A well pattern control degree rapid evaluation method based on potential coefficient

Abstract

The present application relates to the technical field of oil development, in particular to a well pattern control degree rapid evaluation method based on potential coefficient. The method divides the evaluation area into layers and grids according to logging and geological data; the adjacent sample grids are optimized by comprehensively considering spatial distance and sedimentary facies characteristics, and the correction weight is calculated based on attribute difference to suppress abnormal interference, so that the improved interpolation algorithm is used to obtain high-precision grid interpolation attribute data; the injection-production connecting line is constructed based on the distribution of oil and water wells, and the potential coefficient of each grid is calculated by combining the minimum resistance path length and average permeability between the grid, the connecting line and the well point; the injection-production imperfect area is screened by using the potential coefficient. The present application can quickly and accurately locate the remaining oil enrichment area by introducing geological characteristics and noise-resistant mechanism to optimize the interpolation precision, and combining the flow resistance path to quantitatively evaluate the control degree, so as to provide a scientific basis for well pattern adjustment.

Description

Technical Field

[0001] This invention relates to the field of petroleum development technology, specifically to a rapid evaluation method for the degree of well network control based on potential coefficients. Background Technology

[0002] In oil and gas field development, the degree of water-drive reserve control is a key indicator for evaluating the effectiveness of injection-production well networks in reservoir control, and also an important basis for determining whether existing well networks require adjustments such as infill drilling, perforation repair, or injection conversion. When evaluating the degree of well network control across the entire evaluation area, continuous reservoir property data (such as reservoir thickness, permeability, and porosity) are needed. However, oilfield sites only possess measured data at the wellbore level (such as well logging interpretation results), lacking direct measurement data for large areas between wells, thus necessitating the reliance on spatial interpolation algorithms for inference.

[0003] Existing kriging interpolation algorithms typically search for neighboring sample points at a fixed number or distance, assuming a stationary normal distribution and generating a smooth attribute field. However, subsurface reservoirs often exhibit strong heterogeneity, with complex characteristics such as abrupt changes in sedimentary facies, fault shielding, and large permeability gradients. Existing kriging interpolation algorithms only consider spatial relationships while ignoring differences in geological features, easily leading to erroneous mixing of attribute data from different sedimentary facies zones or on both sides of faults, resulting in distorted interpolation results at boundaries (i.e., incorrect interpolation across geological boundaries). Furthermore, since oil and water wells are often unevenly distributed, sparsely distributed areas are susceptible to the influence of individual anomalous data points, producing a striping effect that further reduces the accuracy of attribute field reconstruction. The aforementioned interpolation errors directly cause subsequent calculations of inter-well flow resistance and control level evaluation results to deviate from reality, failing to accurately guide oilfield development adjustments. Summary of the Invention

[0004] To address the technical problem that existing Kriging interpolation algorithms cannot accurately perform interpolation, thus failing to accurately guide oilfield development adjustments, the present invention aims to provide a rapid evaluation method for well network control based on potential coefficients. The specific technical solution adopted is as follows:

[0005] This invention provides a method for rapid evaluation of well network control based on potential coefficients, the method comprising the following steps:

[0006] Acquire attribute data, well logging data, and geological data for each individual well within the area to be evaluated;

[0007] Based on well logging data and geological data, the area to be evaluated is geologically stratified and gridded to determine the interpolation grid and the sample grid of known attributes in each geological layer.

[0008] Based on the distance between each interpolation grid and each sample grid in each geological layer, and the differences in sedimentary facies characteristics, the neighboring sample grids of each interpolation grid are obtained; based on the differences in attribute data between neighboring sample grids, the correction weight of each neighboring sample grid is obtained; based on the correction weight and attribute data, the interpolation attribute data of each interpolation grid is obtained.

[0009] Based on the location distribution of water wells and oil wells in each geological layer, the injection-production connection between oil and water wells in each geological layer is obtained; based on the length of the minimum resistance path and the average permeability of each grid in each geological layer with the injection-production connection, oil well and water well, the potential coefficient of each grid in each geological layer is obtained.

[0010] Areas with incomplete injection and production processes in each geological layer are screened based on potential coefficients.

[0011] Furthermore, the method for obtaining the neighboring sample grid is as follows:

[0012] For any interpolation grid and any sample grid in any geological layer, the Euclidean distance between the center point coordinates of the interpolation grid and the sample grid is used as the distance reference value between the interpolation grid and the sample grid.

[0013] The lithology type of each grid in the geological layer is obtained by using a pixel-based lithology modeling algorithm. Sample grids with the same lithology type as the grid to be interpolated are all used as the first reference grids of the grid to be interpolated.

[0014] Obtain the reciprocal of the Euclidean distance between the interpolation grid and the center point coordinates of each of the first reference grids, and use it as the analysis weight for each first reference grid;

[0015] The analysis weights are arranged in descending order to obtain the analysis weight sequence;

[0016] The first reference grid corresponding to the first preset number of analysis weights in the analysis weight sequence is used as the specified analysis grid.

[0017] The well logging sequences in the well logging data of all the specified analysis grids are subjected to depth normalization, and the obtained sequence is obtained by weighted summation with the analysis weight of the specified analysis grid, which is used as the reference well logging sequence of the grid to be interpolated.

[0018] The DTW value of the well logging sequence of the reference grid and the well logging sequence of the sample grid are obtained by the dynamic time rule algorithm and the depth normalization process, and are used as the reference value of the difference in sedimentary facies characteristics between the grid to be interpolated and the sample grid.

[0019] The result of weighted summation of the distance reference value and the sedimentary facies characteristic difference reference value, followed by negative exponential normalization, is used as the degree of correlation between the grid to be interpolated and the sample grid.

[0020] The correlation degree between the grid to be interpolated and each sample grid in the geological layer is arranged in descending order to obtain a correlation degree sequence;

[0021] The sample grids corresponding to the first second preset number of correlation degrees in the correlation degree sequence are used as the neighboring sample grids of the grid to be interpolated.

[0022] Furthermore, the method for obtaining the corrected weights is as follows:

[0023] For any grid to be interpolated, all neighboring sample grids of the grid to be interpolated are used as analysis grids; for any analysis grid, the sequence of attribute data of the analysis grid is used as the attribute sequence of the analysis grid.

[0024] The result of weighted averaging of the attribute sequences of all analyzed grids is used as the first interpolation sequence for the grid to be interpolated.

[0025] The Euclidean distance between the attribute sequence of each analysis grid and the first interpolation sequence is used as the first reference distance for each analysis grid.

[0026] Based on the three-standard-deviation criterion, interference grids in the analysis grid are identified according to the first reference distance;

[0027] If interfering grids exist, the average of the first reference distances of all interfering grids is used as the second reference distance;

[0028] The normalized result of the ratio of the second reference distance to the sum of the first reference distance and the preset minimum positive number is used as the correction weight for each analysis grid.

