专利摘要:
Systems, methods and computer readable support for predicting a duration to complete a simulation of a reservoir simulation model. A processor may perform a simulation of a reservoir simulation model for a predetermined period of time associated with a first calculation step of the simulation. The processor may measure a simulation performance result for the first predetermined period of time. The performance result may include an elapsed clock time and a simulated time period. The processor can then predict a remaining time to complete the simulation of the reservoir simulation model based on a performance result.
公开号:FR3040809A1
申请号:FR1657479
申请日:2016-08-01
公开日:2017-03-10
发明作者:Richard Edward Hinkley；Terry Wayne Wong；Graham Christopher Fleming；Amit Kumar
申请人:Halliburton Energy Services Inc；
IPC主号:

专利说明:

PREDICTION OF DURATION UNTIL THE END OF SIMULATION
TECHNICAL AREA
The present technology relates to prediction models and more specifically the prediction of the duration until the end of a simulation of a reservoir simulation model.
BACKGROUND The increase in energy demand and the depletion of hydrocarbon resources has accelerated the need for complex reservoir studies. In order to plan the optimal development of a gas or oil field, engineers turned to software modeling of deposits, wells and above-ground facilities to evaluate the potential hydrocarbon yield of one or more deposits for feed the point of sale. However, despite advances in computer technology and improvements in software performance, performing simulation of these complex models may take several days. Therefore, a mechanism for predicting simulation times would be beneficial for the allocation of computing resources and planning for analysis and activities of the study of deposits, for example.
BRIEF DESCRIPTION OF THE DRAWINGS
In order to describe how the above-mentioned advantages and features, and many others, of this disclosure can be obtained, a more specific description of the principles briefly described above will be presented with reference to specific embodiments thereof. which are illustrated in the accompanying figures. It should be understood, however, that these drawings represent only exemplary embodiments of the invention and therefore should not be perceived as limiting its scope, principles and will be described and explained in more detail and specificities. thanks to the use of the attached figures:
Figure 1 illustrates a diagram of an example of an embodiment system of a simulation prediction of the duration to the end;
FIG. 2 illustrates an exemplary method for generating a reservoir simulation and duration estimation model to complete a simulation;
FIG. 3 illustrates an exemplary method for performing a simulation of a reservoir simulation model and the prediction of a duration to complete the simulation;
Figure 4 illustrates an example of a method for predicting a duration to complete a simulation based on a stochastic model;
FIG. 5 is a graphical representation of a real completion and an estimated completion for a simulation example of a reservoir simulation model;
FIG. 6 illustrates an exemplary method for stochastic modeling of the simulation method using the stochasticPrediction function of the Example; and
Fig. 7 is a graphical representation of the duration of the simulation and a clock time for an embodiment of the stochasticPrediction function of the Example.
DETAILED DESCRIPTION
Various embodiments of the disclosure are set forth in detail below. Even if specific implementations are presented, it must be understood that they are only realized for illustrative purposes. A specialist in the relevant field will recognize that other components and configurations may be used without departing from the spirit and scope of the disclosure.
Additional features and advantages of the disclosure will be set forth in the description which follows, and will be partly obvious when reading the description and others may appear from the practice of the invention. The characteristics and advantages of the disclosure can be realized and obtained by means of instruments and combinations particularly emphasized in the appended statements. These and other features of the disclosure will become even more apparent upon reading the following description and the appended claims, or may be learned by practicing the principles described herein.
The approaches that are put forward here can be used to provide a prospective and continually updated estimate of the time required to complete a simulation of a reservoir model. During the construction of the model, the estimate can take into account the complexities of the model and computing resources and can help to access the relative impact of the different modeling choices on the execution time of the simulation. Once the simulation is started, the estimate can use a real simulation performance and can provide more useful monitoring of simulation progress. A Simulation Model Complexity (SMC) score can also be generated at each stage of model construction. The SMC score can take into account various characteristics of the model and can provide an assessment of the level of support needed to interpret the simulation results.
Systems, methods and a computer readable storage medium for predicting a duration to complete a simulation of a reservoir simulation model are disclosed. A processor may perform a simulation of a reservoir simulation model for a predetermined period of time associated with a first calculation step of the simulation. The processor may measure a simulation performance result for the first predetermined period of time. The performance result may include an elapsed clock time and a simulated time period. The processor can then predict a remaining time (e.g., extrapolation, moving averages, probabilistic models, etc.) to complete the simulation of the reservoir simulation model based on a performance result.
Before executing the simulation, the processor may determine one or more characteristics associated with an oil or gas field. The oil or gas field may comprise one or more of a deposit, a well and / or a surface installation. In addition, the characteristics can be calculated by the processor based on data captured by one or more sensors. The processor may generate a reservoir simulation model based on one or more of the features.
Then, the processor can determine one or more parameters for the reservoir simulation model. The processor can then calculate an estimated time to complete the simulation of the reservoir simulation model based on one or more of the parameters. If a change in one or more parameters is detected, the processor may update the estimated duration to complete the simulation of the reservoir simulation model based on the change. This can improve system processing and simulation efficiency. The processor may also use one or more of the parameters to compute a complexity score of the simulation model.
FIG. 1 illustrates an exemplary embodiment of a reservoir simulation system including a predictor of the duration until the end of the simulation. The reservoir simulation system 100 may include one or more processing units 110 and a bus system 105 that couples various system components, such as system memory 115, ROM 120, and RAM 125, to the processor. 110. The system 100 may include a high speed cache connected directly to, near, or integrated with the processor 110. The system 100 may copy data from the memory 115 and / or the storage device 130 to the memory cache 112 for fast access by a processor 110. In this way, the cache memory can boost performance to avoid delays in the processor 110 while waiting for data reception. The processor 110 may comprise one or more multi-purpose processors and a hardware module or software module, such as a reservoir simulation module 132, a prediction module 134 and a parameter module 136 stored in a storage device 130. modules and many others can control or can be configured to control the processor 110 to perform various actions, including running a predictor of the duration until the end of the simulation, as disclosed herein. The processor 110 may also include one or more specialized processors in which software instructions are incorporated into the actual construction of the processor.
