A microcosmic residual oil evaluation method based on topological tracking of time sequence microscopic images and flow field inversion

Abstract

The application discloses a microcosmic residual oil evaluation method based on topological tracking of time sequence microscopic images and flow field inversion, S1: oil-water-matrix three-phase segmentation and binary mask extraction are carried out; S2: the circularity and Euler number are calculated; S3: evolution result statistics of residual oil are carried out according to a judgment rule; S4: a velocity field and a pressure field in a pore scale are calculated; S5: a multi-dimensional criterion is constructed to judge the occurrence type of residual oil; and S6: a microcosmic producing potential index evaluation function is constructed to quantitatively evaluate the producing potential of residual oil under current displacement conditions. Through image stabilization processing and three-phase segmentation on time sequence images in the whole process of microcosmic oil displacement experiments, the dynamic evolution behavior and microcosmic stress mechanism of residual oil are analyzed by using the topological tracking and lattice Boltzmann flow field inversion method, and multi-dimensional fusion is carried out on geometric shapes, evolution courses and mechanical parameters, so that accurate discrimination of the occurrence type of microcosmic residual oil and quantitative evaluation of the producing potential are realized.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and in particular to a microscopic residual oil evaluation method based on time-series microscopic image topology tracing and flow field inversion. Background Technology

[0002] Currently, most of my country's major oilfields have entered the late stage of development with high or ultra-high water cut. Besides the large areas of remaining oil in unaffected areas, the remaining oil in affected areas is highly dispersed and its occurrence is extremely complex, posing a significant challenge for research. To clarify the microscopic formation mechanism of remaining oil and explore its utilization potential, visualized oil displacement experiments based on microfluidic chips have become a core tool in the field of petroleum engineering for studying microscopic seepage patterns. Researchers can directly observe the flow morphology and distribution characteristics of fluids in complex porous media.

[0003] Existing analytical methods for microscopic oil displacement experiments mainly focus on static residual oil saturation calculation and simple morphological statistics. However, these methods still have significant limitations and technical bottlenecks in practical applications, such as: a lack of ability to track the dynamic evolution of residual oil throughout the entire process; when an isolated oil phase is observed within the field of view, existing techniques cannot determine whether it is formed due to the retention of original oil or the formation of large oil clusters after stretching and fracturing, making it difficult to reveal the evolutionary patterns of phenomena such as jamming, coalescence, and emulsification; a lack of pore-scale flow field information; limited by the scale of experimental devices, existing techniques cannot place sensors inside the chip to directly measure the flow velocity and pressure distribution inside the pore throat, and the formation of residual oil is often inferred through visual observation and experience; and a lack of quantitative evaluation indicators to guide engineering time; after identifying the type of residual oil, it is impossible to answer key questions such as "which part of the residual oil is more easily utilized" and "how much driving force is needed for reuse." Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a microscopic residual oil evaluation method based on time-series microscopic image topology tracing and flow field inversion.

[0005] The objective of this invention is achieved through the following technical solution: a method for evaluating microscopic residual oil based on time-series microscopic image topology tracing and flow field inversion, comprising the following steps:

[0006] S1: Obtain the image sequence of the microscopic oil displacement experiment, stabilize the input image by extracting feature points, and perform three-phase segmentation of oil-water-matrix and binary mask extraction by RGB-HSV color space conversion;

[0007] S2: Calculate the remaining oil identified in each frame of the image, calculate its roundness and Euler number, and quantify the geometric shape of the remaining oil by converting the pixels into mathematical vectors.

[0008] S3: Calculate the overlap rate and similarity of the remaining oil in adjacent time step images, and perform statistical analysis on the evolution results of the remaining oil according to the judgment rules;

[0009] S4: Calculate the velocity and pressure fields within the pore scale;

[0010] S5: Construct multi-dimensional criteria to determine the occurrence type of remaining oil;

[0011] S6: Construct a micro-level utilization potential index evaluation function to quantitatively evaluate the utilization potential of remaining oil under current displacement conditions.

