulator. The instructions further cause the one or more processors to record pertinent results from the simulation in one or more tables. The pertinent results include control parameters that determine whether a constraint is satisfied. Furthermore, the instructions further cause the one or more processors to monitor the operation to determine whether the constraint is satisfied during the operation.
Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings, in which:
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.
During the clean-up period of a well test, various operations may be implemented. For instance, pre-job wellhead surface equipment optimization may be implemented for a wellbore clean-up to maximize some objective function, F. For instance, F may non-exclusively include a function to maximize clean-up quality in at least amount of time, minimize CO2 emissions, minimize an amount of area occupied by wellhead equipment and connections, minimize sound emission, and/or other suitable functions that may be used.
With the foregoing in mind,
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, the 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.
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 802.11x 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,
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., xn=1). Using these CV values, the computing device 254 computes a corresponding F for the count (e.g., Fn) (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 not less than or equal to 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., xn) 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.
The computing device 254 initializes the CVs, counts, and risk aversion factor j (block 336) similar to block 306. The computing device 254 selects one uncertain value (Uk) from the uncertainty samples using the count (block 338). Using these CV values, the risk aversion factor, and the selected uncertainty sample, the computing device 254 computes a corresponding F for the count (e.g., Fn) (block 340). As previously noted and as is described in further detail below, the computation of the corresponding F using the CV values, risk aversion factor, and selected uncertainty sample 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 uncertainty samples to be used for the set of CVs (block 342). If more uncertainty samples are to be used, the computing device 254 selects another uncertainty sample (e.g., U1) (block 338) and computes an alternative corresponding function (block 340).
If there are no more uncertainty samples, the objective function is to be evaluated (block 343). F=μ−λ σ is a generic optimization under uncertainty algorithm where μ is the mean and σ is a standard deviation. Using the CV values and corresponding uncertainty samples, 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(xn|λj, U)=μ(xn|U)−λj σ (xn|U), wherein xn are the CVs, λj is the risk aversion factor indexed with count j.
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 max, the process 330 ends. If the count j has not reached its max, the computing device 254 increments count j (block 350). If the count j has reached its maximum number, the process 330 ends. If the count j has not reached its max, the 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., xn) 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, to ensure correct simulator response for each facility scenario and the associated wellbore clean-up, two or more simulators may be used. Two simulators, a wellbore clean-up simulator and a commercial process facility simulator, are to be coupled together in a single optimization “trial.” In this trial, previously defined CVs are fed into both wellbore clean-up simulator and (if necessary) commercial process facility simulator to furnish a user-specified objective function, F, in tandem.
Accordingly,
Moreover, in some embodiments, the solution accuracy using the process 370 may be enhanced and/or accelerated by performing pre-job sensitivity analysis and identification of key drivers that impact the objective function, F.
Additionally or alternatively, history matching (HM) may be used with the process 370 to enable real-time (RT) application of the couple simulators. In other words, previous results may be HM with RT data to utilize the RT data. HM may include feedback and/or fine-tuning of matches and associated controls, such as those performed using a proportional integral derivative (PID) controller and/or any other feedback-oriented controller types. Practical implementation of any real-time model is contingent upon sufficient compute resources and sufficient simulation controls to enable a match of realized rates and pressures with those predicted. To HM a wellbore clean-up, the wellbore clean-up simulator is to adjust key parameters at any point in time. The quality of HM is established by fitting to outputs, such as well head pressure PwH, bottom hole pressure PBH, surface gas flow rate QG, surface oil flow rate QO, surface mixture flow rate Qm, and the like.
The HM may entail varying various parameters in time to facilitate a fit to historical data. Some parameters, such as reservoir parameters, may be adjustable only at some times (e.g., at initialization) and not at others (e.g., during a wellbore clean-up). Thus, new adjustable parameters may be generated to be adjustable at times that were not previously adjustable. For instance, this new parameter/keyword may be designated as a HM reservoir (HMRES).
The new parameter/keyword may be defined by defining a condition in the wellbore clean-up simulator that indicates that CONSTPRODIDX is specified in the wellbore clean-up simulator and whether a specific parameter (e.g., perforation type) is declared. The new parameter/keyword may also have a defined time that may be coupled to the CONSTPRODIDX and the parameter. Furthermore, a skin factor is multiplied over all layers, and a radius multiplier of zone with damaged permeability is applied over all layers.
Additionally or alternatively, the new parameter/keyword may be defined by defining a condition in the wellbore clean-up simulator that indicates that CONSTPRODIDX is specified in the wellbore clean-up simulator. The new parameter/keyword may also have a defined time that may be coupled to the CONSTPRODIDX. A productivity index (PI) multiplier may be applied over all layers.
In some embodiments, a simple global multiplier, J, may be a part of PI. J operates over all layers declared in the reservoir. In certain embodiments, instead of all multipliers being applied over all zones, at least some zone-specific multipliers may be defined. Furthermore, other parameters in other wellbore clean-up simulator keywords may be eligible candidates as uncertainty samples.
