Determining hydrocarbon mobilization potential for enhanced oil recovery

Abstract

A technique for determining the amount of hydrocarbons recoverable from a porous reservoir by miscible gas flooding is disclosed, including a method, an apparatus, and a computer program product. The technique includes: after performing a multiphase flow simulation to obtain a prediction of the flow behavior of oil in the presence of water flooding of the porous reservoir, retrieving a representation of a physical porous reservoir sample (porous reservoir) including pore space and grain space data corresponding to the porous reservoir; locating substantially immobile clumps or patches of oil in the retrieved representation of the porous reservoir; and for N substantially immobile clumps or patches of oil (clumps), evaluating a change in the mobility of the clumps for two or more iterations at an effort level of a given EOR technique, the first of the two or more iterations expending a first effort level and the second of the two or more iterations expending a second, higher effort level, to estimate the amount by which the mobility of the clumps changes between the first and second iterations of the given EOR technique.

Description

Background Art

[0001] The present disclosure relates to multiphase fluid flow simulation, and in particular to the simulation or modeling of fluid displacement processes.

[0002] Two-phase relative permeability (e.g., relative permeability curve (kr)) describes how easily one fluid (e.g., oil) can move through a porous medium in the presence of another fluid (e.g., water). It is an important characteristic of hydrocarbon reservoir rocks and a key input to oil and gas reservoir modeling and simulation activities.

[0003] In the case of water displacement of oil in porous rocks, water is injected through injection wells to displace the oil and push the oil toward production wells. During displacement, some oil may be trapped in the pore spaces of the porous rock. Such oil is considered immobile and cannot be displaced by typical displacement processes. Immobile oil trapped in the pore spaces of porous rocks cannot be displaced unless enhanced oil recovery (EOR) methods are applied. EOR methods attempt to mobilize the trapped oil, thereby increasing oil recovery.

[0004] The term "wettability" is used to measure the tendency of a fluid to adhere to (or wet) a surface compared to a second fluid in the same pore space, i.e., the tendency of one fluid to spread or adhere on a solid surface in the presence of other immiscible fluids. In oil environments, wettability is the tendency of a reservoir surface to preferentially contact a particular fluid in a multiphase or two-phase fluid system.

[0005] Relative permeability and multiphase flow through porous media generally depend on various characteristics of the fluid-fluid-rock system, including rock surface properties, physical properties of each fluid, and flow conditions. One flow characteristic is the dimensionless "capillary number" (Ca), which represents the ratio of viscous force to capillary force. The "capillary number" (Ca) is usually defined as the superficial fluid velocity multiplied by the viscosity of the reference fluid divided by the fluid-fluid interfacial tension. Another characteristic is "wettability", which represents the rock surface's preference for one fluid over another and is manifested as a measurable property called the contact angle. Wettability is usually classified as water-wet (average contact angle 0-90 degrees), neutral-wet (~90 degrees), oil-wet (90-180 degrees), or some variation thereof (such as strongly water-wet or weakly oil-wet).

[0006] Wettability has a strong influence on the behavior of multiphase flows in porous materials, such as oil and water, oil and gas, or water and gas flowing through reservoir rocks. Reservoir rocks (unlike man-made materials) tend to have spatially varying wettabilities, i.e., contact angles vary from location to location on the surface within the pore space of the rock. The distribution of contact angles is a result of the mineralogical composition and geological history of the hydrocarbon-bearing rock, as well as surface texture, chemical composition of the fluids (e.g., water, oil) in contact with the rock, etc. Rocks are almost always naturally water-wet prior to contact with oil; as oil invades the pore space over geological time, the initial water-wetting characteristics are modified in certain locations due to adhesion of certain species in the oil phase. The mechanism of wettability modification depends on various local system properties, such as pressure, temperature, mineral type, and fluid composition. Summary of the invention

[0007] The disclosed subject matter provides an effective technique for predicting the effectiveness of various EOR methods by analyzing immobile oil (trapped oil), and provides guidance for planning EOR studies. A computing system performs multiphase flow simulation to obtain a prediction of the flow behavior of oil in the presence of water flood in porous reservoir rocks. After performing the multiphase flow simulation, immobile oil blobs or patches in porous reservoir rocks are located and identified. These oil blobs in the sample pore space are analyzed for size, shape, surrounding pore space characteristics (such as boundary surfaces and connected pore space areas). The oil blobs in the sample pore space are also analyzed for the wettability of the rock near the blobs and local and global flow conditions (such as pressure and velocity fields). Each analyzed blobs are evaluated in iterations of changing conditions to determine the conditions for moving the oil blobs. The integration of all oil blobs will result in an evaluation of the efficiency of a certain EOR method.

[0008] In one aspect, a computer-implemented method for estimating the amount of hydrocarbons recoverable from a porous reservoir by at least one enhanced oil recovery (EOR) technique comprises: retrieving, by a computing system, a representation of a physical porous reservoir sample (porous reservoir), the representation comprising pore space and grain space data corresponding to the porous reservoir; locating substantially immobile clumps or patches of oil in the retrieved representation of the porous reservoir after performing a multiphase flow simulation to obtain a prediction of the flow behavior of oil in the presence of water flooding of the porous reservoir; and for N substantially immobile clumps or patches of oil (clumps), evaluating a change in the mobility of the clumps for two or more iterations of a given EOR technique, wherein a first iteration of the two or more iterations expends a first level of effort and a second iteration of the two or more iterations expends a second, higher level of effort, thereby estimating the amount of change in the mobilization of the clumps between the first iteration and the second iteration of the given EOR technique.

[0009] Other aspects include a computer program product tangibly stored on a non-transitory computer readable medium and a computing system such as a computer system, a computer server.

[0010] The following are some additional features of one or more of the above aspects.

[0011] All iterations at different effort levels, including a first and a second effort level for a given EOR technology, are integrated to provide an approximation of the oil increment of recoverable oil for the given EOR technology based on the effort level. The given EOR technology is a first EOR technology, and evaluating the mass further includes evaluating the mobility of the mass for two or more iterations of a different second EOR technology, wherein a first iteration of the two or more iterations of the second EOR technology expends a first effort level and a second iteration of the two or more iterations of the different second EOR technology expends a second, higher effort level to estimate an amount by which the mobility of the mass changes between the first iteration and the second iteration of the different second EOR technology.