[0029] If there are no interfering grids, the preset weights are set as the corrected weights for each analysis grid.

[0030] Furthermore, the method for obtaining the interpolation attribute data is as follows:

[0031] For any grid to be interpolated, the kriging weight coefficients of each neighboring sample grid are obtained by using the kriging interpolation algorithm;

[0032] The normalized product of the corrected weights and kriging weights of each neighboring sample grid of the grid to be interpolated is used as the comprehensive weight of each neighboring sample grid.

[0033] The weighted sum of the combined weights and attribute sequences of each neighboring sample grid of the grid to be interpolated is used as the interpolation attribute data of the grid to be interpolated.

[0034] Furthermore, the method for obtaining the injection-import connection is as follows:

[0035] For any water well in any geological layer, a search area is constructed with the water well as the center and the preset injection-production distance as the radius. All oil wells falling into the search area are considered as candidate oil wells.

[0036] For any candidate oil well, if there is a geological fault between the water well and the candidate oil well, the candidate oil well shall be eliminated.

[0037] Establish a connection between this water well and each remaining candidate oil well after the elimination operation, and use them as the initial injection-production connection;

[0038] Obtain the azimuth angle of each initial injection-sampling line. If the included angle between two initial injection-sampling lines is less than a preset angle threshold, the initial injection-sampling line with the longer length will be removed.

[0039] The initial injection-production connection remaining after removing all water wells in the geological layer is taken as the final injection-production connection for that geological layer.

[0040] Furthermore, the method for obtaining the potential coefficient is as follows:

[0041] For any grid in any geological layer, the Dijkstra algorithm is used to obtain the minimum seepage resistance path between the grid and the nearest water well in the geological layer as the first path, and the minimum seepage resistance path between the grid and the nearest oil well in the geological layer as the second path.

[0042] The length of the first path is taken as the water well resistance distance of the grid; the length of the second path is taken as the oil well resistance distance of the grid; the vertical distance between the grid and the nearest injection-production line is taken as the mainstream line distance of the grid.

[0043] If there is no valid injection-mining connection within the search area where the grid is located, the potential coefficient of the grid is directly set to the preset maximum value;

[0044] If a valid injection-production line exists, the location of the nearest oil well is taken as the origin, the direction vector from the nearest oil well to the nearest water well is taken as the reference axis, and the direction vector from the nearest oil well to the grid is taken as the target axis. The angle between the reference axis and the target axis is taken as the position angle.

[0045] Compare the mainstream line distance, water well resistance distance, and oil well resistance distance. If the mainstream line distance is the smallest, the potential coefficient calculation formula for this grid is: ;

[0046] If the well resistance distance is minimized, the potential coefficient of the grid is calculated using the following formula: ;

[0047] If the oil well resistance distance is minimized, the potential coefficient of this grid is calculated using the following formula: In the formula, S is the potential coefficient of the grid. This represents the distance between the main lines of the grid. This represents the well resistance distance within the grid. This represents the distance of the oil well resistance in this grid. To pre-determine a reasonable injection-production well spacing; This is the ratio of the average penetration rate of the first path to the average penetration rate of the second path. θ is the position angle; cos is the cosine function.

[0048] Furthermore, the method for obtaining the average penetration rate is as follows:

[0049] For either the first or second path, the formula for calculating the average permeability of that path is: ; In the formula, B is the average permeability of the path; B is the underground fluid volume coefficient of the path. Where is the fluid viscosity; I is the total number of grid cells traversed by the path; This represents the minimum seepage resistance along this path; Let this be the length of the i-th grid on the path, starting from this grid. The thickness of the i-th grid on the path, starting from this grid. Let i be the permeability of the i-th grid on the path, starting from this grid.

[0050] Furthermore, the method for screening incomplete injection-production areas in each geological layer based on potential coefficients is as follows:

[0051] For any grid in any geological layer, when the potential coefficient of the grid is greater than the preset potential coefficient threshold, the grid area is marked as a local imperfect area with weak injection-production control.

[0052] All locally imperfect areas in this geological layer are considered as imperfect injection and production areas in this geological layer.

[0053] Furthermore, the method for geologically stratifying the area to be evaluated based on well logging data and geological data is as follows:

[0054] Based on the sonic transit time curve and spontaneous potential curve in the well logging data, identify the lithological characteristics of the strata and the sandstone-mudstone interface in the area to be evaluated.

[0055] Based on the lithological identification results and sedimentary facies analysis results in the geological data, the sedimentary facies types at different depths in the area to be evaluated are obtained;

[0056] Depth intervals with the same sedimentary facies type and continuous lithological characteristics are divided into the same geological layer, thereby dividing the area to be evaluated into several geological layers vertically.

[0057] Furthermore, the interpolation grid is a grid region in each geological layer that does not contain measured attribute data from a single well; the sample grid is a grid region in each geological layer that contains measured attribute data from a single well.

[0058] The present invention has the following beneficial effects:

[0059] This invention first geologically stratifies the area to be evaluated based on well logging and geological data, and then divides it into grids. This facilitates the separate evaluation of the heterogeneous characteristics of different strata, eliminating inter-stratum interference. Simultaneously, it identifies the interpolation grids and sample grids with known attributes in each geological layer, which is beneficial for constructing a unified spatial computing unit. To overcome the problem of incorrect interpolation across geological boundaries caused by relying solely on spatial distance, it further obtains neighboring sample grids for each interpolation grid based on the distance between each interpolation grid and each sample grid in each geological layer, as well as the differences in sedimentary facies characteristics. This helps to select reliable samples with similar spatial and geological characteristics. Considering the striping effect that may be caused by uneven well network distribution, it further obtains the correction weight of each neighboring sample grid based on the differences in attribute data between neighboring sample grids. This accurately reflects the local outlier degree of the sample points, which is beneficial for automatically identifying and suppressing abnormal interference data. This process eliminates the striping effect; furthermore, based on corrected weights and attribute data, it accurately obtains the interpolation attribute data for each grid to be interpolated, which is beneficial for constructing a high-precision, geologically consistent grid attribute field for the entire region; to clarify the mainstream displacement direction, it obtains the injection-production connection between oil and water wells in each geological layer based on the location distribution of water and oil wells, which is beneficial for establishing a mainstream benchmark for evaluating the fluid sweep range; furthermore, based on the length of the minimum resistance path and the average permeability of each grid in each geological layer with the injection-production connection, oil well, and water well, it obtains the potential coefficient of each grid in each geological layer, which accurately reflects the strength of well network control at each location and the possibility of remaining oil enrichment, which is beneficial for quantifying the difficulty of water injection sweep in heterogeneous reservoirs; furthermore, based on the potential coefficient, it accurately screens the imperfect injection-production areas in each geological layer, which is beneficial for providing intuitive guidance for well network densification or parameter adjustment, and effectively improving the final recovery rate of the oilfield. Attached Figure Description

[0060] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic flowchart illustrating a rapid evaluation method for well network control based on potential coefficients, provided in one embodiment of the present invention.

