Recognition method and device for oil reservoir dominant seepage channel

Abstract

The invention provides a method and a device for identifying an oil reservoir dominant seepage channel. The method comprises the following steps: establishing a time-varying regression function between oil reservoir parameters and water drive multiples; the time-varying regression functions comprise a time-varying regression function between the permeability and the water flooding multiple, a time-varying regression function between the water-phase relative permeability and the water flooding multiple and a time-varying regression function between the oil-phase relative permeability and the water flooding multiple; determining an output result of each grid in the oil reservoir grid system model under each time step based on a time-varying regression function between the oil reservoir parameters and the water drive multiples; establishing a source catchment water phase tracking model based on an output result of each grid in the grid system model under each time step; identifying a dominant seepage channel based on a source catchment water phase tracking model; therefore, the flowing process of the fluid in the oil reservoir can be dynamically evolved by using the time-varying regression function of the oil reservoir parameters and the water drive multiple, and finally the streamline distribution result of the water phase can be determined, so that the dominant seepage channel can be accurately identified according to the streamline distribution result.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas development, and in particular to a method and device for identifying a dominant seepage channel in an oil reservoir. Background Art

[0002] At present, most domestic oil fields have entered a period of high water content. After long-term water drive development, the sand production and particle migration of the reservoir have caused significant changes in the physical properties of sandstone reservoirs compared with the initial development. The enhanced heterogeneity in the plane, between layers and within the layers has aggravated the contradictions of the reservoir in the plane and vertical direction, which has put forward higher requirements for clarifying the oil-water interaction law of the reservoir and realizing the accurate identification of the dominant seepage channel.

[0003] Conventional identification methods of dominant seepage channels do not take into account the changes in particle migration and oil-water seepage characteristics caused by strong scouring during long-term water flooding. At the same time, for injection-production units with multiple wells, conventional identification methods are difficult to determine the evolution direction of dominant seepage channels, which directly affects the accuracy of dynamic dominant seepage channel identification. Summary of the invention

[0004] In view of the problems existing in the prior art, an embodiment of the present invention provides a method and device for identifying dominant seepage channels in an oil reservoir, so as to solve or partially solve the technical problem that the prior art cannot accurately identify dominant seepage channels in an oil reservoir with high water content.

[0005] A first aspect of the present invention provides a method for identifying a dominant seepage channel in an oil reservoir, the method comprising:

[0006] Establishing a time-varying regression function between reservoir parameters and water drive multiples; the time-varying regression function includes: a time-varying regression function between permeability and water drive multiples, a time-varying regression function between water phase relative permeability and water drive multiples, and a time-varying regression function between oil phase relative permeability and water drive multiples;

[0007] Determine the output result corresponding to each grid in the reservoir grid system model at each time step based on the time-varying regression function between the reservoir parameters and the water drive multiple;

[0008] A source-sink water phase tracking model is established based on the output results corresponding to each grid in the grid system model at each time step; the source-sink water phase tracking model is used to characterize the streamline distribution results of the water phase;

[0009] The dominant seepage channels are identified based on the source-sink phase tracing model.

[0010] In the above scheme, before establishing the time-varying regression function between each reservoir parameter and the water flooding multiple, the method further includes:

[0011] Based on the water flooding physical simulation experimental data, the formula is used to determine The water drive multiple E corresponding to all grids in the reservoir grid system model;

[0012] The water drive multiple range is determined according to the maximum and minimum values ​​of the water drive multiples corresponding to all grids;

[0013] The Q t is the cumulative water volume of any grid, V POR is the effective pore volume of the core.

[0014] In the above scheme, the step of establishing a time-varying regression function between reservoir parameters and water flooding multiples includes:

[0015] When the time-varying regression function is a time-varying regression function between permeability and water flooding multiple, the permeability of each rock sample at different water flooding multiples is obtained; the initial permeability, sedimentary microfacies and porosity of each rock sample are different;

[0016] According to the formula Determine the time-varying regression function between the permeability change rate and the water flooding multiple; where,

[0017] M is the permeability change rate, K 0 is the initial permeability before water flooding, the K t is the final permeability obtained when the water flooding multiple is t, and f(E) is the time-varying regression function between the permeability change rate and the water flooding multiple.

[0018] In the above scheme, the step of establishing a time-varying regression function between reservoir parameters and water flooding multiples includes:

[0019] When the time-varying regression function is a time-varying regression function between the relative permeability of the water phase and the water flooding multiple, the residual oil saturation and the irreducible water saturation under different water flooding multiples are obtained;

[0020] Based on the formula A time-varying regression function between water phase relative permeability and water flooding multiple is established; among them,

[0021] K rwr is the relative permeability of the water phase corresponding to the residual oil saturation before water flooding, S WCR is the bound water saturation before water flooding, S OWCR is the residual oil saturation before water flooding, S w is the water saturation, and the relative permeability of the water phase before water flooding is K1. rw , the relative permeability of the water phase after water flooding is K1 rw ', n is a constant determined according to the pore structure of the rock.