[0012] Preferably, step S1 further includes the following step:

[0013] S11: Extract image sampling points;

[0014] S12: Filter image sampling points.

[0015] ;

[0016] in, Let be the projection residual of the k-th feature matching pair. These are the coordinates of the feature point observed at the current moment. These are the theoretical coordinates assuming the rock skeleton is at rest;

[0017] like If the point exhibits non-rigid displacement, it is considered to belong to a moving fluid region and is therefore excluded; if The point was determined to be subject to rigid displacement and belonged to the static skeleton region; therefore, it was retained. The filtering threshold;

[0018] S13: Reconstruct the original image.

[0019] ;

[0020] in, The grayscale value of the target pixel after resampling. The weighting coefficients are calculated based on the surrounding 4×4 neighboring pixels. and These are the normalized local coordinates within the grid cell;

[0021] S14: Extract extreme values ​​from the RGB three channels , Sum and Difference The optical signal is decoupled into a luminance component V and a color saturation component S.

[0022] ;

[0023] (like ≠0);

[0024] ;

[0025] S15: Establish a matrix-oil-water three-phase discrimination model using hue (H), saturation (S), and luminance component (V):

[0026]

[0027] in, The labels correspond to the three phases of oil, water, and matrix within the microscopic visible area;

[0028] Perform morphological opening operations on the generated binarized mask:

[0029] ;

[0030] in As a structural element, For corrosion operation, This is an expansion operation.

[0031] Preferably, step S2 further includes the following step:

[0032] S21: Traverse the segmented oil phase binary image, record the adjacency relationships between pixels, and group spatially connected oil phase pixels into the same independent oil cluster. Assign a unique index ;

[0033] S22: Calculate the pixel area S of each oil cluster. oil Perimeter L of the oil-water interface p The current stress state of the oil cluster is determined by calculating the roundness and aspect ratio;

[0034] S23: Calculate the complexity of the two-dimensional Euler quantized oil cluster structure.

[0035]

[0036] in, It is a two-dimensional topological connectivity index. The number of enclosed aqueous regions within the oil cluster, when When =1, it means the connected domain is simple; When ≤0, it indicates that the interconnected domains of oil are complex.

[0037] Preferably, in step S22, the formula for calculating roundness is:

[0038] ;

[0039] The equivalent ellipse of the oil cluster was fitted using the least squares method to obtain the major axis L. max With short axis L min Calculate the aspect ratio:

[0040] .

[0041] Preferably, step S3 further includes the following step:

[0042] S31: Define T t The oil cluster set U is the source node at time step. Using time set V as the target node, calculate the hybrid association weight of oil clusters between two frames. ,

[0043] ;

[0044] in, Let m be the m-th oil droplet at time t. for At time n, the oil droplet For node pairs, for At time m, oil cluster for At time n, oil cluster numbered n This represents the total number of pixels occupied by oil clusters in the image. The area of ​​the overlapping region between the two time points;

[0045] like Then let ,in, This is the connection threshold;

[0046] S32: Statistically determine the out-degree of nodes. and in-degree Perform logical judgment, and the judgment rules are as follows:

[0047] Split Event: Then, a large oil cluster will break into multiple smaller oil clusters in the next moment;

[0048] Mergers and acquisitions: Then, multiple small oil clusters will converge into a large oil cluster in the next moment;

[0049] Migration events: The oil clusters then move smoothly along the channel;

[0050] Disappearance Event: The oil clusters disappear from the visible range due to the displacement phase.

[0051] Preferably, step S4 further includes the following step:

[0052] S41: Iterative solution using discrete Boltzmann equations, decomposing fluid motion into two processes: migration and collision.

[0053] ;

[0054] in, Let be the fluid particle distribution function. It is a local equilibrium distribution. The relaxation time is dimensionless. It is a discrete velocity vector;

[0055] S42: Calculate the local thermodynamic equilibrium state.