[0062] Figure 2 The diagram shows a structure of a rapid evaluation system for well network control based on potential coefficients, provided in one embodiment of the present invention.

[0063] Figure 3 This is a schematic diagram of a computer device provided according to an embodiment of the present invention. Detailed Implementation

[0064] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for rapid evaluation of well network control based on potential coefficients proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0066] The following description, in conjunction with the accompanying drawings, details a specific scheme for a rapid evaluation method for well network control based on potential coefficients provided by this invention.

[0067] Example 1:

[0068] This invention proposes a rapid evaluation method for well network control based on potential coefficients. Please refer to [link / reference]. Figure 1 The diagram illustrates a schematic flowchart of a rapid evaluation method for well network control based on potential coefficients, according to an embodiment of the present invention. The method includes the following steps:

[0069] Step S1: Obtain the attribute data, well logging data, and geological data of each single well within the area to be evaluated.

[0070] Specifically, in order to construct a high-precision grid attribute data body that can accurately reflect the heterogeneity of underground reservoirs and the well network control status, this embodiment acquires the attribute data, logging data, and geological data of each individual well within the proposed evaluation area through a data acquisition interface. The individual wells include all producing oil wells and water injection wells within the proposed evaluation area.

[0071] Attribute data includes static attribute data, fluid property data, and dynamic production data. Static attribute data includes porosity, permeability, effective thickness, and oil saturation of a single well. Static attribute data is typically derived from core analysis or well logging interpretation results and is used to characterize the reservoir's physical properties. Fluid property data includes formation fluid viscosity and volume factor. Dynamic production data mainly includes time-series data such as cumulative oil production, cumulative water injection, daily fluid production, water cut, and bottomhole flowing pressure of a single well, used to reflect the reservoir's development history and fluid flow status.

[0072] The well logging data mainly consists of standard format logging curves, including sonic transit time curves, spontaneous potential curves, natural gamma curves, and deep / shallow lateral resistivity curves. Among these, sonic transit time curves and spontaneous potential curves are primarily used to identify sandstone-mudstone interfaces, assisting in stratigraphic correlation and sedimentary facies classification; natural gamma curves are used to identify lithological characteristics, particularly distinguishing between mudstone and sandstone; and deep / shallow lateral resistivity curves are used to determine oil and water layers.

[0073] Geological data includes geological stratification data, sedimentary facies maps, and fault distribution data. Specifically, the geological stratification data comprises the top, bottom, and depth data of each individual well, used to determine the geological stratigraphic position of the area to be evaluated; the sedimentary facies map is a distribution map of sedimentary facies zones within the area to be evaluated, used to constrain anisotropic characteristics during the interpolation process; and the fault distribution data includes fault strike, dip, and displacement information, used to identify geological obstructions and eliminate invalid injection-production relationships when constructing injection-production connections.

[0074] It should be noted that the proposed evaluation area was set by the implementer based on the actual situation and is not limited here.

[0075] Step S2: Based on well logging data and geological data, the area to be evaluated is geologically layered and gridded to determine the interpolation grid and the sample grid of known attributes in each geological layer.

[0076] Specifically, considering the distinct layered distribution of underground reservoirs in terms of geological structure, the sedimentary environment and physical parameters of strata often undergo abrupt changes in the vertical direction, meaning that the physical properties of different geological strata differ significantly. For example, over time, different geological strata such as interbedded sandstone and mudstone, positive rhythmic sedimentary layers, and negative rhythmic sedimentary layers have emerged. In order to achieve refined vertical evaluation and eliminate interference between geological strata, so that the evaluation results can truly reflect the reservoir conditions of each independent flow unit, and improve the vertical resolution and accuracy of well network evaluation, the area to be evaluated is geologically stratified based on well logging data and geological data. This facilitates subsequent accurate attribute interpolation and evaluation of the heterogeneous characteristics of different geological strata, effectively avoiding homogenization errors caused by mixing and interpolating the attributes of strata with different properties.

[0077] To accurately interpolate and evaluate the heterogeneous characteristics of different geological layers and ensure a refined characterization of the heterogeneity of residual oil distribution between wells, this embodiment divides each geological layer into a grid to construct a unified spatial computing unit. The grid size is set to [size missing]. To ensure sufficient mesh accuracy to capture the details of physical property changes between wells while also considering computational efficiency, implementers can set the mesh size according to actual conditions (such as the size of the evaluation area and computational resource limitations), and no limitation is imposed here. It should be noted that if the boundary of each geological layer is not large enough to fit a single mesh, the remaining boundary area will still be treated as a single mesh by default.

[0078] Given the well location coordinates and measured attribute data (such as porosity and permeability) of each well, the interpolation grid and the sample grid with known attributes in each geological layer can be directly determined. The interpolation grid is the grid region in each geological layer that does not contain measured attribute data for a single well, while the sample grid is the grid region in each geological layer that does contain measured attribute data for a single well. It should be noted that if a single well appears in at least two grids, then all grids containing that single well are considered sample grids.

[0079] Preferably, in one feasible manner of this embodiment, the method for geologically stratifying the area to be evaluated based on well logging data and geological data is as follows: based on the sonic transit time curve and spontaneous potential curve in the well logging data, the lithological characteristics and sandstone-mudstone interfaces of the strata in the area to be evaluated are identified; based on the lithological identification results and sedimentary facies analysis results in the geological data, the sedimentary facies types at different depths in the area to be evaluated are obtained; depth intervals with the same sedimentary facies type and continuous lithological characteristics are divided into the same geological layer, thereby dividing the area to be evaluated into several geological layers in the vertical direction.

[0080] This accurately identifies the interpolation grid and the sample grid with known attributes in each geological layer, which is beneficial for subsequent attribute data interpolation calculations based on both spatial and geological characteristics, and for constructing a continuous grid attribute field.

[0081] Step S3: Based on the distance between each interpolation grid and each sample grid in each geological layer, and the differences in sedimentary facies characteristics, obtain the neighboring sample grids of each interpolation grid; based on the differences in attribute data between neighboring sample grids, obtain the correction weight of each neighboring sample grid; based on the correction weight and attribute data, obtain the interpolation attribute data of each interpolation grid.

