A permeability model construction method and system and electronic device

Abstract

The present application relates to a kind of permeability model construction method and system and electronic equipment, the method includes: the core porosity data and permeability data obtained according to porosity data size range, core porosity data and permeability data are evenly divided into several pore permeability data group, and make the porosity interval of each pore permeability data group not more than first preset threshold;Judge the sample number of each pore permeability data group whether greater than second preset threshold, if yes, then the pore permeability data group is determined as high confidence group, if not, then the pore permeability data group is determined as low confidence group;Determine the permeability value corresponding to each porosity data under each preset cumulative distribution probability, obtain corresponding multiple permeability cumulative distribution probability curve, interpolation calculation is carried out according to cumulative distribution probability curve data, to obtain permeability probability distribution atlas. It can realize the permeability probabilistic prediction based on porosity data, provides theoretical basis and quantitative method for the characterization of permeability uncertainty.

Description

Technical Field

[0001] This invention relates to a method, system, and electronic device for constructing a penetration rate model. Background Technology

[0002] Permeability is the most critical parameter for evaluating the flow capacity of fluids in porous media, and it is also the most critical parameter for determining the production capacity of underground oil and gas reservoirs. Currently, permeability under formation conditions cannot be directly measured; it is mainly obtained indirectly through calculation based on directly measurable porosity parameters. The porosity-permeability model used to calculate permeability requires cross-plot analysis based on porosity and permeability data from core analysis. Summary of the Invention

[0003] The inventors of this invention have discovered that, in practice, the intersection of porosity and permeability data is quite dispersed. Samples with the same porosity often exhibit permeability differences of 2-4 orders of magnitude. Under such data conditions, the calculated results of the regressed porosity-permeability model have significant uncertainty compared to actual conditions. This significant uncertainty will further propagate to subsequent reservoir evaluation and geological modeling processes, adversely affecting the comprehensive evaluation of reservoir quality and the predictive ability of reservoir dynamic and static geological models. In view of the above problems, this invention proposes a permeability model construction method, system, and electronic equipment to solve or partially solve these problems. The technical solution proposed by this invention is as follows:

[0004] As a first aspect of the present invention, the present invention provides a method for constructing a penetration rate model, comprising:

[0005] The obtained core porosity and permeability data are evenly divided into several porosity and permeability data groups according to the size range of the porosity data, and the porosity range of each group of porosity and permeability data does not exceed the first preset threshold.

[0006] Determine whether the number of samples in each pore permeation data group is greater than a second preset threshold. If so, the pore permeation data group is determined as a high-confidence group; otherwise, the pore permeation data group is determined as a low-confidence group.

[0007] For the identified multiple high-confidence groups and multiple low-confidence groups, the permeability value corresponding to each porosity data under each preset cumulative distribution probability is determined, and multiple corresponding permeability cumulative distribution probability curves are obtained.

[0008] A permeability probability distribution map is obtained based on the multiple cumulative permeability distribution probability curves.

[0009] In one or more embodiments, determining the permeability value corresponding to each porosity data point under each preset cumulative distribution probability for a given plurality of high-confidence groups and a plurality of low-confidence groups, and obtaining a plurality of corresponding cumulative permeability distribution probability curves, includes:

[0010] For each of the identified high-confidence groups, the high-confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability is calculated according to the cumulative distribution probability function.

[0011] For the identified multiple low-confidence groups, based on a preset extrapolation method and the distribution relationship between the multiple high-confidence groups and the multiple low-confidence groups, the low-confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability is determined.

[0012] Based on the high-confidence penetration rate value and low-confidence penetration rate value corresponding to each preset cumulative distribution probability, multiple corresponding cumulative distribution probability curves of penetration rate are obtained.

[0013] In one or more embodiments, determining the permeability value corresponding to each porosity data point under each preset cumulative distribution probability for a given plurality of high-confidence groups and a plurality of low-confidence groups, and obtaining a plurality of corresponding cumulative permeability distribution probability curves, includes:

[0014] Based on the determined high-confidence groups and low-confidence groups, the porosity data is substituted into a preset permeability prediction function corresponding to each preset cumulative distribution probability to determine the permeability value corresponding to each porosity data point for each preset cumulative distribution probability; the preset permeability prediction function is obtained by fitting a Sigmodal function.

[0015] Based on the permeability value corresponding to each porosity data corresponding to each preset cumulative distribution probability, multiple permeability cumulative distribution probability curves are obtained.

