Residual oil enrichment scale classification evaluation method and device

Abstract

The invention relates to the technical field of oil reservoir development, in particular to a remaining oil enrichment scale classification evaluation method and device.The method comprises the steps that oil saturation data at the current moment in a numerical model is filtered according to the minimum remaining oil saturation threshold value, and a target saturation model is constructed; marking the remaining oil concentrate of the saturation model; dividing a rich body into three types by using a distance coefficient; carrying out potential quality classification on the three types of scale enriched bodies; carrying out spatial form classification and discrimination on the first-class and second-class scale concentrates, and respectively carrying out deposition feature, internal interlayer feature and physical property feature classification and discrimination on the first-class and second-class scale concentrates according to the three-dimensional geologic model; and performing potential quality classification on the first-class and second-class scale concentrates according to the judgment result. The method achieves the quick evaluation of the potential quality of the target object based on geological information, solves the recognition problem of potential tapping objects with different measures, is suitable for different development means, and lays a foundation for real-time potential tapping.

Description

Technical Field

[0001] This invention relates to the field of reservoir development technology, and in particular to a method and apparatus for classifying and evaluating the scale of remaining oil enrichment. Background Technology

[0002] High-water-cut old oilfields, which constitute the majority of my country's oil reserves and production (accounting for over 70% each), have generally entered the high-water-cut development stage. The distribution of remaining oil is shifting from relatively concentrated and contiguous areas to locally concentrated and highly dispersed areas, increasing the difficulty of tapping potential and leading to a gradual decline in development efficiency. Under the dual pressure of low oil prices and rising costs, the accuracy of understanding the spatial enrichment characteristics of remaining oil directly determines the effectiveness of efficient oilfield development and is one of the key issues that urgently need to be addressed.

[0003] Currently, commonly used methods for evaluating remaining oil mainly include numerical simulation, well group recovery rate calculation, well logging data analysis, and core well data analysis. Among these, numerical simulation has high accuracy and is effective in guiding development plan formulation. However, remaining oil evaluation methods based on numerical models have three main drawbacks: First, the quantitative characterization of remaining oil relies mainly on single evaluation indicators such as remaining oil saturation, remaining oil geological reserves, remaining oil reserve abundance, and remaining oil recoverable reserve abundance. While these indicators can reveal macroscopic distribution characteristics, they cannot quantitatively evaluate the scale of remaining oil enrichment in three-dimensional space, making it difficult to guide the three-dimensional tapping of remaining oil depth in the later stages of ultra-high water cut. Second, remaining oil distribution studies mainly focus on two-dimensional planar or vertical distribution evaluation, which can qualitatively delineate remaining oil potential areas or layers, but cannot determine the three-dimensional spatial distribution pattern of remaining oil. Third, remaining oil evaluation lacks geological factor analysis. Although potential areas can be delineated based on indicators such as saturation, in actual development, geological factors such as inter-well facial transitions, interlayers within single sand bodies, and differences in physical properties at different locations make it difficult to effectively develop potential areas. Summary of the Invention

[0004] This invention proposes a method and apparatus for classifying and evaluating the scale of remaining oil enrichment, in order to solve the problems of existing methods in recognizing and characterizing the scale and distribution pattern of remaining oil enrichment, evaluating the heterogeneous characteristics of reservoirs, and the poor timeliness of deep three-dimensional development.

[0005] According to one aspect of the present invention, a method for classifying and evaluating the enrichment scale of residual oil is provided, comprising:

[0006] Obtain the three-dimensional geological model of the study area, the numerical model at the current moment, and the development characteristics of the study area;

[0007] Based on the aforementioned development characteristics, a minimum threshold for remaining oil saturation is determined, and the oil saturation data at the current moment in the numerical model is filtered according to this threshold to construct a target saturation model.

[0008] Identify and label the remaining oil-rich collectives in the grid-connected target saturation model, and output a new data volume containing the labels of the rich collectives;

[0009] The rich groups in the new data volume are divided into three categories based on size: Class I, Class II, and Class III rich groups using distance coefficients.

[0010] Based on the three-dimensional geological model, the potential quality of the three types of large-scale rich landmasses is classified.

[0011] Based on the regularity and geometric characteristics of the rich aggregate morphology, the rich aggregates of the first and second scales are classified and identified by spatial morphology. Based on the three-dimensional geological model, the rich aggregates of the first and second scales are classified and identified by sedimentary characteristics, internal interlayer characteristics and physical property characteristics, respectively.

[0012] Based on the discrimination results, the potential quality classification is performed on the first and second categories of large-scale rich collectives.

[0013] Preferably, the minimum threshold for residual oil saturation includes, but is not limited to: residual oil saturation under different permeability conditions, the minimum oil saturation that meets the conditions for profitable development, and the oil saturation corresponding to the water content limit of the target area.

[0014] Preferably, in the target saturation model, the grid corresponding to each individual well contains at least sedimentary facies, lithology, and permeability data.

[0015] Preferably, the development characteristics are obtained based on the development history of the study area, the analysis of multiple rounds of adjustment schemes in history, and their development effects.

[0016] Preferably, the method for identifying and marking residual oil-rich aggregates connected to the target saturation model grid includes:

[0017] The 26-neighborhood connected component labeling algorithm is used to identify and label the remaining oil-rich collectives in the grid-connected target saturation model.

[0018] Preferably, the method for classifying rich groups in the new data volume into three categories of large-scale rich groups using distance coefficients includes:

[0019] Use equation (1) to determine the distance coefficient of each rich group in the new data volume;

[0020] (1);

[0021] In the formula: L is the distance coefficient for the i-th rich group; well The minimum well spacing for oil and water wells, in meters (m); L model Let m be the planar mesh step size of the numerical model; j is the number of meshes in the i-th rich set.

[0022] If the distance coefficient is less than the first predetermined value, it belongs to category three; if it is greater than or equal to the first predetermined value and less than the second predetermined value, it belongs to category two; if it is greater than or equal to the second predetermined value, it belongs to category one.

[0023] Preferably, the method for classifying the potential quality of the three types of large-scale rich landmasses based on the three-dimensional geological model includes:

[0024] Output well location data based on the three-dimensional geological model;

[0025] The spatial relative position of each rich collective in the three types of rich collectives to the production well in the well location data volume is determined. If there is a production well location in the rich collective and its surrounding area, the rich collective is determined to be a type three movable; if there is no production well location, the rich collective is determined to be a type three stagnant.

