Computing uncertainties associated with a multi physics simulation of injection of a fluid in a subsurface

Abstract

The disclosure provides a method for computing uncertainties associated with a multiphysics simulation of injection of a fluid in a subsurface. The simulation is a multiphase reservoir, mechanical, thermal, geochemical, non-linear, time-dependent simulation of monophase or multiphase injection. The simulation comprises one or more parameters, each parameter taking its values in a respective range of values. The method comprises performing the simulation, thereby outputting a set of time-dependent and / or space-dependent values. The method further comprises using an uncertainty-computation module to generate a plurality of instances of the simulation based on at least some outputted values. The generation of the plurality of instances is by varying the value of at least one of the one or more parameters. The uncertainty-computation module is configured for varying the values of the parameters and thereby generating instances of the simulation each corresponding to a different set of values for the one or more parameters.

Description

[0001] COMPUTING UNCERTAINTIES ASSOCIATED WITH A MULTIPHYSICS SIMULATION OF INJECTION OF A FLUID IN A SUBSURFACE

[0002] TECHNICAL FIELD

[0003] The disclosure relates to the field of geomodeling, and more specifically to a method, system and program for computing uncertainties associated with a multiphysics simulation of injection of a fluid in a subsurface.

[0004] BACKGROUND

[0005] Geomodeling (or geologic modeling) refers to methods for determining data representing a geological environment, including for example determining data configured for geological simulation and / or performing geological simulation.

[0006] Geological simulation refers to all techniques for performing computer simulations related to a geological environment. These techniques assist actors by providing them with computerized representations of real, estimated or predicted states and / or processes related to the geological environment. Geological simulation techniques include dynamic simulations, such as hydrocarbon flow simulations (which may simply be referred to as "flow simulation") or geo-mechanical simulations. Flow simulation provides useful information as to how hydrocarbons (i.e. gas and / or oil) flow in a reservoir environment. Flow simulation may for example represent real, estimated or predicted pressure, saturations, flow paths, flow rates, flowing compositions, and / or evolutions of these quantities over time. Geomechanical simulations are introduced to accurately determine the combined effects of hydrocarbon production and changes in rock properties due to geomechanical effects.

[0007] In many existing solutions, geological simulations are performed based on geological simulation grids (also referred to as "geomodels"). A geological simulation grid comprises a geometrical grid which represents a geological environment, such as a subsurface (e.g. a geostorage). The geometrical grid may conform to shapes of geological structures, such as horizons, fault surfaces, channels and / or reservoirs. For example, horizons may correspond to layer structures of the geometrical grid and fault surfaces may correspond to stair-stepped structures of the geometrical grid. Geological simulation grids may further comprise parameters (also referred to as "properties") which represent geological properties of the geological structures, such as flow parameters, and which are assigned to geometrical structures of the geometrical grid. A geological simulation grid may be inputted to a simulator which performs the simulation, according to the shapes of the geometrical grid and / or to the values of the geological properties conveyed by the parameters.

[0008] Within this context, simulation of injection of a fluid in a subsurface (e.g. a geostorage) is gaining wide importance. Such simulation allows to take decisions and / or infer design parameters related to fluid injection for storage and / or for hydrocarbon production, and to perform physical actions based on those decisions and / or design parameters. However, for that, it is often important to assess uncertainties associated with the simulation. For example, fault properties may be hard to quantify and therefore contain uncertainties. Experimental design helps to understand which properties play an important role in fault behavior and what are the optimal conditions in the context of CO2 injection for example.

[0009] However, within this context, uncertainty computation for multiphysics simulation, involving notably reservoir simulation and geo-mechanical simulation, need improvement. Indeed, current solutions / workflows consist in performing the reservoir simulation, and then the performing the geo-mechanical simulation. The uncertainties are then computed only for the geo-mechanical simulation, which is not entirely satisfactory as the simulation is multi-physics.

[0010] Within this context, there is a need for improved solutions for uncertainty computation in the context of multiphysics simulation of injection of a fluid in a subsurface.

[0011] SUMMARY

[0012] It is therefore provided a computer-implemented method for computing uncertainties associated with a multiphysics simulation of injection of a fluid in a subsurface. The simulation is a multiphase reservoir, mechanical, thermal, geochemical, non-linear, time-dependent simulation of monophase or multiphase injection. The simulation comprises one or more parameters, each parameter taking its values in a respective range of values. The method comprises performing the simulation, thereby outputting a set of time-dependent and / or space-dependent values. The method further comprises using an uncertainty-computation module to generate a plurality of instances of the simulation based on at least some outputted values. The generation of the plurality of instances is by varying the value of at least one of the one or more parameters. The uncertainty-computation module is configured for varying the values of the parameters and thereby generating instances of the simulation each corresponding to a different set of values for the one or more parameters.