[0016] In one or more embodiments, obtaining the permeability probability distribution map based on the plurality of cumulative permeability probability curves includes:

[0017] The permeability probability distribution map is obtained by interpolation calculation based on the multiple cumulative permeability distribution probability curves.

[0018] In one or more embodiments, before interpolating the permeability probability distribution map based on the plurality of cumulative permeability distribution probability curves, the process includes:

[0019] The permeability values ​​of multiple cumulative permeability distribution probability curves are converted into logarithmic values, and the first outlier data point in the low confidence group that deviates from the trend of the high confidence group is removed.

[0020] In one or more embodiments, based on the permeability probability distribution map and the porosity curve obtained from single-well logging interpretation, the permeability curve for each well under different cumulative distribution probabilities is determined.

[0021] In one or more embodiments, before uniformly dividing the acquired core porosity and permeability data into several porosity-permeability data groups according to the porosity data size range, and ensuring that the porosity range of each group of porosity-permeability data does not exceed a first preset threshold, the method further includes:

[0022] The second abnormal data point in the obtained core porosity and permeability data was removed.

[0023] In one or more embodiments, before uniformly dividing the acquired core porosity and permeability data into several porosity-permeability data groups according to the porosity data size range, and ensuring that the porosity range of each group of porosity-permeability data does not exceed a first preset threshold, the method further includes:

[0024] The core porosity and permeability data are divided according to rock characteristics to obtain core porosity and permeability data corresponding to each rock characteristic; the rock characteristics include at least one of lithology, microfacies type, rock type and logging facies type.

[0025] In one or more embodiments, the penetration rate model construction method further includes:

[0026] Based on the permeability probability distribution map corresponding to each rock feature and the porosity curve corresponding to each rock feature obtained from single-well logging interpretation, the permeability curves for each rock feature of each well under different cumulative distribution probabilities are determined.

[0027] As a second aspect of the present invention, the present invention provides a method for permeability coarsening based on the permeability probability distribution map determined by the permeability model construction method, comprising:

[0028] Based on the preset coarsening target scale and the porosity data of the fine grid, the porosity distribution range of each coarsening grid is determined.

[0029] Based on the permeability probability distribution map, determine the cumulative permeability distribution probability parameter for each porosity distribution interval of the coarsened grid.

[0030] As a third aspect of the present invention, the present invention provides a penetration rate model construction system, comprising:

[0031] The data partitioning module is used to divide the acquired core porosity data and permeability data into several porosity-permeability data groups evenly according to the size range of the porosity data, and to ensure that the porosity range of each group of porosity-permeability data does not exceed a first preset threshold.

[0032] The judgment module is used to determine whether the number of samples in each pore permeation data group is greater than a second preset threshold. If so, the pore permeation data group is determined as a high confidence group; otherwise, the pore permeation data group is determined as a low confidence group.

[0033] The first calculation module is used to determine the permeability value corresponding to each porosity data under each preset cumulative distribution probability for the determined multiple high confidence groups and multiple low confidence groups, and to obtain the corresponding multiple permeability cumulative distribution probability curves.

[0034] The second calculation module is used to obtain a permeability probability distribution map based on the multiple cumulative permeability distribution probability curves.

[0035] As a fourth aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the penetration rate model construction method and the penetration rate coarsening method as described above.

[0036] As a fifth aspect of the present invention, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the penetration rate model construction method and the penetration rate coarsening method as described above.

[0037] Based on the above technical solution, the beneficial effects of the present invention compared with the prior art are as follows:

[0038] The permeability model construction method provided by this invention divides core porosity data and permeability data into multiple high-confidence groups and multiple low-confidence groups, and obtains the cumulative permeability distribution probability curve across the entire porosity range by constructing a permeability probability distribution function, thereby establishing a permeability probability distribution map across the entire porosity range. This enables probabilistic permeability prediction based on porosity data, providing a theoretical basis and quantitative method for characterizing permeability uncertainty.

[0039] The permeability model construction method provided by this invention, through the application of the permeability probability distribution function, can calculate multiple permeability values ​​with different cumulative distribution probabilities based on the single-well logging curve and permeability probability distribution map during single-well logging interpretation. These multiple permeability values ​​with different cumulative distribution probabilities can be used for comprehensive evaluation of reservoir quality, establishment of dynamic and static geological models of oil reservoirs, and analysis of geological model uncertainties.