[0012] These aspects also include generating an assessment of each of the first EOR technique and the second EOR technique. The assessment mass also includes integrating all iterations at different effort levels including the first and second effort levels for each of the first EOR technique and the second EOR technique to provide an approximate value of the increment of recoverable oil for each of the first EOR technique and the second EOR technique according to the effort level. The mass includes connected oil phase regions and / or oil patches as oil attached to the wall of the porous reservoir. The effort includes increasing the amount of the parameter of the given EOR technique. The effort includes one or two or more of the type of the injection surfactant and the injection flow rate. The effort includes increasing the amount of the parameter of each of the first EOR technique and the second EOR technique applied to the porous reservoir. The effort includes changing the value of one or two or more of the type of the injection surfactant and the injection flow rate.

[0013] One or more of the above aspects may provide one or more of the following advantages.

[0014] The above aspects provide an estimation of the efficiency of various potential EOR methods, which can be applied to reservoirs without the need to run the actual simulation of each potential EOR method. This efficiency estimation can be compared and provide guidance for the deployment of a specific method in the EOR method, and therefore reduces the degree of testing required for the detection EOR design space. These aspects can increase the value of the relative permeability or capillary pressure simulation in multiphase flow simulation, and provide insight into the statistical distribution of mobilization efficiency. The aspects can be used for the optimization of the kr simulation configuration settings and the explanation of the results. For example, a result can be a prediction of whether a fluid is displaced by another fluid above or below the critical capillary number (wherein the capillary number is the ratio of viscous force to capillary force, and the critical capillary number is the value of the capillary number, which defines the onset of the oil mobilization that causes oil recovery). These aspects can also be applied to the fluid distribution in the pore space, which is obtained from the 3D image obtained from the physical rock sample in the laboratory fluid displacement experiment.

[0015] Other features and advantages of the invention will be apparent from the following description and from the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a block diagram of a system for predicting the relative effectiveness of different enhanced oil recovery techniques.

[0017] Figure 2 A flow chart showing operations for predicting the relative effectiveness of different enhanced oil recovery techniques is described.

[0018] Figure 3 A flow chart showing the operation of a gas flood simulation for estimating oil recovery is depicted.

[0019] Figure 4 Depicted is a 2D illustration of mass mobility through the throat body.

[0020] Figure 5 An example of a throat-body-throat tube bundle is depicted.

[0021] Figure 6 The pressure distribution along the centerline of the pore throat / body is depicted.

[0022] Figure 7 yes Figure 5 Plot of theoretically predicted mobilization pressure versus simulated mobilization pressure in the example.

[0023] Figure 8 It is a graph of the relative permeability (kr) curve of Berea Sandstone B200.

[0024] Fig. 9 is a visualization of the clump volume and a plot of the clump driving force based on clump length and number of clumps.

[0025] Fig. 10A and 10B are bar graphs of two capillary numbers.

[0026] Fig.11 Comparison of the volume of oil clumps before and after mobilization is depicted. DETAILED DESCRIPTION

[0027] Enhanced oil recovery (EOR), commonly referred to as tertiary recovery, includes various techniques for extracting oil from a reservoir that was not extracted during primary or secondary recovery. At the microscopic level, unextracted oil is often associated with trapped or immobile oil. All EOR methods attempt to minimize trapped or immobile oil. Two types of displacement efficiency are considered microscopic displacement efficiency and macroscopic displacement efficiency.

[0028] Microscopic displacement efficiency can be described in the form of a relative permeability function. The two-phase relative permeability (kr) describes how easily one fluid can move through a porous medium in the presence of another fluid. The relative permeability curve also includes the amount of trapped oil, which is usually called residual oil. The relative permeability curve is a characteristic of hydrocarbon reservoir rocks and is an input to oil and gas reservoir modeling and simulation activities. Relative permeability and multiphase flow through porous media generally depend on various characteristics of the fluid-fluid-rock system, including rock surface properties, physical properties of each fluid, and flow conditions.

[0029] One flow condition is the dimensionless capillary number (Ca), which represents the ratio of viscous to capillary forces and is generally defined as the average fluid velocity times the viscosity of the reference fluid divided by the fluid-fluid interfacial tension. Another property is "wettability," the preference of a rock surface for one fluid over another. Wettability manifests itself in a measurable property called the contact angle. Wettability is generally classified as water-wet (average contact angle of 0 to about 90 degrees), neutral-wet (about 90 degrees), oil-wet (slightly above 90 degrees to 180 degrees), or some variation thereof, such as strongly water-wet or weakly oil-wet.

[0030] The mobilization of the oil mass will result in at least partial recovery of the oil mass. Mobilization increases oil recovery. The integration of all oil mass mobilizations for one effort level of an EOR technology will correspond to a certain amount of incremental oil recovery. Then, for a specific EOR technology (e.g., surfactant injection), the process evaluates more than one effort (e.g., a continuous increase in surfactant concentration) and determines the increase in the mobilization of all oil mass in the reservoir. The recovery can be determined by integrating the results for all oil mass for each effort level (e.g., an increase in surfactant concentration), so multiple effort levels will provide multiple integrated results, which can be used and plotted or otherwise rendered for evaluation.

[0031] refer to Figure 1, a system 10 for determining the relative amount of hydrocarbons recoverable from a porous reservoir by one or two or more enhanced oil recovery techniques is shown. In this implementation, the system 10 is based on a client-server or cloud-based architecture and includes a server system 12 and a client system 14 implemented as a massively parallel computing system 12 (standalone or cloud-based), the server system 12 including a memory 18, a bus system 22, an interface 20 (e.g., a user interface / network interface / display or monitor interface, etc.) and a processing device 24.

[0032] In memory 18 is a 3D imaging engine 32 that generates, for example, a digital representation of a porous reservoir rock and an image analysis engine 32b that determines the characteristics of the geometry of the pore space network in the porous reservoir rock based on the analysis of the digital representation of the porous reservoir rock. Also in memory 18 is a simulation engine 34 that performs numerical multiphase flow simulations for various saturation values ​​to obtain predictions of flow behavior through the digital representation of the porous reservoir rock, including relative permeability as a function of saturation. In some embodiments, the simulated multiphase flow behavior occurs while passing through the reservoir rock adjacent to a gas or oil well (e.g., a drilling rig 37).