[0026] Preferably, the method for spatial morphological classification and discrimination of the first- and second-class large-scale large-scale collectives based on the regularity and geometric characteristics of the large-scale collective morphology includes:

[0027] Based on the regularity and geometric characteristics of the rich collective morphology, the rich collective is divided into three spatial morphology types: ellipsoidal, oblong, and irregular.

[0028] One method for classifying wealthy communities into three spatial morphological types includes:

[0029] The morphological regularity of the rich collective is evaluated using fractal dimension, and the rich collective is divided into regular and irregular shapes.

[0030] In regular-shaped rich aggregates, the geometric characteristics of the rich aggregates are evaluated using volume ellipticity, and regular-shaped rich aggregates are divided into ellipsoidal and oblong shapes.

[0031] Preferably, the method for evaluating the morphological regularity of the rich collective using fractal dimension and classifying the rich collective into regular and irregular shapes includes:

[0032] Use equation (2) to determine the fractal dimension of the rich collective;

[0033] (2);

[0034] Where: N ( () represents the current side length of the box. The minimum number of boxes required at that time;

[0035] If D f If ≈ the third predetermined value, then it is a regular shape; if D f The third predetermined value is an irregular shape.

[0036] Preferably, the method for evaluating the geometric characteristics of the enriched mass using volume ellipticity and classifying regular-shaped enriched masses into ellipsoidal and oblong shapes includes:

[0037] The volume ellipticity of the rich aggregate is determined using equation (3);

[0038] (3);

[0039] In the formula: a, b, and c are the three principal axes radii of the rich collective;

[0040] If e v If e ≤ the fourth predetermined value, then it is an ellipsoid; if e v The fourth predetermined value is an elongated shape.

[0041] Preferably, the method for classifying and distinguishing the sedimentary characteristics of the Class I and Class II rich landmasses based on the three-dimensional geological model includes:

[0042] Output sedimentary facies data volume based on the aforementioned three-dimensional geological model;

[0043] Based on the grid location of each rich aggregate, relevant information in the sedimentary facies data volume is extracted. According to the proportion of different facies grids, the rich aggregates are divided into three sedimentary facies types: homogeneous, single-phase variation, and multi-phase variation.

[0044] One method for classifying rich aggregates into three sedimentary facies types includes:

[0045] If the proportion of the same phase grid is greater than the first predetermined percentage, it is a single phase; if there are two or more phase types, and the total proportion of any two of them is greater than the first predetermined percentage, it is a single-phase change; if there are two or more phase types, and the total proportion of any two of them is less than the first predetermined percentage, it is a multi-phase change.

[0046] Preferably, the method for classifying and identifying the internal interlayer characteristics of the Class I and Class II large-scale rich landmasses based on the three-dimensional geological model includes:

[0047] Output lithological data volume based on the three-dimensional geological model;

[0048] Based on the grid location of each rich aggregate, mudstone structures in the lithological data volume are extracted. Based on the continuity of mudstone, the rich aggregates are divided into three interlayer feature types: unobstructed, partially obstructed, and continuously obstructed.

[0049] Among them, the method for classifying rich collectives into three types of mezzanine features includes:

[0050] If the mudstone continuity in a rich aggregate exceeds the second predetermined percentage, it is considered continuous shading; if the mudstone continuity in a rich aggregate exceeds the third predetermined percentage, it is considered partial shading; otherwise, it is considered unshading.

[0051] Preferably, the mudstone continuity is calculated using equation (4);

[0052] (4);

[0053] In the formula: The number of grids with mudstone as the lithology in the j-th layer of the vertical grid from top to bottom in the i-th rich aggregate; Let be the total number of grid cells in the j-th layer vertically from top to bottom in the i-th rich cluster.

[0054] Preferably, the method for classifying and distinguishing the physical property characteristics of the Class I and Class II large-scale rich landmasses based on the three-dimensional geological model includes:

[0055] Output permeability data based on the three-dimensional geological model;

[0056] Based on the grid location of each rich aggregate, relevant information in the permeability data volume is extracted, and the rich aggregates are divided into three physical property types: relatively homogeneous, moderately heterogeneous, and strongly heterogeneous, according to the Moran index of permeability.

[0057] One method for classifying rich collectives into three types of physical property characteristics includes:

[0058] If the Moran's index of penetration is greater than the fifth predetermined value, it is relatively homogeneous; if the Moran's index of penetration is between -the fifth predetermined value and the fifth predetermined value, it is moderately heterogeneous; if the Moran's index of penetration is less than -the fifth predetermined value, it is strongly heterogeneous.

[0059] Preferably, the Moran index of permeability is calculated using equation (5);

[0060] (5);

[0061] In the formula: n is the number of grid cells in the connected component; k i and k j These are the permeability values ​​at positions i and j, respectively. The average of all permeability values; w ij Let be the element in the spatial weight matrix, representing the distance between positions i and j; W is the distance between all w. ij A comprehensive summary.

[0062] Preferably, the method for classifying the potential quality of the first-class and second-class large-scale collectives based on the discrimination result includes:

[0063] Based on the spatial morphology, sedimentary characteristics, internal interlayer characteristics, and physical property characteristics, potential quality evaluation parameters are constructed, and the evaluation parameters are divided into two categories, high quality and general, using a clustering algorithm.

[0064] Among them, the potential quality evaluation parameters are as shown in equation (6);

[0065] (6);

[0066] In the formula: a, b, c, and d are the weights of the evaluation parameters for spatial morphology, sedimentary characteristics, interlayer characteristics, and physical properties, respectively; F morph For spatial morphology evaluation parameters; F facies For sedimentary facies evaluation parameters; F lith For evaluating the characteristics of the interlayer; F per These are parameters for evaluating physical properties.

[0067] Preferably, in equation (6), the values ​​of a, b, c, and d are 0.4, 0.2, 0.1, and 0.3, respectively;

[0068] Among them, F morph The value is 3 for an ellipsoid, 2 for an oblong shape, and 1 for an irregular shape; F facies The value is 3 for in-phase conversion, 2 for single-phase conversion, and 1 for multi-phase conversion; F lith The value is 3 for no occlusion, 2 for partial occlusion, and 1 for continuous occlusion; F per The value is 3 for relatively homogeneous conditions, 2 for moderately heterogeneous conditions, and 1 for strongly heterogeneous conditions.