In order to allow a user interaction with the field simulation system 100, an input device 145 may represent any number of input mechanisms, such as a microphone for the voice, a touch screen for the gesture or a graphic input, keyboard, mouse, motion capture, etc. An input device 142 may also be one or more of a number of output mechanisms known to those skilled in the art, such as a display, a speaker, and so on. In some cases, multimodal systems may allow a user to provide multiple types of inputs to communicate with the reservoir simulation system 100. There are no restrictions on operation on any given hardware arrangement and therefore the The basic features described here can be replaced by improved hardware or firmware arrangements as they are developed.
The reservoir simulation system 100 may also include one or more communication interfaces 140 which may comprise different physical interfaces. The communication interfaces 140 may include any type of communication channel, connector, data communication networks, or other links. For example, the communication interfaces 140 may comprise a wireless or wired network, a LAN, a WAN, a private network, a public network (such as the Internet), a Wi-Fi network, a network that includes a satellite link, or any other type of data communication network.
The storage device 130 may be a non-volatile memory and may be a hard disk or other types of computer readable media that can store data that is accessible by a computer, such as magnetic cassettes, memory cards. flash, semiconductor memory devices, digital versatile discs, cartridges, RAMs 125, ROMs 120, and hybrids thereof. The storage device 130 may be connected directly to the system bus 105 or may wirelessly communicate with the various components in the system 100. As previously described, the storage device 130 may include a reservoir simulator module 132, a forecast module 134 and a parameter module 136, even if other hardware or software modules are envisaged. The modules may include machine readable instructions that may be interpreted or executed by the processor 110 to perform one or more of the operations related to FIGs. 2-6. The modules may include machine-readable instructions for creating a graphical user interface (GUI) of a reservoir simulator with a prediction of the duration until the end of the simulation. The modules can receive input data, such as modules used to generate a reservoir model, to simulate a reservoir model, predict a duration to complete a simulation of a reservoir model, or any other type of data, from the memory 115, the ROM 120, the RAM 135, the storage device 130, another local source, an input device 145, or one or more other remote sources (e.g. through communication interfaces 140). The modules may generate output data and store output data in the memory 115, the ROM 120, there RAM 135, the storage device 130, on another local medium or on one or more remote devices (e.g. by sending the output data through the communication interfaces 140). In one aspect, a hardware module that performs a given function may include the software component stored on a computer readable medium in relation to the necessary hardware components, such as processor 110, bus 105, output device 142, and so on. , to execute the function.
The reservoir simulation module 132 may contain software instructions executable by a processor 110 for generating a GUI for rendering and simulation of a reservoir model. Through the use of a GUI, a user can enter data to create a reservoir model. The data used to create the model can be obtained, eg by using equipment or sensors to perform or obtain measurements on a gas or oil drilling site, from data stored inside the reservoir module 132 or from another local or distant source, etc. The deposit module 132 may also store (or download from a remote location) archival or real-time deposit-related data that covers various aspects of well planning, construction and completion processes such as, for example, eg, drilling, cementing, wireline logging, well analysis and simulation. In addition, such data may include, for example, open well log data, well trajectories, rock petrophysical property data, surface data, fault data, well data. environment, data derived from geostatistics, etc. At each step of creating a reservoir model, a parameter module 136 may collect various parameters and various features related to the complexities of the model as well as the number and type of processors 110 and other local or remote computing resources used by the reservoir simulation system 100. The prediction module 134 can use the parameters and / or the characteristics collected by the parameter module 136 to calculate, by the processor 110, an estimation of the duration to complete a simulation of a model. deposit. The prediction module 134 may also use the parameters and / or characteristics to compute a simulation model complexity (SMC) score. The estimated duration and the SMC score may be stored and transmitted to the processor 110 and / or the field simulation module 132 to display an estimated duration to the user, eg, on a GUI. If, at any time, a change is detected at one or more parameters or characteristics of the reservoir model, the parameter module 136 may update the parameters and characteristics collected to reflect this change. The forecasting module 134 can then use the updated parameters and / or features to recalculate the estimate of the time to complete a simulation of the reservoir model and the SMC score and can store and transmit the recalculated estimate and the SMC score. to the processor 110 and / or a reservoir simulation module 132 for display on a GUI. The GUI created by the processor 110 using the instructions stored in the reservoir simulation module 132 can also allow a user to start a simulation of the reservoir model, to monitor the execution of a simulation, to analyze the results of the simulation, etc. When the simulation is started, a forecast module 134 can measure a simulation performance result for a predetermined period of time associated with the simulation calculation step. The forecasting module 134 may use the performance result to predict a remaining time until the end of the reservoir model simulation and may store and transmit the predicted time to the processor 110 and / or the reservoir simulation module 132 for a display on a GUI. The forecast module 134 may also react to the predicted duration, eg, by requesting an increase or by allowing a decrease in the computing power attributed to the simulation. The prediction module 134 may repeat the aforementioned steps for subsequent steps of calculating the simulation, as described in FIG. 3 to update the previously predicted duration or to predict a new remaining time to complete a simulation of the reservoir model. Although a specific configuration of a reservoir simulation system with a prediction of the duration until the end of the simulation is illustrated in FIG. 1, it will be apparent to one skilled in the art that different configurations are possible without departing from the scope of the present disclosure. It can be envisioned that the deposit simulation system 100 may have multiple processors 110 or be part of a group or pool of networked computing devices to provide superior processing capability. The reservoir simulation system can utilize any number of computer systems or computer networks and can realize different computer model infrastructures, such as a cloud computing infrastructure, a computer network or a grid of information. computers. The program modules, such as a reservoir simulation module 132, a forecast module 134 and a parameter module 136, can be located on other local or remote computer storage media and / or a memory and can be associated or divided into any number of individual modules.
Having disclosed certain basic components and concepts of the system, the disclosure now discloses exemplary embodiments of methods illustrated in FIGs. 2-6. For the sake of clarity, the methods are described in terms of processor 110, as shown in FIG. 1, configured to perform the methods. The steps described here may be implemented in any combination thereof, including combinations that exclude, add, or modify certain steps.