[0033] The digital representation of the porous reservoir formation and the image analysis performed by the engine 32 to determine the characteristics of the pore space network may be a third party application executed on a system different from the server 12. The system 10 requires only that the digital representation of the formation and the analysis of the pore space network be available as input to the simulation engine 34. One method of providing a digital representation of a reservoir sample is to obtain the representation, for example, from a 3D image of the porous reservoir formation produced by a micro-CT scan of the reservoir sample(s).

[0034] The memory 18 also stores parameters used by the engine 34, such as the particle surface characteristics obtained by assigning mineral types 33a to particles to determine the surface characteristics of each mineral type, and surface texture and roughness characteristics. The memory 18 also stores parameters, such as fluid characteristics 33b, for example, the fluid density and viscosity of each expected fluid (e.g., two or more of water, gas, oil) and fluid-fluid interfacial tension characteristics. The memory 18 also stores parameters for specific mineral types, such as the chemical composition data 33c of the fluid and the affinity data 33d of the fluid components. The memory 18 also stores the separation pressure 33e of each mineral type combined with the fluid, and the selected aging time 33f used by the aging engine 32. In addition, reservoir pressure and temperature data are also stored. The mineral types evaluated can be those found or expected at the actual location of the reservoir.

[0035] The simulation engine 34 includes a module (not shown) for establishing a reservoir sample simulation environment and a module (not shown) for performing a pore-scale numerical simulation of water flooding. The system also includes an analysis module 34a, which enables the study of the relative effectiveness of different effort levels of each of one or two or more EOR methods by analyzing immobile oil (trapped oil) after simulated water flooding of porous reservoirs, thereby providing guidance for planning EOR studies.

[0036] Typically, for a given reservoir rock sample, the simulation only needs to be run once, and upon completion of the simulation, the analysis engine 34a is used to determine the relative effectiveness effort level(s) of each of one or two or more EOR methods. For most embodiments, the computational cost (processor, memory resources, etc., and time, etc.) of the execution of the analysis engine 34a is likely to be lower than that of the simulation engine 34.

[0037] The analysis engine (module) 34a analyzes each oil mass in the pore space for its size, shape, the surrounding geometry of the pore space (boundaries and interconnected pore space regions), and the wettability and local and global flow conditions (pressure and velocity fields) near the mass in one or two or more iterations of varying parameters for a given EOR technique. In some embodiments, one iteration may be used per EOR technique, while in other embodiments, two or more iterations may be required. In some embodiments, the analysis will involve two or more increased effort levels for one or more EOR methods, and in other embodiments, the analysis will involve one or more increased effort levels for two or more EOR methods.

[0038] U.S. patent application Ser. No. 16 / 545,387, filed on August 20, 2019, and entitled “Determination of Oil Removed by Gas via Miscible Displacement in Reservoir Rock,” which is incorporated herein by reference, describes examples of modules for establishing a reservoir rock sample simulation environment and modules for performing pore-scale numerical simulations of waterflooding.

[0039] After obtaining data from these analyses, each mass remaining after the water flooding simulation of porous reservoir rock is located. The remaining mass is an oil mass or patch that does not move substantially (e.g., slowly moves or is trapped). For N of these substantially immobile oil mass or patch (mass), module 34a evaluates the mass at each effort level for each technology in one or two or more EOR technologies, so as to estimate the mobility of the mass at each effort level for each technology in this one or two or more EOR technologies. In other words, the change of the mobility of each oil mass is analyzed to determine the optimal effort level that makes this oil mass move required for each EOR technology. Module 34a performs the integration of the mobility on all oil masses at each place in multiple applied efforts for each EOR technology, so as to provide the measurement of the prediction efficiency of each EOR technology.

[0040] Reference now Figure 2 , module 34a performs EOR screening 40. Module 34a is configured 42 to perform a single simulation of waterflooding and record each trapped oil mass for size, geometric extent, surrounding particle space with wetting conditions, local flow field (pressure, velocity), and global flow field (pressure velocity). Analysis module 34a is configured 44 to perform several analysis iterations for each effort level of each of one or two or more different EOR techniques.

[0041] That is, the simulation engine 34 provides the state of the pore space after water flooding. This state of the pore space is a representation 35, which is used by the analysis engine (module) 34a to perform an analysis of (one or more) effort levels and (one or more) EOR techniques, so as to determine the optimal amount of (one or more) effort levels for (one or more) given EOR techniques. The representation 35 can be in the form of a data structure that stores data describing the state of the pore space after the simulation in a structured manner (including the characteristics of the trapped oil mass). In other embodiments, a flat file or a graphic or other computer structure can be used. The representation 35 is fed to the analysis module 34a and used by it to analyze 46 the results of the simulation. The representation 35 is operated by the analysis engine 34a to determine the subsequent characteristics of the trapped oil mass, as determined from the analysis engine 34a by applying a set of characteristics to the mass during each iteration of the effort level, the set of characteristics representing the given effort level. Each iteration of the effort generates data representing the effectiveness of the iteration by determining the relative increased mass mobility. The generated data is stored 48 for each iteration of the effort and each EOR technique. The module 34a is configured to determine 50 whether it is the last effort iteration for the given EOR technique. If it is not the last effort iteration 50, the module 34a is configured to change 52 the effort level of the set of characteristics and perform 46 another effort iteration using the given EOR technique with a new (i.e., changed) set of characteristics representing the new effort level.

[0042] A set of characteristics for a given EOR technology will be dominated by the specific technology. For example, characteristics may include flow rate, relative concentrations and types of surfactants, polymers and surfactant-polymer combinations, water alternating gas (WAG) circulation and (one or more) duty cycles, etc. Other examples include injection of other types of chemicals dissolved in brine or water, injection of steam, injection of gas, etc. Other characteristics include driving forces (head, body force, injection flow rate) vs miscible displacement (CO2 injection) vs wettability changes vs mobility changes, etc. The set of characteristics will depend on the type of EOR technology being simulated. Therefore, efforts are made to iteratively modify one or two or more characteristics of a given EOR technology to analyze how the modification affects changes in mass mobility.