[0069] According to one aspect of this disclosure, a residual oil enrichment scale classification and evaluation device is provided, comprising:

[0070] The acquisition unit is used to acquire the three-dimensional geological model of the study area, the numerical model at the current time, and the development characteristics of the study area.

[0071] The saturation model construction unit is used to determine the minimum threshold of remaining oil saturation based on the development characteristics, and to filter the oil saturation data at the current moment in the numerical model to construct the target saturation model.

[0072] Rich collective labeling unit is used to identify and label the remaining oil rich collectives in the grid connected to the target saturation model, and output a new data volume containing rich collective labels;

[0073] Rich collective classification unit, used to classify rich collectives in a new data volume into three categories of large-scale rich collectives using distance coefficients;

[0074] The three-category rich collective potential evaluation unit is used to classify the potential quality of the three-category rich collectives based on the three-dimensional geological model.

[0075] The first and second category rich aggregate classification and discrimination unit is used to classify and discriminate the first and second category rich aggregates in terms of spatial morphology based on the regularity and geometric characteristics of the rich aggregate morphology. Based on the three-dimensional geological model, the first and second category rich aggregates are classified and discriminated based on sedimentary characteristics, internal interlayer characteristics and physical property characteristics, respectively.

[0076] The first and second category rich collective potential evaluation unit is used to classify the potential quality of the first and second category large-scale rich collectives based on the discrimination results.

[0077] The present invention has at least the following beneficial effects:

[0078] This invention proposes a method and apparatus for classifying and evaluating the scale of remaining oil enrichment. By labeling remaining oil-rich areas on selected target saturation models and classifying the scale of these enrichment areas, the method combines a three-dimensional geological model with different categories for classification and discrimination, ultimately determining the final potential quality classification result. This enables rapid evaluation of the potential quality of target objects based on geological information, solves the problem of identifying potential targets for different development measures, is applicable to various development methods, and lays the foundation for real-time potential tapping. Attached Figure Description

[0079] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present invention and, together with the specification, serve to explain the technical solutions of the present invention.

[0080] Figure 1 A flowchart illustrating a method for classifying and evaluating the enrichment scale of residual oil according to an embodiment of the present invention is shown. Detailed Implementation

[0081] Various exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0082] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0083] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0084] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0085] Figure 1 A flowchart illustrating a method for classifying and evaluating the enrichment scale of residual oil according to an embodiment of the present invention is shown. Figure 1 As shown, a method for classifying and evaluating the scale of remaining oil enrichment includes: Step S01: acquiring a three-dimensional geological model of the study area, a numerical model at the current time, and the development characteristics of the study area; Step S02: determining the minimum threshold for remaining oil saturation based on the development characteristics, filtering the oil saturation data at the current time in the numerical model based on the threshold, and constructing a target saturation model; Step S03: identifying and labeling the remaining oil-rich groups connected to the grid of the target saturation model, and outputting a new data volume containing the rich group labels; Step S04: using a distance coefficient to classify the new data volume... The rich aggregates are divided into Class I, Class II, and Class III scale rich aggregates; Step S05: According to the three-dimensional geological model, the three scale rich aggregates are classified into potential quality categories; Step S06: According to the morphological regularity and geometric characteristics of the rich aggregates, the Class I and Class II scale rich aggregates are classified into spatial morphology categories, and according to the three-dimensional geological model, the Class I and Class II scale rich aggregates are classified into sedimentary characteristics, internal interlayer characteristics, and physical property characteristics, respectively; Step S07: According to the classification results, the Class I and Class II scale rich aggregates are classified into potential quality categories.

[0086] The residual oil enrichment scale classification and evaluation method provided in this embodiment of the invention specifically includes the following steps:

[0087] Step S01: Obtain the three-dimensional geological model of the study area, the numerical model at the current time, and the development characteristics of the study area.

[0088] In this invention, the development characteristics are obtained based on the development history of the study area, the analysis of multiple rounds of adjustment schemes in history, and their development effects.

[0089] In this embodiment of the invention, basic data collection and organization are performed. This includes collecting the three-dimensional geological model of the study area, the numerical model of the study area up to the current time (Model-0), and development characteristics. Specifically, this includes:

[0090] Collect a three-dimensional geological model containing static data of all single wells and geological stratification data of the target stratum; output well location data volume (Well-model), sedimentary facies data volume (Facies-model), lithology data volume (Lith-model) and permeability data volume (Per-model) based on the three-dimensional geological model.

[0091] Obtain the numerical model (Model-0) of the study area at the current moment and determine the grid information of the numerical model;

[0092] Determine the development history of the study area, the multiple rounds of adjustment plans in history and their development effects, and determine the development characteristics based on these.

[0093] Taking a certain study region M as an example, the area of ​​study region M is 198 km². 2 There are 14,664 wells and 100 sedimentary units. It has been developed for 58 years and currently has a water cut of 95.1%. The reservoir model has a planar grid of 30 meters × 30 meters and a vertical grid divided according to sedimentary units.

[0094] Step S02: Based on the development characteristics, determine the minimum threshold of remaining oil saturation, filter the oil saturation data at the current moment in the numerical model according to it, and construct the target saturation model.

[0095] In this invention, the minimum threshold for residual oil saturation includes, but is not limited to: residual oil saturation under different permeability conditions, the minimum oil saturation that meets the conditions for profitable development, and the oil saturation corresponding to the water content limit of the target area.

[0096] In this invention, the target saturation model includes at least sedimentary facies, lithology, and permeability data in the grid corresponding to each individual well.

[0097] In this embodiment of the invention, based on the economic and technical limits (development characteristics) of water-driven precision tapping potential in the M study area, the minimum threshold for remaining oil saturation is determined to be 50%.

[0098] Using software such as Petrel, the current oil saturation data (So-Model1) in the numerical model (Model-0) is called. The model (So-Model1) is filtered according to the set minimum threshold of remaining oil saturation, and only grid cells with oil saturation higher than 50% are retained to construct the target saturation model (So-Model2). The grids corresponding to all single wells after filtering contain data such as sedimentary facies, lithology, and permeability.