[0056] ;

[0057] in, For macroscopic density, For speed, These are the weighting coefficients;

[0058] S43: Mark the matrix pixels in the image as solid walls, and use a half-step bounce format to achieve slip-free boundaries.

[0059] ;

[0060] in, The opposite direction of k;

[0061] S44: The flow field velocity distribution is calculated based on the lattice Boltzmann equation, and mechanical parameters are obtained.

[0062] ;

[0063] ;

[0064] in, This represents the number of capillaries in a given area. The dynamic viscosity of the displacing phase, The local flow velocity within the pore space. This represents the interfacial tension between the displaced phase and the oil phase. This refers to the additional resistance generated when fluid passes through a constricted throat due to the difference in curvature radii at the front and rear ends. Let be the radius of curvature of the oil droplet tip. Let be the radius of curvature at the tip of the oil droplet.

[0065] Preferably, in step S5, three typical residual oil occurrence types are established, including Jamin effect card break-type oil clusters, heterogeneous flow-around type oil clusters, and blind-end retention type oil clusters.

[0066] Preferably, in step S6, residual oil targets are screened by constructing a microscopic residual oil utilization potential index evaluation function.

[0067] ;

[0068] in, As a micro-level index of remaining oil utilization potential, This represents the number of capillaries in a given area. For micro-displacement initiation threshold, As a resistance to the Jia Min effect, For local driving pressure difference;

[0069] Based on the oil cluster type selected in step S5, the benchmark value is... , and Assign a value:

[0070] When it is a heterogeneous flow-around type oil cluster, then , , ;

[0071] If it is a Jamin effect type of oil cluster, then , , ;

[0072] If it is a blind-end stagnant oil cluster, then .

[0073] This invention has the following advantages: By stabilizing and segmenting the time-series images of the entire process of micro-displacement oil experiments, and then using topology tracing and lattice Boltzmann flow field inversion methods, the dynamic evolution behavior and micro-force mechanism of the remaining oil are analyzed. Furthermore, the geometric morphology, evolutionary history, and mechanical parameters are multidimensionally integrated, thereby achieving accurate identification of the micro-remaining oil occurrence type and quantitative evaluation of its exploitation potential. This provides a scientific basis for formulating potential tapping strategies to improve oil recovery rates in the later stages of oilfield development. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of the process of a microscopic residual oil evaluation method based on time-series microscopic image topology tracing and flow field inversion;

[0075] Figure 2 This is a schematic diagram of the matrix-oil-water three-phase distribution at the initial moment;

[0076] Figure 3 This is a schematic diagram of the matrix-oil-water three-phase distribution after oil cluster migration;

[0077] Figure 4 A schematic diagram illustrating the evolution of oil clusters;

[0078] Figure 5 This is a schematic diagram of the pore velocity field based on LBM simulation.

[0079] Figure 6 A schematic diagram showing the distribution of the circularity characteristics of oil clusters;

[0080] Figure 7 A schematic diagram for evaluating the micro-level mobilization potential index;

[0081] Figure 8 A schematic diagram illustrating the ease or difficulty of mobilizing each oil cluster;

[0082] Figure 9 This is a schematic diagram showing the scatter points corresponding to roundness and ROPI index. Detailed Implementation

[0083] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0084] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0085] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other.

[0086] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0087] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are only used for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. In addition, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0088] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0089] In this embodiment, as Figures 1-9 As shown, a method for evaluating microscopic residual oil based on time-series microscopic image topology tracing and flow field inversion includes the following steps:

[0090] S1: Obtain the image sequence of the microscopic oil displacement experiment, stabilize the input image by extracting feature points, and perform three-phase segmentation of oil-water-matrix and binary mask extraction by RGB-HSV color space conversion;

[0091] S2: Calculate the remaining oil identified in each frame of the image, calculate its roundness and Euler number, and quantify the geometric shape of the remaining oil by converting the pixels into mathematical vectors.