Figure 2 illustrates an example of a method for creating a reservoir simulation model and estimating a duration until the end of a simulation. In step 200, the processor 110 may determine one or more characteristics associated with an oil or gas field. The oil or gas field may include one or more of a deposit, a well and a surface installation. The determined characteristics may be based on data entered or selected by a user, eg, through one or more input devices 145. The features may also be based on data stored in local or remote databases and on storage media. The data may include, eg, physical properties, material properties, geometric properties, geological data, fluid data, fracture data, processing data, and any other data necessary for the definition of the data. one or more deposits, wells, surface facilities and the surrounding area. The data may come from, for example, measurements made using equipment or sensors at a drilling site of an oil or gas field, geological studies or theoretical calculations, from databases stored in local or remote storage sources, etc. In step 210, the processor 110 may create a reservoir simulation model based on the characteristics determined in step 200. The reservoir simulation model may include one or more of a reservoir model, a a well model, a surface facility model, a geological model, a geomechanical model, a fracture model, a flow model and any other model needed to access the potential hydrocarbon recovery. The processor 110 may create the reservoir simulation model to respond to a selection or preference of the user. The reservoir simulation model can be rendered and displayed to the user, e.g., through one or more output devices 142. In step 220, the processor 110 can determine one or more parameters for the simulation model. deposit. The parameters may include one or more of a number of components, a number of block grids, a number of processors or nodes, a start time of the simulation, an end time of the simulation, a lapse of time. time of simulation of the reservoir simulation model, a material factor, a surface network complexity, a type of displacement (eg, water injection, gas injection, nearly miscible, etc.), a type wells (eg, a gas and water well, a multilateral well, an intelligent well, a well with downhole devices, etc.), a well control level, a pressure type, volume-temperature (eg, black oil, gas-water, water-oil, American Petroleum Institute, state of the equation, etc.), a type of relative permeability (eg, hysteresis, interfacial tension, close to the critic, etc.), a type of porosity (eg, single porosity, double porosity, etc.), a geometric type omechanical (eg, simple compressibility, compaction tables, coupled geomechanical model, etc.), thermal type (eg, single temperature, no flash steam, full steam / water vapor, etc.) .), a scale factor, and a diverse property of the reservoir simulation model. The data associated with one or more determined parameters can be stored on a local or remote storage medium.
After determining the parameters, the processor 110 may use one or more of the parameters to calculate an estimated time to complete a simulation of the reservoir simulation model in step 230. The processor 110 may calculate the estimated time before execution of the simulation. simulation of a deposit simulation model. The estimated duration may be displayed to the user, eg, via one or more output devices 142, and / or may be stored on a local or remote storage medium. As a non-limiting example, the estimated duration can be calculated using the following equations (1) and (2), in which TTFS represents the duration until the end of the simulation in hours, NC represents the number of components, NB represents the number of block grids, NP represents the number of processors or nodes, TSPAN represents the time span of the simulation in years, HW represents the hardware factor, SNET represents the complexity of the surface network, DISP represents the type of displacement , and exl, ex2, ex3 and ex4 are factors of the exponent. Exponent factors ex 1-4 may be simulator-dependent and may be optimized based on information gathered during the simulation progress and / or results from previous simulations.
(1)
(2)
The processor 110 may also use one or more of the parameters to calculate an SMC score for the reservoir simulation model. The SMC score may include ranges that correspond to the complexity of the reservoir simulation model and may indicate the level of support required to interpret the simulation results of the reservoir simulation model. The SMC score may be displayed to the user, e.g., through one or more output devices 142, and / or may be stored on a local or remote storage medium. In step 240, the processor 110 may continuously monitor the reservoir simulation model to determine if a change is made to one or more of the parameters. In case of detection of a change, the processor 110 may proceed to step 220 to update the settings of the reservoir simulation model to reflect the change. The processor 110 can then recalculate the estimated duration to complete the simulation of the reservoir simulation model in step 230. The recalculated estimate may be displayed to the user, eg, through a several output devices 142, and / or may be stored on a local or remote storage medium.
Figure 3 illustrates an example of a method for performing a simulation of a reservoir simulation model and the projection of a duration to complete the simulation. In step 300, the processor 110 may execute a simulation of a reservoir simulation model for a predetermined period of time associated with a calculation step of the simulation. The calculation step may be associated with a simulation time period, one or more simulation iterations, a calculation, a function, a process, a stage or a state of the simulation, a duration, etc. The processor 110 may provide one or more convergent solutions to the simulation for the predetermined period of time, such as a convergent solution with one or more Newton iterations. The criterion for convergence can be determined by processor 110 based on the data entered by a user, on default values stored on a storage medium, and so on. In step 310, the processor 110 may measure a simulation performance result for the predetermined period of time. The performance result may include one or more of an elapsed clock time, a simulation time period that has been simulated for a predetermined period of time, a number of iterations executed by the processor 110, and during the predetermined period of time, a result related to one or more convergent solutions, and any other result indicative of the amount of computing time or power required to execute the simulation of the reservoir simulation model during the the predetermined period of time. The performance result can be stored on a local or remote storage medium. In step 320, the processor 110 may use the measured performance result to predict a remaining time to complete the simulation of the reservoir simulation model after a predetermined period of time. As a non-limiting example, the predicted remaining time can be calculated by the following equation (3), in which TTFS represents the remaining clock time to complete the simulation, AND represents the elapsed clock time, TSIM represents the lapse of time. simulated time to ET and TSPAN represents the total time of the simulation.
(3)
The predicted duration can also be calculated by extrapolating (eg linear, polynomial, etc.) the performance result through the remainder of the simulation, a moving average of the performance result with one or more previously measured performance results, a comparison of the performance result to database performance results associated with previously executed simulations, a probabilistic model (eg, a stochastic model, a Monte Carlo simulation, etc.), any combination of these, etc. The estimated remaining time can be displayed to the user, e.g. via one or more output devices 142, and / or can be stored on a local or remote storage medium.