[0040] The permeability coarsening method provided by this invention can determine the cumulative permeability distribution probability parameter of each porosity distribution interval of the coarsened grid by using the permeability probability distribution map obtained by the permeability model construction method and the porosity distribution interval of the coarsened grid. It can quantitatively characterize the uncertainty range of permeability during the permeability attribute coarsening process. Compared with the traditional deterministic averaging algorithm, it has more flexible adjustment space and also has a reliable theoretical basis. Attached Figure Description

[0041] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0042] Figure 1 This is a cross-plot of porosity and permeability data from a carbonate reservoir in Group M of an oilfield in the Middle East.

[0043] Figure 2 This is a flowchart illustrating the penetration rate model construction method provided in Embodiment 1 of the present invention. Figure 1 ;

[0044] Figure 3 This is a diagram showing the grouping of core pore permeability data for the permeability model construction method provided in Embodiment 1 of the present invention;

[0045] Figure 4 yes Figure 3 Grouped core porosity and permeability data statistical parameter diagram;

[0046] Figure 5 This is a schematic diagram of the cumulative distribution probability curves of permeability in different confidence intervals determined by the permeability model construction method provided in this embodiment of the invention;

[0047] Figure 6 This is a flowchart illustrating step S103 of the penetration rate model construction method provided in Embodiment 1 of the present invention. Figure 1 ;

[0048] Figure 7 This is a flowchart illustrating step S103 of the penetration rate model construction method provided in Embodiment 1 of the present invention. Figure 2 ;

[0049] Figure 8 The first permeability probability distribution map determined by the permeability model construction method provided in Embodiment 1 of the present invention;

[0050] Figure 9 The second permeability probability distribution map determined by the permeability model construction method provided in this embodiment of the invention;

[0051] Figure 10 This is a flowchart illustrating the penetration rate model construction method provided in Embodiment 1 of the present invention. Figure 2 ;

[0052] Figure 11 This is a schematic diagram of the penetration rate model construction system provided in Embodiment 3 of the present invention;

[0053] Figure 12 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention. It should be noted that, unless otherwise specified, the various features in the embodiments of this invention can be combined with each other, all of which are within the protection scope of this invention. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed with a different module division or in a different order than that shown in the device schematic diagram or the flowchart.

[0055] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0056] Example 1

[0057] Reference Figure 1 The figure shows a cross-plot of core porosity and core permeability in a carbonate reservoir of Group M in an oilfield in the Middle East. The dashed lines represent the upper and lower limits of permeability assumed based on the distribution trend of the cross-plot data points. The first solid line from top to bottom is the cross-plot curve obtained from exponential regression, and the second solid line is the cross-plot curve obtained from power function regression. According to the model calculation, the permeability at a porosity of 22% is 10(10) / 2000. -3 μm 2 According to actual core data analysis, the permeability at a porosity of 22% is 0.18-3000 (10). -3 μm 2The permeability models obtained through exponential regression and power function regression are 0.018 times and 300 times higher than those calculated by the models, respectively, demonstrating their limitations and inapplicability. Therefore, this invention provides a method for constructing a permeability model for oil and gas reservoir rocks. Figure 2 As shown, it includes:

[0058] S101. Divide the acquired core porosity and permeability data into several porosity-permeability data groups evenly according to the porosity data range, ensuring that the porosity range of each group does not exceed a first preset threshold; the grouping of core porosity and permeability data is as follows. Figure 3 As shown;

[0059] S102. Determine whether the number of samples in each pore permeation data group is greater than the second preset threshold. If yes, then the pore permeation data group is determined as a high confidence group; otherwise, the pore permeation data group is determined as a low confidence group.

[0060] S103. For the determined multiple high-confidence groups and multiple low-confidence groups, determine the permeability value corresponding to each porosity data under each preset cumulative distribution probability, and obtain the corresponding multiple permeability cumulative distribution probability curves; the multiple permeability cumulative distribution probability curves are as follows: Figure 4 and Figure 5 As shown;

[0061] S104. Based on the multiple cumulative permeability distribution probability curves, a permeability probability distribution map is obtained.