[0013] The method may comprise one or more of the following:

[0014] - before using the uncertainty-computation module, the method comprises: o automatically converting the at least some outputted values into a format readable by the uncertainty-computation module;

[0015] - prior to the automatic conversion, the method comprises: o automatically selecting values to be mandatorily converted, and possibly selecting, by user action, additional values to be converted;

[0016] - the values to be mandatorily converted include values of a subsurface minimal constraint and / or values related to fault reactivation, induced seismicity prediction, subsurface initial stress state, subsurface initial pore pressure state, and / or any value related to the initialization of the simulation;

[0017] - the one or more parameters include at least one parameter influencing a design of an injection well or a group / pattern of injection wells and / or a design of a geostorage;

[0018] - the one or more parameters include one or more of: o one or more cap rock minimal and / or integrity constraints; o one or more parameters related to cap rock rigidity; o one or more flow-related parameters; and / or o one or more parameters related to the fully coupled physics involved in the simulation; - the method further comprises assessing uncertainties associated with the simulation based on the outputted plurality of instances of the simulation; and / or

[0019] - the method further comprises using the uncertainties to design an injection well, or a group / pattern of injection wells and / or a design of a geostorage.

[0020] It is further provided a computer program comprising instructions for performing the method.

[0021] It is further provided a computer readable storage medium having recorded thereon the computer program.

[0022] It is further provided a system comprising a processor coupled to a memory, the memory having recorded thereon the computer program.

[0023] BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Non-limiting examples will now be described in reference to the accompanying drawings, where:

[0025] FIG.s 1 to 5B illustrate the method; and

[0026] FIG. 6 shows an example of the system.

[0027] DETAILED DESCRIPTION

[0028] It is proposed a computer-implemented method for computing uncertainties associated with a multiphysics simulation of injection of a fluid in a subsurface. The simulation is a multiphase reservoir, mechanical, thermal, geochemical, non-linear, time-dependent simulation of monophase or multiphase injection. The simulation comprises one or more parameters, each parameter taking its values in a respective range of values. The method comprises performing the simulation, thereby outputting a set of time-dependent and / or space-dependent values. The method further comprises using an uncertainty-computation module to generate a plurality of instances of the simulation based on at least some outputted values. The generation of the plurality of instances is by varying the value of at least one of the one or more parameters. The uncertainty-computation module is configured for varying the values of the parameters and thereby generating instances of the simulation each corresponding to a different set of values for the one or more parameters.

[0029] This constitutes an improved solution for computing uncertainties associated with a multiphysics simulation of injection of a fluid in a subsurface.

[0030] First, the method computes uncertainties for the multiphysics simulation, and not for a physics sub-simulation thereof (e.g. the geomechanical part of the simulation). The uncertainty computation is thus more accurate than in prior art solutions which were previously discussed. Furthermore, the method takes advantage of the existence of recent known software solutions that can perform multiphysics simulations (including both geomechanical and reservoir) of injection of a fluid in a subsurface, such as the simulation at stake herein. Examples of such solutions include the well-known GEOS software solution. The method directly uses the simulations performed by such software solutions in an uncertainty-computation module (possibly up to an appropriate format conversion step as further discussed hereinafter), which is a new approach and workflow. Using GEOS for fully coupled models is particularly advantageous since it allows fast simulation run time with large models and since it is an open-source code.

[0031] The method is for computing uncertainties associated with a simulation. This means that the method outputs, for a given simulation, uncertainties related to the results of the simulation. The uncertainties associated with the simulation can be defined as follows: the simulation comprises one or more parameters of which values belong to a range. Computing the uncertainties consists in varying the values of at least some of these parameters within their corresponding ranges. These variations result in simulation instances, i.e. instances of the simulation, that is variations of the simulation results that correspond to the variations of the parameters.

[0032] The simulation is a multiphysics simulation, which means that it concerns several fields of physics (e.g. both simulation of reservoir and geomechanical simulation). The simulation is that of simulation of injection of a fluid in a subsurface, such as a geostorage. Specifically, the simulation is a multiphase reservoir, mechanical, thermal, geochemical, non-linear, time-dependent simulation of monophase or multiphase injection simulation. As previously discussed, the simulation comprises one or more parameters. Each parameter takes its values in a respective range of values. Up to a scaling if necessary, the range may be, for each parameter, The simulation may be a simulation of CO2 injection in a reservoir, for hydrocarbon production. The simulation may alternatively be a simulation of H2 injection, for storage.