The processor 110 may also react to the predicted remaining time. If the predicted duration is greater than a threshold, the processor 110 may request the user to modify, or may automatically modify, one or more parameters of the reservoir simulation model to produce an updated reservoir simulation model. Such a threshold may be defined by the user or may be defined by the processor 110. The processor 110 may then execute a simulation of the updated reservoir simulation model for a predetermined period of time, measure a simulation performance result during a simulation. the predetermined time period and predict a remaining time to complete the simulation of the updated reservoir simulation model. The processor 110 may also notify a user of an expected or pending failure of a simulation based on the predicted remaining time and / or information from the calculation of the predicted remaining time. In addition, the processor 110 may allocate computing resources based on the predicted remaining time, eg, by requesting additional computing or processing power when the predicted remaining time is greater than a maximum threshold, or allowing a decrease in computing or processing power when the remaining predicted time is less than a minimum threshold. In step 330, the processor 110 can determine whether the simulation of the reservoir simulation model is complete. If the simulation is not complete, the processor 110 may return to step 300 and execute the simulation of the reservoir simulation model for a subsequent predetermined time period associated with a subsequent calculation step of the simulation. From there, the processor 110 can measure a simulation performance result for the next predetermined time period and can update the predicted time remaining time to complete the simulation of the reservoir simulation model, as specified herein. -above. The estimated remaining time remaining can be displayed to the user, eg via one or more output devices 142, and / or can be stored on a local or remote storage medium.
Once the simulation is complete, the processor 110 can transmit the results of the simulation to the user and / or store the results on a local or remote storage medium. The processor 110 can also transmit to the user a summary of the estimated duration to complete the simulation calculated before the execution of the simulation and the predicted duration to complete the simulation calculated during the execution of the simulation. The processor 110 can store all calculations of the estimated duration, the predicted duration and the performance results on a local or remote storage medium or in a database. These data can be analyzed (eg, by machine learning) and used by processor 110 in future simulations to increase the accuracy of estimated and predicted times to complete a simulation of a reservoir simulation model. The processor 110 can also transmit the results of the simulation to sensors or equipment located at a prospecting or previously constructed oil or gas field drilling site for the execution or for adjustments of the drilling operations.
Figure 4 illustrates an example of a method for predicting a duration to complete a simulation based on a stochastic model. In general, the method can provide a stochastic model of the progress of the simulation in terms of a relationship between a clock time and a simulated time. In addition, the method can stochastically model the relationship of the clock time and simulated time between a given simulation start time and a given simulation end time to provide an estimated clock time to complete the simulation and / or a percentage of the simulation that has been completed. Such a method can be used, for example, when calculating an estimated time to complete a simulation of a reservoir simulation model before running the simulation, and when predicting a remaining time for complete the simulation of a reservoir simulation model during the execution of the simulation. In both situations, the processor 110 may create and evaluate the stochastic model one or more times for each predetermined period of time associated with a simulation calculation step. One or more evaluations of the stochastic model can predict the simulation process up to the end time of the simulation and can obtain an estimate of the total remaining clock time to complete the simulation of the reservoir simulation model. If the stochastic model is evaluated once again, the average of the remaining total clock time estimates can be calculated to complete the simulation obtained at each evaluation to improve the accuracy of the estimate.
The process of FIG. 4 can begin at step 400 with the processor 110 generating a stochastic model of a simulation of a reservoir simulation model comprising one or more random variables. As previously described, the stochastic model can be used to model the progress of the simulation in terms of a relationship between a clock time and a simulated time, in the presence of a simulated start time and a simulated end time. The random variables can correspond to one or more of a convergent or non-convergent prediction of the simulation, of a number of iterations used to reach the convergence, of a fraction of the calculation step which must be attempted as a a new computational step in the non-convergence of the simulation, a clock time taken to provide a solution to one or more iterations of the simulation, the reservoir simulation model (eg, a time frame of the simulation , etc.), the simulation itself (eg, an elapsed clock time of the simulation, etc.), a result of the simulation calculation step (eg, a measured performance result) , etc., etc. The stochastic model may also include other variables and / or constant scalar values such as one or more of a maximum or minimum size of a calculation step, a maximum number of iterations allowed to complete a calculation step , a growth factor of a calculation step, a maximum or minimum cutting factor of a calculation step, an elapsed clock time, an increment of the elapsed clock time, an average clock time taken to solving an iteration of a calculation step, a standard deviation of the clock time taken to solve an iteration of a calculation step, a simulation start time, an end of simulation time, a total simulation time etc. Each of the random variables, other variables, and / or constant scalar values can be initialized during the generation of the stochastic model based on data entered by a user, default data stored on a storage medium, and so on. In step 410, the processor 110 may evaluate the stochastic model for a predetermined period of time associated with a calculation step of the simulation. The evaluation of the stochastic model may include carrying out calculations related to one or more of the random variables, other variables and scalar constant values of the stochastic model to estimate a clock time taken to complete the simulation of the period. predetermined time. The evaluation may also perform calculations to predict whether a simulation solution during the current predetermined time period reaches convergence. Calculations made during the evaluation of the stochastic model, such as calculations performed to determine a solution to the random variables, may use current or calculated values determined during one or more previous predetermined time periods, may assume a probability distribution of one or more of the variables (eg, discrete uniform distribution, Bayesian probability, etc.), any combination thereof, etc. Each of the calculations and their results can be displayed to the user and / or stored on a local or remote storage medium. In step 420, the processor 110 can check whether a solution to the simulation during the current predetermined time period reaches a convergence based on the calculation made in step 410. If the solution is not convergent, the Processor 110 may decrease or cut the predetermined period of time by a calculated or user-determined cutting factor. The cutting factor can have a maximum or minimum value so that the predetermined period of time cut does not fall below a minimum threshold. The processor 110 may also increment the elapsed clock time by an estimated clock time taken to complete the simulation of the predetermined time period, but may not increment the simulated time. The values for the elapsed clock time and the simulated time can be stored, displayed to the user, and / or used to predict a remaining time to complete the simulation of the reservoir simulation model. From there, the processor 110 may return to step 410 and evaluate the stochastic model for the new predetermined decreased time period.