[0082] Specifically, existing Kriging interpolation algorithms, based on searching a fixed number or distance of neighboring sample grids, tend to generate smooth fields from the attribute data of these neighboring sample grids for interpolation. This approach fails to consider the strong heterogeneity and structural differences within a single well. It struggles to capture fine-scale discontinuous changes in permeability and porosity across different geological types, easily leading to excessive diffusion of these local disturbances into the surrounding area. This results in significant deviations in the interpolated attribute data near sedimentary facies boundaries and fault zones. Therefore, considering only spatial location characteristics and neglecting attribute data abrupt changes caused by geological mutations makes the algorithm insensitive to these disturbances. Consequently, interpolating the target grid based on attribute data from neighboring sample grids with different geological conditions leads to substantial deviations in the actual interpolation results. To overcome the problem of incorrect interpolation across geological boundaries caused by relying solely on spatial distance, this embodiment obtains neighboring sample grids for each grid to be interpolated based on the distance between each grid to be interpolated and each sample grid in each geological layer, as well as the differences in sedimentary facies characteristics. This facilitates the selection of sample grids that are highly similar to the grid to be interpolated in both spatial location and geological characteristics, thereby improving the reliability of the interpolated samples from the outset. The Kriging interpolation algorithm is a well-known technique and will not be described in detail here.

[0083] Because the spatial distribution of individual wells may be sparse and uneven, some neighboring sample grids may exist within the searched neighboring sample grid. These neighboring sample grids may have data distribution patterns that differ significantly from the data distribution patterns of all neighboring sample grids. This striping effect can easily distort the interpolation results, failing to reflect the true attribute data characteristics of the grid to be interpolated. To address the problem of striping effect affecting the accuracy of interpolation results in the Kriging interpolation algorithm, this embodiment obtains a correction weight for each neighboring sample grid based on the attribute data differences between neighboring sample grids. This accurately reflects the degree of local outliers in each neighboring sample grid and its contribution to the reliability of the interpolation results, facilitating the automatic identification and suppression of the influence of local outliers and eliminating the striping effect. A larger correction weight indicates that the data distribution of the corresponding neighboring sample grid more closely matches the local overall trend, and its reliability as an interpolation reference is higher.

[0084] Given the measured attribute data and corresponding correction weights of each neighboring sample grid, in order to obtain high-precision grid attribute data that conforms to geological laws, this embodiment obtains the interpolation attribute data of each grid to be interpolated based on the correction weights and attribute data, accurately determining the reservoir physical parameters at any location within the evaluation area. This is beneficial for subsequent accurate analysis of the connectivity of the injection-production well network and provides precise data support for the degree of control.

[0085] Preferably, in one feasible embodiment, the method for obtaining neighboring sample grids is as follows: for any grid to be interpolated and any sample grid in any geological layer, the Euclidean distance between the center point coordinates of the grid to be interpolated and the sample grid is used as a distance reference value between the grid to be interpolated and the sample grid; the larger the distance reference value, the farther the grid to be interpolated and the sample grid are spatially, and the weaker the spatial correlation; in order to initially screen out sample grids with similar geological backgrounds and avoid confusion of attribute data of different lithologies, this embodiment further obtains the lithology type of each grid in the geological layer through a pixel-based lithology modeling algorithm, and uses sample grids with the same lithology type as the grid to be interpolated as the first reference grid of the grid to be interpolated, which is beneficial to improve the pertinence and computational efficiency of subsequent sedimentary facies feature comparison;

[0086] Considering that the interpolation grid itself lacks actual well logging curves, it is necessary to reconstruct its geological characteristic benchmark based on the statistical characteristics of grids of the same lithology. Furthermore, the varying formation thickness in different well sections leads to inconsistent well logging data points, making direct calculation of statistical characteristics impossible. To accurately predict the well logging data of the interpolation grid, this embodiment obtains the reciprocal of the Euclidean distance between the interpolation grid and the center point coordinates of each of its first reference grids, using this as the analysis weight for each first reference grid. A larger analysis weight corresponds to a more meaningful first reference grid, as well as the gradual changes in well logging response characteristics at different depositional sites, even within the same lithology. The analysis weights are then arranged in descending order to obtain an analysis weight sequence. The first reference grids corresponding to the first preset number of analysis weights in the analysis weight sequence are all designated analysis grids. In this embodiment, the first preset number is set to 10; however, the implementer can set the size of the first preset number according to actual conditions, which is not limited here. If there are fewer than 10 elements in the analysis weight sequence, then the first reference grids corresponding to all elements in the analysis weight sequence are used as designated analysis grids. The well logging sequences in all specified analysis grids are depth normalized, and the obtained sequence is obtained by weighted summation with the analysis weights of the specified analysis grids, which is used as the reference well logging sequence for the grid to be interpolated. In this embodiment, the well logging sequences of different lengths are uniformly mapped to a fixed length (e.g., 100 data points) by linear resampling interpolation, thereby realizing the depth normalization of the well logging sequences.

[0087] To quantify the similarity in sedimentary facies sequence morphology between the interpolation grid and the sample grid, and to overcome the limitations of Euclidean distance in handling sequence time shifts and nonlinear alignment, a dynamic time rule algorithm is used to obtain the DTW value of the depth-normalized result of the reference logging sequence of the interpolation grid and the logging sequence of the sample grid. This value serves as a reference value for the difference in sedimentary facies characteristics between the interpolation grid and the sample grid. The larger the reference value for the difference in sedimentary facies characteristics, the greater the difference in sedimentary facies evolution sequence between the interpolation grid and the sample grid, and the lower the similarity in geological attributes.

[0088] To comprehensively characterize the spatial and geological similarity between the interpolation grid and the sample grid, the weighted sum of distance reference values ​​and sedimentary facies difference reference values, after being normalized by a negative exponent, is used as the correlation degree between the interpolation grid and the sample grid. A higher correlation degree indicates a higher overall similarity between the interpolation grid and the sample grid, and a stronger reliability of the sample grid as an interpolation reference grid. The formula for calculating the correlation degree is as follows: In the formula, The correlation between the k-th interpolation grid and the n-th sample grid; exp is an exponential function with the natural constant as the base. This is a reference value for the distance between the k-th grid to be interpolated and the n-th sample grid; This serves as a reference value for the difference in sedimentary facies characteristics between the k-th interpolation grid and the n-th sample grid; As the first weighting coefficient, As the second weighting coefficient, this embodiment sets... It is 0.6. The value is set to 0.4, which implementers can adjust based on their specific circumstances (e.g., whether spatial proximity or geological similarity is more important). and The size is not limited here, but The value is 1, so that the two types of features can be comprehensively evaluated under a unified dimension;

[0089] To select the most representative sample grids for interpolation calculations and reduce interference from sample grids that are far apart or have large geological differences, the correlation degree between the grid to be interpolated and each sample grid in the same geological layer is arranged in descending order to obtain a correlation degree sequence. Then, the sample grids corresponding to the first second preset number of correlation degrees in the correlation degree sequence are used as the neighboring sample grids of the grid to be interpolated. In this embodiment, the second preset number is set to 10. Implementers can set the size of the second preset number according to actual conditions, and it is not limited here. The methods for obtaining Euclidean distance, the lithological modeling algorithm for pixels, the linear resampling interpolation method, and the dynamic time rule algorithm are all well-known technologies and will not be described in detail here.