[0043] If it is the last time 50 in the configured iteration, the module 34a is configured to determine 54 whether the engine 34a has simulated the last EOR technology. If it is not the last EOR technology, the analysis module 34a is configured to change 56 the technology and provide a new EOR technology. The analysis module 34a is configured with the new EOR technology, and performs another analysis of the recorded characteristics of each trapped oil mass for size, geometric range, surrounding particle space with wetting conditions, local flow field (pressure, velocity) and global flow field (pressure velocity) according to the new EOR technology. The analysis module 34a analyzes 46 the simulation results (recorded characteristics) of each iteration of different effort levels for a given number of effort iterations (a set of characteristics for a given EOR technology) with different effort levels. If it is the last EOR technology, the module 34a is configured to evaluate 58 the results from all iterations of each EOR technology and generate 60 an evaluation for the various EOR technologies simulated.

[0044] Reference now Figure 3 , starting with water flooding, module 34a performs a first EOR simulation 70. For example, module 34a performs a gas flood and calculates how much oil is produced by the gas flood and how much of the produced oil is due to direct displacement vs. extraction and swelling. The system 10 accesses a data repository 38 storing 2D and / or 3D grids, coordinate systems, and libraries that can be used in simulations using any known computing technique such as computational fluid dynamics or the so-called Lattice Boltzmann method.

[0045] In other examples, instead of using gas flooding to represent the state of the pore space of the reservoir rock, surfactant or polymer flooding is used. The reason is that gas injection is a third phase (oil, water, gas), and while it is usually acceptable, surfactant or polymer flooding can also be used. Surfactant or polymer flooding is not considered a new phase. Instead, water or brine is modified in basic physical properties (viscosity, interfacial tension) using surfactants or polymers to enhance recovery.

[0046] After receiving or retrieving 72 the digital representation of the pore space and grain space of the reservoir rock sample, the simulation engine module 34b sets the input parameters and conditions to perform a numerical simulation of the pore scale of the water drive, and uses the set simulation environment to perform 74 a numerical simulation of the pore scale of the water drive. After performing the water drive 74, the simulation engine 34 stores 76 the state of the pore space in the memory, and performs a gas drive simulation 78. After performing the gas drive 78, the simulation engine 34 also stores 80 the state of the pore space after performing the gas drive simulation 78 in the memory. Using the results of the gas drive simulation, the estimation engine that can be part of the simulation engine calculates 46a the amount of oil recovered by the gas drive due to direct displacement, and calculates 46b the amount of oil recovered due to extraction and expansion.

[0047] The system 10 is described as using LBM techniques to perform simulations such as waterflooding and gasflooding; however, as described above, any of a variety of well-known computational techniques in computational fluid dynamics may be used.

[0048] For the purpose of explanation, a brief description of multiphase simulation for fluid flow simulation is provided using the "Lattice Boltzmann Method" (LBM). Unlike other computational fluid dynamics techniques that numerically solve conservation equations for macroscopic properties (i.e., mass, momentum, and energy), the LBM technique models fluids as "particles", and such particles perform a continuous propagation and collision process on a grid composed of voxels. Voxels can have different sizes and are used as the basic unit area for fluid flow analysis. Voxels are associated with properties that describe the state of the fluid in a particular region (e.g., including a velocity vector that describes the velocity of the fluid in the region). During each time step of the simulation, the fluid can move from one voxel to another according to the velocity vector. Collision operators describe the effects of collisions of different fluids due to fluid motion.

[0049] LBM technology can be efficiently implemented on scalable computer platforms and operates with great robustness for time-unstable flows and complex boundary conditions. In one example, the results of multiphase fluid flow simulations at the pore scale can be used to understand the fluid displacement mechanism in the reservoir rock. Micro-CT images of the reservoir rock can be used to construct the geometry of the pores, which is used as input for each simulation.

[0050] The system 10 using LBM techniques determines relative permeability and multiphase flow through porous media, which generally depends on various characteristics of the fluid-fluid-rock system, including rock surface properties, physical properties of each fluid, and flow conditions. One flow condition is the dimensionless capillary number (Ca), which represents the ratio of viscous forces to capillary forces and is generally defined as the average fluid velocity multiplied by the viscosity of the reference fluid divided by the fluid-fluid interfacial tension. Another property is wettability, which represents the preference of the rock surface for one fluid relative to another and is manifested as a measurable property called the contact angle.

[0051] When configured to perform simulations of water and gas flooding, the system 10 is first configured to perform a water flooding simulation. To simulate water flooding, the simulation is set to a two-fluid simulation, in which water is a fluid injected into the pore space and oil is another fluid. The simulated water flooding process is basically a simulation of the displacement caused by the injection of water. For a typical wettability process, the process is set to an iterative process, in which wettability changes are performed, followed by fluid phase redistribution. The process is repeated until the engine 34 determines that the wettability changes converge (such as by further repeated measurements without significantly changing the results (e.g., contact angle)). Process 40 refers to an established threshold value, which provides the amount of contact angle that needs to be changed in order to stop further iterations of the wettability change simulation. One such process is disclosed in U.S. patent application serial number 16 / 243,285, entitled "DETERMINING FLUIDFLOW CHARACTERISCS OFPOROPUS MEDIUM", filed on January 9, 2019, the entire contents of which are incorporated herein by reference. The wettability procedure uses the two-phase relative permeability (kr) procedure to determine how easily one fluid (eg, oil) moves in the presence of another fluid (eg, water).

[0052] Two-phase relative permeability is an important property of hydrocarbon reservoir rocks and is a key input to oil and gas reservoir modeling and simulation activities. A technique for determining two-phase relative permeability is disclosed in U.S. Patent Application Serial No. 14 / 277,909, filed on May 15, 2014, entitled "MASS EXCHANGE MODEL FOR RELATIVE PERMABILITY SIMULATION," published on November 20, 2014 as US-2014-0343858-A1, the entire contents of which are incorporated herein by reference.