If a solution for the simulation during the predetermined period of time is determined to be convergent, the processor 110 may increase or increase the predetermined period of time by a calculated or user-determined factor. The growth factor may have a minimum or maximum threshold so that the increased predetermined time period does not exceed a maximum threshold. The increased predetermined time period can be used to perform the next stochastic model evaluation, if any. The processor 110 may also increment the elapsed clock time by an estimated clock time taken to complete the simulation of the predetermined time period, and may increment the simulated time by the current predetermined time period. The values for the elapsed clock time and the simulated time can be stored, displayed to the user, and / or used to predict a remaining time to complete the simulation of the reservoir simulation model. From there, the processor 110 may proceed to step 430 where it can determine whether the stochastic model has been evaluated for the entire simulation. Such a determination can be made, e.g., by comparing the simulated time of the stochastic model with the given end time of the simulation. If the evaluation of the stochastic model is not complete, the processor 110 can return to step 410 and the process summarized above is repeated. If the evaluation of the stochastic model is complete, the processor 110 may proceed to step 440 and may use the total clock time elapsed, the simulated time elapsed, one or more random variables and / or other results calculated for determine a relationship between the clock time and the simulated time and / or predict a remaining time to complete the simulation of the reservoir simulation model. The predicted remaining time may be displayed to the user, eg, through one or more output devices 142, and / or may be stored on a local or remote storage medium.
FIG. 5 is a graphical representation of a real completion and an estimated completion for an example simulation of a reservoir simulation model. The x-axis represents the simulated time in days of the reservoir simulation model. The y-axis can represent the percentage of the simulation that has been completed. The actual completion percentage can be determined after completion of the simulation and can be calculated, e.g., by the processor 110, by dividing the elapsed clock time by the total clock time taken to complete the simulation. The estimated time (illustrated in dashed lines) can be calculated using any of the methods described herein.
To facilitate a better understanding of the present disclosure, the following example of certain aspects of the description is given. In any case, the following example shall not be construed as limiting, or defining, the scope of disclosure.
EXAMPLE
This example is based on hypothetical data, and the results are produced by running a computer stochastic programming routine that can be incorporated into a reservoir simulator.
When the reservoir simulator starts, the reservoir simulator produces a state of the evolution field by progressing through time by discrete time intervals, called time steps. Each time step represents a forward projection obtained by solving a coupled system of nonlinear partial differential equations. The solution for each time step and obtained by an iterative scheme which, itself, includes multiple nested iterative schemes. All of these iterative patterns within a time step must converge toward or reach a satisfactory state in order to obtain a successful time step solution. As such, the size of the time step is very variable and one is only partially controlled by the user, and it is also a powerful function of the difficulty of the solution which, itself, and a function of Several things. The criteria for convergence are programmed in the simulator for which parameters can be defined by the user integrated in the input data for the simulation, or can be defined by default by the simulator if the necessary parameters are not specifically defined by the simulator. the user. The usage also captures the end date for the simulation built into the input data. The simulator can report, through a graphical interface (GUI), and / or text, an estimate of the remaining clock time to complete the simulation. The remaining time can also be expressed as a percentage of the simulation that has been completed.
During the simulation process, the simulator records the evolution of the simulator. This data is also generally provided to the user in the form of a data file or via the GUI. An example of the evolution data output by the simulator is given in Table I.
TABLE I
In this example of a reservoir simulator, a function, hereinafter called stochasticPrediction, provides a method for stochastically modeling the simulation process in terms of the relationship between clock time and simulated time between start data values. and end-of-simulated time values.
FIG. 6 illustrates an exemplary method for stochastic modeling of the simulation method in the example of a reservoir simulator using the stochasticPrediction function. Such a method may be performed by a processor associated with a reservoir simulator using instructions stored on a storage medium, etc. In step 600, the stochastic model can be initialized, e.g., by initializing the stochasticPrediction function. Then, the reservoir simulator can begin a reservoir simulation (step 605) and can set the time to solve the reservoir simulation, t, at an initial time to (step 610). The time to can be, for example, the start time of the reservoir simulation. In step 615, the reservoir simulator can execute a reservoir simulation solver to compute a solution for the simulation at time t. The calculation may include the resolution of a coupled system of nonlinear partial differential equations. The solution can be a convergent solution and can be obtained by an iterative scheme which, itself, comprises multiple nested iterative schemes. In step 620, the stochasticPrediction can be evaluated at least once in order to predict the simulation method of time t to the final end time of the simulation, tfin. Then, the stochasticPredicion can obtain an estimate for the total clock time taken to simulate time t up to time tfin in step 625 based on the results of step 620. If the stochasticPrediction is evaluated more than Once, an average of the total clock times estimated by the function can be used to obtain a more realistic value of the clock time needed to complete the simulation. In both cases, the evolution of the simulation can be reported as an estimated remaining clock time to complete the simulation or as a percentage of the simulation completed and / or remaining in step 630. These values can be continually updated as the simulation progresses at new time steps. In step 635, time t can be compared to tfjn to determine whether the simulation of the deposit is complete. If the simulation of the deposit is complete, the simulation can be stopped and the results can be presented to a user. If the simulation of the deposit is not completed, the process may proceed to step 640 and may increment time t by a predetermined or calculated time step. Next, it can be determined whether the updating methods for the stochastic model are to be updated at step 645. If no updating is necessary, the method can return to step 615 and repeat the method described above. If an update is needed, such as in cases where a Bayesian update is used, the stochastic model update processes can be updated in step 650 to take into account the known values of the random variables based on the determined information. during one or more time steps. From there, the process can go back to step 615 and repeat the process described above.