[0033] The method comprises performing the simulation. Performing the simulation may consist in applying any suitable software for performing the simulation at stake in the method. Such software may for example be GEOS, as previously discussed. Any alternative software capable of performing a fully coupled multiphase reservoir, mechanical, thermal, geochemical, non-linear, time-dependent simulation of monophase or multiphase injection could be used instead.

[0034] Performing the simulation may first comprise selecting a software dedicated to that purpose, such as the GEOS software. Alternatively, the simulation may always be performed with the same software (e.g. GEOS). Performing the simulation may then comprise, in said software, defining a grid. The grid represents a geometry of the subsurface, or of a region of interest comprising the subsurface. The grid may be a geological simulation grid, the concept of geological simulation grid having been previously discussed. Defining the grid may notably comprise defining / selecting a time discretization of the simulation and / or a space discretization provided by the grid. Performing the simulation may then comprise defining (e.g. selecting) the parameters of the simulation (e.g. by user interaction with appropriate tools of the simulation software) and selecting (e.g. also by user interaction with appropriate tools of the simulation software) one or more solvers to be used to perform the simulation. Performing the simulation may then comprise running the simulations using the software, which uses the selected solvers to find all the values in space and time of the variables involved in the simulation. The difference between a variable of the simulation and a parameter of the simulation is as follows: a variable of the simulation is herein a free variable of the simulation of which value is varied / found by the solver(s) performing the simulation, while a parameter is a variable of which value is fixed during the simulation (but may however be varied during the uncertainty computation). Performing the simulation outputs a set of time-dependent and / or spacedependent value, i.e. the results of the simulation which are the values of all the simulation (free) variables for each element of the grid and for each time step. The output of the simulation may be a file (e.g. a table file, such as a .XML file) comprising all these outputted values, together with a description of the parameters of the simulation and of the solver(s) used.

[0035] The method then comprises the step of using the uncertainty-computation module to generate a plurality of instances of the simulation. The uncertaintycomputation module may be any module or software capable of computing uncertainties of the simulation based on the values outputted by the simulation and on said description of the parameters of the simulation and of the solver(s) used. The module may be any module implementing the functionalities disclosed in reference Chugunova, Tatiana, Trani, Mario, and Natalia Shchukina, "Integrated Uncertainty Study for Resources Evaluation Under Operational Constraints.", Paper presented at the Offshore Technology Conference, Houston, Texas, May 2019. doi: and / or in reference Busby, D., Chugunova T.,

[0036] (2015), Embedded Response Surface Approach for Uncertainty Quantification, (IPTC) International Petroleum Technology Conference 6-9Dec2015, Doha, Qatar. IPTC- 18441-MS, which are both integrated herein by reference. The generation of the plurality of instances of the simulation is based on at least some outputted values by varying the value of at least one of the one or more parameters (e.g. all). The uncertainty-computation module is configured for varying the values of the parameters and thereby generating instances of the simulation each corresponding to a different set of values for the one or more parameters.

[0037] In specific, this step may comprise defining all the possible combinations of values of the parameters where the parameters take the extreme values (min or max) of their respective ranges (thus, for 3 parameters, there are 8 combinations of all possible extreme values of the parameters). Up to a scaling, these extreme values may be -1 and 1. The uncertainty-computation module uses the values outputted by the simulation and the described solver(s) to recompute several instances of the simulation, one for each combination. The module thereby generates instances of the simulation, each instance corresponding to a different set of values for the (free) variables of the simulation, obtained for a respective combination of the parameter's extreme values. The uncertainty-computation module may then plot the instances resulting from these computations with the instances grouped according to the parameters values. A net separation of the two groups indicates a high impact of that parameter on that specific output.

[0038] The method may further comprise, before using the uncertainty-computation module, automatically converting the at least some outputted values into a format readable by the uncertainty-computation module. The automatic conversion may in implementations consist in the following steps:

[0039] - Request inside (e.g. GEOS) simulation input data file (e.g. xml file) the "displacement history" output file. This file is in hdf5 format and contains calculated 3D displacement field for the entire grid at requested time steps;

[0040] - Read HDF5 file;

[0041] - Get Global Coordinate of Nodal Point;

[0042] - Load Displacement Components;

[0043] - Extract Curve of the displacements on the top surface or / and displacement along the fault;

[0044] - Save the curve in txt file with the following header = [[' Distance', ' DispO', ' Displ', ' Disp2']] where Distance represents the position with respect to the chosen observation point , DispO', ' Displ', ' Disp2' are X, Y and Z component of displacement;

[0045] - Read this txt file in the uncertainty-computation module.