[0090] Preferably, in one feasible embodiment, the method for obtaining the corrected weights is as follows: for any grid to be interpolated, all neighboring sample grids of the grid to be interpolated are taken as analysis grids; for any analysis grid, the sequence of attribute data of the analysis grid is taken as the attribute sequence of the analysis grid; in order to construct a benchmark sequence that reflects the local overall trend, so as to identify abnormal analysis grids that deviate from the trend, the result of weighted averaging of the attribute sequences of all analysis grids is taken as the first interpolation sequence of the grid to be interpolated; wherein, the specific method for obtaining the first interpolation sequence is: using the correlation degree between each analysis grid and the grid to be interpolated as the weight, the attribute sequences of all analysis grids are weighted, summed, and normalized (i.e., divided by the sum of weights) to obtain the first interpolation sequence;

[0091] Then, the Euclidean distance between the attribute sequence of each analysis grid and the first interpolation sequence is used as the first reference distance for each analysis grid, accurately reflecting the degree to which the data distribution pattern of the analysis grid deviates from the local overall trend (i.e., the degree of outlier). In order to effectively identify abnormal interference data (the source of striping effect) caused by well logging errors or local geological changes, interference grids in the analysis grid are identified based on the three-standard-deviation criterion and the first reference distance. This helps to mark grids with a deviation significantly greater than the statistical regularity of the population as unreliable samples. The three-standard-deviation criterion is a well-known technique and will not be elaborated further.

[0092] If interfering grids exist, to quantify the average outlier level of the interfering data and use it as a benchmark for evaluating the relative reliability of other grids, the mean of the first reference distances of all interfering grids is used as the second reference distance. This accurately reflects the overall anomaly intensity of the interfering sample group and is beneficial for constructing a reference benchmark for noise-resistant weights. To suppress the influence of outlier grids, samples with smaller deviations are assigned larger weights to eliminate the striping effect. The result of linearly normalizing the ratio of the second reference distance to the sum of the first reference distance and the preset minimum positive number is used as the correction weight for each analysis grid. This accurately reflects the credibility of the corresponding analysis grid relative to the interfering group. The larger the correction weight, the smaller the first reference distance of the corresponding analysis grid, i.e., the more it conforms to the local overall trend, and the higher its data quality. In this embodiment, the preset minimum positive number is set to 0.001 to avoid a denominator of 0 and to ensure the numerical stability of the calculation process. Implementers can set the size of the preset minimum positive number according to the actual situation, and it is not limited here.

[0093] If no interfering grids exist, it indicates that the data distribution of all neighboring sample grids is relatively consistent, with no significant abnormal interference. In this case, the preset weight is set as the corrected weight for each analysis grid. In this embodiment, the preset weight is set to 1, indicating that all sample grids have equal reliability in the noise resistance dimension, and no additional weight reduction processing is performed.

[0094] Preferably, in one feasible manner of this embodiment, the method for obtaining interpolation attribute data is as follows: for any grid to be interpolated, the kriging weight coefficients of each neighboring sample grid of the grid to be interpolated are obtained by the kriging interpolation algorithm; wherein, the method for obtaining the kriging weight coefficients is a well-known technique and will not be described in detail; the result of linearly normalizing the product of the corrected weights and the kriging weight coefficients of each neighboring sample grid of the grid to be interpolated is used as the comprehensive weight of each neighboring sample grid, which accurately reflects the comprehensive contribution of the corresponding neighboring sample grid in both spatial statistical significance (characterized by the kriging coefficients) and local data reliability (characterized by the corrected weights). The larger the comprehensive weight, the more likely the corresponding neighboring sample grid is to be strongly correlated with the grid to be interpolated in terms of spatial variability function and has high reliability in terms of local data distribution, making it the optimal interpolation reference source;

[0095] To ensure the correct numerical magnitude of the interpolation result and avoid systematic bias caused by the sum of weights not being 1, the sequence of weighted summation of the comprehensive weights and attribute sequences of each neighboring sample grid of the grid to be interpolated is used as the interpolation attribute data of the grid to be interpolated.

[0096] Step S4: Based on the location distribution of water wells and oil wells in each geological layer, obtain the injection-production connection between oil and water wells in each geological layer; based on the length of the minimum resistance path and the average permeability of each grid in each geological layer with the injection-production connection, oil well, and water well, obtain the potential coefficient of each grid in each geological layer.

[0097] Specifically, in order to clarify the direct displacement relationship between individual wells and use this as a baseline to evaluate the mainstream direction of the fluid sweep range, this embodiment further obtains the injection-production connection line between oil and water wells in each geological layer based on the location distribution of water wells and oil wells in each geological layer. This is beneficial for subsequent quantitative calculation of the degree of deviation of each grid from the mainstream displacement line, and accurately reflects the effective range of water injection and the spatial distribution differences of displacement intensity.

[0098] It is known that the flow of fluid in underground porous media follows the principle of minimum energy consumption. Its actual flow path is not a straight line, but rather a path of minimum resistance along the high-permeability zone. In order to realistically simulate the underground migration trajectory of fluid and overcome the defect that the evaluation of straight-line distance cannot accurately characterize the characteristics of heterogeneous seepage, this embodiment obtains the potential coefficient of each grid in each geological layer based on the length of the minimum resistance path between each grid and the injection-production line, oil well, and water well, and the average permeability. This accurately reflects the degree of control of each grid by the current injection-production well network and the possibility of remaining oil enrichment. The larger the potential coefficient, the farther the corresponding grid is from the mainstream displacement channel or the greater the seepage resistance, the higher the difficulty of water injection, and the more likely it is to be a potential area for remaining oil enrichment (i.e., weak control). Targeted potential tapping measures (such as infill wells and perforation conversion) are needed for adjustment.

[0099] Preferably, in one feasible method of this embodiment, the method for obtaining the injection-production connection is as follows: for any water well in any geological layer, a search area is constructed with the water well as the center and a preset injection-production distance as the radius, and all oil wells falling into the search area are regarded as candidate oil wells; in this embodiment, the preset injection-production distance is set to 200m, referring to the limit injection-production well distance of the area to be evaluated. The implementer can set the size of the preset injection-production distance according to the actual situation, which is not limited here. For any candidate oil well, if there is a geological fault between the water well and the candidate oil well, considering that the fault has a shielding effect and the fluid cannot form an effective direct connection, the candidate oil well is eliminated. Furthermore, a line is established connecting the water well and each remaining candidate oil well after the elimination operation, which is used as the initial injection-production connection line. Considering that if there are multiple oil wells in the same direction, water injection usually preferentially affects the closer oil wells, while the influence of water injection in the farthest oil wells on the water in this direction is shielded or greatly weakened, this embodiment obtains the azimuth angle of each initial injection-production connection line. The method for obtaining the azimuth angle is as follows: with due north as the reference (0 degrees), the angle between the injection-production connection line vector and the reference direction is calculated clockwise.