[0022] In the above scheme, the step of establishing a time-varying regression function between reservoir parameters and water flooding multiples includes:

[0023] When the time-varying regression function is a time-varying regression function between the oil phase relative permeability and the water flooding multiple, the residual oil saturation and the irreducible water saturation under different water flooding multiples are obtained;

[0024] Based on the formula A time-varying regression function between oil phase relative permeability and water flooding multiple is established;

[0025] The S WCR is the irreducible water saturation before water flooding, the S OWCR is the residual oil saturation before water flooding, the S w is water saturation, the K rorw is the oil phase relative permeability corresponding to the irreducible water saturation before water flooding, and the oil phase relative permeability K2 before water flooding is K ro , the relative permeability of the oil phase after water flooding is K2 ro ′, the S w is water saturation, and m is a constant determined according to the pore structure of the rock.

[0026] In the above scheme, the output result corresponding to each grid in the reservoir grid system model at each time step is obtained based on the time-varying regression function between the reservoir parameters and the water drive multiple, including:

[0027] Initializing the grid system model using geological parameters and fluid parameters;

[0028] The time-varying regression function between the reservoir parameters and the water drive multiple is called, and the grid system model of the reservoir is simulated and calculated according to the preset time step to obtain the water drive multiple, permeability, fluid velocity, pressure and endpoint value of the phase permeability curve of each grid at each time step.

[0029] In the above scheme, the time-varying regression function between the reservoir parameters and the water drive multiple is called to simulate and calculate the grid system model of the reservoir according to a preset time step, and the water drive multiple, permeability, fluid velocity, pressure and endpoint of the phase permeability curve of each grid at each time step are obtained, including:

[0030] Based on the time-varying regression function between the reservoir parameters and the water flooding multiple, the control equation is solved by using the incomplete LU decomposition preconditioned conjugate gradient method to obtain the pressure of each grid at different time steps;

[0031] Determining a water drive factor and a fluid velocity for each grid based on the pressure;

[0032] The endpoints of the permeability and relative permeability curves are determined based on the water flooding multiple.

[0033] In the above scheme, the source-sink phase tracking model is established based on the output results corresponding to each grid in the grid system model at each time step, including:

[0034] In the grid system model, the injected water and the edge and bottom water of each injection well are calibrated, and the target grid through which the injected water flows is determined;

[0035] Determine the time length of the fluid passing through the target grid based on the fluid velocity of each target grid at each time step;

[0036] Determine the position where the fluid leaves each of the target grids based on the time duration for the fluid to pass through the target grid;

[0037] Taking the injection well as the starting coordinate and the production well as the destination coordinate, a streamline distribution map is determined based on the position where the fluid leaves each target grid; the streamline distribution map is the source-sink phase tracking model.

[0038] In the above solution, the method of identifying the dominant seepage channel based on the source-sink phase tracking model includes:

[0039] Determine streamline-dense areas from the source-sink phase tracing model;

[0040] The streamline-dense area is used as a dominant seepage channel.

[0041] A second aspect of the present invention provides a device for identifying a dominant seepage channel in an oil reservoir, the device comprising:

[0042] The first establishing unit is used to establish a time-varying regression function between reservoir parameters and water drive multiples; the time-varying regression function includes: a time-varying regression function between permeability and water drive multiples; a time-varying regression function between water phase relative permeability and water drive multiples; and a time-varying regression function between oil phase relative permeability and water drive multiples;

[0043] A determination unit, used to determine the output result corresponding to each grid in the reservoir grid system model at each time step based on the time-varying regression function between the reservoir parameter and the water drive multiple;

[0044] The second establishing unit is used to establish a source-sink water phase tracking model based on the output results corresponding to each grid in the grid system model at each time step; the source-sink water phase tracking model is used to characterize the streamline distribution results of the water phase;

[0045] An identification unit is used to identify a dominant seepage channel based on the source-sink phase tracing model.

[0046] The present invention provides a method, device, medium and equipment for identifying a dominant seepage channel of an oil reservoir. The method comprises: establishing a time-varying regression function between oil reservoir parameters and water drive multiples; the time-varying regression function comprises: a time-varying regression function between permeability and water drive multiples, a time-varying regression function between water phase relative permeability and water drive multiples, and a time-varying regression function between oil phase relative permeability and water drive multiples; determining an output result corresponding to each grid in an oil reservoir grid system model at each time step based on the time-varying regression function between the oil reservoir parameters and the water drive multiples; establishing a source-sink water phase tracking model based on the output result corresponding to each grid in the grid system model at each time step; the source-sink water phase tracking model is used to characterize the streamline distribution result of the water phase; identifying a dominant seepage channel based on the source-sink water phase tracking model; in this way, the time-varying regression function between the oil reservoir parameters and the water drive multiples can be used to dynamically evolve the flow process of the fluid in the oil reservoir, and finally the streamline distribution result of the water phase can be determined, so as to accurately identify the dominant seepage channel according to the streamline distribution result. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] By reading the detailed description of the preferred embodiment below, various other advantages and benefits will become clear to those of ordinary skill in the art. The accompanying drawings are only used for the purpose of illustrating the preferred embodiment and are not considered to be limitations of the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings.