[0062] This invention, based on the range of porosity data from core analysis, divides the core porosity and permeability data into several porosity-permeability data groups. Statistical analysis is performed on each group to obtain the cumulative permeability probability distribution function within that porosity range. The cumulative permeability probability distribution curves of all groups are then combined and interpolated to obtain a continuous permeability probability distribution map. Using this permeability probability distribution map, combined with porosity data interpreted from well logging, a probabilistic expression of the calculated permeability parameters can be achieved. Furthermore, during the establishment of geological models at different scales, the probabilistic expression of permeability parameters in the coarse grid model is determined comprehensively based on the porosity distribution and the permeability probability distribution map, providing a theoretical basis and quantitative method for characterizing permeability uncertainty. Moreover, through the application of the cumulative permeability probability distribution function, multiple permeability values ​​with different cumulative distribution probabilities can be calculated during single-well well logging interpretation. These determined permeability values ​​with different cumulative distribution probabilities can be used for the establishment of reservoir geological models and the analysis of geological model uncertainties.

[0063] The first and second preset thresholds described in the embodiments of the present invention can be set according to actual needs, such as based on the amount of core porosity data and permeability data. Under the premise that the porosity range of each group of porosity and permeability data does not exceed the first preset threshold, the number of samples in each group of porosity and permeability data should be greater than the second preset threshold as much as possible.

[0064] In one embodiment, step S103 above, for the determined plurality of high-confidence groups and plurality of low-confidence groups, determines the permeability value corresponding to each porosity data under each preset cumulative distribution probability, and obtains the corresponding plurality of permeability cumulative distribution probability curves, such as... Figure 6 As shown, it specifically includes:

[0065] S1031a. For the determined multiple high-confidence groups, calculate the high-confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability according to the cumulative distribution probability function.

[0066] The specific process described in step S1031a above for calculating the high-confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability is as follows: Based on the grouping results of step S102 above, such as Figure 4 As shown, each porosity data point corresponds to multiple permeability data points. For each porosity data point, the number of permeability data points corresponding to that porosity data point is counted, and the mean of all permeability data points is calculated. Based on the magnitude of the permeability data points, statistical analysis is performed to calculate the permeability value for each preset cumulative distribution probability. The number of all original permeability data points within each porosity interval is counted to obtain the number of sample points for each porosity interval.

[0067] The specific process described in step S1031a above for calculating the high-confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability can also be as follows: determine the intermediate porosity of each porosity interval; count the number of sample points of permeability data corresponding to each intermediate porosity, calculate the mean of permeability data corresponding to each intermediate porosity, perform statistical analysis based on the size of the permeability data, calculate the permeability value under each preset distribution probability corresponding to each intermediate porosity; perform interpolation calculation on the permeability value corresponding to each intermediate porosity to obtain the high-confidence permeability value corresponding to each porosity.

[0068] S1032a. For the determined multiple low-confidence groups, based on a preset extrapolation method and according to the distribution relationship between the multiple high-confidence groups and the multiple low-confidence groups, determine the low-confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability.

[0069] The extrapolation method described in step S1032a above is based on the pore permeability data adjacent to the low confidence group; Figure 5For example, porosity data between 26% and 30% belong to the high confidence group, while data greater than 30% belong to the low confidence group. Based on the porosity data between 26% and 30%, a power function, parabolic equation, or exponential function is used for fitting, and the fitted function is extrapolated to the region with porosity greater than 30%. The same method can be used for the low-value portion, such as porosity data between 0% and 2% and 4%.

[0070] The extrapolation method described in step S1032a above can also be to fit the permeability data of all pores in the high-confidence group using the Logistic function to obtain a permeability prediction function, and then deduce the low-confidence permeability value of the low-confidence group from this function. Of course, those skilled in the art can also refer to the descriptions of data fitting methods in the prior art; therefore, no specific limitations are imposed on this embodiment of the invention.

[0071] S1033a. Based on the high-confidence penetration rate value and low-confidence penetration rate value corresponding to each preset cumulative distribution probability, obtain multiple corresponding cumulative distribution probability curves of penetration rate.

[0072] In one embodiment, in step S103 above, for the determined plurality of high-confidence groups and plurality of low-confidence groups, the permeability value corresponding to each porosity data under each preset cumulative distribution probability is determined, resulting in multiple corresponding cumulative permeability distribution probability curves, such as... Figure 7 As shown, it specifically includes:

[0073] S1031b: Based on the determined multiple high-confidence groups and multiple low-confidence groups, substitute the porosity data into the preset permeability prediction function corresponding to each preset cumulative distribution probability to determine the permeability value corresponding to each porosity data corresponding to each preset cumulative distribution probability; the preset permeability prediction function is obtained by fitting the Sigmodal function;

[0074] As a specific example, a set of permeability prediction functions is obtained by fitting the Sigmodal function. Taking the cumulative distribution probabilities of 10%, 25%, 50%, 75%, 90%, and 95% as examples, the specific fitting results are as follows:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] Wherein, Por is porosity data.