[0092] S3: Calculate the overlap rate and similarity of the remaining oil in adjacent time step images, and perform statistical analysis on the evolution results of the remaining oil according to the judgment rules;

[0093] S4: Calculate the velocity and pressure fields within the pore scale;

[0094] S5: Construct multi-dimensional criteria to determine the occurrence type of remaining oil;

[0095] S6: Construct a micro-level utilization potential index evaluation function to quantitatively evaluate the utilization potential of remaining oil under current displacement conditions. By stabilizing and segmenting the time-series images of the entire micro-level oil displacement experiment, and then using topology tracing and lattice Boltzmann flow field inversion methods, the dynamic evolution behavior and micro-level stress mechanism of the remaining oil are analyzed. Furthermore, the geometric morphology, evolutionary history, and mechanical parameters are multi-dimensionally integrated to achieve accurate identification of the micro-level remaining oil occurrence type and quantitative evaluation of its utilization potential. This provides a scientific basis for formulating strategies to enhance oil recovery in the later stages of oilfield development.

[0096] Furthermore, step S1 also includes the following steps:

[0097] S11: Extract image sampling points;

[0098] S12: Filter image sampling points.

[0099] ;

[0100] in, Let be the projection residual of the k-th feature matching pair. These are the coordinates of the feature point observed at the current moment. These are the theoretical coordinates assuming the rock skeleton is at rest;

[0101] like If the point exhibits non-rigid displacement, it is considered to belong to a moving fluid region and is therefore excluded; if The point was determined to be subject to rigid displacement and belonged to the static skeleton region; therefore, it was retained. Filter threshold, filter threshold The value is related to the input image resolution and is approximately 0.1% of the image diagonal length.

[0102] S13: Reconstruct the original image.

[0103] ;

[0104] in, The grayscale value of the target pixel after resampling. The weighting coefficients are calculated based on the surrounding 4×4 neighboring pixels. and These are the normalized local coordinates within the grid cell;

[0105] S14: Extract extreme values ​​from the RGB three channels , Sum and Difference The optical signal is decoupled into a luminance component V and a color saturation component S.

[0106] ;

[0107] (like ≠0);

[0108] ;

[0109] S15: Establish a matrix-oil-water three-phase discrimination model using hue (H), saturation (S), and luminance component (V):

[0110] ;

[0111] in, The labels correspond to the three phases of oil, water, and matrix within the microscopic visible area;

[0112] Perform morphological opening operations on the generated binarized mask:

[0113] ;

[0114] in As a structural element, For corrosion operation, The expansion operation, i.e., corrosion followed by expansion, effectively removes isolated noise points and smooths the oil-water-matrix interface. Specifically, in step S11, to eliminate image displacement caused by equipment vibration during the experiment and to provide reference coordinates for step S4, an image reference coordinate system is established. A feature extraction algorithm (SIFT) with multi-scale anti-interference capabilities is used. By simulating the changes in image resolution at different levels, stable points on the rock skeleton that do not change with image scaling or blurring are selected as reference points for image registration. Specifically, a multi-scale space is constructed using a Gaussian difference model, with the function defined as:

[0115] ;

[0116] in, This is the difference in Gaussian response value. For flow field image space at different fuzzy scales, The scale factor of the Gaussian kernel corresponds to the characteristic dimensions of the rock pore structure, such as the pore throat radius. This represents the scaling factor between adjacent scale spaces. In step S12, the feature points extracted by SIFT are filtered, retaining the static rock skeleton structure and removing oil and water areas. The Random Sampling Consensus (RANSAC) algorithm is introduced, using geometric projection error to distinguish between static and moving sampling points. In step S13, when performing geometric corrections on the image, such as rotating or translating the image, non-integer displacement of the pixel grid can lead to the loss of original grayscale information or the generation of jagged edges. To avoid this error affecting the subsequent oil cluster analysis results, a bicubic interpolation algorithm is used to reconstruct the original image. In step S14, due to the different illumination and dye concentrations in different experiments, a sample sampling method is used to determine the threshold. Histograms are drawn from 30×30 pixel sample blocks of each of the oil, water, and matrix phases in the initial image, and the interval of two standard deviations is used as the segmentation threshold to extract the extreme values ​​in the RGB three channels. , and difference The optical signal is decoupled into a luminance component V and a color saturation component S.