[0053] When configured to perform water and gas flooding simulations, the system 10 can also track blobs in the formation. One technique for determining two-phase relative permeability is disclosed in U.S. Patent No. 14 / 660,019, filed on March 17, 2015, entitled "FLUID BLOB TRACKING FOREVALUATION OF MULTIPHASE FLOW SIMULATIONS," which was published as US-2015-0268080-A1 on September 24, 2015, the entire contents of which are incorporated herein by reference.

[0054] After performing the waterflood simulation, a gasflood simulation (of a particular EOR type) is performed. The simulation engine 34 is therefore configured to perform a gasflood simulation starting from the state of the pore space resulting from the waterflood simulation, wherein residual water in the pore space is one of the fluids and hydrocarbons (miscible gas+oil) are another fluid. The gasflood simulation is performed using the techniques discussed above, except that the simulation is configured for hydrocarbons (combined amounts of gas and oil injected) and residual water.

[0055] Although the exemplary simulation techniques of water drive and gas drive simulation have been described above, it should be understood that these are exemplary. Therefore, in some embodiments of estimating the predicted amount of potential oil recovery due to EOR technology, other simulation techniques can be used. In addition, in some embodiments, actual field data of the state of the pore space can be used instead of simulating.

[0056] Described below is an example of an analysis model performed on the results of a simulation (eg, the recorded characteristics of trapped oil masses determined from the simulation engine 34) at various iterations of effort levels.

[0057] Reference now Figure 4 , a two-dimensional (2D) model illustrating oil mobilization in a multi-fluid flow of water-flooding oil shows an oil droplet 83 (circle in the center) trapped in front of a pore throat 85, with the pressure P at the top 84 of the throat 1 With the pressure P at the bottom of the throat 87 0 The oil agglomerates are pushed towards the pore boundaries and hence no water flow is possible, which means that there are no viscous forces.

[0058] The resistance is defined by the radius of the throat and can be expressed using Laplace's law (see Equation 1 below). To push the oil mass through the throat, the applied pressure differential P 1 -P 0 Need to be equal to or greater than P L Or P 1 -P 0 ≥P L , where P L is the capillary pressure (given as PL =σ / r). When P 1 -P 0 ≥P L When the curvature of the surface of the oil mass is 1 / r, the curvature 1 / r required to make the oil mass 82 invade the pore throat 89. Therefore, as long as the entrapment mechanism is known (in this example: the oil mass blocking the throat), the force required to move the oil mass can be predicted / estimated.

[0059] Example 1: Throat-body-throat bundle

[0060] Reference now Figure 5 , shows Example 1 of using a model as a throat-body-throat bundle CDC (capillary desaturation curve). In this first example, the bundle has or consists of a series of simple pore throat-pore body tubes, which are used as a model for evaluating the effort level(s) of(s) EOR technique(s).

[0061] The Capillary Desaturation Curve method is a method in which the capillary number is increased incrementally and the remaining oil saturation is measured and plotted as described above. In this example, each pore body (in Figure 5 The vertical center of the pore (not labeled) is cylindrical and has a radius of 10 voxels. The pore throat (not labeled) has a cylindrical shape and has various radii. The largest radius on the left has 6 voxel radii and the smallest pore throat radius on the right is 2 voxels. The non-wetting fluid is initialized only in the body of the pore. The model is periodic in the z direction, connecting the top and bottom of the fluid domain. The body force is used as the driving force. The reduction of the pore radius from the body to the throat (pore aspect ratio) leads to the entrapment of the non-wetting fluid.

[0062] Reference now Figure 6 , showing the Figure 5 Pressure distribution at the centerline of the pore throat / body. Figure 6 A graph of static pressure as a function of Z (position lattice length) is shown. Mobilization of a trapped non-wetting fluid requires a driving force (e.g., bulk force) at least as high as the resistive force, the capillary pressure, which is related to the pore throat radius. The driving force is larger than the capillary force caused by the geometric extension of the pore throat in the direction of flow and by the material properties of the pore space boundaries (e.g., wettability).

[0063] The following equation describes this force balance, where the left-hand side represents the driving force due to gravity and the right-hand side represents the capillary force described by Laplace's law:

[0064]

[0065] Reference now Figure 7 , showing Figure 4 Theoretical predicted mobilization pressure versus simulated mobilization pressure for an example. As shown, the required driving force matches relatively well with the theoretical estimate using Laplace's law (Equation 1). In this case, the required driving force for mobilization is known by considering the theoretical comparison. This is possible because the topology of the geometry is precisely known and the geometry topology is simple, such as the radii of the pore throat and pore body (R1 and R2) and the length of the pore throat (L top and L bottom ) and the length of the pore body (L middle ).

[0066] Another way to determine the onset of mobilization is to measure the pressure (P top ) and the pressure directly below the non-wetting fluid (P bottom ). The force balance defining the start of the mobilization is given by:

[0067]

[0068] In complex pore spaces, such as those of porous rocks, the topology of the geometry coupled with the fluid distribution is not simple, and theoretical estimation methods may not be reliable.

[0069] However, the method described herein studies the force balance by measuring all force contributors in the vicinity of a trapped fluid body (mass), and calculating an estimate of the increase in driving force required to move the trapped fluid body. In this case, this means measuring P top ,P bottom ,L middle and R 1 and R 2 All of these quantities can be measured in the pore space and the driving force (eg, bulk force) required to move the trapped fluid body can be estimated.

[0070] Example 2: Berea Sandstone CDC

[0071] In the pore space of physical rocks, the pore space and fluid flow are more complex than in Example 1 above. For water-wet rocks (where oil is a non-wetting fluid), the force balance as described in Example 1 is still valid and can be applied. To explain the basic concepts, some simplifications are introduced. In the pore space of real rocks, trapped oil masses usually do not block the flow of wetting fluids, which leads to the simplification of equation (2) because P top ,Pbottom To further simplify the example, we can assume that the radius R of the pore body is 1 Specific throat radius R 2 These simplifications are presented in order to provide a simple explanation of the potential of the disclosed subject matter, but are not particularly required for practicing the subject matter described herein.

[0072] The resulting simplified equation is as follows:

[0073]

[0074] The left-hand side of formula 3 represents the action on a certain length L middle , the driving force on the trapped mass, and the right hand side represents the throat radius R of the next throat trapped by the mass 2 To demonstrate the potential of pore-scale EOR screening, a relatively small domain (B200) was selected as a test case.