[0082] S1032b: Based on the permeability value corresponding to each porosity data corresponding to each preset cumulative distribution probability, obtain multiple corresponding permeability cumulative distribution probability curves.

[0083] In one embodiment, obtaining the permeability probability distribution map based on the plurality of cumulative permeability probability curves in step S104 above includes:

[0084] S1041. The permeability probability distribution map is obtained by interpolation calculation based on the multiple cumulative permeability distribution probability curves. The determined permeability probability distribution map is as follows: Figure 8 and Figure 9 As shown, Figure 8 The permeability probability distribution map is determined by interpolation calculation based on multiple cumulative permeability probability curves defined in S1031a-S1033a. Figure 9 The permeability probability distribution map is determined by interpolation calculation based on multiple cumulative permeability probability curves defined in S1031b-S1033b. Figure 8 and Figure 9 The resulting permeability probability sub-maps are generally quite similar. Figure 8 The permeability probability distribution map obtained by direct calculation has relatively poor smoothness. Therefore, it is preferable to use steps S1031b-S1033b to determine the multiple permeability cumulative distribution probability curves.

[0085] In one embodiment, before obtaining the permeability probability distribution map by interpolation calculation based on the plurality of cumulative permeability distribution probability curves as described in step S1041 above, the method includes:

[0086] S1040. Convert the permeability values ​​of the multiple cumulative permeability distribution probability curves into logarithmic values, and remove the first outlier data point in the low confidence group that deviates from the trend of the high confidence group.

[0087] In step S1040 above, the permeability value is logarithmized to facilitate the subsequent interpolation calculation of multiple cumulative distribution probability curves of permeability. Since the permeability values ​​differ by thousands of times, logarithmization can avoid the occurrence of singularities during the interpolation calculation, which would cause some data points to deviate from the overall trend.

[0088] In one embodiment, based on the permeability probability distribution map determined in steps S101-S104 above and the porosity curve obtained from single-well logging interpretation, the permeability curve for each well under different cumulative distribution probabilities is determined.

[0089] For example, assuming the porosity interpreted from a single well logging operation is 15%, the permeability value corresponding to each preset cumulative distribution probability under the condition of 15% porosity can be determined based on the permeability probability distribution map, thus determining the permeability probability distribution under the condition of 15% porosity. By using the permeability probability distribution corresponding to each porosity in the porosity curve, the permeability curve under different cumulative distribution probabilities can be determined.

[0090] In one embodiment, before step S101, which involves uniformly dividing the acquired core porosity and permeability data into several porosity-permeability data groups based on the porosity data size range, and ensuring that the porosity range of each group of porosity-permeability data does not exceed a first preset threshold, the method further includes:

[0091] S100. Remove second abnormal data points from the acquired core porosity and permeability data. To perform quality control on conventional core porosity and permeability data, second abnormal data points caused by factors such as sample damage are removed. One method is to remove tagged core porosity and permeability data; the tags indicate whether the sample is damaged. Another method is to perform a porosity-permeability cross-comparison of all core porosity and permeability data; if a data point significantly deviates from the overall data trend, it can be considered a second abnormal point and removed.

[0092] In one embodiment, as another specific implementation of the present invention, the penetration rate model construction method, such as Figure 10 As shown, it includes:

[0093] S200, Remove the second abnormal data point from the obtained core porosity and permeability data;

[0094] S201. The obtained core porosity data and permeability data are divided according to rock characteristics to obtain core porosity data and permeability data corresponding to each rock characteristic; the rock characteristics include at least one of lithology, microfacies type, rock type and logging facies type;

[0095] S202. For the core porosity data and permeability data corresponding to each rock feature, the core porosity data and permeability data are evenly divided into several porosity-permeability data groups according to the size range of the porosity data, and the porosity range of each group of porosity-permeability data does not exceed the first preset threshold.

[0096] S203. Determine whether the number of samples in each pore permeation data group is greater than the second preset threshold. If yes, then the pore permeation data group is determined as a high confidence group; otherwise, the pore permeation data group is determined as a low confidence group.

[0097] S204. For the determined multiple high-confidence groups and multiple low-confidence groups, determine the permeability value corresponding to each porosity data under each preset cumulative distribution probability, and obtain multiple permeability cumulative distribution probability curves corresponding to each rock feature.