[0117] Furthermore, step S2 also includes the following steps:

[0118] S21: Traverse the segmented oil phase binary image, record the adjacency relationships between pixels, and group spatially connected oil phase pixels into the same independent oil cluster. Assign a unique index ;

[0119] S22: Calculate the pixel area S of each oil cluster. oil Perimeter L of the oil-water interface p The current stress state of the oil cluster is determined by calculating its roundness and aspect ratio; preferably, in step S22, the formula for calculating roundness is:

[0120] ;

[0121] Specifically, roundness The closer a value is to 1, the more it indicates that the oil phase is mainly controlled by interfacial tension and is in a steady state, with good roundness. The closer the value is to 0, the stronger the oil phase is constrained by pores or subjected to shear and stretching forces. This usually indicates that the oil film or elongated oil clusters attached to the matrix surface are in an unsteady state.

[0122] The equivalent ellipse of the oil cluster was fitted using the least squares method to obtain the major axis L. max With short axis L min Calculate the aspect ratio:

[0123] ;

[0124] Used to characterize the stretching degree of oil clusters, aspect ratio The larger the value, the more severe the shear and stretching effect on the surface oil phase, and it is mainly used to identify long strip-shaped film-like oil clusters in the flow state.

[0125] S23: Calculate the complexity of the two-dimensional Euler quantized oil cluster structure.

[0126]

[0127] in, It is a two-dimensional topological connectivity index. The number of enclosed aqueous regions within the oil cluster, when When =1, it means the connected domain is simple; When the value is ≤0, it indicates that the oil-phase interconnected domains are complex. Specifically, in step S21, based on the registered clear experimental image, discrete pixels are transformed into analyzable independent objects. A two-pass scanning algorithm is used to traverse the segmented oil phase binary image, recording the adjacency relationships between pixels and grouping spatially connected oil phase pixels into the same independent oil cluster. And assign a unique index .

[0128] In this embodiment, step S3 further includes the following step:

[0129] S31: Define T t The oil cluster set U is the source node at time step. Using time set V as the target node, calculate the hybrid association weight of oil clusters between two frames. This weight is the product of the inclusion coefficient and the similarity coefficient, used to ensure the accuracy of recognizing large-scale deformation and shape maintenance of residual oil in the image.

[0130] ;

[0131] in, Let m be the m-th oil droplet at time t. for At time n, the oil droplet For node pairs, for At time m, oil cluster for At time n, oil cluster numbered n This represents the total number of pixels occupied by oil clusters in the image. The area of ​​the overlapping portion of the images at two time points is denoted as . Specifically, the inclusion coefficient records the jamming and splitting events during the migration of oil clusters within the pores, and is used to solve matching problems with large scale differences. The similarity coefficient records the events of the remaining oil migrating smoothly within the pores, and measures the degree of overlap of the overall contours of the two oil clusters before and after. It is sensitive to shape changes and can ensure the confidence of the matching.

[0132] like Then let ,in, The connection threshold is used to filter out false correlations between images caused by noise.

[0133] S32: Statistically determine the out-degree of nodes. and in-degree Perform logical judgment, and the judgment rules are as follows:

[0134] Split Event: Then, a large oil cluster will break into multiple smaller oil clusters in the next moment;

[0135] Mergers and acquisitions: Then, multiple small oil clusters will converge into a large oil cluster in the next moment;

[0136] Migration events: The oil clusters then move smoothly along the channel;

[0137] Disappearance Event: The oil clusters disappear from the visible range due to the displacement phase. Specifically, denoted as the oil cluster splitting number, representing the number of oil droplets that split from the previous oil droplet into the next oil droplet. The number of merged oil clusters represents the number of oil clusters formed by merging several oil clusters from the previous time step.