[0048] In the attached picture:

[0049] Figure 1 A schematic diagram of a process flow of a method for identifying a dominant seepage channel in an oil reservoir according to an embodiment of the present invention is shown;

[0050] Figure 2 A schematic diagram of flow lines according to an embodiment of the present invention is shown;

[0051] Figure 3 A schematic diagram of the structure of a device for identifying a dominant seepage channel in an oil reservoir according to an embodiment of the present invention is shown;

[0052] Figure 4a A schematic diagram showing a case where the water drive ratio is less than 1PV according to an embodiment of the present invention is shown;

[0053] Figure 4b A schematic diagram showing a water drive multiple of 1PV to 50PV according to an embodiment of the present invention is shown;

[0054] Figure 4c A schematic diagram showing a water flooding multiple of 50PV to 100PV according to an embodiment of the present invention is shown;

[0055] Figure 4dA schematic diagram showing a water flooding multiple greater than 100 PV according to an embodiment of the present invention is shown;

[0056] Figure 5a A time-varying relationship diagram between water flooding multiple and permeability is shown according to an embodiment of the present invention, where the initial permeability is 510 mD and the sedimentary facies type is a braided channel;

[0057] Figure 5b A comparison diagram of initial oil / water relative permeability (oil phase relative permeability and water phase relative permeability) and oil / water relative permeability after water flooding 100PV according to one embodiment of the present invention is shown;

[0058] Figure 6a A schematic diagram of an initial permeability field according to an embodiment of the present invention is shown;

[0059] Figure 6b A schematic diagram of a time-varying permeability field according to an embodiment of the present invention is shown;

[0060] Figure 7a A schematic diagram of the initial water phase relative permeability field according to one embodiment of the present invention is shown;

[0061] Figure 7b A schematic diagram of a time-varying water phase relative permeability field according to an embodiment of the present invention is shown;

[0062] Figure 8a A schematic diagram of the initial oil phase relative permeability field according to one embodiment of the present invention is shown;

[0063] Figure 8b A schematic diagram of a time-varying oil phase relative permeability field according to an embodiment of the present invention is shown;

[0064] Figure 9a A schematic diagram showing the streamline distribution of injected water in an injection well according to an embodiment of the present invention is shown;

[0065] Figure 9b A schematic diagram of the streamline distribution of bottom water in an injection well according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0066] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0067] The present invention provides a method for identifying a dominant seepage channel in an oil reservoir, such as Figure 1 As shown, the method comprises the following steps:

[0068] S110, establishing a time-varying regression function between reservoir parameters and water drive multiples; the time-varying regression function includes: a time-varying regression function between permeability and water drive multiples, a time-varying regression function between water phase relative permeability and water drive multiples, and a time-varying regression function between oil phase relative permeability and water drive multiples.

[0069] Considering that the particle migration and changes in oil-water seepage characteristics caused by strong scouring during long-term water flooding will affect the fluid path and thus affect the dominant seepage channel, it is necessary to establish a time-varying regression function between reservoir parameters and water flooding multiples based on water flooding physical experimental data. Among them, the time-varying regression function between reservoir parameters and water flooding multiples includes: the time-varying regression function between permeability and water flooding multiples, the time-varying regression function between water phase relative permeability and water flooding multiples, and the time-varying regression function between oil phase relative permeability and water flooding multiples.

[0070] Before determining the time-varying regression function between the reservoir parameters and the water drive multiple, it is necessary to determine the water drive multiple range of the reservoir. In one embodiment, before establishing the time-varying regression function between each reservoir parameter and the water drive multiple, the method further includes:

[0071] Based on the water flooding physical simulation experimental data, the formula is used to determine The water drive multiple E corresponding to the grid in the reservoir grid system model;

[0072] The water drive multiple range is determined according to the maximum and minimum values ​​of the water drive multiples corresponding to all grids; where Q t is the cumulative water volume of any grid, V POR is the effective pore volume of the core.

[0073] Specifically, the reservoir numerical simulation model refers to the mathematical model of the reservoir solved by computer. In order to facilitate simulation calculation, the model is generally gridded. In this way, the reservoir numerical simulation model will be divided into multiple grids.

[0074] Then, a number of rock samples with different reservoir characteristics are selected according to the sedimentary microfacies type, permeability range and porosity range of the target oil reservoir block. Among them, the sedimentary microfacies type may include: meandering river channel, braided river channel, floodplain, point bar, nearshore beach bar, etc.

[0075] Then, a water flooding physical simulation experiment is carried out to obtain the corresponding experimental data, and the water flooding multiple E of all grids at each time step is calculated according to formula (1).

[0076]

[0077] In formula (1), Q tis the cumulative water volume of any grid, V POR is the effective pore volume of the core.

[0078] From all the water drive multiples determined above, the maximum and minimum values ​​of the water drive multiples are selected as the water drive multiple range.

[0079] After the water drive multiple range is determined, single-phase (water phase) long-term displacement experiments are carried out on rock samples in combination with the water drive multiple range to establish a time-varying regression function between reservoir parameters and water drive multiples.

[0080] Then in one embodiment, a time-varying regression function between reservoir parameters and water flooding multiples is established, including:

[0081] When the time-varying regression function is a time-varying regression function between permeability and water flooding multiple, the permeability of each rock sample at different water flooding multiples is obtained; the initial permeability, sedimentary microfacies and porosity of each rock sample are all different;

[0082] The time-varying regression function between the permeability change rate and the water flooding multiple is determined according to formula (2); where:

[0083] M is the permeability change rate, K 0 is the initial permeability before water flooding, in mD; K t is the final permeability obtained when the water flooding multiple is t, and f(E) is the time-varying regression function between the permeability change rate and the water flooding multiple.