[0098] S205. Based on the multiple cumulative permeability distribution probability curves corresponding to each rock feature, a permeability probability distribution map corresponding to each rock feature is obtained.

[0099] S206. Based on the permeability probability distribution map corresponding to each rock feature and the porosity curve corresponding to each rock feature obtained from the interpretation of single-well logging, determine the permeability curves of each rock feature in each well under different cumulative distribution probabilities.

[0100] Example 2

[0101] This invention provides a method for permeability coarsening based on the permeability probability distribution map determined by the permeability model construction method described in Embodiment 1, comprising:

[0102] S601. Based on the preset coarsening target scale and the porosity data of the fine grid, determine the porosity distribution range of each coarsening grid.

[0103] S602. Based on the permeability probability distribution map, determine the cumulative permeability distribution probability parameter for each porosity distribution interval of the coarsened grid.

[0104] In existing technologies, the permeability distribution range of a porosity range is typically determined based on the probability distribution of permeability values ​​at the midpoint of the porosity distribution range. Assuming... Figure 9 The dashed line represents a specific porosity distribution range within the coarsened grid, specifically 12%-16% porosity. If the permeability distribution within this range is determined using a permeability probability distribution with a porosity of 14%, the determined permeability range will be too large relative to the permeability value at 12% porosity and too small relative to the permeability value at 16% porosity. Therefore, this method of determining the permeability distribution range within this porosity range is inaccurate. To overcome the shortcomings of the prior art, this embodiment determines the permeability distribution range within the 12%-16% porosity range based on data points from P10 (porosity of 12%) to P95 (porosity of 16%) in the permeability probability distribution map. By analyzing the intersection of porosity and permeability, the permeability value corresponding to each porosity within the coarsened grid under different cumulative distribution probabilities can be determined. Wherein, P10, P25, P50, P75, P90, and P95 correspond to the permeability values ​​under cumulative distribution probabilities of 10%, 25%, 50%, 75%, 90%, and 95%, respectively. Figure 5For example, taking data with a porosity between 8-12% as a sample, and performing statistical analysis based on the permeability, the permeability distribution within this porosity range can be obtained. Through the permeability probability distribution map on the porosity-permeability cross-plot, the uncertainty range of permeability can be quantitatively characterized during the coarsening of permeability attributes. Compared with traditional deterministic averaging algorithms, this approach offers more flexible adjustment options and also has a reliable theoretical basis.

[0105] The method for determining the cumulative permeability distribution probability parameter of each porosity distribution interval in the aforementioned fine grid is the same as the method for determining the cumulative permeability distribution probability parameter of each porosity distribution interval in the aforementioned coarsened grid. Specifically, based on the second preset coarsening target scale, and based on the porosity data of the well logging interpretation curve corresponding to each small grid of the fine grid, the porosity distribution interval of each small grid is determined; based on... Figure 9 The diagram shows how, based on the permeability probability distribution map and the porosity distribution range of each small grid, the cumulative permeability distribution probability parameter, i.e., the permeability probability value, is determined for each small grid's porosity distribution range. This allows for permeability coarsening at different geological model scales, from well logging interpretation curves, fine grids, and coarse grids. Different geological model scales refer to a range from centimeter-scale plunger core samples (typically 1-inch cylinders, approximately 2.54 cm in diameter) to geological model grids (typically 50-100 meters horizontally and 0.5-5 meters vertically).

[0106] Example 3

[0107] This embodiment further describes the permeability construction methods of Embodiments 1 and 2 above, based on core analysis of porosity and permeability data from a carbonate reservoir in Group M of an oilfield in the Middle East. Figure 1 This is a cross-plot of porosity and permeability data from all core analyses of the M group carbonate reservoir in a certain oilfield. There are 5981 valid data point pairs. Based on these data points, this embodiment establishes a permeability probability distribution map of the M reservoir. The steps for constructing the permeability probability distribution map include:

[0108] S300: Perform quality control on core porosity and permeability data pairs to remove second abnormal data points caused by experimental conditions or sample damage;

[0109] S301: Group all 5981 data points according to porosity intervals, with each porosity distribution interval being 2%. In this embodiment, the data are grouped into 20 groups: [0-2%], (2%-4%], (4%-6%], (6%-8%], (8%-10%], (10%-12%], (12%-14%], (14%-16%], (16%-18%], (18%-20%], (20%-22%], (22%-24%], (24%-26%], (26%-28%], (28%-30%], (30%-32%], (32%-34%], (34%-36%], (36%-38%], and (38%-40%). See details. Figure 4 ;

[0110] S302: Divide the high confidence interval and low confidence interval with the second preset threshold of 50, and count the number of samples in each pore permeability data group. In this embodiment, the number of data in [0-2%], (32%-34%], (34%-36%], (36%-38%] and (38%-40%] is less than 50. Since the number of samples in this embodiment is large, the sample number requirement for the high confidence interval can be appropriately increased. Set the second preset threshold to 100, divide the [0-4%] and (30%-40%] intervals into low confidence intervals, and divide (4%-30%] into high confidence intervals.