[0099] Step S03: Identify and label the remaining oil-rich collectives in the grid-connected target saturation model, and output a new data volume containing the labels of the rich collectives.

[0100] In this invention, the method for identifying and marking the remaining oil-rich collectives connected to the target saturation model grid includes: using a 26-neighborhood connected component marking algorithm to identify and mark the remaining oil-rich collectives connected to the target saturation model grid.

[0101] In this embodiment of the invention, for the target saturation model (So-Model2), the 26-neighborhood connected component labeling (CCL) algorithm is used to label the remaining oil-rich collectives in the grid-connected network. i The system identifies and labels 16,024 rich groups, generates a unique label for each rich group, and outputs a new data volume (So-Model3) containing the labels of the 16,024 rich groups.

[0102] Among them, the remaining oil-rich collective Oil-connected i In the equation, i = 1, 2, ..., n, where n is the total number of remaining oil-rich groups.

[0103] Step S04: Use distance coefficients to classify the rich groups in the new data volume into three categories of large-scale rich groups.

[0104] In this invention, the method of classifying rich groups in a new data volume into three categories of rich groups using distance coefficients includes: determining the distance coefficient of each rich group in the new data volume using equation (1);

[0105] (1);

[0106] In the formula: L is the distance coefficient for the i-th rich group; well The minimum well spacing for oil and water wells, in meters (m); L model Let m be the planar mesh step size of the numerical model; j is the number of meshes in the i-th rich set.

[0107] If the distance coefficient is less than the first predetermined value, it belongs to category three; if it is greater than or equal to the first predetermined value and less than the second predetermined value, it belongs to category two; if it is greater than or equal to the second predetermined value, it belongs to category one.

[0108] In this embodiment of the invention, the remaining oil-rich collectives (Oil-connected) of each individual tag in the new data volume (So-Model3) are traversed. i Using the distance coefficient Node as shown in equation (1) i Oil-connected iThe dataset is divided into three enrichment scales: Class I, Class II, and Class III, and the output is a new data volume (So-Model4) containing the classification results, with 1646 Class I rich groups, 3147 Class II rich groups, and 11231 Class III rich groups.

[0109] The first predetermined value is 0.5, and the second predetermined value is 2.0. If Node i If <0.5, it belongs to the third category of large-scale affluent collectives, suitable for policy adjustments; if Node i If the value is between [0.5, 2.0), it is considered a second-class large-scale collective, suitable for targeted potential tapping; if Node i If the value is ≥2.0, it is considered a large-scale collective, suitable for targeted potential tapping or local well network adjustment.

[0110] Step S05: Based on the three-dimensional geological model, classify the potential quality of the three types of large-scale rich collectives.

[0111] In this invention, the method for classifying the potential quality of the three types of rich aggregates based on the three-dimensional geological model includes: outputting a well location data volume based on the three-dimensional geological model; determining the spatial relative position of each rich aggregate in the three types of rich aggregates to the production well in the well location data volume; if there is a production well location in the rich aggregate and its surrounding area, the rich aggregate is determined to be of type three, which is easily movable; if there is no production well location, the rich aggregate is determined to be of type three, which is stagnant.

[0112] In this embodiment of the invention, the large-scale rich aggregates with initial potential types of three are traversed in the new data volume (So-Model4). Based on the spatial relative position of each rich aggregate to the production well in the well location data volume (Well-model), the three types of large-scale rich aggregates are classified into three categories of mobile and three categories of stagnant. The specific rules are as follows:

[0113] If there are production wells in the rich collective and its eight directly adjacent grids, the rich collective is classified as a Class III easily movable collective; if there are no production wells in the rich collective and its eight directly adjacent grids, the rich collective is classified as a Class III stagnant collective.

[0114] The final list of three types of easily movable wealthy groups is 4,942, and the three types of stagnant wealthy groups is 6,289. The potential quality classification results of the three types of large-scale wealthy groups are output (Type3-Model).

[0115] Step S06: Based on the regularity and geometric characteristics of the rich aggregate morphology, the rich aggregates of Class I and Class II scales are classified and identified in terms of spatial morphology. Based on the three-dimensional geological model, the rich aggregates of Class I and Class II scales are classified and identified in terms of sedimentary characteristics, internal interlayer characteristics, and physical property characteristics.

[0116] In this invention, the method for spatially classifying rich aggregates of type I and type II based on their morphological regularity and geometric characteristics includes: classifying rich aggregates into three spatial morphological types: ellipsoidal, elongated, and irregular, based on their morphological regularity and geometric characteristics; wherein, the method for classifying rich aggregates into these three spatial morphological types includes: evaluating the morphological regularity of the rich aggregate using fractal dimension, classifying the rich aggregate into regular and irregular shapes; and in regular rich aggregates, evaluating their geometric characteristics using volume ellipticity, classifying regular rich aggregates into ellipsoidal and elongated shapes.

[0117] In this invention, the method of evaluating the morphological regularity of the rich collective using fractal dimension and dividing the rich collective into regular and irregular shapes includes: determining the fractal dimension of the rich collective using equation (2).

[0118] (2);

[0119] Where: N ( () represents the current side length of the box. The minimum number of boxes required at that time;

[0120] If D f If ≈ the third predetermined value, then it is a regular shape; if D f The third predetermined value is an irregular shape.

[0121] In this invention, the method for evaluating the geometric characteristics of the enriched mass using volume ellipticity and classifying regular-shaped enriched masses into ellipsoidal and oblong shapes includes:

[0122] The volume ellipticity of the rich aggregate is determined using equation (3);

[0123] (3);

[0124] In the formula: a, b, and c are the three principal axes radii of the rich collective;

[0125] If e v If e ≤ the fourth predetermined value, then it is an ellipsoid; if e v The fourth predetermined value is an elongated shape.