[0046] The method may further comprise, prior to the automatic conversion, automatically selecting values to be mandatorily converted. Optionally this may further comprise selecting, by user action, additional values to be converted. The values to be mandatorily converted may include values of a subsurface minimal constraint, values related to fault reactivation, induced seismicity prediction, subsurface initial stress state, subsurface initial pore pressure state, and / or any value related to the initialization of the simulation. The one or more parameters may include at least one parameter influencing a design of an injection well or a group / pattern of injection wells and / or a design of a geostorage. The one or more parameters may for example include one or more of: one or more cap rock minimal and / or integrity constraints one or more parameters related to cap rock rigidity (e.g. Young modulus and Poisson coefficient), one or more flow-related parameters (such as bottom hole pressure, head pressure, bottom hole temperature, volume, composition, and / or any of thermodynamics flow parameters), and / or one or more parameters related to the fully coupled physics involved in the simulation. The balance of the cap rock-related parameters is affected by the injection. In the above, "cap rock" refers to an impermeable cover (e.g. compacted clay levels and / or salt) above a permeable lithology where injection is to be done. The method accounts for these parameters combined with flow related parameters (pressure, intact rock permeability, fault permeability, fluid composition, injection rate), and other physical parameters (e.g. temperature).

[0047] The method may further comprise assessing uncertainties associated with the simulation based on the outputted plurality of instances of the simulation. This may for example comprises displaying, to the user, the instances, as previously described, and by the user, determining the parameter(s) having the most impact on the simulation (i.e. the parameter(s) for which value modification yields significantly different simulation instances. This may also comprise grouping the instances as previously discussed. Instead of user-based analysis of the plots, the method may alternatively comprise any automated computerized analysis of the instances (based on their plots or not if they are not plotted), using suitable uncertainty-analysis techniques.

[0048] The method may further comprise using the uncertainties to design an injection well, or a group / pattern of injection wells and / or a design of a geostorage. In particular, the uncertainties may concern parameters related to this design / these designs, and the assessment of the uncertainties computed based on these parameters allows to perform these designs. Using the uncertainties for design may comprise performing derisking for drilling injection wells, for example by deciding to perform physical measurements on points for which rock rigidity uncertainties have been identifies, and optionally performing these measurements. The method may further comprise performing one or more physical actions based on this design, such as drilling injection wells at locations which are suitable based on the results of the derisking.

[0049] An application case of the method is now discussed. As illustrated by FIG. 1, the model here comprises a single fault plane and a sandy reservoir. It is assumed that initial reservoir pressure is 35.0 MPa and an injection with a uniform pressure change of 20 bars is applied to the hanging wall of the fault. The fault divides the reservoir into two parts. For the sake of simplicity 3 parameters are investigated. Hence 2A3 = 8 experiments are generated and then simulated. FIG. 2 shows the total vertical displacement in meters, and FIG. 3 shows the shear stress change in Pa. The simulations are here performed with GEOS.

[0050] The results are then converted in a formal readable by the uncertaintycomputation module, and a choice of the variables to use is performed as previously discussed. FIG.s 4A-4B illustrate the selected variables. FIG.s 5A-5B illustrate the results of the simulation.

[0051] Other possible outputs of the workflow / method may include Shear Slip along Fault's Surface and Normal Opening along Fault's Surface.

[0052] The method is computer-implemented. This means that steps (or substantially all the steps) of the method are executed by at least one computer, or any system alike. Thus, steps of the method are performed by the computer, possibly fully automatically, or, semi-automatically. In examples, the triggering of at least some of the steps of the method may be performed through user-computer interaction. The level of user-computer interaction required may depend on the level of automatism foreseen and put in balance with the need to implement user's wishes. In examples, this level may be user-defined and / or pre-defined.

[0053] A typical example of computer-implementation of a method is to perform the method with a system adapted for this purpose. The system may comprise a processor coupled to a memory and a graphical user interface (GUI), the memory having recorded thereon a computer program comprising instructions for performing the method. The memory may also store a database. The memory is any hardware adapted for such storage, possibly comprising several physical distinct parts (e.g. one for the program, and possibly one for the database).