[0111] S303: Calculate the permeability values ​​of samples with porosity distribution in the high confidence interval based on the cumulative probability distribution function, namely the permeability values ​​of (4%-6%), (6%-8%), (8%-10%), (10%-12%), (12%-14%), (14%-16%), (16%-18%), (18%-20%), (20%-22%), (22%-24%), (24%-26%), (26%-28%), and (28%-30%). (Refer to...) Figure 5 As shown, in this embodiment, the permeability values ​​at six quantiles with cumulative distribution probabilities of 10%, 25%, 50%, 75%, 90%, and 95% were calculated. From bottom to top in the figure, these are the permeability values ​​with cumulative distribution probabilities of 10%, 25%, 50%, 75%, 90%, and 95%.

[0112] S304: For the low confidence group, refer to... Figure 5 As shown, the same method was used to calculate the permeability values ​​for six quantiles of cumulative distribution probabilities: 10%, 25%, 50%, 75%, 90%, and 95%. However, the calculation results for the low confidence interval (i.e., the solid line portion) are less reliable. Therefore, in this preferred embodiment, the extrapolation method in Embodiment 1 is used to determine the low confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability based on the high confidence permeability value of the high confidence group.

[0113] S305: Based on the high-confidence group permeability values ​​determined in step S303 and the low-confidence group permeability values ​​determined in step 304, multiple cumulative permeability probability curves are obtained; the multiple cumulative permeability probability curves are as follows: Figure 4 and Figure 5 As shown;

[0114] S306: Convert permeability values ​​to logarithmic values, remove the first outlier data point in the low confidence zone that deviates significantly from the normal trend in the high confidence zone, and calculate the permeability probability distribution under different porosity conditions through interpolation, as shown in the appendix. Figure 9 As shown.

[0115] Example 3

[0116] Based on the same inventive concept, embodiments of the present invention provide a penetration rate model construction system, such as... Figure 11 As shown, it includes:

[0117] 401. Data partitioning module, used to divide the acquired core porosity data and permeability data into several porosity-permeability data groups evenly according to the porosity data size range, and to ensure that the porosity range of each group of porosity-permeability data does not exceed a first preset threshold.

[0118] 402. Judgment module, used to determine whether the number of samples in each pore permeation data group is greater than a second preset threshold. If so, the pore permeation data group is determined as a high confidence group; otherwise, the pore permeation data group is determined as a low confidence group.

[0119] 403. The first calculation module is used to determine the permeability value corresponding to each porosity data under each preset cumulative distribution probability for the determined multiple high confidence groups and multiple low confidence groups, and to obtain the corresponding multiple permeability cumulative distribution probability curves.

[0120] 404. The second calculation module is used to obtain a permeability probability distribution map based on the multiple cumulative permeability distribution probability curves.

[0121] The specific implementation of the penetration rate model construction system provided in the embodiments of the present invention can be referred to the detailed description of the penetration rate model construction method in Embodiment 1 and Embodiment 2 above. Of course, in the implementation process, those skilled in the art can also refer to the detailed description in the prior art. Where there are repetitions, they will not be repeated.

[0122] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the permeability model construction method and the permeability coarsening method as described above.