[0126] In this embodiment of the invention, the first and second class rich groups in the new data volume (So-Model4) are traversed, and based on the morphological regularity and geometric characteristics of the rich groups, the rich groups (Oil-connected) are classified. i It is divided into three spatial morphological types: ellipsoidal, oblong, and irregular. Specifically, it includes:

[0127] Using fractal dimension D fTo evaluate the morphological regularity of this rich collective, the rich collective is divided into regular and irregular shapes; where the fractal dimension D... f The box counting method is used to calculate the fractal dimension, which involves placing the connected object in a progressively finer grid and counting the number of boxes required to cover the entire object each time. As the grid size decreases, the number of boxes required increases. By plotting the logarithmic relationship between the number of boxes and the box size, the fractal dimension can be estimated from the slope. The formula is shown in equation (2).

[0128] The third predetermined value is 3. If D f If ≈3, then it is a regular shape; if D f If the value is greater than 3, then it is an irregular shape.

[0129] In a regular-shaped rich aggregate, the volume ellipticity e is used. v To evaluate the geometric characteristics of the rich aggregate, the regular rich aggregate is divided into ellipsoidal and oblong shapes. The formula for calculating the volume ellipticity is shown in equation (3). The fourth predetermined value is 0.5. If e v If e ≤ 0.5, then it is an ellipsoid; if e v If the value is greater than 0.5, it is an elongated shape.

[0130] Finally, 4793 spatial morphological classification results for Type I and Type II large-scale rich collectives were output (Type 1-connected). morph Type 2-connected morph ).

[0131] In this invention, the method for classifying and identifying sedimentary characteristics of Class I and Class II rich aggregates based on the three-dimensional geological model includes: outputting sedimentary facies data volumes based on the three-dimensional geological model; extracting relevant information from the sedimentary facies data volumes based on the grid positions of each rich aggregate; and classifying the rich aggregates into three sedimentary facies types—homophonic, single-facies variation, and multi-facies variation—based on the proportion of different facies grid numbers. The method for classifying rich aggregates into these three sedimentary facies types includes: if the proportion of the same facies grid number is greater than a first predetermined percentage, it is considered holophagic; if there are two or more facies types, and the total proportion of any two of them is greater than the first predetermined percentage, it is considered single-facies variation; if there are two or more facies types, and the total proportion of any two of them is less than the first predetermined percentage, it is considered multi-facies variation.

[0132] In this embodiment of the invention, the first and second-class rich collectives in the new data volume (So-Model4) are traversed. Relevant information from the sedimentary facies data volume (Facies-model) is extracted based on the grid location of each rich collective. Based on the proportion of grids of different facies types, the rich collectives (Oil-connected) are classified. i It is classified into three sedimentary facies types: homophase, single-phase variation, and multiphase variation.

[0133] The first predetermined percentage is 80%. If the percentage of the same phase grid is greater than 80%, it is a single phase; if there are more than two phase types, but the total percentage of any two phases is greater than 80%, it is a single-phase change; if there are more than two phase types, but the total percentage of any two phases is less than 80%, it is a multi-phase change.

[0134] Finally, 4793 classification results of rich collective sedimentary features of Type I and Type II scales were output (Type 1-connected) facies Type 2-connected facies ).

[0135] In this invention, the method for classifying and identifying the internal interlayer characteristics of Class I and Class II rich aggregates based on the three-dimensional geological model includes: outputting lithological data volumes based on the three-dimensional geological model; extracting mudstone structures from the lithological data volumes according to the grid position of each rich aggregate; and classifying the rich aggregates into three interlayer characteristic types—unobstructed, partially obstructed, and continuously obstructed—based on mudstone continuity; wherein, the method for classifying rich aggregates into the three interlayer characteristic types includes: if the mudstone continuity in the rich aggregate is greater than a second predetermined percentage, it is continuously obstructed; if the mudstone continuity in the rich aggregate is greater than a third predetermined percentage, it is partially obstructed; otherwise, it is unobstructed.

[0136] In this invention, the mudstone continuity is calculated using equation (4);

[0137] (4);

[0138] In the formula: The number of grids with mudstone as the lithology in the j-th layer of the vertical grid from top to bottom in the i-th rich aggregate; Let be the total number of grid cells in the j-th layer vertically from top to bottom in the i-th rich cluster.

[0139] In this embodiment of the invention, the first and second-class rich aggregates in the data volume (So-Model4) are traversed, and the mudstone structure (Shale-model) in the lithological data volume (Lith-model) is extracted according to the grid position of each rich aggregate. i Based on the continuity of mudstone, the oil-connected aggregates are classified as... i The features are categorized into three types: unoccluded, partially occluded, and continuously occluded.

[0140] The mudstone continuity is calculated according to equation (8). The second predetermined percentage is 80%, and the third predetermined percentage is 40%. If the i-th enriched mass contains... If >80%, it is considered continuous occlusion; if the i-th rich set contains If the obstruction rate is greater than 40%, it is considered partial obstruction; the rest is considered unobstructed.

[0141] Finally, 4793 classification results of interlayer features within large-scale groups of Type I and II were output (Type 1-connected). lith Type 2-connected lith ).

[0142] In this invention, the method for classifying and identifying the physical properties of Class I and Class II rich aggregates based on the three-dimensional geological model includes: outputting permeability data volumes based on the three-dimensional geological model; extracting relevant information from the permeability data volumes based on the grid position of each rich aggregate; and classifying the rich aggregates into three physical property types—relatively homogeneous, moderately heterogeneous, and strongly heterogeneous—based on the Moran's permeability index. The method for classifying rich aggregates into these three physical property types includes: if the Moran's permeability index is greater than a fifth predetermined value, it is considered relatively homogeneous; if the Moran's permeability index is between -5 and 5 predetermined values, it is considered moderately heterogeneous; and if the Moran's permeability index is less than -5 predetermined values, it is considered strongly heterogeneous.

[0143] In this invention, the Moran index of permeability is calculated using equation (5);

[0144] (5);

[0145] In the formula: n is the number of grid cells in the connected component; k i and k j These are the permeability values ​​at positions i and j, respectively. The average of all permeability values; w ij Let be the element in the spatial weight matrix, representing the distance between positions i and j; W is the distance between all w. ij A comprehensive summary.

[0146] In this embodiment of the invention, the first and second-class rich clusters in the data volume (So-Model4) are traversed, and relevant information in the permeability data volume (Per-model) is extracted according to the grid position of each rich cluster. Based on the Moran's permeability index, the rich clusters (Oil-connected) are then classified. i The physical properties are classified into three types: relatively homogeneous, moderately heterogeneous, and strongly heterogeneous.