[0138] Furthermore, step S4 also includes the following steps:

[0139] S41: To describe fluid motion at the microscale, discrete Boltzmann equations are used for iterative solutions, and fluid motion is decomposed into two processes: migration and collision.

[0140] ;

[0141] in, Let f be the fluid particle distribution function, which characterizes the statistical motion of fluid particles at the microscopic scale. This represents a local equilibrium distribution, reflecting the thermodynamic steady state of the fluid under the current pressure and velocity. The dimensionless relaxation time reflects the different flow resistance characteristics of the oil and water phases, and is related to the kinematic viscosity of the fluid. The relationship is , This is a discrete velocity vector, with values ​​ranging from (0,0) for k=0 and (1~4). k=5~8 .

[0142] The pixels of the image are mapped to LBM grid nodes. To ensure the physical realism of the simulation results, the conversion between physical units and grid units is performed according to the Reynolds number and capillary number similarity criteria. The inlet velocity boundary condition is obtained from the experimental pumping velocity, and the outlet boundary adopts a constant pressure boundary condition. Furthermore, the relative velocity error across the entire field is less than 10. -6 The time is defined as convergence.

[0143] S42: Calculate the local thermodynamic equilibrium state. In each iteration, the particle distribution always tends towards the local thermodynamic equilibrium state. This state is determined by the current macroscopic density. and speed Determine the equilibrium distribution function calculation:

[0144] ;

[0145] in, For macroscopic density, For speed, These are the weighting coefficients. ;

[0146] S43: Mark the matrix pixels in the image as solid walls, and use a half-step bounce format to achieve slip-free boundaries.

[0147] ;

[0148] in, It is the opposite direction of k; it effectively simulates the process of a particle returning along its original path after colliding with the wall, ensuring the no-slip condition of macroscopic velocity u=0 at the wall.

[0149] S44: The flow field velocity distribution is calculated based on the lattice Boltzmann equation, and mechanical parameters are obtained.

[0150] ;

[0151] ;

[0152] in, This represents the number of capillaries in a given area. The dynamic viscosity of the displacing phase, The local flow velocity within the pore space. This represents the interfacial tension between the displaced phase and the oil phase. This refers to the additional resistance generated when fluid passes through a constricted throat due to the difference in curvature radii at the front and rear ends. Let be the radius of curvature of the oil droplet tip. Let be the radius of curvature at the tip of the oil droplet.

[0153] Furthermore, in step S5, three typical residual oil occurrence types are established, including Jamin effect-induced breakage oil clusters, heterogeneous flow-around oil clusters, and blind-end retention oil clusters. Specifically, (1) the formation mechanism of Jamin effect-induced breakage oil clusters is that when oil droplets pass through the orifice throat, they break and become retained due to capillary resistance exceeding the driving pressure difference. The discrimination criteria are as follows: ① Morphological criteria: (in =0.8), indicating that its shape is rounded and its state is stable; ② Historical criterion: The remaining oil has undergone splitting events during its evolution; ③ Mechanical criterion: (Local driving pressure difference), Jia Minli is greater than the driving pressure difference, which leads to blockage during the migration of the remaining oil. (2) The formation mechanism of heterogeneous flow-around type oil clusters is affected by the heterogeneity of pores. The injected fluid flows along the dominant channel, resulting in the oil phase in the low permeability area not being affected. The judgment criteria are as follows: ① Morphological criteria: (in =0.4), extremely irregular shape, large specific surface area; ② During the evolution of historical residual oil, it has been in a state of long-term migration or stagnation, without obvious oil phase topological fracture process; ③ Mechanical criteria , usually located in the unaffected area, and are weakly affected by fluid shear. (3) The formation mechanism of blind-end stagnant oil clusters is that the fluid cannot form an effective displacement circuit due to the limitation of unidirectional interconnected pore topology. Morphological criterion: pore coordination number Z=1 (only one interconnected pore throat).