[0123] This invention also provides an electronic device 500, such as... Figure 2 As shown, it includes a memory 52, a processor 51, and a computer program stored in the memory 52 and executable on the processor 51. When the processor 51 executes the program, it implements the permeability model construction method and the permeability coarsening method as described.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; under the concept of the present invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for constructing a penetration rate model, characterized in that, include: The obtained core porosity and permeability data are evenly divided into several porosity and permeability data groups according to the size range of the porosity data, and the porosity range of each group of porosity and permeability data does not exceed the first preset threshold. Determine whether the number of samples in each pore permeation data group is greater than a second preset threshold. If so, the pore permeation data group is determined as a high-confidence group; otherwise, the pore permeation data group is determined as a low-confidence group. For the identified multiple high-confidence groups, the high-confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability is calculated according to the cumulative distribution probability function; for the identified multiple low-confidence groups, the low-confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability is determined based on the distribution relationship between the multiple high-confidence groups and the multiple low-confidence groups using a preset extrapolation method. Based on the high-confidence penetration rate value and low-confidence penetration rate value corresponding to each preset cumulative distribution probability, multiple corresponding cumulative distribution probability curves of penetration rate are obtained; or, Based on the determined high-confidence groups and low-confidence groups, the porosity data is substituted into a preset permeability prediction function corresponding to each preset cumulative distribution probability to determine the permeability value corresponding to each porosity data point for each preset cumulative distribution probability; the preset permeability prediction function is obtained by fitting a Sigmodal function. Based on the permeability value corresponding to each porosity data corresponding to each preset cumulative distribution probability, multiple permeability cumulative distribution probability curves are obtained. The permeability values ​​of multiple cumulative permeability probability curves are converted into logarithmic values, and the first outlier data point in the low confidence group that deviates from the trend of the high confidence group is removed. The permeability probability distribution map is obtained by interpolation calculation based on the multiple cumulative permeability distribution probability curves.

2. The method for constructing a penetration rate model according to claim 1, characterized in that, Also includes: Based on the permeability probability distribution map and the porosity curve obtained from the single-well logging interpretation, the permeability curve for each well under different cumulative distribution probabilities is determined.

3. The method for constructing a penetration rate model according to claim 1, characterized in that, Before dividing the acquired core porosity and permeability data into several porosity-permeability data groups according to the porosity data range, and ensuring that the porosity range of each group of porosity-permeability data does not exceed a first preset threshold, the process also includes: The second abnormal data point in the obtained core porosity and permeability data was removed.

4. The method for constructing a penetration rate model according to claim 1, characterized in that, Before dividing the acquired core porosity and permeability data into several porosity-permeability data groups according to the porosity data range, and ensuring that the porosity range of each group of porosity-permeability data does not exceed a first preset threshold, the process also includes: The core porosity and permeability data are divided according to rock characteristics to obtain core porosity and permeability data corresponding to each rock characteristic; the rock characteristics include at least one of lithology, microfacies type, rock type and logging facies type.

5. The method for constructing a penetration rate model according to claim 4, characterized in that, Also includes: Based on the permeability probability distribution map corresponding to each rock feature and the porosity curve corresponding to each rock feature obtained from single-well logging interpretation, the permeability curves for each rock feature of each well under different cumulative distribution probabilities are determined.

6. A penetration rate model construction system, characterized in that, include: The data partitioning module is used to divide the acquired core porosity data and permeability data into several porosity-permeability data groups evenly according to the size range of the porosity data, and to ensure that the porosity range of each group of porosity-permeability data does not exceed a first preset threshold. The judgment module is used to determine whether the number of samples in each pore permeation data group is greater than a second preset threshold. If so, the pore permeation data group is determined as a high confidence group; otherwise, the pore permeation data group is determined as a low confidence group. The first calculation module is used to calculate, for the determined multiple high-confidence groups, the high-confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability according to the cumulative distribution probability function; and to determine, for the determined multiple low-confidence groups, the low-confidence permeability value corresponding to each porosity data under each preset cumulative distribution probability according to the distribution relationship between the multiple high-confidence groups and the multiple low-confidence groups based on a preset extrapolation method. Based on the high-confidence penetration rate value and low-confidence penetration rate value corresponding to each preset cumulative distribution probability, multiple corresponding cumulative distribution probability curves of penetration rate are obtained; or, Based on the determined high-confidence groups and low-confidence groups, the porosity data is substituted into a preset permeability prediction function corresponding to each preset cumulative distribution probability to determine the permeability value corresponding to each porosity data point for each preset cumulative distribution probability; the preset permeability prediction function is obtained by fitting a Sigmodal function. Based on the permeability value corresponding to each porosity data corresponding to each preset cumulative distribution probability, multiple permeability cumulative distribution probability curves are obtained. The second calculation module is used to convert the permeability values ​​of multiple cumulative permeability distribution probability curves into logarithmic values ​​and remove the first outlier data points in the low confidence group that deviate from the trend of the high confidence group; and to perform interpolation calculations based on the multiple cumulative permeability distribution probability curves to obtain a permeability probability distribution map.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the penetration rate model construction method as described in any one of claims 1-5.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the penetration rate model construction method as described in any one of claims 1-5.

Images