[0147] The Moran's index for penetration is shown in equation (5). The fifth predetermined value is 0.5. If I per If I > 0.5, then it is relatively homogeneous. per If the value is between -0.5 and 0.5, it is considered moderately heterogeneous; if I per If the value is less than -0.5, it is considered strongly heterogeneous.

[0148] Finally, 4793 classification results of rich aggregate property features of the first and second class were output (Type 1-connected) per Type 2-connected per ).

[0149] Step S07: Based on the discrimination results, perform potential quality classification on the first and second categories of large-scale rich collectives.

[0150] In this invention, the method for classifying the potential quality of the first-class and second-class large-scale rich collectives according to the discrimination result includes: constructing potential quality evaluation parameters based on the spatial morphology, sedimentary characteristics, internal interlayer characteristics and physical property characteristics, and dividing the evaluation parameters into two categories, high-quality and general, by a clustering algorithm; wherein, the constructed potential quality evaluation parameters are shown in Equation (6);

[0151] (6);

[0152] In the formula: a, b, c, and d are the weights of the evaluation parameters for spatial morphology, sedimentary characteristics, interlayer characteristics, and physical properties, respectively; F morph For spatial morphology evaluation parameters; F facies For sedimentary facies evaluation parameters; F lith For evaluating the characteristics of the interlayer; F per These are parameters for evaluating physical properties.

[0153] In this embodiment of the invention, based on the morphology of rich collectives (Type-connected) morph ), sedimentary facies (Type-connected) facies Type-connected features lith ) and physical properties (Type-connected) per The potential quality evaluation parameter Factor is constructed as shown in Equation (6), and the Factor parameter is divided into high-quality and general by K-means clustering algorithm. Finally, a total of 623 high-quality potentials and 4169 general potentials are divided, and the potential quality classification results of the first and second class scale rich groups (Type12-Model) are output.

[0154] In this invention, in equation (6), the weights a, b, c, and d are 0.4, 0.2, 0.1, and 0.3 respectively, based on the reservoir characteristics and development method of the study area;

[0155] Among them, the spatial morphology evaluation parameter F morph The value is 3 for ellipsoidal shapes, 2 for oblong shapes, and 1 for irregular shapes; sedimentary facies evaluation parameter Ffacies The value is 3 for in-phase, 2 for single-phase change, and 1 for multi-phase change; Sandwich characteristic evaluation parameter F lith The value is 3 for no occlusion, 2 for partial occlusion, and 1 for continuous occlusion; Physical property evaluation parameter F per The value is 3 for relatively homogeneous conditions, 2 for moderately heterogeneous conditions, and 1 for strongly heterogeneous conditions.

[0156] In this embodiment of the invention, the potential quality classification results of three types of enriched collectives (Type3-Model), namely three types of mobile and three types of stagnant, and the potential quality classification results of one and two types of enriched collectives (Type12-Model), namely high quality and general quality, are combined; and the Petrel software is used to characterize the remaining oil space of 16,024 different enrichment scales and different potential qualities.

[0157] It is understood that the various method embodiments mentioned above in this invention can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this invention will not elaborate further.

[0158] The entity executing the residual oil enrichment scale classification and evaluation method can be a residual oil enrichment scale classification and evaluation device. For example, the method can be executed by a terminal device, server, or other processing device. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, the residual oil enrichment scale classification and evaluation method can be implemented by a processor calling computer-readable instructions stored in memory.

[0159] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0160] In this invention, a residual oil enrichment scale classification and evaluation device includes: an acquisition unit for acquiring a three-dimensional geological model of the study area, a numerical model at the current time, and the development characteristics of the study area; a saturation model construction unit for determining a minimum threshold for residual oil saturation based on the development characteristics, filtering the oil saturation data at the current time in the numerical model based on the threshold, and constructing a target saturation model; a rich-collection labeling unit for identifying and labeling residual oil rich-collections connected to the grid of the target saturation model, and outputting a new data volume containing rich-collection labels; and a rich-collection category classification unit for classifying rich-collections in the new data volume using a distance coefficient. The geological model classifies rich agglomerates into three categories: Category I, Category II, and Category III. A Category III rich agglomerate potential evaluation unit is used to classify the potential quality of these rich agglomerates based on the three-dimensional geological model. A Category I and Category II rich agglomerate classification and discrimination unit is used to classify the Category I and Category II rich agglomerates based on their spatial morphology regularity and geometric characteristics, and to classify them based on sedimentary characteristics, internal interlayer characteristics, and physical properties according to the three-dimensional geological model. A Category I and Category II rich agglomerate potential evaluation unit is used to classify the potential quality of these rich agglomerates based on the discrimination results.

[0161] In some embodiments, the functions or modules and units included in the apparatus provided in this disclosure can be used to execute the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0162] This disclosure also proposes a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the aforementioned method for classifying and evaluating the enrichment scale of residual oil. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0163] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-described method for classifying and evaluating the enrichment scale of residual oil. The electronic device may be provided as a terminal, a server, or other form of device.

[0164] This invention solves the problem of existing technologies being unable to classify and evaluate the scale of remaining oil spatial enrichment. Based on screening oil-saturation enrichment zones with specific thresholds, it classifies remaining oil into three scales of enriched zones through enriched zone calibration and distance coefficient calculation. This allows for potential type classification for different development methods such as local well network adjustment, targeted potential tapping, and measure adjustment. Using the relative spatial location of wells, the three scales of enriched zones with relatively poor potential are classified into two potential quality types: easily movable and stagnant, providing a reference for the formulation of measure adjustment strategies. The fractal dimension and volume ellipticity of the enriched zones are used to classify and evaluate the spatial enrichment morphology of remaining oil. Furthermore, the proportion of facies grids, mudstone continuity, and Moran's permeability index are used to classify and evaluate various geological information, constructing potential quality evaluation parameters that integrate the spatial morphology and geological information of remaining oil. This enables the classification and evaluation of the potential quality of first and second scale enriched zones, providing support and guidance for the formulation of schemes such as local well network adjustment, in-situ deep three-dimensional potential tapping, and targeted potential tapping of remaining oil at different enrichment scales.