[0154] In this embodiment, in step S6, residual oil targets are screened by constructing a microscopic residual oil utilization potential index evaluation function.

[0155] ;

[0156] in, As a micro-level index of remaining oil utilization potential, This represents the number of capillaries in a given area. For micro-displacement initiation threshold, As a resistance to the Jia Min effect, For local driving pressure difference; specifically, viscous driving term: V α = This is used to characterize the dragging ability of the displacing phase fluid on the remaining oil, and is presented in logarithmic form to balance the changes in capillary number across orders of magnitude; morphological variable: S β = A higher value indicates a more irregular oil phase shape, a larger oil-water contact area, and greater susceptibility to deformation or peeling under shear forces; Capillary resistance term: R γ = The higher the resistance ratio, the lower the utilization potential.

[0157] Based on the oil cluster type selected in step S5, the benchmark value is... , and Assign a value:

[0158] When it is a heterogeneous flow-around type oil cluster, then , , ;

[0159] If it is a Jamin effect type of oil cluster, then , , ;

[0160] If it is a blind-end stagnant oil cluster, then .

[0161] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating microscopic residual oil based on time-series microscopic image topology tracing and flow field inversion, characterized in that: Includes the following steps: S1: Obtain the image sequence of the microscopic oil displacement experiment, stabilize the input image by extracting feature points, and perform three-phase segmentation of oil-water-matrix and binary mask extraction by RGB-HSV color space conversion; S2: Calculate the remaining oil identified in each frame of the image, calculate its roundness and Euler number, and quantify the geometric shape of the remaining oil by converting the pixels into mathematical vectors. S3: Calculate the overlap rate and similarity of the remaining oil in adjacent time step images, and perform statistical analysis on the evolution results of the remaining oil according to the judgment rules; S4: Calculate the velocity and pressure fields within the pore scale; S5: Construct multi-dimensional criteria to determine the occurrence type of remaining oil; S6: Construct a micro-level utilization potential index evaluation function to quantitatively evaluate the utilization potential of remaining oil under current displacement conditions.

2. The method for evaluating microscopic residual oil based on time-series microscopic image topology tracing and flow field inversion according to claim 1, characterized in that: Step S1 further includes the following steps: S11: Extract image sampling points; S12: Filter image sampling points. ； in, Let be the projection residual of the k-th feature matching pair. These are the coordinates of the feature point observed at the current moment. These are the theoretical coordinates assuming the rock skeleton is at rest; like If the point exhibits non-rigid displacement, it is considered to belong to a moving fluid region and is therefore excluded; if The point was determined to be subject to rigid displacement and belonged to the static skeleton region; therefore, it was retained. The filtering threshold; S13: Reconstruct the original image. ； in, The grayscale value of the target pixel after resampling. The weighting coefficients are calculated based on the surrounding 4×4 neighboring pixels. and These are the normalized local coordinates within the grid cell; S14: Extract extreme values ​​from the RGB three channels , Sum and Difference The optical signal is decoupled into a luminance component V and a color saturation component S. ； (like ≠0); ； S15: Establish a matrix-oil-water three-phase discrimination model using hue (H), saturation (S), and luminance component (V): in, The labels correspond to the three phases of oil, water, and matrix within the microscopic visible area; Perform morphological opening operations on the generated binarized mask: ； in As a structural element, For corrosion operation, This is an expansion operation.

3. The method for evaluating microscopic residual oil based on time-series microscopic image topology tracing and flow field inversion according to claim 2, characterized in that: Step S2 further includes the following steps: S21: Traverse the segmented oil phase binary image, record the adjacency relationships between pixels, and group spatially connected oil phase pixels into the same independent oil cluster. Assign a unique index ; S22: Calculate the pixel area S of each oil cluster. oil Perimeter L of the oil-water interface p The current stress state of the oil cluster is determined by calculating the roundness and aspect ratio; S23: Calculate the complexity of the two-dimensional Euler quantized oil cluster structure. in, It is a two-dimensional topological connectivity index. The number of enclosed aqueous regions within the oil cluster, when When =1, it means the connected domain is simple; When ≤0, it indicates that the interconnected domains of oil are complex.