[0100] If the azimuth angle between two initial injection-production lines is less than a preset angle threshold, it indicates that these two initial injection-production lines are highly overlapping in spatial azimuth and have a competitive relationship in terms of injection-production effectiveness. In order to identify the true effective displacement relationship and avoid redundant lines interfering with the calculation of the subsequent mainstream line distance, the longer initial injection-production line is removed to ensure that the most direct and effective injection-production correspondence is retained. In this embodiment, the preset angle threshold is set to 10° to ensure that well groups with similar azimuths but different effective layers can be distinguished. Implementers can set the size of the preset angle threshold according to the actual situation, which is not limited here.

[0101] Finally, the initial injection-production lines remaining after removing all water wells in the geological layer are taken as the final injection-production lines for that layer. It should be noted that if an oil or water well has been abandoned, or the ratio of the cumulative injected or produced fluid volume to the pore volume is less than 0.1, it indicates that the corresponding single well has a very short production history or a very small operating volume, and has almost no substantial impact on the underground flow field. In this case, the influence of the single well is not considered, and the single well can be ignored when calculating the injection-production lines.

[0102] Preferably, in one feasible embodiment, the potential coefficient is obtained as follows: For any grid in any geological layer, the Dijkstra algorithm is used to obtain the minimum seepage resistance path between the grid and the nearest water well in the geological layer as the first path, and the minimum seepage resistance path between the grid and the nearest oil well in the geological layer as the second path, accurately determining the dominant channel for actual fluid flow in heterogeneous reservoirs. The length of the first path is then used as the water well resistance distance of the grid; the length of the second path is used as the oil well resistance distance of the grid; to quantify the degree to which the grid deviates from the mainstream displacement channel, the vertical distance between the grid and the nearest injection-production line is used as the value of the grid. The distance to the main flow line accurately reflects the direct impact range of water injection. It should be noted that if no effective injection-production connection exists within the search area of ​​the grid, i.e., the distance to the main flow line exceeds the preset limit distance or there is a closed fault separating the grid from the nearest injection-production connection, preventing the search for an effective connection, it indicates that the grid is in a well network control blind zone. In this case, the potential coefficient of the grid is directly set to the preset maximum value (e.g., 100 or infinity), and no further calculations are performed. The implementer can set the preset limit distance and the preset maximum value according to the actual situation; no restrictions are imposed here. If an effective injection-production connection can be found within the search area of ​​the grid, subsequent analysis continues.

[0103] To introduce a direction correction factor, particularly for refined evaluation of the stagnation zone behind oil wells, the location of the nearest oil well in the grid is used as the origin. The direction vector from the nearest oil well to the nearest water well is used as the reference axis, and the direction vector from the nearest oil well to the grid is used as the target axis. The angle between the reference axis and the target axis is taken as the position angle. The larger the position angle, the more the grid deviates from the mainstream injection and production direction. The Dijkstra algorithm is a well-known technique and will not be elaborated further.

[0104] Comparing the distances to the main flow line, water well resistance, and oil well resistance, if the distance to the main flow line is the smallest, it indicates that the grid is located in the main flow zone between injection and production wells, and is most directly affected by water injection. In this case, the formula for calculating the potential coefficient of the grid is: Because the grid is controlled by bidirectional flow at this point, its potential mainly depends on the degree of deviation from the axis and the permeability difference along the fluid path. If the well resistance distance is minimal, it indicates that the grid is located in the non-mainstream zone on one side of the well (i.e., the stagnant zone behind the well). In this case, the formula for calculating the potential coefficient of the grid is: Because the grid is located in the opposite direction of the injection end, the impact is mainly controlled by the distance to the water well, so the weight of the water well distance in the potential evaluation needs to be strengthened; if the oil well resistance distance is the minimum, it means that the grid is located in the non-mainstream zone area on one side of the oil well (i.e., the stagnant zone behind the oil well). In this case, the formula for calculating the potential coefficient of the grid is: Because the grid is located in the opposite direction of the production end at this time, it belongs to a typical dead oil zone. It is necessary to combine the distance and direction angle (cosine correction) to evaluate the degree to which it is difficult to be affected.

[0105] In the formula, S is the potential coefficient of the grid. This represents the distance between the main lines of the grid. This represents the well resistance distance within the grid. This represents the distance of the oil well resistance in this grid. To pre-set a reasonable injection-production well spacing, this embodiment sets it to 200m, referencing the technical well spacing within the proposed evaluation area; The ratio of the average permeability of the first path to the average permeability of the second path accurately reflects the difference in relative conductivity between the water well and oil well directions. This is beneficial for characterizing the impact of reservoir heterogeneity on sweep efficiency. When the permeability in the oil well direction is better... The smaller the value, the smaller the calculated potential coefficient S, indicating a higher degree of well network control, which conforms to physical laws; Let be the position angle; cos is the cosine function; it should be noted that... The value of is in the range of 0 to 1, therefore, There is no case where the value is 0. Implementers can set a preset reasonable injection-production well spacing according to actual conditions. The size is not limited here.

[0106] The average permeability is obtained as follows: For any path in the first or second path, the formula for calculating the average permeability of that path is: ; In the formula, B is the average permeability of the path; B is the underground fluid volume coefficient of the path. Where is the fluid viscosity; I is the total number of grid cells traversed by the path; This represents the minimum seepage resistance along this path; Let this be the length of the i-th grid on the path, starting from this grid. The thickness of the i-th grid on the path, starting from this grid. This represents the permeability of the i-th grid along the path, starting from this grid. This is the total length of the path; This represents the average effective thickness of the path. Since the seepage resistance along the path is the sum of the flow resistances of each grid cell, the average permeability obtained by back-calculating the total flow resistance more accurately reflects the overall ease or difficulty of fluid flow along the path than a simple arithmetic average. It should be noted that all physical parameters in the above formula (such as permeability, thickness, viscosity, etc.) must be converted to SI units or have appropriate unit conversion factors introduced before being substituted into the calculation to ensure the consistency of the dimensions of the calculation results.

[0107] Step S5: Screen areas with incomplete injection and production in each geological layer based on the potential coefficient.