[0054] FIG. 6 shows an example of the system, wherein the system is a client computer system, e.g. a workstation of a user.

[0055] The client computer of the example comprises a central processing unit (CPU) 1010 connected to an internal communication BUS 1000, a random access memory (RAM) 1070 also connected to the BUS. The client computer is further provided with a graphical processing unit (GPU) 1110 which is associated with a video random access memory 1100 connected to the BUS. Video RAM 1100 is also known in the art as frame buffer. A mass storage device controller 1020 manages accesses to a mass memory device, such as hard drive 1030. Mass memory devices suitable for tangibly embodying computer program instructions and data include all forms of nonvolatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks. Any of the foregoing may be supplemented by, or incorporated in, specially designed ASICs (application-specific integrated circuits). A network adapter 1050 manages accesses to a network 1060. The client computer may also include a haptic device 1090 such as cursor control device, a keyboard or the like. A cursor control device is used in the client computer to permit the user to selectively position a cursor at any desired location on display 1080. In addition, the cursor control device allows the user to select various commands, and input control signals. The cursor control device includes a number of signal generation devices for input control signals to system. Typically, a cursor control device may be a mouse, the button of the mouse being used to generate the signals. Alternatively or additionally, the client computer system may comprise a sensitive pad, and / or a sensitive screen.

[0056] The computer program may comprise instructions executable by a computer, the instructions comprising means for causing the above system to perform the method. The program may be recordable on any data storage medium, including the memory of the system. The program may for example be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The program may be implemented as an apparatus, for example a product tangibly embodied in a machine-readable storage device for execution by a programmable processor. Method steps may be performed by a programmable processor executing a program of instructions to perform functions of the method by operating on input data and generating output. The processor may thus be programmable and coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. The application program may be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired. In any case, the language may be a compiled or interpreted language. The program may be a full installation program or an update program. Application of the program on the system results in any case in instructions for performing the method. The computer program may alternatively be stored and executed on a server of a cloud computing environment, the server being in communication across a network with one or more clients. In such a case a processing unit executes the instructions comprised by the program, thereby causing the method to be performed on the cloud computing environment.

Claims

CLAIMS1. A computer-implemented method for computing uncertainties associated with a multiphysics simulation of injection of a fluid in a subsurface, the simulation being a multiphase reservoir, mechanical, thermal, geochemical, non-linear, time-dependent simulation of monophase or multiphase injection and comprising one or more parameters, each parameter taking its values in a respective range of values, the method comprising:- performing the simulation, thereby outputting a set of time-dependent and / or space-dependent values; and- using an uncertainty-computation module to generate a plurality of instances of the simulation based on at least some outputted values by varying the value of at least one of the one or more parameters, the uncertaintycomputation module being configured for varying the values of the parameters and thereby generating instances of the simulation each corresponding to a different set of values for the one or more parameters.

2. The method of claim 1, wherein before using the uncertainty-computation module, the method comprises:- automatically converting the at least some outputted values into a format readable by the uncertainty-computation module.

3. The method of claim 2, wherein prior to the automatic conversion, the method comprises:- automatically selecting values to be mandatorily converted, and possibly selecting, by user action, additional values to be converted.

4. The method of claim 3, wherein the values to be mandatorily converted include values of a subsurface minimal constraint and / or values related to fault reactivation, induced seismicity prediction, subsurface initial stress state, subsurface initial pore pressure state, and / or any value related to the initialization of the simulation.

5. The method of any one of claims 1 to 4, wherein the one or more parameters include at least one parameter influencing a design of an injection well or a group / pattern of injection wells and / or a design of a geostorage.

6. The method of claim any one of claims 1 to 5, wherein the one or more parameters include one or more of:- one or more cap rock minimal and / or integrity constraints;- one or more parameters related to cap rock rigidity;- one or more flow-related parameters; and / or- one or more parameters related to the fully coupled physics involved in the simulation.

7. The method of any one of claims 1 to 6, wherein the method further comprises assessing uncertainties associated with the simulation based on the outputted plurality of instances of the simulation.

8. The method of claim 7, wherein the method further comprises using the uncertainties to design an injection well, or a group / pattern of injection wells and / or a design of a geostorage.

9. A computer program comprising instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8.

10. A computer-readable data storage medium having recorded thereon the computer program of claim 9.

11. A computer system comprising a processor coupled to a memory, the memory having recorded thereon the computer program of claim 9.

Images