[0084]

[0085] In one embodiment, establishing a time-varying regression function between reservoir parameters and water flooding multiples includes:

[0086] When the time-varying regression function is a time-varying regression function between the relative permeability of the water phase and the water flooding multiple, the residual oil saturation and the irreducible water saturation under different water flooding multiples are obtained;

[0087] Based on the formula A time-varying regression function between water phase relative permeability and water flooding multiple is established; among them,

[0088] K rwr is the relative permeability of the water phase corresponding to the residual oil saturation before water flooding, S WCR is the bound water saturation before water flooding, S OWCR is the residual oil saturation before water flooding, S w is the water saturation (which changes at each time step), and the relative permeability of the water phase before water flooding is K1. rw , the relative permeability of the water phase after water flooding is K1 rw '(Please refer to the following Figure 5bto understand), n is a constant determined according to the pore structure of the rock.

[0089] In one embodiment, establishing a time-varying regression function between reservoir parameters and water flooding multiples includes:

[0090] When the time-varying regression function is a time-varying regression function between oil phase relative permeability and water drive multiple, the residual oil saturation and irreducible water saturation under different water drive multiples are obtained;

[0091] Based on the formula A time-varying regression function between oil phase relative permeability and water flooding multiple is established;

[0092] K rorw is the oil phase relative permeability corresponding to the irreducible water saturation before water flooding, and the oil phase relative permeability K2 before water flooding is K ro , the relative permeability of the oil phase after water flooding is K2 ro '(Please refer to the following Figure 5b to understand), m is a constant determined according to the pore structure of the rock.

[0093] It can be seen that the relative permeability curve shape can be obtained from S WCR , S WOCR , K rorw , K rwr Based on the long-term water flooding physical simulation experiment, the S at different time steps can be obtained. wc , S or , then based on S wc , S or The established time-varying regression function between oil phase relative permeability and water drive multiple, and the time-varying regression function between water phase relative permeability and water drive multiple are equivalent to a time-varying feature, which can dynamically characterize the relationship between water drive multiple and oil / water phase relative permeability in real time, and also obtain a real-time updated dynamic time-varying relative permeability curve.

[0094] It should be noted that the above three time-varying regression functions are different for different sedimentary microfacies and different permeabilities. For example, assuming that at a certain time step, the sedimentary microfacies of a grid is a meandering river channel, and the permeability range is 0 to 500, then the time-varying regression function between the permeability change rate and the water drive multiple is called 1 (E); If the permeability of the grid changes from 500 to 2000 in the next time step, the time-varying regression function between the permeability change rate and the water flooding multiple is f 2 (E).

[0095] S111, determining the output result corresponding to each grid in the reservoir grid system model at each time step based on the time-varying regression function between the reservoir parameters and the water drive multiple.

[0096] In practical applications, for reservoirs with different sedimentary facies characteristics, the relative permeability curves are quite different; and the changes in the pore throat structure and the law of oil-water interaction (oil-water relative permeability curve) have different evolution characteristics during long-term water flooding development. Therefore, when finely simulating the fluid flow laws of reservoirs with different sedimentary facies and obtaining the corresponding output results for each grid at each time step, the differences in sedimentary microfacies and pore throat structures should be considered.

[0097] When performing a fine simulation of the fluid flow law, this embodiment essentially calls the time-varying regression function between the reservoir parameters and the water drive multiple to simulate and calculate the reservoir grid system model, and then obtains the output result corresponding to each grid at each time step. It can be understood that when calling the time-varying regression function between the reservoir parameters and the water drive multiple to perform flow simulation calculations on the reservoir grid system model, the reservoir grid system model is essentially converted into a numerical simulation model that takes into account different time-varying characteristics. Therefore, the output result also has a time-varying characteristic, which can dynamically characterize the time-varying dynamic evolution law of the subsequent streamline.

[0098] In one embodiment, the output result corresponding to each grid in the reservoir grid system model at each time step is obtained based on the time-varying regression function between the reservoir parameters and the water flooding multiple, including:

[0099] Initialize the grid system model using geological parameters and fluid parameters;

[0100] The time-varying regression function between reservoir parameters and water drive multiples is called, and the grid system model of the reservoir is simulated and calculated according to the preset time step to obtain the water drive multiple, permeability, fluid velocity, pressure and endpoint values ​​of the phase permeability curve of each grid at each time step; wherein, the phase permeability curve includes the oil phase phase permeability curve and the water phase phase permeability curve, and the endpoints of the phase permeability curve include the irreducible water saturation before water drive, the residual oil saturation before water drive, the irreducible water saturation after water drive and the residual oil saturation after water drive.

[0101] Among them, geological parameters mainly include: sedimentary microfacies, porosity, etc.; fluid parameters mainly include: fluid PVT (fluid pressure, volume and temperature), oil / water relative permeability, capillary force, through pressure coefficient, capillary pressure at oil-water interface

[0102] When the reservoir grid system model is initialized, the above geological parameters and fluid parameters are initially assigned in the model. After the initialization is completed, the time-varying regression function between the reservoir parameters and the water drive multiple is called to perform fluid simulation calculations to obtain the output results corresponding to each grid in the reservoir grid system model at each time step.

[0103] It is worth noting that when performing simulation calculations, a time-varying model is introduced in the first time step of the simulation; in the calculation process of the second time step and subsequent time steps, the cumulative water drive multiple of each grid is first calculated, and then the sedimentary microfacies and permeability ranges satisfied by each grid are determined, and then the corresponding time-varying regression function between permeability and water drive multiple, the time-varying regression function between water phase relative permeability and water drive multiple, and the time-varying regression function between oil phase relative permeability and water drive multiple are called.