During operations measured data is HM'd to create a proxy of the wellbore clean-up simulator. A proxy of the wellbore clean-up simulator may be a lighter weight approximation of the wellbore clean-up simulator instead of the full-blown model used in the wellbore clean-up simulator. Using this proxy, operations may be optimized for real-time or near-real-time output. One example of the proxy may be a neural network proxy, such as implemented in the neural network(s) 264, or some other form of machine learning proxy. The machine learning proxies use training, but such data suitable for training may be restricted and/or have relatively low amount of data. This is at least partially due to the asset-specific nature of such proxies and that the machine learning proxy is for the specific asset without physics modelling and may be unsuited for forecasting of properties outside the domain of its training for the specific asset. In fact, the machine learning proxy may be directed to a specific outcome, such as PwH or some other parameter. The neural network(s) 264 may include one or more deep or conditional neural networks with any suitable number of hidden layers between input and output layers.
In some embodiments, different sets of constraints may be used. For instance, simple and complex constraints may be used where simple constraints may be determined directly either linearly or non-linearly while complex constraints use solutions via the wellbore clean-up simulator to determine whether the particular complex constraint has been violated or not. For example, simple constraints may include facility capacity for handling produced gas during a cleanup (flaring limits), facility capacity for handling produced oil during a cleanup (burning), facility capacity for handling produced water during cleanup, specified limits for combined fluids, maximum solids as mass or volumetric flow rate, and/or other constraints. Complex restraints may include complex situations, such as drawdown pressure at a formation that may cause sand production if drawdown is too high, erosional velocity where the pipe is eroded by a choke orifice being too open, and/or other like constraints. Some estimates may be made for complex constraints (e.g., drawdown pressure as a function of flow rate), but since such estimates may be problematic (e.g., drawdown may vary along the length of the wellbore due to wellbore friction and local inflow variations due to rock heterogeneity), some analysis in the wellbore clean-up simulator may be applied causing the constraint to be a complex constraint. In some situations, a simple constraint (e.g., burner and flare operations) may be limited in time. For instance, batch burning may be intermittent or periodic rather than continuous as required by regulations and/or client policy. Similarly, flaring may be permissible within only certain times (e.g., during daylight hours). The time constraints in combination with the simple constraints may be facilitated in the wellbore clean-up simulator similar to complex constraints.
At least some of the constraints may be related to environmental factors. For example, the commercial process facility simulator may include a flare simulator that may be used to analyze heat radiation while considering local environment conditions along with targets and/or constraints. For instance, the local environment conditions may include wind speed, wind direction, humidity, ambient temperature, solar radiation, background noise, and/or other relevant local environment conditions. These conditions may be computed using the coupled simulators previously discussed. Additionally or alternatively, at least some a priori tabulation may be used as illustrated in
Regardless of using simulations in response to input values and/or using interpolation via the tables resulting from a priori tabulation, input values (e.g., wind speed and direction, etc.) may be used to determine output values (e.g., noise, heat radiation, temperature, etc.).
As noted, an amount of noise (e.g., in dB) emitted from a burner or flare stack may be computed using a priori tabulation and/or simulation to establish whether a result is likely to violate any prescribed noise limits that may be defined by regulation or client policy. Although this is a relatively straightforward constraint to consider during a cleanup operation when a priori tabulation is available, the issue is defining the range of values necessary to generate the aforementioned table as throughputs and equipment types will vary. This issue may be remedied using coupled simulations. Furthermore, in some embodiments, the a priori tabulation tables may be updated when new simulations are run using the coupled simulators.
The commercial process facility simulator may also provide information to provide radiation information at specific points. For instance,
Another constraint previously discussed may be dispersion of emissions. The constraint may be that a threshold mole fraction of emission concentrations is not exceeded from a given location. The dispersion of emissions may be shown using a map, similar to the isopleth 440. Additionally or alternatively, the dispersion of emissions may be represented using an elevation map, such as the elevation map 450 of
The commercial process facility simulator may also be used to evaluate a quantity of assistance fluids for smokeless flare uses as a function of throughput and assign their associated cost to the objective function. Such quantities may be computed a priori as a function of composition and throughput and a reasonable cost assigned to better define the objective function, F.
To mitigate some of the aforementioned environmental emissions, the impact of appropriate shielding may be computed from the coupled simulators. Shielding elevates emission limits that may impact a priori tabulation. However, the cost of such shielding may be defined for the objective function, F.
Burner and flare operating time window issues may be at least partially addressed using a new keyword or argument in the wellbore clean-up simulator. For example, the keyword may be called SHUTIN to indicate when a wellbore operation (e.g., cleanup, production, etc.) is delayed, paused, and/or stopped. This may be implemented as an argument in another keyword (e.g., CHOKE) and/or as a dedicated keyword. The insertion of text ‘SHUTIN’ as an argument may be accompanied by the clock-time duration of the shut-in itself, after which wellbore operation (e.g., clean-up) will recommence. Table 1 below shows an embodiment of SHUTIN used as an argument in the CHOKE keyword.