4. The method for evaluating microscopic residual oil based on time-series microscopic image topology tracing and flow field inversion according to claim 3, characterized in that: In step S22, the formula for calculating roundness is: ； The equivalent ellipse of the oil cluster was fitted using the least squares method to obtain the major axis L. max With short axis L min Calculate the aspect ratio: 。 5. The method for evaluating microscopic residual oil based on time-series microscopic image topology tracing and flow field inversion according to claim 4, characterized in that: Step S3 further includes the following steps: S31: Define T t The oil cluster set U is the source node at time t. Using time set V as the target node, calculate the hybrid association weight of oil clusters between two frames. , ； in, Let m be the m-th oil droplet at time t. for At time n, the oil droplet For node pairs, for At time m, oil cluster for At time n, oil cluster numbered n This represents the total number of pixels occupied by oil clusters in the image. The area of ​​the overlapping region between the two time points; like Then let ,in, This is the connection threshold; S32: Statistically determine the out-degree of nodes. and in-degree Perform logical judgment, and the judgment rules are as follows: Split Event: Then, a large oil cluster will break into multiple smaller oil clusters in the next moment; Mergers and acquisitions: Then, multiple small oil clusters will converge into a large oil cluster in the next moment; Migration events: The oil clusters then move smoothly along the channel; Disappearance Event: The oil clusters disappear from the visible range due to the displacement phase.

6. The method for evaluating microscopic residual oil based on time-series microscopic image topology tracing and flow field inversion according to claim 5, characterized in that: Step S4 also includes the following steps: S41: Iterative solution using discrete Boltzmann equations, decomposing fluid motion into two processes: migration and collision. ； in, Let be the fluid particle distribution function. It is a local equilibrium distribution. The relaxation time is dimensionless. It is a discrete velocity vector; S42: Calculate the local thermodynamic equilibrium state. ； in, For macroscopic density, For speed, These are the weighting coefficients; S43: Mark the matrix pixels in the image as solid walls, and use a half-step bounce format to achieve slip-free boundaries. ； in, The opposite direction of k; S44: The flow field velocity distribution is calculated based on the lattice Boltzmann equation, and mechanical parameters are obtained. ； ； in, This represents the number of capillaries in a given area. The dynamic viscosity of the displacing phase, The local flow velocity within the pore space. This represents the interfacial tension between the displaced phase and the oil phase. This refers to the additional resistance generated when fluid passes through a constricted throat due to the difference in curvature radii at the front and rear ends. Let be the radius of curvature of the oil droplet tip. Let be the radius of curvature at the tip of the oil droplet.

7. The method for evaluating microscopic residual oil based on time-series microscopic image topology tracing and flow field inversion according to claim 6, characterized in that: In step S5, three typical residual oil occurrence types are established, including Jamin effect card breakage type oil cluster, heterogeneous flow around type oil cluster, and blind end retention type oil cluster.

8. The method for evaluating microscopic residual oil based on time-series microscopic image topology tracing and flow field inversion according to claim 7, characterized in that: In step S6, residual oil targets are screened by constructing a microscopic residual oil utilization potential index evaluation function. ； in, As a micro-level index of remaining oil utilization potential, This represents the number of capillaries in a given area. For micro-displacement initiation threshold, As a resistance to the Jia Min effect, For local driving pressure difference; Based on the oil cluster type selected in step S5, the benchmark value is... , and Assign a value: When it is a heterogeneous flow-around type oil cluster, then , , ; If it is a Jamin effect type of oil cluster, then , , ; If it is a blind-end stagnant oil cluster, then .

Images