[0104] That is, when the sedimentary microfacies and permeability range of the grid change, the three time-varying regression functions called above will also change.

[0105] In one embodiment, a time-varying regression function between reservoir parameters and water flooding multiples is called to simulate and calculate the grid system model of the reservoir according to a preset time step to obtain the water flooding multiple, permeability, fluid velocity, pressure and endpoints of the phase permeability curve of each grid at each time step, including:

[0106] Based on the time-varying regression function between the reservoir parameters and the water flooding multiple, the control equation is solved implicitly by using the incomplete LU decomposition preconditioned conjugate gradient method to obtain the pressure of each grid at different time steps;

[0107] Determine the water displacement multiple and fluid velocity of each grid based on the pressure;

[0108] The endpoints of the permeability and relative permeability curves are determined based on the water flooding multiple.

[0109] It should be noted that when determining the output results of each grid at each time step, for the same grid, the calculation of the current time step needs to rely on the output results of the previous time step.

[0110] For example, for the first time step, the flow rate, water drive multiple, fluid velocity, etc. in each grid can be calculated; starting from the second time step, the simulation calculation of the current time step is performed by reading the sedimentary microfacies model to which each grid belongs and the flow rate, water drive multiple and fluid velocity in the grid of the previous time step.

[0111] Specifically, the control equations include the oil phase control equation (Formula 2) and the water phase control equation (Formula 3):

[0112]

[0113] In formula (2), K rw is the relative permeability of water phase before water flooding, K is the absolute permeability of reservoir, μ rw is the viscosity of the water phase, mP·s; γ rw is the density of the water phase, N / m 3 ;q vrw is the flow rate of water phase injected into the reservoir per unit time and per unit volume, D is the depth, m, S rw is the water phase saturation, B rw Water phase volume factor, p rw is the water phase pressure.

[0114]

[0115] In formula (3), K ro is the relative permeability of the oil phase before water flooding, K is the absolute permeability of the reservoir, p ro is the oil phase pressure, μ ro is the oil phase viscosity, mP·s; γ ro is the density of the oil phase, N / m 3 ;q vro is the flow rate of oil phase produced in the reservoir per unit time and per unit volume, D is, S ro is the oil phase saturation, B ro Oil phase volume coefficient.

[0116] By solving the above control equations implicitly, the oil phase pressure and water phase pressure of different grids at different time steps and the total pressure p of the sum of the oil phase pressure and the water phase pressure are obtained.

[0117] Then, the water drive multiple and fluid velocity of each grid are determined based on the total pressure of each grid; the permeability and the endpoint of the phase permeability curve are determined based on the water drive multiple. These can be determined based on the well-known technical means in the art, and will not be repeated here.

[0118] S112, establishing a source-sink water phase tracking model based on the output results corresponding to each grid in the grid system model at each time step; the source-sink water phase tracking model is used to characterize the streamline distribution results of the water phase;

[0119] In one embodiment, a source-sink phase tracking model is established based on the output results corresponding to each grid in the grid system model at each time step, including:

[0120] In the grid system model, the injected water and the edge and bottom water of each injection well are calibrated, and the target grid through which the injected water flows is determined;

[0121] Determine the time length for the fluid to pass through the target grid based on the fluid velocity of each target grid at each time step;

[0122] Determine the position where the fluid leaves each target grid based on the time it takes the fluid to traverse the target grid;

[0123] Taking the injection well as the starting coordinate and the production well as the destination coordinate, the streamline distribution map is determined based on the position where the fluid leaves each target grid; the streamline distribution map is a source-sink phase tracking model.

[0124] Specifically, after the fluid velocity is determined, the streamline distribution diagram can be determined based on the fluid velocity. Source water refers to the water flowing out of the injection well, and sink water refers to the water flowing into the production well. Edge and bottom water refers to the water inside the reservoir. The present invention is mainly intended to determine the streamline distribution of the injected water, and then determine the dominant seepage channel. Therefore, in order to distinguish between edge and bottom water and injected water, it is necessary to calibrate the edge and bottom water and the injected water so that the flow areas of different water phases can be clearly determined in the streamline distribution diagram.

[0125] Assuming that the fluid is regarded as a particle, the streamline of the particle's movement and propagation trajectory in space is actually an instantaneous curve in space, and the tangent direction of each point on the curve is the direction of the flow velocity at that point. Then the velocity component in the x-axis direction is:

[0126]

[0127] In formula (4), V xo V is the velocity of the particle at the origin in the x direction; x is the velocity of the particle with coordinate x in the x direction; is the velocity gradient in the grid, in S -1 .

[0128] By integrating formula (4), we can obtain the time Δt that the particle takes to pass through the grid in the x direction: x :

[0129]

[0130] In formula (5), x o is the origin coordinate; x i is the horizontal coordinate of the position where the particle enters the grid; x e V is the horizontal coordinate of the particle leaving the grid; x,o is the velocity of the origin in the x direction, m / s.