The stochasticPrediction function simulates the evolution of the simulation in terms of elapsed clock time and simulation time, taking the start and end times of the simulation as arguments, named respectively tStartSimulation and tEndSimulation, both given in time. simulated in units of days. The definitions for this one, and other variables used by the stochasticPrediction function, are summarized in Table II. The algorithm for the stochasticPrediction in the present example can start by setting maxNewtons, tau, a, dtMax, dtMin, dtGrowthFactor, dtmaxNew, minAlpha, maxAlpha to constant scalar values according to the parameters entered by the user for the simulation period between tStartSimulation and tEndSimulation. Then, the stochasticPrediction can initialize tElapsed to 0 and dtSolve to 0. From there, the stochasticPrediction can execute a While loop that ends when the condition tSolve> tEndSimulation is satisfied. Within this loop, the following steps can be executed in sequence: Set n to a discounted value of n; Set isConverged to a discounted value of isConverged; Set epsilon to a present value of epsilon; Set alpha to a discounted value of alpha;
If isConverged is TRUE, then set dtElapsed = n * tau * (l + epsilon) and dtSolve = dtmaxNew, otherwise set dtElapsed = maxNewtons * tau * (l + epsilon);
If isConverged is TRUE, then set dtMaxNew = the smallest of dtMax and dtGrowthFactor * dtMaxNew, otherwise set dtMaxNew = the largest of alpha * dtMaxNew and dtMin; Define tSolve = dtSolve + tSolve; Define tElapsed = dtElapsed + tElapsed;
Store the values of tElapsed and tSolve.
TABLE II
In this example, the calculation of dtMaxNew is taken as dtSolve for the next time step (that is, in the next iteration of the While Loop). This is done to take into account that, when convergence is not achieved, the next time step must be cut according to a minimum dtMin, otherwise it must be advanced by a growth factor on the dtSolve based a maximum dtMax. However, the non-convergence of a time step is not the only reason to cut or grow time steps. Other reasons, such as an indication by the user, may also affect the size of the time step. In such cases, a user-specified value can be specified at dtMaxNew and dtSolve in the While Loop. If these are unknown, an updated value method can be used to model these two variables through appropriate probability distributions. These concepts can be captured by the same isConverged variable. In addition, quantities that are known to affect the size of a time step, such as block grid volumes, the rate at which the primary and secondary variables change, and the types of physical processes that are in progress can also be taken into account by the stochasticPrediction function. This can be done by introducing new variables into the stochasticPrediction function or by modifying the refresh processes for one or more of the random variables.
In addition, the discounted values of n, isConverged, epsilon and alpha, illustrated in the algorithm described above can be calculated in different ways. In some cases, the updated values may take the values obtained from a preceding time step. For example, it can be assumed that at the time step for which the simulation method is predicted for the stochasticPrediction function, the same number of iterations of Newton can be performed as during the simulation process for the time step. preceding (this information is known during the simulation process). In the same way, we can suppose that if the simulation of a preceding time step is convergent, the simulation can converge also at the level of the current time step. In addition, for the random variables epsilon and alpha, it can be assumed that the values are the same as those determined during the preceding time step (this information is known during the simulation process).
In some cases, one might assume that the number of Newton iterations needed at the current time step follows a discrete uniform distribution with a minimum of 1 and a maximum of maxNewtons. Similarly, it can be assumed that isConverged, indicating whether the Newton's iteration set at the current time steps gives a convergence, follows a uniform distribution that may take a TRUE or FALSE value. In such a case, the probability of having a convergence may follow the same distribution as that of having a "face" in a stack or face. If one or more random variables are known to follow more complex processes, such as a Poisson method or a jumping Poisson method, an appropriate updating method for these variables may be used. The updating method may use one or more sampling methods to ensure that the relevant random variable follows a given probability distribution. Any robust sampling method, such as Monte Carlo, Latin Hypercube, Hammersley Squares, etc., can be used to derive a sample of the given probability distribution in the discounting process for a random variable (i.e. -d., n, isConverged, epsilon or alpha).
Between the cases where the random variables of the stochastic model are either completely determined by the previous results or are completely independent of the previous results, there are other possible ways to calculate the values. For example, one or more of the random variables can be updated by the Bayesian process during the evolution of the simulation and the results of the previous time steps are known. During the Bayesian update step, the known value of the random variable obtained by the simulator at one or more previous time steps can be used to update the probability distribution for the random variable. In the case of the Bayesian update (also known as Bayesian interference), the probability distribution ("previous density") for the random variable assumed to be true at the beginning of the simulation may be distorted when it is actualized. on a new distribution ("posterior density") by the actual found value ("observed data"), which can be known at each step of time as the simulation progresses.
An example of a graph of the simulation time and the clock time for a realization of the stochasticPrediction function is shown in FIG. 7. The values shown in this graph are obtained by using the algorithm described above to evaluate a simulation between a simulation time of 0 to 100 days. It is assumed that the random values in this example follow a probabilistic distribution, without updating the observed data.
Methods as described above may be implemented using computer executable instructions that are stored or otherwise available from a computer readable medium. Such instructions may include instructions or data that cause or otherwise configure a general purpose computer, a specialized computer, or a specialized processing device to perform a certain function or group of functions. Parts of the computer resources used can be accessed through a network. The computer executable instructions may be intermediate format binary instructions, such as an assembly language, firmware, or source code. A computer-readable medium that can be used to store instructions, information used and / or information created during the methods as described above includes magnetic or optical discs, flash memory, USB devices with memory non-volatile, networked storage devices, etc.
For the sake of clarity of explanation, in some cases the technology of the present invention may be presented as comprising individual functional blocks comprising functional blocks comprising devices, device components, steps or routines in a realized process. in software, or combinations of hardware and software.
The memories, media, computer-readable storage devices may include a wireless cable or signal containing a bit stream, and so on. However, when it is mentioned, a computer-readable non-transitory storage medium expressly excludes a medium such as energy, carrier signals, electromagnetic waves and signals as such.
Devices implementing the methods according to these disclosures may include hardware, firmware and / or software, and may assume a variety of form factors. Such form factors may include laptops, smart phones, small form factor personal computers, personal digital assistants, rail mount devices, stand-alone devices, and the like. The functionality described here can also be embodied in devices or expansion cards. Such functionality may also be implemented on a circuit board among different chips or different methods running on a single device.