[0165] This invention addresses the challenges of recognizing and characterizing the scale and distribution morphology of remaining oil accumulation, evaluating reservoir heterogeneity, and improving the timeliness of deep three-dimensional development. It features three key innovations: First, it provides criteria for judging the scale of remaining oil accumulation, identifying target objects for screening and making oil-rich reservoirs of different sizes more targeted and applicable to different development methods. Second, it achieves spatial morphological classification of oil-rich reservoirs through quantitative evaluation of their morphological regularity and geometric characteristics, solving the problem of identifying potential targets for different development measures. Third, it enables rapid evaluation of the potential quality of target objects based on geological information evaluation through the nesting of oil-rich reservoirs and geological models, laying the foundation for real-time potential tapping.

[0166] The present invention has at least the following beneficial effects: 1. The present invention relates to the data basis of technical methods, which is to evaluate the reservoir model that is essential for formulating development strategies in oilfields and to establish a classification evaluation method for the enrichment scale of remaining oil without increasing any costs; 2. The evaluation indicators have a high degree of integration and strong reference value, taking into account both the saturation and spatial morphology required for development and the heterogeneous characteristics of the reservoir, reducing the workload of geological and remaining oil step-by-step analysis and improving work efficiency; 3. It has a wide range of applications, is flexible and easy to operate, and provides more scientific guidance for the implementation of development adjustment measures.

[0167] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for classifying and evaluating the scale of residual oil enrichment, characterized in that, include: Obtain the three-dimensional geological model of the study area, the numerical model at the current moment, and the development characteristics of the study area; Based on the aforementioned development characteristics, a minimum threshold for remaining oil saturation is determined, and the oil saturation data at the current moment in the numerical model is filtered according to this threshold to construct a target saturation model. Identify and label the remaining oil-rich collectives in the grid-connected target saturation model, and output a new data volume containing the labels of the rich collectives; The rich groups in the new data volume are divided into three categories based on size: Class I, Class II, and Class III rich groups using distance coefficients. Based on the three-dimensional geological model, the potential quality of the three types of large-scale rich landmasses is classified. Based on the regularity and geometric characteristics of the rich aggregate morphology, the rich aggregates of the first and second scales are classified and identified by spatial morphology. Based on the three-dimensional geological model, the rich aggregates of the first and second scales are classified and identified by sedimentary characteristics, internal interlayer characteristics and physical property characteristics, respectively. Based on the discrimination results, the potential quality classification is performed on the first and second categories of large-scale rich collectives.

2. The method for classifying and evaluating the scale of residual oil enrichment according to claim 1, characterized in that: The minimum threshold for residual oil saturation includes, but is not limited to: residual oil saturation under different permeability conditions, the minimum oil saturation that meets the conditions for profitable development, and the oil saturation corresponding to the water content limit of the target area.

3. The method for classifying and evaluating the scale of residual oil enrichment according to claim 1, characterized in that: In the target saturation model, the grid corresponding to each single well contains at least sedimentary facies, lithology, and permeability data.

4. The method for classifying and evaluating the scale of residual oil enrichment according to claim 1, characterized in that: The development characteristics were obtained based on the development history of the study area, the analysis of multiple rounds of adjustment schemes in history, and their development effects.

5. The method for classifying and evaluating the scale of residual oil enrichment according to claim 1, characterized in that, The method for identifying and marking residual oil-rich aggregates in a grid-connected target saturation model includes: The 26-neighborhood connected component labeling algorithm is used to identify and label the remaining oil-rich collectives in the grid-connected target saturation model.

6. The method for classifying and evaluating the scale of residual oil enrichment according to claim 1, characterized in that, The method for classifying rich groups in a new data volume into Class I, Class II, and Class III rich groups using distance coefficients includes: Use equation (1) to determine the distance coefficient of each rich group in the new data volume; （1）； In the formula: L is the distance coefficient for the i-th rich group; well The minimum well spacing for oil and water wells, in meters (m); L model Let m be the planar mesh step size of the numerical model; j is the number of meshes in the i-th rich set. If the distance coefficient is less than the first predetermined value, it belongs to category three; if it is greater than or equal to the first predetermined value and less than the second predetermined value, it belongs to category two; if it is greater than or equal to the second predetermined value, it belongs to category one.

7. The method for classifying and evaluating the scale of residual oil enrichment according to claim 1, characterized in that, The method for classifying the potential quality of the three types of large-scale rich landmasses based on the three-dimensional geological model includes: Output well location data based on the three-dimensional geological model; The spatial relative position of each rich collective in the three types of rich collectives to the production well in the well location data volume is determined. If there is a production well location in the rich collective and its surrounding area, the rich collective is determined to be a type three movable; if there is no production well location, the rich collective is determined to be a type three stagnant.

8. The method for classifying and evaluating the scale of residual oil enrichment according to claim 1, characterized in that, The method for spatial morphological classification and discrimination of the first- and second-class large-scale large collectives based on the regularity and geometric characteristics of the large collective morphology includes: Based on the regularity and geometric characteristics of the rich collective morphology, the rich collective is divided into three spatial morphology types: ellipsoidal, oblong, and irregular. One method for classifying wealthy communities into three spatial morphological types includes: The morphological regularity of the rich collective is evaluated using fractal dimension, and the rich collective is divided into regular and irregular shapes. In regular-shaped rich aggregates, the geometric characteristics of the rich aggregates are evaluated using volume ellipticity, and regular-shaped rich aggregates are divided into ellipsoidal and oblong shapes.

9. The method for classifying and evaluating the scale of residual oil enrichment according to claim 8, characterized in that, The method for evaluating the morphological regularity of the rich collective using fractal dimension and classifying the rich collective into regular and irregular shapes includes: Use equation (2) to determine the fractal dimension of the rich collective; （2）； Where: N ( () represents the current side length of the box. The minimum number of boxes required at that time; If D f If ≈ the third predetermined value, then it is a regular shape; if D f The third predetermined value is an irregular shape.

10. The method for classifying and evaluating the enrichment scale of residual oil according to claim 8, characterized in that, The method for evaluating the geometric characteristics of the enriched mass using volume ellipticity and classifying regular-shaped enriched masses into ellipsoidal and oblong shapes includes: The volume ellipticity of the rich aggregate is determined using equation (3); （3）； In the formula: a, b, and c are the three principal axes radii of the rich collective; If e v If e ≤ the fourth predetermined value, then it is an ellipsoid; if e v The fourth predetermined value is an elongated shape.