[0108] Specifically, the larger the known potential coefficient, the lower the degree of control of the corresponding grid area by the current injection-production well network, and the greater the remaining oil potential. In order to accurately locate the remaining oil-rich areas with weak well network control, so that well network adjustment measures can be targeted and maximize the recovery rate, and further screen the imperfect injection-production areas in each geological layer based on the potential coefficient, it is beneficial to provide intuitive spatial guidance for well network densification, sidetracking, or injection-production parameter adjustment.

[0109] Preferably, in one feasible embodiment of this method, the method for screening imperfect injection-production areas in each geological layer based on the potential coefficient is as follows: For any grid in any geological layer, when the potential coefficient of the grid is greater than a preset potential coefficient threshold, it indicates that the grid is in an injection-production blind zone or a stagnant zone, and thus the grid area is marked as a locally imperfect area with weak injection-production control; then all locally imperfect areas in the geological layer are taken as imperfect injection-production areas in the geological layer. In this embodiment, the preset potential coefficient threshold is set to 1 to ensure that areas with a control level significantly lower than the average level (benchmark value) can be screened out. Implementers can set the size of the preset potential coefficient threshold according to actual conditions (such as oilfield development stage and economic evaluation indicators, etc.), which is not limited here.

[0110] In summary, this embodiment stratifies and grids the area to be evaluated based on well logging and geological data; it optimizes neighboring sample grids by considering spatial distance and sedimentary facies characteristics, and calculates correction weights based on attribute differences to suppress abnormal interference, thereby obtaining high-precision grid interpolation attribute data using an improved interpolation algorithm; it constructs injection-production lines based on the distribution of oil and water wells, and calculates the potential coefficient of each grid by combining the minimum resistance path length and average permeability between grids, lines, and well points; and it uses the potential coefficient to screen areas with incomplete injection-production processes. This invention optimizes interpolation accuracy by introducing geological features and noise reduction mechanisms, and combines flow resistance path quantification to evaluate the degree of control, enabling rapid and accurate location of remaining oil-rich areas and providing a scientific basis for well network adjustment.

[0111] Example 2:

[0112] This invention also proposes a rapid evaluation system for well network control based on potential coefficients. Please refer to [link / reference]. Figure 2 The diagram illustrates a structure of a rapid evaluation system for well network control based on potential coefficients, provided by an embodiment of the present invention. The system includes: a data acquisition module 10, a grid division module 20, an interpolation module 30, a potential coefficient acquisition module 40, and a data processing module 50.

[0113] The data acquisition module 10 is used to acquire attribute data, well logging data and geological data of each single well in the area to be evaluated;

[0114] The grid division module 20 is used to geologically stratify the area to be evaluated based on well logging data and geological data, and to perform grid division to determine the interpolation grid and the sample grid with known attributes in each geological layer.

[0115] The interpolation module 30 is used to obtain the neighboring sample grids of each grid to be interpolated based on the distance between each grid to be interpolated and each sample grid in each geological layer, as well as the differences in sedimentary facies characteristics; to obtain the correction weight of each neighboring sample grid based on the differences in attribute data between neighboring sample grids; and to obtain the interpolation attribute data of each grid to be interpolated based on the correction weight and attribute data.

[0116] The potential coefficient acquisition module 40 is used to acquire the injection-production connection between oil and water wells in each geological layer based on the location distribution of water wells and oil wells in each geological layer; and to acquire the potential coefficient of each grid in each geological layer based on the length of the minimum resistance path between each grid and the injection-production connection, oil well, and water well, and the average permeability.

[0117] Data processing module 50 is used to screen areas with incomplete injection and production in each geological layer based on potential coefficients.

[0118] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the embodiment of a rapid evaluation system for well network control based on potential coefficient and the embodiment of a rapid evaluation method for well network control based on potential coefficient are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0119] Example 3:

[0120] This invention also proposes a device for rapidly evaluating the degree of well network control based on a potential coefficient. The device includes a memory and a processor. The memory stores executable program code, and the processor calls and executes the executable program code to perform a method for rapidly evaluating the degree of well network control based on a potential coefficient provided in the embodiments of this application. Specifically, the device may be a chip, component, or module. The chip may include a connected processor and memory; the memory stores instructions, and when the processor calls and executes the instructions, the chip can perform the method for rapidly evaluating the degree of well network control based on a potential coefficient provided in the above embodiments.

[0121] Furthermore, this application also protects a computer device; please refer to [link to relevant documentation]. Figure 3 The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. When the processor 402 executes the computer program 403, the computer device can execute any of the aforementioned methods for rapid evaluation of well network control based on potential coefficients.

[0122] Example 4:

[0123] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement the rapid evaluation method for well network control based on potential coefficients provided in the above embodiment.

[0124] Example 5:

[0125] This embodiment also provides a computer program product. When the computer program product is run on a computer, it causes the computer to perform the above-mentioned related steps to realize the rapid evaluation method for well network control based on potential coefficient provided in the above embodiment.

[0126] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0127] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0128] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A rapid evaluation method for well pattern control based on potential coefficients, characterized in that, The method includes the following steps: Acquire attribute data, well logging data, and geological data for each individual well within the area to be evaluated; Based on well logging data and geological data, the area to be evaluated is geologically stratified and gridded to determine the interpolation grid and the sample grid of known attributes in each geological layer. Based on the distance between each interpolation grid and each sample grid in each geological layer, and the differences in sedimentary facies characteristics, the neighboring sample grids of each interpolation grid are obtained; based on the differences in attribute data between neighboring sample grids, the correction weight of each neighboring sample grid is obtained; based on the correction weight and attribute data, the interpolation attribute data of each interpolation grid is obtained. Based on the location distribution of water wells and oil wells in each geological layer, the injection-production connection between oil and water wells in each geological layer is obtained; based on the length of the minimum resistance path and the average permeability of each grid in each geological layer with the injection-production connection, oil well and water well, the potential coefficient of each grid in each geological layer is obtained. Areas with incomplete injection and production processes in each geological layer are screened based on potential coefficients.

2. The rapid evaluation method for well network control based on potential coefficient as described in claim 1, characterized in that, The method for obtaining the neighboring sample grid is as follows: For any interpolation grid and any sample grid in any geological layer, the Euclidean distance between the center point coordinates of the interpolation grid and the sample grid is used as the distance reference value between the interpolation grid and the sample grid. The lithology type of each grid in the geological layer is obtained by using a pixel-based lithology modeling algorithm. Sample grids with the same lithology type as the grid to be interpolated are all used as the first reference grids of the grid to be interpolated. Obtain the reciprocal of the Euclidean distance between the interpolation grid and the center point coordinates of each of the first reference grids, and use it as the analysis weight for each first reference grid; The analysis weights are arranged in descending order to obtain the analysis weight sequence; The first reference grid corresponding to the first preset number of analysis weights in the analysis weight sequence is used as the specified analysis grid. The well logging sequences in the well logging data of all the specified analysis grids are subjected to depth normalization, and the obtained sequence is obtained by weighted summation with the analysis weight of the specified analysis grid, which is used as the reference well logging sequence of the grid to be interpolated. The DTW value of the well logging sequence of the reference grid and the well logging sequence of the sample grid are obtained by the dynamic time rule algorithm and the depth normalization process, and are used as the reference value of the difference in sedimentary facies characteristics between the grid to be interpolated and the sample grid. The result of weighted summation of the distance reference value and the sedimentary facies characteristic difference reference value, followed by negative exponential normalization, is used as the degree of correlation between the grid to be interpolated and the sample grid. The correlation degree between the grid to be interpolated and each sample grid in the geological layer is arranged in descending order to obtain a correlation degree sequence; The sample grids corresponding to the first second preset number of correlation degrees in the correlation degree sequence are used as the neighboring sample grids of the grid to be interpolated.