[0131] Similarly, the time Δt of the particle passing through the grid in the x direction in the Y and Z directions can be obtained y ,Δt z Finally, the time Δt for the fluid to pass through the grid is determined according to formula (6): e :

[0132] Δt e=min(Δt x ,Δt y ,Δt z )(6)

[0133] Taking water injection as an example, after determining the time of crossing the grid, the position where the fluid leaves the grid can be obtained. After determining the position of leaving the grid, the fluid particle uses this point as the inflow point to enter the next grid and continues to cross the new grid until it converges to the grid where a production well is located; in this process, several points can be determined by the above method, and a streamline can be fully expressed by connecting the coordinates of the injection well that emits the fluid particle, all the points passed through, and the coordinates of the production well where the fluid particle arrives with a smooth curve. The streamline is as follows Figure 2 shown.

[0134] After many time-step cycles, many streamlines can be obtained to form a dynamic source-sink water phase tracking model; that is, the source-sink water phase tracking model can characterize the streamline distribution results of the water phase and reflect the flow dynamics of the reservoir fluid.

[0135] S113, identifying the dominant seepage channel based on the source-sink phase tracking model.

[0136] In the above steps, the source and sink directions of different water phases were calibrated to obtain the streamline distribution results, that is, each streamline starts from the injection well or edge and bottom water (edge ​​water and bottom water), and shoots different numbers of streamlines to neighboring wells in different directions; among them, the flow represented by each streamline is equal and fixed. Therefore, the greater the flow rate, the denser the streamlines, and the stronger the flow field; the injection well has a dominant flow field in the direction of dense streamlines.

[0137] Then, in one embodiment, identifying the dominant seepage channel based on the source-sink phase tracing model includes:

[0138] Determine streamline-dense areas from source-sink phase tracing models;

[0139] The streamline-dense area is regarded as the dominant seepage channel.

[0140] Through long-term water drive physical simulation experiments, the permeability and time-varying evolution of oil-water phase permeability of different sedimentary phase characteristics and porosity and permeability characteristics are obtained, and the corresponding time-varying characterization model is established using the time-varying regression function, and the corresponding results are output; a source-sink water phase tracking model is established. At the same time, a time-varying characterization model with different time-varying characteristics is introduced; through the time-varying dynamic evolution law of streamlines during the simulation process, the dominant seepage area of ​​water drive is identified, providing a basis for later oilfield management measures. The dominant channel identification method and process used in the long-term water drive process of medium and high permeability reservoirs in this application can effectively identify the development and evolution process of dominant seepage channels during long-term water drive.

[0141] Based on the same inventive concept as in the above-mentioned embodiment, this embodiment also provides a device for identifying a dominant seepage channel in an oil reservoir, such as Figure 3 As shown, the device comprises:

[0142] The first establishing unit 31 is used to establish a time-varying regression function between reservoir parameters and water drive multiples; the time-varying regression function includes: a time-varying regression function between permeability and water drive multiples; a time-varying regression function between water phase relative permeability and water drive multiples; and a time-varying regression function between oil phase relative permeability and water drive multiples;

[0143] A determination unit 32, configured to determine an output result corresponding to each grid in the reservoir grid system model at each time step based on a time-varying regression function between the reservoir parameter and the water drive multiple;

[0144] The second establishing unit 33 is used to establish a source-sink water phase tracking model based on the output result corresponding to each grid in the grid system model at each time step; the source-sink water phase tracking model is used to characterize the streamline distribution result of the water phase;

[0145] The identification unit 34 is used to identify the dominant seepage channel based on the source-sink phase tracing model.

[0146] Since the device introduced in the embodiment of the present invention is a device used to implement the method for identifying the dominant seepage channel of the reservoir in the embodiment of the present invention, based on the method introduced in the embodiment of the present invention, the person skilled in the art can understand the specific structure and deformation of the device, so it is not repeated here. All devices used in the method of the embodiment of the present invention belong to the scope of protection of the present invention.

[0147] Through one or more embodiments of the present invention, the present invention has the following beneficial effects or advantages:

[0148] The present invention provides a method and device for identifying a dominant seepage channel of an oil reservoir. The method comprises: establishing a time-varying regression function between oil reservoir parameters and water drive multiples; the time-varying regression function comprises: a time-varying regression function between permeability and water drive multiples, a time-varying regression function between water phase relative permeability and water drive multiples, and a time-varying regression function between oil phase relative permeability and water drive multiples; determining an output result corresponding to each grid in an oil reservoir grid system model at each time step based on the time-varying regression function between oil reservoir parameters and water drive multiples; establishing a source-sink water phase tracking model based on the output result corresponding to each grid in the grid system model at each time step; the source-sink water phase tracking model is used to characterize the streamline distribution result of the water phase; identifying a dominant seepage channel based on the source-sink water phase tracking model; in this way, the time-varying regression function between oil reservoir parameters and water drive multiples can be used to dynamically evolve the flow process of a fluid in an oil reservoir, and finally the streamline distribution result of the water phase can be determined, so as to accurately identify the dominant seepage channel according to the streamline distribution result.