11. The method for classifying and evaluating the enrichment scale of residual oil according to claim 1, characterized in that, The method for classifying and distinguishing the sedimentary characteristics of the Class I and Class II rich landmasses based on the three-dimensional geological model includes: Output sedimentary facies data volume based on the aforementioned three-dimensional geological model; Based on the grid location of each rich aggregate, relevant information in the sedimentary facies data volume is extracted. According to the proportion of different facies grids, the rich aggregates are divided into three sedimentary facies types: homogeneous, single-phase variation, and multi-phase variation. One method for classifying rich aggregates into three sedimentary facies types includes: If the proportion of the same phase grid is greater than the first predetermined percentage, it is a single phase; if there are two or more phase types, and the total proportion of any two of them is greater than the first predetermined percentage, it is a single-phase change; if there are two or more phase types, and the total proportion of any two of them is less than the first predetermined percentage, it is a multi-phase change.

12. The method for classifying and evaluating the scale of residual oil enrichment according to claim 1, characterized in that, The method for classifying and identifying the internal interlayer characteristics of the Class I and Class II large-scale rich landmasses based on the three-dimensional geological model includes: Output lithological data volume based on the three-dimensional geological model; Based on the grid location of each rich aggregate, mudstone structures in the lithological data volume are extracted. Based on the continuity of mudstone, the rich aggregates are divided into three interlayer feature types: unobstructed, partially obstructed, and continuously obstructed. Among them, the method for classifying rich collectives into three types of mezzanine features includes: If the mudstone continuity in a rich aggregate exceeds the second predetermined percentage, it is considered continuous shading; if the mudstone continuity in a rich aggregate exceeds the third predetermined percentage, it is considered partial shading; otherwise, it is considered unshading.

13. The method for classifying and evaluating the enrichment scale of residual oil according to claim 12, characterized in that: The continuity of the mudstone is calculated using equation (4); （4）； In the formula: The number of grids with mudstone as the lithology in the j-th layer of the vertical grid from top to bottom in the i-th rich aggregate; Let be the total number of grid cells in the j-th layer vertically from top to bottom in the i-th rich cluster.

14. The method for classifying and evaluating the scale of residual oil enrichment according to claim 1, characterized in that, The method for classifying and distinguishing the physical properties of Class I and Class II large-scale rich landmasses based on the three-dimensional geological model includes: Output permeability data based on the three-dimensional geological model; Based on the grid location of each rich aggregate, relevant information in the permeability data volume is extracted, and the rich aggregates are divided into three physical property types: relatively homogeneous, moderately heterogeneous, and strongly heterogeneous, according to the Moran index of permeability. One method for classifying rich collectives into three types of physical property characteristics includes: If the Moran's index of penetration is greater than the fifth predetermined value, it is relatively homogeneous; if the Moran's index of penetration is between -the fifth predetermined value and the fifth predetermined value, it is moderately heterogeneous; if the Moran's index of penetration is less than -the fifth predetermined value, it is strongly heterogeneous.

15. The method for classifying and evaluating the enrichment scale of residual oil according to claim 14, characterized in that: The Moran index of penetration rate is calculated using equation (5); （5）； In the formula: n is the number of grid cells in the connected component; k i and k j These are the permeability values ​​at positions i and j, respectively. The average of all permeability values; w ij Let be the element in the spatial weight matrix, representing the distance between positions i and j; W is the distance between all w. ij A comprehensive summary.

16. The method for classifying and evaluating the enrichment scale of residual oil according to any one of claims 1-15, characterized in that, The method for classifying the potential quality of the first-class and second-class large-scale collectives based on the discrimination result includes: Based on the spatial morphology, sedimentary characteristics, internal interlayer characteristics, and physical property characteristics, potential quality evaluation parameters are constructed, and the evaluation parameters are divided into two categories, high quality and general, using a clustering algorithm. Among them, the potential quality evaluation parameters are as shown in equation (6); （6）； In the formula: a, b, c, and d are the weights of the evaluation parameters for spatial morphology, sedimentary characteristics, interlayer characteristics, and physical properties, respectively; F morph For spatial morphology evaluation parameters; F facies For sedimentary facies evaluation parameters; F lith For evaluating the characteristics of the interlayer; F per These are parameters for evaluating physical properties.

17. The method for classifying and evaluating the enrichment scale of residual oil according to claim 16, characterized in that: In equation (6), the values ​​of a, b, c, and d are 0.4, 0.2, 0.1, and 0.3, respectively; Among them, F morph The value is 3 for an ellipsoid, 2 for an oblong shape, and 1 for an irregular shape; F facies The value is 3 for in-phase conversion, 2 for single-phase conversion, and 1 for multi-phase conversion; F lith The value is 3 for no occlusion, 2 for partial occlusion, and 1 for continuous occlusion; F per The value is 3 for relatively homogeneous conditions, 2 for moderately heterogeneous conditions, and 1 for strongly heterogeneous conditions.

18. A device for classifying and evaluating the scale of residual oil enrichment, characterized in that, include: The acquisition unit is used to acquire the three-dimensional geological model of the study area, the numerical model at the current time, and the development characteristics of the study area. The saturation model construction unit is used to determine the minimum threshold of remaining oil saturation based on the development characteristics, and to filter the oil saturation data at the current moment in the numerical model to construct the target saturation model. Rich collective labeling unit is used to identify and label the remaining oil rich collectives in the grid connected to the target saturation model, and output a new data volume containing rich collective labels; Rich collective classification unit, used to classify rich collectives in a new data volume into three categories of large-scale rich collectives using distance coefficients; The three-category rich collective potential evaluation unit is used to classify the potential quality of the three-category rich collectives based on the three-dimensional geological model. The first and second category rich aggregate classification and discrimination unit is used to classify and discriminate the first and second category rich aggregates in terms of spatial morphology based on the regularity and geometric characteristics of the rich aggregate morphology. Based on the three-dimensional geological model, the first and second category rich aggregates are classified and discriminated based on sedimentary characteristics, internal interlayer characteristics and physical property characteristics, respectively. The first and second category rich collective potential evaluation unit is used to classify the potential quality of the first and second category large-scale rich collectives based on the discrimination results.

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