The instructions, support for transmitting such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures. Although a variety of information has been used to explain the aspects within the scope of the appended claims, no limitation of the claims should be inferred based on the given features or arrangement, since a person skilled in the art would be able to derive a large variety of implementations. Furthermore and even if some of the objects of the invention have been described in a language specific to the structural features and / or the steps of the process, it should be understood that the object of the invention defined in the appended claims is not necessarily limited to those features or actions described. Such functionality may be distributed differently or made in components other than those identified herein. Instead, the features and steps described are disclosed as possible components of the systems and methods within the scope of the appended claims. In addition, the language of the claim indicating "at least one" of a game indicates that an element of the game or multiple elements of the game satisfy the claim. DISCLOSURE STATEMENTS INCLUDE: Statement 1: A method comprising: performing, by a processor, a simulation of a reservoir simulation model for a first predetermined time period associated with a first simulation calculation step , measuring a simulation performance result for the first predetermined time period, the performance result including an elapsed time and a simulated time step, and the prediction, based on the performance result, of a duration remaining to complete the simulation of the deposit simulation model. Statement 2: The method according to statement 1, further comprising: prior to execution of the simulation, determining a plurality of parameters from the reservoir simulation model, and the parameter-based calculation of an estimated time to complete the simulation of the deposit simulation model. Statement 3: The method according to statement 1 or statement 2, further comprising: detecting a change at one or more of a plurality of parameters, and updating, based on the change , of the estimated duration to complete the simulation of the deposit simulation model. Statement 4: The method according to any one of preceding 1 to 3, further comprising: calculating a complexity score of the simulation model based on at least one of a model size, a well type, well control level, pressure-volume-temperature type, relative permeability type, porosity type, geomechanical type and thermal type . Statement 5: The precedent according to any one of the preceding statements 1 to 4, also comprising: generating a stochastic model of the simulation of the reservoir simulation model, the stochastic model comprising a plurality of random variables, the execution by the processor of the stochastic model to determine a convergent solution to one or a plurality of random variables of the stochastic model, and the prediction, based on the convergent solution, of the remaining time to complete the simulation of the reservoir simulation model . Statement 6: The precedent according to any of the preceding statements 1 to 5, further comprising: allocating computing resources for the remaining time-based simulation to complete the simulation of the reservoir simulation model. Statement 7: The precedent according to any one of the preceding statements 1 to 6, also comprising: determining a plurality of characteristics associated with an oil or gas field, the oil or gas field comprising at least one of a deposit, a well and a surface installation, the generation of the deposit simulation model based on the plurality of characteristics, and the determination of a plurality of parameters associated with the reservoir simulation model, the plurality of parameters comprising at least one of a number of components, a number of block gates, a number of processors, a time period of the simulation of the reservoir simulation model, a hardware factor , a complexity of the surface network and a type of displacement. Statement 8: The method of any one of the preceding Statements 1 to 7, wherein the determination of the plurality of features comprises computing one or more characteristics of the oil or gas field based on data captured through the one or more sensors. Statement 9: The precedent according to any one of preceding 1 to 8, further comprising: executing, by a processor, the simulation of the reservoir simulation model for a second predetermined time period associated with a second step of calculating the simulation, measuring a second simulation performance result for the second predetermined period of time, and predicting, based on the second performance result, a remaining time to complete the simulation of the simulation model deposit after a second predetermined period of time. Statement 10: The precedent according to any one of the preceding statements 1 to 9, also comprising: calculating a moving average of the performance result and the second performance result, the discounting, based on the moving average, of the remaining time to complete the simulation of the deposit simulation model. Statement 11: The precedent according to any one of preceding 1 to 10, further comprising: when the remaining time is greater than a threshold, modifying one or more parameters from the reservoir simulation model to provide a model an updated reservoir simulation simulation, performing a second simulation of the updated reservoir simulation model for a second predetermined period of time of the simulation, and predicting a remaining updated duration to complete the second simulation of the simulation model. updated deposit. Statement 12: A system comprising: a processor, and a computer readable storage medium having instructions stored thereon, which, when executed by a processor, cause the processor to perform operations including: a simulation of a reservoir simulation model for a first predetermined period of time associated with a first calculation step of the simulation, the calculation of a simulation performance result for the first predetermined period of time, the result performance model comprising an elapsed clock time and a simulated time step, and the prediction, based on the performance result, of a remaining time to complete the simulation of the reservoir simulation model. Statement 13: The system according to Statement 12, wherein prior to executing the simulation, the processor determines a plurality of parameters from the reservoir simulation and calculation model, based on the parameters, an estimated time to complete simulation of the reservoir simulation model. Statement 14: The system according to Statement 12 or Statement 13, wherein the processor detects a change at one or more of a plurality of parameters and updates, based on the change, the estimated duration to complete the simulation of the reservoir simulation model. Statement 15: The system according to any one of the preceding 12-14 statements, further comprising: a sensor coupled to the processor for sending sensed data at a petroleum or gas field to the processor. Statement 16: The system according to any one of the 12-15 statements, wherein the processor determines a plurality of features associated with the oil or gas field based on the data from the sensor and generates the plurality-based reservoir simulation model. characteristics. Statement 17: A computer-readable non-transitory storage medium having instructions stored thereon which, when executed by a processor, causes the processor to perform operations including: performing a simulation of a reservoir simulation model for a first predetermined time period associated with a first calculation step of the simulation, the measurement of a simulation performance result for the first predetermined time period, the performance result including a an elapsed clock time and a simulated time step, and the prediction, based on the performance result, of a remaining time to complete the simulation of the reservoir simulation model. Statement 18: The computer-readable non-transitory storage medium according to Statement 17, storing additional instructions, which, when executed by the processor, cause the processor to perform operations including: before executing the simulation, determining a plurality of parameters from the reservoir simulation model, and calculating, based on the parameters, an estimated time to complete the simulation of the reservoir simulation model. Statement 19: The computer-readable non-transitory storage medium in accordance with Statement 17 or 18, storing additional instructions, which, when executed by the processor, cause the processor to perform the operations including: detecting a changing at one or more of a plurality of parameters, and updating, based on the change, the estimated time to complete the simulation of the reservoir simulation model. Statement 20: The computer-readable non-transitory storage medium according to any one of the preceding Statements 17-18, storing additional instructions, which, when executed by the processor, cause the processor to perform the operations comprising: Assignment of computing resources for the remaining time-based simulation to complete the simulation of the reservoir simulation model.