[0149] In practical applications, when the method and device for identifying the dominant seepage channel of an oil reservoir provided in the above embodiment are used to identify the dominant seepage channel of a target block of an oil reservoir, the following is achieved:

[0150] Typical cores of the target block are obtained, as shown in Table 1: There are six types of sedimentary facies in the target block: meandering channel, braided channel, floodplain, point bar, nearshore beach bar; the overall reservoir permeability range is 0-15000mD. Combined with the field coring data, several rock samples with different sedimentary and porosity characteristics are selected, and the feasibility of the experiment is considered in combination with the reservoir pore volume, total water drive, edge and bottom water range of the target block and the preliminary numerical simulation results. Figures 4a to 4d As shown, the water drive multiples of the target block are finally determined to be in the range of 0 to 100 PV. Figure 4a Schematic diagram showing the water drive ratio is less than 1PV. Figure 4b Schematic diagram showing water drive multiples from 1PV to 50PV. Figure 4c Schematic diagram showing water drive multiples of 50PV to 100PV. Figure 4d Schematic diagram showing the water drive ratio being greater than 100PV.

[0151] By analyzing the experimental results, schematic diagrams of the time-varying relationship between the permeability change rate and the water drive multiple for different permeability levels of meandering channel, braided channel, floodplain, point bar, and nearshore beach bar sedimentary types were obtained. Figure 5a The figure shows the time-varying relationship between water flooding multiple and permeability when the initial permeability is 510 mD and the sedimentary facies type is braided channel; Figure 5bThis is a comparison chart of the initial oil / water relative permeability (oil phase relative permeability and water phase relative permeability) of the core and the oil / water relative permeability after water flooding 100PV. Similarly, the time-varying relationship chart of permeability, oil / water relative permeability and water flooding multiples of all rock samples can be obtained.

[0152] exist Figure 5b In, S OWCR is the residual oil saturation before water flooding, S′ OWCR is the residual oil saturation after water flooding, S WCR is the irreducible water saturation before water flooding, S′ WCR is the irreducible water saturation after water flooding, S w is water saturation, K rwr is the relative permeability of water phase corresponding to the residual oil saturation before water flooding, K r ' wr is the relative permeability of water phase corresponding to the residual oil saturation after water flooding, K rorw is the relative permeability of the oil phase corresponding to the irreducible water saturation before water flooding, K r ' orw is the relative permeability of the oil phase corresponding to the irreducible water saturation after water flooding.

[0153] Then, based on the corresponding time-varying relationship diagram, a time-varying regression function between the corresponding reservoir parameters and the water drive multiple is established.

[0154] Then, based on the time-varying regression function between the reservoir parameters and the water drive multiple, the output result corresponding to each grid in the reservoir grid system model at each time step is determined.

[0155] Specifically, a time-varying regression function is introduced in the first time step of the simulation; in each calculation step, the cumulative water drive multiple of each grid is first calculated, and then the sedimentary microfacies and permeability ranges satisfied by each grid are determined, and then the corresponding time-varying regression function between permeability and water drive multiple, the time-varying regression function between water phase relative permeability and water drive multiple, and the time-varying regression function between oil phase relative permeability and water drive multiple are called.

[0156] Finally, reference Figure 6a and 6b , the initial permeability field is Figure 6a As shown in the figure, after using the time-varying regression function between the permeability change rate and the water flooding multiple for simulation calculation, the time-varying permeability field obtained is as follows Figure 6b shown.

[0157] refer to Figure 7a and 7b , the initial water phase relative permeability field is as follows Figure 7a As shown in the figure, after simulating the time-varying regression function between water phase relative permeability and water flooding multiple, the time-varying water phase relative permeability field is obtained as follows: Figure 7bshown.

[0158] refer to Figure 8a and 8b The initial oil phase relative permeability field is as follows: Figure 8a As shown in the figure, after simulating the time-varying regression function between oil phase relative permeability and water flooding multiple, the time-varying oil phase relative permeability field is obtained as follows: Figure 8b shown.

[0159] Then, a source-sink water phase tracking model is established based on the output results corresponding to each grid in the grid system model at each time step; and the dominant seepage channel is identified based on the source-sink water phase tracking model.

[0160] Among them, the time-varying dominant seepage zone of water injected into a certain injection well is as follows: Figure 9a As shown in Figure 1, the streamline distribution of the injected water can be seen from the time-varying dominant seepage area; the time-varying dominant seepage area of ​​the bottom water of a certain injection well is shown in Figure 1. Figure 9b As shown in Figure 1, the streamline distribution of bottom water can be seen from the time-varying dominant seepage zone. Figure 9a For example, from Figure 9a The flow path of the source and sink water phases can be clearly seen, and the dominant seepage channel can be accurately identified. Figure 9a The dotted box in the figure is the location of the injection well, and the denser streamlines in the dotted box are the dominant seepage channels.

[0161] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying the dominant seepage channel in an oil reservoir. It is characterized in that The method comprises: Establishing a time-varying regression function between reservoir parameters and water drive multiples; the time-varying regression function includes: a time-varying regression function between permeability and water drive multiples, a time-varying regression function between water phase relative permeability and water drive multiples, and a time-varying regression function between oil phase relative permeability and water drive multiples; Determine the output result corresponding to each grid in the reservoir grid system model at each time step based on the time-varying regression function between the reservoir parameters and the water drive multiple; A source-sink water phase tracking model is established based on the output results corresponding to each grid in the grid system model at each time step; the source-sink water phase tracking model is used to characterize the streamline distribution results of the water phase; The dominant seepage channels are identified based on the source-sink phase tracing model.

2. The method according to claim 1, It is characterized in that Before establishing the time-varying regression function between each reservoir parameter and the water flooding multiple, the method further includes: Based on the water flooding physical simulation experimental data, the formula is used to determine The water drive multiple E corresponding to all grids in the reservoir grid system model; The water drive multiple range is determined according to the maximum and minimum values ​​of the water drive multiples corresponding to all grids; wherein, the Q t is the cumulative water volume of any grid, V POR is the effective pore volume of the core.