[0075] Figure 8 Permeability is shown relative to water saturation and a relative permeability kr curve is depicted, which was simulated for the Berea sandstone B200 at a capillary number of 1.0E-5, used as a test case. The residual oil saturation is 42%. [This is in Figure 8 81 is described by (100% - the water saturation value at point 81 (58%)). The bottleneck radius (e.g., critical throat radius) of this pore space is 2.2 voxels. For further simplicity, this demonstration assumes that all pore throats have a radius of 2.2 voxels, such as a uniform pore space. Other embodiments may include all relevant details to obtain a numerically accurate pore throat radius in the flow direction for each trapped mass by measuring the throat size where each mass is trapped.

[0076] Reference now Fig. 9 , showing a visualization of cluster volume (left) and a plot of cluster driving force based on cluster length and number of clusters (right). The pore geometry (pores in the rock) is not visualized. The number of clusters (bars) exposed to a certain driving force is determined by the z-extension of these clusters (cluster length L). The driving force can be approximated as the product of the cluster length L, the fluid density rho of the cluster, and the driving force g (gravity). The fluid is driven by a bulk force g in the z-direction (up and down). The horizontal lines represent capillary forces for certain throat radii. Line 91 represents the capillary force required to overcome the bottleneck pore throat size. There are no clusters where the driving force exceeds the capillary force required to overcome the bottleneck pore throat.

[0077] Reference now Fig. 10A and 10B, the two capillary number bar graphs show the two largest clumps 92A, 92B ( Fig. 9 ), and in the bar graph at ρ*G*L=0.02( Fig. 10A ) are removed. These clumps are removed by having no clumps on line 91 (represented by Fig. 10B 91 is the capillary pressure required for the bottleneck radius. This shows that the entrapment criterion holds for this example. In order to move the two largest clumps 92a, 92b, an increase in the driving force (here the bulk force) is required. This will result in a higher capillary number than in the bar graph, for example, and it can be seen that a 50% increase in the bulk force (and therefore the capillary number) will most likely move the two largest clumps.

[0078] Reference now Fig.11 , shows a comparison of the volume of oil clumps before and after mobilization. It can be seen that when the simulation is continued at a higher capillary number of 1.5E-5, the two largest clumps 92a, 92b are moved as expected. Comparing the two bar graphs, it can also be seen that the two largest clumps 92a, 92b not only disappear, but they split into several smaller clumps.

[0079] Again, at Ca=1.5E-5, there is no agglomerate of ρ*G*L>0.02. Due to mobilization, residual oil saturation is reduced from 42% to 36%. It should be noted that when the oil agglomerate is moved, it is not necessary to recover the entire oil agglomerate and therefore remove it from the pore space. On the contrary, it only indicates that the agglomerate begins to move, and eventually experiences a sudden disappearance phenomenon (splitting event). The resulting smaller agglomerate may travel to the next pore and merge with the agglomerate downstream, which may lead to the subsequent mobilization phenomenon. The precise determination of how much oil has been recovered due to increased mobilization can be estimated based on correlation, and it is not a trivial determination. Other methods can use machine learning methods to associate the reduction of the pore space and oil agglomerate topology with the oil agglomerate volume after mobilization. Although the agglomerate does not disappear but splits into several smaller agglomerates, the agglomerate occupies some amount of the oil actually recovered, so it is removed from the pore space.

[0080] The determined slug state after mobilization is stored in a database of mobilization simulations.The database of mobilization simulations can be used to generate correlations (with or without machine learning techniques) to provide an estimate of the overall capillary desaturation curve.

[0081] One method to provide an estimate of the overall capillary desaturation curve is as follows:

[0082] The system 10 simulates a first kr curve for a certain capillary number to provide a first point of the CDC curve. The first capillary number is usually very small. The system 10 performs a pore-scale analysis on the mobility and thus estimates the capillary number required to achieve a certain incremental oil production. This is the second point of the CDC curve. The system repeats the pore-scale analysis to obtain a third point.

[0083] Another consequence of this type of pore-scale analysis is that based on how "close" the agglomerates are to being produced, it can be seen whether the flow regime is within the capillary number dominated regime. This is because each rock has a different specific critical capillary number that defines the onset of mobilization. In summary, for Example 2, the process applied is consistent with that explained in the description of the basic concepts.

[0084] The fluid and flow near the trapped mass are studied: the pressure difference is measured (or set to 0 here), and the driving force on the mass is estimated using ρ*G*L. The pore space near the trapped mass is studied: the pore throat radius (here: the bottleneck radius is used), which is needed to estimate the resistance, is determined. A relationship in the form of a force balance is established to evaluate the mobilization criteria. A method is proposed to estimate the incremental oil produced and to determine whether the displacement is above, below or at the critical capillary number (mobilization threshold). The mobilization efforts and incremental oil recovery are integrated across the domain.

[0085] Example 3: Pinned Oil (Patch)

[0086] When particles prefer to be in contact with oil rather than water, and therefore in oil-wet rocks, the residual oil is usually not located in the center of the pore body, but is usually attached to the wall in the shape of a relatively thin patch. The oil patch may be completely trapped by pinning (spinning), such as a shift in wettability, or the oil patch may be produced very slowly by film flow. Both cases require different mobilization mechanisms. In the case of pinned oil patches, contact angle hysteresis must be overcome. The driving force can be generated by pressure drop or by viscous forces caused by bypass flow of other fluid phases. The drag force is due to the shift in contact angle - in a sense, this is also a capillary force.

[0087] The process of estimating the effort required to overcome the pinning effect and thus mobilize the oil mass / patch is essentially the same as described for the basic concept and above Example 2. The method includes investigating the fluids and flows near the trapped mass, investigating the pore space near the trapped mass, establishing a force balance and thus a mobilization criterion, estimating incremental oil recovery, and integrating the mobilization effort and incremental oil recovery for the entire domain.

[0088] Example 4: Oil film

[0089] In this case, the rock is also oil wet, but the oil patch is continuous, i.e., the oil patch is an oil film. In this case, no entrapment occurs, but oil recovery may be very slow and uneconomical. Instead of mobilization, the goal may be to accelerate oil production, so that the time scale of oil production replaces the term of incremental oil recovery. The process is the same as above. Instead of optimizing incremental oil recovery, the rate of incremental oil recovery is the optimized feature of the EOR process employed.