权利要求:
Claims (20)
[1" id="c-fr-0001]
CLAIMS We claim:
A method comprising: performing, by a processor of a simulation, a reservoir simulation model for a first predetermined time period associated with a first simulation calculation step; measuring a simulation performance result for the first predetermined period of time, the performance result including an elapsed time and a simulated time period; and the prediction, based on the performance result, of a remaining time to complete the simulation of the reservoir simulation model.
[2" id="c-fr-0002]
The method of claim 1, further comprising: prior to executing the simulation, determining a plurality of parameters from the reservoir simulation model; and the parameter-based calculation of an estimated time to complete the simulation of the reservoir simulation model.
[3" id="c-fr-0003]
The method of claim 2, further comprising: detecting a change at one or more of a plurality of parameters; and the change-based discounting of the estimated duration to complete the simulation of the reservoir simulation model.
[4" id="c-fr-0004]
The method of claim 1, further comprising: calculating a complexity score of the simulation model based on at least one of a model size, a type of well, a level of well control, a pressure-volume-temperature type, a relative permeability type, a porosity type, a geomechanical type and a thermal type.
[5" id="c-fr-0005]
The method of claim 1, further comprising: generating a stochastic model of the simulation of the reservoir simulation model, the stochastic model comprising a plurality of random variables; the processor performing the stochastic model to determine a convergent solution to one or a plurality of random variables of the stochastic model; the prediction, based on the convergent solution, of the remaining time to complete the simulation of the reservoir simulation model.
[6" id="c-fr-0006]
The method of claim 1, further comprising: allocating computing resources for the remaining time-based simulation to complete the simulation of the reservoir simulation model.
[7" id="c-fr-0007]
7. The method of claim 1, further comprising: determining a plurality of characteristics associated with an oil or gas field, the oil or gas field comprising at least one of a deposit, a well and a a surface installation; generating the reservoir simulation model based on the plurality of features; and determining a plurality of parameters associated with the reservoir simulation model, the plurality of parameters comprising at least one of a number of components, a number of block gates, a number of processors, a timeframe for simulation of the reservoir simulation model, a material factor, a complexity of the surface network and a type of displacement.
[8" id="c-fr-0008]
The method of claim 7, wherein determining the plurality of features comprises computing one or more characteristics of the oil or gas field based on data captured through one or more sensors.
[9" id="c-fr-0009]
The method of claim 1, further comprising: executing, by the processor, a simulation of a reservoir simulation model for a second predetermined period of time associated with a second simulation calculation step; measuring a simulation performance result for the second predetermined period of time; and predicting, based on the second performance result, the remaining time to complete the simulation of the reservoir simulation model after a second predetermined period of time.
[10" id="c-fr-0010]
The method of claim 9, further comprising: calculating a moving average of the performance result and the second performance result; the update, based on the moving average, of the remaining time to complete the simulation of the deposit simulation model.
[11" id="c-fr-0011]
The method of claim 1, further comprising: when the remaining time is greater than a threshold, modifying one or more parameters from the reservoir simulation model to provide an updated reservoir simulation model; performing a second simulation of the updated reservoir simulation model for a second predetermined time period associated with at least one simulation calculation step; and predicting a remaining updated duration to complete the second simulation of the updated reservoir simulation model.
[12" id="c-fr-0012]
12. System comprising: a processor; and a computer readable storage medium having instructions stored thereon which, when executed by a processor, causes the processor to perform operations including: performing a simulation of a simulation model deposit during a first predetermined period of time with a first step of calculating the simulation; calculating a simulation performance result for the first predetermined period of time, the performance result including an elapsed time and a simulated time period; and the prediction, based on the performance result, of a remaining time to complete the simulation of the reservoir simulation model.
[13" id="c-fr-0013]
The system of claim 12, wherein prior to execution of the simulation, the processor determines a plurality of parameters from the reservoir and calculation simulation model, based on the parameters, an estimated time to complete the simulation of the simulation. deposit simulation model.
[14" id="c-fr-0014]
The system of claim 13, wherein the processor detects a change at one or more of a plurality of parameters and updates, based on the change, the estimated time to complete the simulation of the simulation model. deposit.
[15" id="c-fr-0015]
The system of claim 12, further comprising: a sensor coupled to the processor for sending sensed data at a petroleum or gas field to the processor.
[16" id="c-fr-0016]
The system of claim 15, wherein the processor determines a plurality of characteristics associated with the oil or gas field based on the data from the sensor and generates the reservoir simulation model based on the plurality of features.
[17" id="c-fr-0017]
A computer-readable non-transitory storage medium having stored instructions thereon which, when executed by a processor, causes the processor to perform operations including: performing a simulation of a reservoir simulation model for a first predetermined period of time with a first simulation calculation step; measuring a simulation performance result for the first predetermined period of time, the performance result including an elapsed clock time and a simulated time period; and the prediction, based on the performance result, of a remaining time to complete the simulation of the reservoir simulation model.
[18" id="c-fr-0018]
The computer-readable non-transitory storage medium of claim 17, storing additional instructions, which, when executed by the processor, cause the processor to perform operations including: prior to executing the simulation, the determining a plurality of parameters from the reservoir simulation model; and the parameter-based calculation of an estimated time to complete the simulation of the reservoir simulation model.
[19" id="c-fr-0019]
The computer-readable non-transitory storage medium of claim 17, storing additional instructions, which, when executed by the processor, cause the processor to perform the operations including: detecting a change at the level of one or more of a plurality of parameters; and the change-based discounting of the estimated duration to complete the simulation of the reservoir simulation model.
[20" id="c-fr-0020]
The computer-readable non-transitory storage medium of claim 17, storing additional instructions, which, when executed by the processor, cause the processor to perform the operations including: allocating the computing resources for the simulation based on the remaining time to complete the simulation of the deposit simulation model.

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优先权:

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PCT/US2015/048483|WO2017039680A1|2015-09-04|2015-09-04|Time-to-finish simulation forecaster|

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