3. The rapid evaluation method for well network control based on potential coefficient as described in claim 1, characterized in that, The method for obtaining the corrected weights is as follows: For any grid to be interpolated, all neighboring sample grids of the grid to be interpolated are used as analysis grids; for any analysis grid, the sequence of attribute data of the analysis grid is used as the attribute sequence of the analysis grid. The result of weighted averaging of the attribute sequences of all analyzed grids is used as the first interpolation sequence for the grid to be interpolated. The Euclidean distance between the attribute sequence of each analysis grid and the first interpolation sequence is used as the first reference distance for each analysis grid. Based on the three-standard-deviation criterion, interference grids in the analysis grid are identified according to the first reference distance; If interfering grids exist, the average of the first reference distances of all interfering grids is used as the second reference distance; The normalized result of the ratio of the second reference distance to the sum of the first reference distance and the preset minimum positive number is used as the correction weight for each analysis grid. If there are no interfering grids, the preset weights are set as the corrected weights for each analysis grid.

4. The rapid evaluation method for well network control based on potential coefficient as described in claim 3, characterized in that, The method for obtaining the interpolation attribute data is as follows: For any grid to be interpolated, the kriging weight coefficients of each neighboring sample grid are obtained by using the kriging interpolation algorithm; The normalized product of the corrected weights and kriging weights of each neighboring sample grid of the grid to be interpolated is used as the comprehensive weight of each neighboring sample grid. The weighted sum of the combined weights and attribute sequences of each neighboring sample grid of the grid to be interpolated is used as the interpolation attribute data of the grid to be interpolated.

5. The rapid evaluation method for well network control based on potential coefficient as described in claim 1, characterized in that, The method for obtaining the injection-production connection is as follows: For any water well in any geological layer, a search area is constructed with the water well as the center and the preset injection-production distance as the radius. All oil wells falling into the search area are considered as candidate oil wells. For any candidate oil well, if there is a geological fault between the water well and the candidate oil well, the candidate oil well shall be eliminated. Establish a connection between this water well and each remaining candidate oil well after the elimination operation, and use them as the initial injection-production connection; Obtain the azimuth angle of each initial injection-sampling line. If the included angle between two initial injection-sampling lines is less than a preset angle threshold, the initial injection-sampling line with the longer length will be removed. The initial injection-production connection remaining after removing all water wells in the geological layer is taken as the final injection-production connection for that geological layer.

6. The rapid evaluation method for well network control based on potential coefficient as described in claim 5, characterized in that, The method for obtaining the potential coefficient is as follows: For any grid in any geological layer, the Dijkstra algorithm is used to obtain the minimum seepage resistance path between the grid and the nearest water well in the geological layer as the first path, and the minimum seepage resistance path between the grid and the nearest oil well in the geological layer as the second path. The length of the first path is taken as the water well resistance distance of the grid; the length of the second path is taken as the oil well resistance distance of the grid; the vertical distance between the grid and the nearest injection-production line is taken as the mainstream line distance of the grid. If there is no valid injection-mining connection within the search area where the grid is located, the potential coefficient of the grid is directly set to the preset maximum value; If a valid injection-production line exists, the location of the nearest oil well is taken as the origin, the direction vector from the nearest oil well to the nearest water well is taken as the reference axis, and the direction vector from the nearest oil well to the grid is taken as the target axis. The angle between the reference axis and the target axis is taken as the position angle. Compare the mainstream line distance, water well resistance distance, and oil well resistance distance. If the mainstream line distance is the smallest, the potential coefficient calculation formula for this grid is: ; If the well resistance distance is minimized, the potential coefficient of the grid is calculated using the following formula: ; If the oil well resistance distance is minimized, the potential coefficient of this grid is calculated using the following formula: In the formula, S is the potential coefficient of the grid. This represents the distance between the main lines of the grid. This represents the well resistance distance within the grid. This represents the distance of the oil well resistance in this grid. To pre-determine a reasonable injection-production well spacing; This is the ratio of the average penetration rate of the first path to the average penetration rate of the second path. θ is the position angle; cos is the cosine function.

7. The rapid evaluation method for well network control based on potential coefficient as described in claim 6, characterized in that, The method for obtaining the average penetration rate is as follows: For either the first or second path, the formula for calculating the average permeability of that path is: ; In the formula, This represents the average penetration rate along this pathway. B is the underground fluid volume coefficient for this path; For fluid viscosity; I represents the total number of grid cells traversed by the path; This represents the minimum seepage resistance along this path; Let this be the length of the i-th grid on the path, starting from this grid. The thickness of the i-th grid on the path, starting from this grid. Let i be the permeability of the i-th grid on the path, starting from this grid.

8. The rapid evaluation method for well network control based on potential coefficient as described in claim 1, characterized in that, The method for screening areas with incomplete injection and production in each geological layer based on potential coefficients is as follows: For any grid in any geological layer, when the potential coefficient of the grid is greater than the preset potential coefficient threshold, the grid area is marked as a local imperfect area with weak injection-production control. All locally imperfect areas in this geological layer are considered as imperfect injection and production areas in this geological layer.

9. The rapid evaluation method for well network control based on potential coefficient as described in claim 1, characterized in that, The method for geologically stratifying the area to be evaluated based on well logging data and geological data is as follows: Based on the sonic transit time curve and spontaneous potential curve in the well logging data, identify the lithological characteristics of the strata and the sandstone-mudstone interface in the area to be evaluated. Based on the lithological identification results and sedimentary facies analysis results in the geological data, the sedimentary facies types at different depths in the area to be evaluated are obtained; Depth intervals with the same sedimentary facies type and continuous lithological characteristics are divided into the same geological layer, thereby dividing the area to be evaluated into several geological layers vertically.

10. The method for rapid evaluation of well network control based on potential coefficient as described in claim 1, characterized in that, The interpolation grid is a grid region in each geological layer that does not contain measured attribute data from a single well; the sample grid is a grid region in each geological layer that contains measured attribute data from a single well.

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