[0090] Example 5: Oil seepage in dead-end pores or oil in oil-wet clay

[0091] There are many situations where oil is difficult to displace simply because it is difficult to access (direct contact) by other fluids. This may be the case when the oil is in dead-end pores, or when the oil is in oil-wet clays. In this case, gas injection may be the EOR method of choice, and the displacement mechanism is the diffusion of oil into the gas phase, resulting in rich gas production. The mechanism is the same as described above.

[0092] The embodiments of the subject matter and operations described in this specification may be implemented in digital electronic circuits, or in computer software, firmware or hardware, including the structures disclosed in this specification and their structural equivalents, or in a combination of one or more of them. The embodiments of the subject matter described in this specification may be implemented as one or more computer programs (also referred to as data processing programs) (i.e., one or more modules of computer program instructions encoded on a computer storage medium for execution by a data processing device or for controlling the operation of a data processing device). The computer storage medium may be a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them, or may be included in them. The computer storage medium may also be one or more separate physical components or media (e.g., multiple CDs, disks or other storage devices), or be included in one or more separate physical components or media. The present subject matter may be implemented on computer program instructions stored on a non-transitory computer storage medium.

[0093] The operations described in this specification may be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.

[0094] The term "data processing apparatus" includes all kinds of apparatus, devices and machines for processing data, including, for example: a programmable processor, a computer, a system on a chip, or a plurality or combination of the foregoing. The apparatus may include a dedicated logic circuit (e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit)). In addition to hardware, the apparatus may also include code that creates an execution environment for the computer program in question (e.g., code constituting processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them). The apparatus and execution environment may implement a variety of different computing model infrastructures, such as web services, distributed computing, and grid computing infrastructures.

[0095] A computer program (also referred to as a program, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple collaborative files (e.g., files storing one or more modules, subroutines, or code portions). A computer program may be deployed to execute on one computer, or on multiple computers located in one location or distributed in multiple locations and interconnected by a communication network.

[0096] The processes and logic flows described in this specification can be performed by one or more programmable processors that execute one or more computer programs to perform actions by operating on input data and generating output. These processes and logic flows can also be performed by special-purpose logic circuits (e.g., FPGAs (field programmable gate arrays) or ASICs (application-specific integrated circuits)), and the apparatus can also be implemented as special-purpose logic circuits (e.g., FPGAs (field programmable gate arrays) or ASICs (application-specific integrated circuits)).

[0097] Processors suitable for executing computer programs include, for example, general-purpose and special-purpose microprocessors, and any one or more processors of any type of digital computer. Typically, the processor will receive instructions and data from a read-only memory or a random access memory or both. The basic elements of a computer are a processor for performing actions according to instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices (e.g., magnetic disks, magneto-optical disks, or optical disks) for storing data, or be operably coupled to receive data from or transmit data to one or more mass storage devices for storing data, or both; however, a computer need not have such a device. In addition, a computer can be embedded in another device (e.g., a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive)). Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0098] To provide interaction with a user, embodiments of the subject matter described in this specification may be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user, and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other kinds of devices may also be used to provide interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including sound, speech, or tactile input. In addition, a computer may interact with a user by sending documents to and receiving documents from a device used by a user (e.g., by sending a web page to a web browser in response to a request received from a web browser on a user device of the user).

[0099] Embodiments of the subject matter described in this specification may be implemented in a computing system that includes a back-end component (e.g., as a data server), or includes a middleware component (e.g., an application server), or includes a front-end component (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification), or any combination of one or more such back-end, middleware, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include local area networks ("LANs") and wide area networks ("WANs"), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

[0100] A computing system may include a user and a server. The user and the server are usually remote from each other and usually interact through a communication network. The relationship between the user and the server is due to computer programs running on respective computers and having a user-server relationship with each other. In some embodiments, the server sends data (e.g., an HTML page) to a user device (e.g., in order to display data to a user interacting with the user device and to receive user input from the user). Data generated at the user device (e.g., the result of the user interaction) can be received from the user device at the server.

[0101] Although this specification contains many specific implementation details, these should not be interpreted as limitations on the scope of any invention or the scope that can be claimed, but as descriptions of features peculiar to the specific embodiments of specific inventions. Certain features described in the context of separate embodiments in this specification may also be implemented in combination in a single embodiment. On the contrary, the various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments. In addition, although features may be described above as working in certain combinations and even initially claimed as such, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may be directed to a variation of a sub-combination or a sub-combination.

[0102] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that such operations be performed in the particular order shown or in a continuous order, or that all of the operations shown be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0103] Thus, specific embodiments of the present subject matter have been described. Other embodiments are also within the scope of the appended claims. For example, any of the above techniques described with reference to pore spaces may also be performed with reference to or with respect to physical media such as reservoir rock samples. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired results. In addition, the processes described in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain implementations, multitasking and parallel processing may be advantageous.

Claims

1. A computer-implemented method for estimating the amount of hydrocarbons recoverable from a porous reservoir by one or two or more enhanced oil recovery (EOR) techniques, the method comprising: after performing a multiphase flow simulation to obtain a prediction of flow behavior of oil in the presence of waterflooding of the porous reservoir rock, retrieving, by a computing system, a representation of a physical porous reservoir rock sample, the representation including pore space and grain space data corresponding to the porous reservoir rock; locating, by a computing system, substantially immobile clumps or patches of oil in the retrieved representation of the porous reservoir rock; performing, by a computing system, an analysis of given one or more EOR techniques for an immobile oil mass, wherein a first iteration of the given one or more EOR techniques expends a first level of effort, and a second iteration of the given one or more EOR techniques expends a second, higher level of effort, wherein each level of effort represents one or more values ​​of one or more parameters of the given EOR technique; as well as For N basically immobile oil clumps or patches, determining, by a computing system, a change in the mobility of the mass for a given one of the EOR techniques, and An estimate is generated, by a computing system, of an amount by which the mobilization of the mass changes between the first iteration and the second iteration for the given one or more EOR techniques.