In Table 1, there is an eight hour shut-in after two hours of flow with choke set at 48 (e.g., 48/64″). The wellbore clean-up job is then recommenced with four hours of flow with a choke set at 64 (e.g., 64/64 or 1″). If conforming to emission regulations, a specific job-clock keyword may be used, such as CLOCKDATE shown in Table 2.
The arguments of CLOCKDATE may be defined using Table 3 below:
Using Tables 2 and 3 to interpret Table 1 shows that choke defines a shut-in starting at 10:30 pm (i.e., two hours after start of job 8:30 pm (20:30)). The job recommences on May 3 at 6:30 and lasts for four hours (after 8 hours of shut-in).
Wellbore and/or flow modules may be active during shut-in to monitor for gravity segregation and potential fluid ingress back into the near wellbore region. Upon wellbore clean-up recommencement after shut-in, fluid saturation profiles in the well and the near wellbore region may have changed, causing the wellbore clean-up simulator to re-initialize itself accordingly. During shut-in will, by definition, will yield zero flow (or emissions) at surface. However, the subsurface system may be in a state of flux with phase segregation, counter-current flow, ingress, and so on. In some embodiments, the shut-in keyword or other similar operation halts, pauses, or delays may be used when one of the constraints (either a priori computed or using couples simulations) is violated.
As previously noted in relation to block 340 of
It may be difficult to ascertain which uncertain parameters are the most impactful to the objective function, F, identify their bounds, identify their distributions, and determine how to best sample over these distributions. Furthermore, the issue of sample size may become problematic for stand-alone applications. However, cloud computing resources of the cloud 274 may be employed to resolve such limitations.
The question of the appropriate number of samples used to define an uncertainty (to a manageable number) is reduced somewhat, though perhaps not completely eliminated through cloud computation. However, such balance between sample size and compute resources may still be desirable/necessary. Thus, a preliminary sensitivity analysis may be performed prior to any operational optimization. The total number of calls to the simulator, N, that is to be used in each trial in the optimization defined by Equation 1:
K is the total number of uncertainties for the wellbore clean-up problem and (sk|Uk) are the number of samples necessary to define uncertainty Uk. As N is a product, it can become large quickly if sk and K are not selected carefully even though cloud computing may make computations faster. The uncertainty may be declared for the cleanup operation in the wellbore clean-up simulator using a relevant keyword, such as RESERVOIR and CONSTPRODIDX. For example, Table 4 shows an embodiment of a keyword (e.g., CONSTPRODIDX and/or RESERVOIR) describing two producing zones.
Arguments #1 and #2 are measured depths of top and bottom of completed reservoir zone with argument #3 being porosity. Argument #4 is permeability (k stated in mD). Argument #5 is initial reservoir pressure (Bar) with argument #6 representing initial water saturation, SW with argument #7 being salt concentration. Argument #8 is m in the relationship k=k0(ϕ)m that may typically be a constant. Argument #9 is drilling time (in hours). The meaning of argument #10 may be dependent on whether a specific keyword (e.g., CONSTPRODIDX) is declared or not.
In the example with the specific keyword declared, argument #10 represents productivity index (PI) of the zone (stated in Sm3/d/bar). Using conventional $-delimited declarations for uncertainties we specify two such declaration for PI for both layers, as shown in Table 5 below:
According to Table 5, the PI for zone 1 (5780 to 6100 m) is declared as $PIL1$ which is sampled at three equally probable points: Uk=1-3 samples {0:020; 0:022; 0:024}. PI for zone 2 (6198 to 6589 m) is declared as $PIL2$, sampled at three equally probable points: Uk=2-3 samples {0:019; 0:021; 0:023}. Using Equation 1, we have K=2 thus N=9. In other words, a single trial of the wellbore clean-up simulator will require 9 separate (but perhaps parallel) calls to the wellbore clean-up simulator simulation engine. We then obtain values for μ and σ. If there are further uncertainties, such as samples for ϕ, then Equation 1 soon results in an explosion of samples per trial. For example, 2 additional independent samples for ϕ for both zones, then N=36 as K=4, so sK∈{3; 3; 2; 2}. In other words, a single trial will require 36 separate (but perhaps parallel) calls of the wellbore clean-up simulator in order to compute with uncertainties. Although uniform grid sampling may be used, non-uniform grid sampling may be instead and/or also used. Indeed, some uncertainties may be better addressed using non-uniform sampling schemes. Moreover, all uncertainty samples are to be drawn over the uncertainty space using grid sampling or otherwise.
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. Moreover, although various actions are discussed as part of processes in a specific order, at least some of the actions may be performed in different orders. Additionally, at least some of the actions may be performed by one or more processors 256 of suitable computing devices. 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).