3. The method according to claim 2, It is characterized in that The step of establishing a time-varying regression function between reservoir parameters and water drive multiples includes: When the time-varying regression function is a time-varying regression function between permeability and water flooding multiple, the permeability of each rock sample at different water flooding multiples is obtained; the initial permeability, sedimentary microfacies and porosity of each rock sample are different; According to the formula Determine the time-varying regression function between the permeability change rate and the water flooding multiple; where, M is the permeability change rate, K 0 is the initial permeability before water flooding, the K t is the final permeability obtained when the water flooding multiple is t, and f(E) is the time-varying regression function between the permeability change rate and the water flooding multiple.

4. The method according to claim 2, It is characterized in that The step of establishing a time-varying regression function between reservoir parameters and water drive multiples includes: When the time-varying regression function is a time-varying regression function between the relative permeability of the water phase and the water flooding multiple, the residual oil saturation and the irreducible water saturation under different water flooding multiples are obtained; Based on the formula A time-varying regression function between water phase relative permeability and water flooding multiple is established; among them, K rwr is the relative permeability of the water phase corresponding to the residual oil saturation before water flooding, S WCR is the bound water saturation before water flooding, S OWCR is the residual oil saturation before water flooding, S w is the water saturation, and the relative permeability of the water phase before water flooding is K1. rw , the relative permeability of the water phase after water flooding is K1 rw ', n is a constant determined according to the pore structure of the rock.

5. The method according to claim 2, It is characterized in that The step of establishing a time-varying regression function between reservoir parameters and water drive multiples includes: When the time-varying regression function is a time-varying regression function between the oil phase relative permeability and the water flooding multiple, the residual oil saturation and the irreducible water saturation under different water flooding multiples are obtained; Based on the formula A time-varying regression function between oil phase relative permeability and water flooding multiple is established; The S WCR is the irreducible water saturation before water flooding, the S OWCR is the residual oil saturation before water flooding, the S w is water saturation, the K rorw is the oil phase relative permeability corresponding to the irreducible water saturation before water flooding, and the oil phase relative permeability K2 before water flooding is K ro , the relative permeability of the oil phase after water flooding is K2 ro ′, the S w is water saturation, and m is a constant determined according to the pore structure of the rock.

6. The method according to claim 1, It is characterized in that The output result corresponding to each grid in the reservoir grid system model at each time step is obtained based on the time-varying regression function between the reservoir parameters and the water drive multiple, including: Initializing the grid system model using geological parameters and fluid parameters; The time-varying regression function between the reservoir parameters and the water drive multiple is called, and the grid system model of the reservoir is simulated and calculated according to the preset time step to obtain the water drive multiple, permeability, fluid velocity, pressure and endpoint value of the phase permeability curve of each grid at each time step.

7. The method according to claim 6, It is characterized in that The time-varying regression function between the reservoir parameters and the water drive multiple is called, and the grid system model of the reservoir is simulated and calculated according to a preset time step to obtain the water drive multiple, permeability, fluid velocity, pressure and endpoint of the phase permeability curve of each grid at each time step, including: Based on the time-varying regression function between the reservoir parameters and the water flooding multiple, the control equation is solved by using the incomplete LU decomposition preconditioned conjugate gradient method to obtain the pressure of each grid at different time steps; Determining a water drive factor and a fluid velocity for each grid based on the pressure; The endpoints of the permeability and relative permeability curves are determined based on the water flooding multiple.

8. The method according to claim 1, It is characterized in that The source-sink phase tracking model is established based on the output result corresponding to each grid in the grid system model at each time step, including: In the grid system model, the injected water and the edge and bottom water of each injection well are calibrated, and the target grid through which the injected water flows is determined; Determine the time length of the fluid passing through the target grid based on the fluid velocity of each target grid at each time step; Determine the position where the fluid leaves each of the target grids based on the time duration for the fluid to pass through the target grid; Taking the injection well as the starting coordinate and the production well as the destination coordinate, a streamline distribution map is determined based on the position where the fluid leaves each target grid; the streamline distribution map is the source-sink phase tracking model.

9. The method according to claim 1, It is characterized in that The identifying of the dominant seepage channel based on the source-sink phase tracking model includes: Determine streamline-dense areas from the source-sink phase tracing model; The streamline-dense area is used as a dominant seepage channel.

10. A device for identifying the dominant seepage channel of an oil reservoir, It is characterized in that The device comprises: The first establishing unit is used to establish a time-varying regression function between reservoir parameters and water drive multiples; the time-varying regression function includes: a time-varying regression function between permeability and water drive multiples; a time-varying regression function between water phase relative permeability and water drive multiples; and a time-varying regression function between oil phase relative permeability and water drive multiples; A determination unit, used to determine the output result corresponding to each grid in the reservoir grid system model at each time step based on the time-varying regression function between the reservoir parameter and the water drive multiple; The second establishing unit is used to establish a source-sink water phase tracking model based on the output results corresponding to each grid in the grid system model at each time step; the source-sink water phase tracking model is used to characterize the streamline distribution results of the water phase; An identification unit is used to identify a dominant seepage channel based on the source-sink phase tracing model.

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