2. The method according to claim 1, further comprising: For the given EOR technique, all iterations at different effort levels, including the first effort level and the second effort level, are integrated to provide an approximation of the increment of recoverable oil for the given EOR technique as a function of effort level.

3. The method according to claim 1, wherein: The given EOR technique is a first EOR technique, and evaluating the mass further comprises: The mobility of the mass is evaluated for one or two or more iterations of a different second EOR technique, wherein a first iteration of the one or two or more iterations of the second EOR technique expends a first level of effort and a second iteration of the one or two or more iterations of the different second EOR technique expends a second, higher level of effort, thereby estimating an amount by which the mobility of the mass changes between the first iteration and the second iteration of the different second EOR technique.

4. The method according to claim 3, further comprising: An evaluation of each of the first EOR technique and the second EOR technique is generated.

5. The method according to claim 4, wherein: Evaluating the mass further comprises: For each of the first EOR technique and the second EOR technique, all iterations at different effort levels including the first effort level and the second effort level are integrated to provide an approximation of the increment of recoverable oil for each of the first EOR technique and the second EOR technique according to the effort level.

6. The method according to claim 1, wherein: The clusters include interconnected oil phase regions and / or are patches of oil attached to the walls of the porous reservoir rock.

7. The method according to claim 1, wherein: Efforts have been made to include an increasing number of parameters for a given EOR technique.

8. The method according to claim 7, wherein: Such efforts include one or more of the type of surfactant injected and the injection flow rate.

9. The method according to claim 3, wherein: Efforts include increasing the amount of parameters of each of the first EOR technique and the second EOR technique applied to the porous reservoir rock.

10. The method according to claim 9, wherein: Efforts include one or more of the type of surfactant injected and the injection flow rate.

11. A system for determining the amount of hydrocarbons recoverable from a porous reservoir by miscible gas flooding, the system comprising: one or more processor devices; a memory operatively coupled to the one or more processor devices; as well as A computer storage device storing executable program instructions to cause the system to: after performing a multiphase flow simulation to obtain a prediction of flow behavior of oil in the presence of waterflooding of the porous reservoir rock, retrieving a representation of a physical porous reservoir rock sample, the representation including pore space and grain space data corresponding to the porous reservoir rock; locating substantially immobile clumps or patches of oil in the retrieved representation of the porous reservoir rock; performing an analysis of given one or more EOR techniques for the immobile oil mass, wherein a first iteration of the given one or more EOR techniques expends a first level of effort, and a second iteration of the given one or more EOR techniques expends a second, higher level of effort, wherein each level of effort represents one or more values ​​of one or more parameters of the given EOR technique; as well as For N basically immobile oil clumps or patches, determining a change in mobility of the mass for a given one of the EOR techniques, and An estimate of an amount by which mobilization of the mass changes between the first iteration and the second iteration for the given one or more EOR techniques is generated.

12. The system according to claim 11, further configured to: For the given EOR technique, all iterations at different effort levels, including the first effort level and the second effort level, are integrated to provide an approximation of the increment of recoverable oil for the given EOR technique as a function of effort level.

13. The system according to claim 11, wherein: The given EOR technique is a first EOR technique, and the system is further configured to: The mobility of the mass is evaluated for two or more iterations of a different second EOR technique, wherein a first iteration of the two or more iterations of the second EOR technique expends a first level of effort and a second iteration of the two or more iterations of the different second EOR technique expends a second, higher level of effort, thereby estimating an amount by which the mobility of the mass changes between the first iteration and the second iteration of the different second EOR technique.

14. The system according to claim 13, further configured to: For each of the first EOR technique and the second EOR technique, all iterations at different effort levels including the first effort level and the second effort level are integrated to provide an approximation of the increment of recoverable oil for each of the first EOR technique and the second EOR technique according to the effort level.

15. The system of claim 13, wherein: Efforts have been made to include an increasing number of parameters for a given EOR technique.

16. The system according to claim 11, further configured to: performing a first numerical multiphase flow simulation to obtain a prediction of the flow behavior of oil in the presence of water flooding of the porous reservoir rock; and A second numerical multiphase flow simulation is performed to obtain a prediction of flow behavior for gas flooding of the porous reservoir rock.

17. A computer program product tangibly stored on a non-transitory storage device for determining the amount of hydrocarbons recoverable from a porous reservoir by miscible gas flooding, the computer program product comprising instructions for causing a system to: after performing a multiphase flow simulation to obtain a prediction of flow behavior of oil in the presence of waterflooding of the porous reservoir rock, retrieving a representation of a physical porous reservoir rock sample, the representation including pore space and grain space data corresponding to the porous reservoir rock; locating substantially immobile clumps or patches of oil in the retrieved representation of the porous reservoir rock; performing an analysis of given one or more EOR techniques for the immobile oil mass, wherein a first iteration of the given one or more EOR techniques expends a first level of effort, and a second iteration of the given one or more EOR techniques expends a second, higher level of effort, wherein each level of effort represents one or more values ​​of one or more parameters of the given EOR technique; and For N basically immobile oil clumps or patches, determining a change in mobility of the mass for a given one of the EOR techniques, and An estimate of an amount by which mobilization of the mass changes between the first iteration and the second iteration for the given one or more EOR techniques is generated.

18. The computer program product of claim 17, further comprising instructions to: For the given EOR technique, all iterations at different effort levels, including the first effort level and the second effort level, are integrated to provide an approximation of the increment of recoverable oil for the given EOR technique as a function of effort level.

19. The computer program product of claim 17, the given EOR technique being a first EOR technique, the computer program product further comprising instructions to: evaluating the mobility of the mass for two or more iterations of a second, different EOR technique, wherein a first iteration of the two or more iterations for the second EOR technique expends a first level of effort and a second iteration of the two or more iterations for the second, different EOR technique expends a second, higher level of effort, thereby estimating an amount by which the mobility of the mass changes between the first iteration and the second iteration for the second, different EOR technique; and For each of the first EOR technique and the second EOR technique, all iterations at different effort levels including the first effort level and the second effort level are integrated to provide an approximation of the increment of recoverable oil for each of the first EOR technique and the second EOR technique according to the effort level.

20. The computer program product of claim 17, further comprising instructions to: The effort level is varied by changing one or more of the type of surfactant injected and the injection flow rate.

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