Three-dimensional geological modeling method, device, medium, equipment and system
By de-clustering adjustment of horizontal well data, the clustering effect problem of horizontal well data in three-dimensional geological modeling is solved, and the modeling accuracy and actual representativeness of the results are improved.
Patent Information
- Application Number
- CN202311091406.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-28
- Publication Date
- 2025-02-28
AI Technical Summary
In three-dimensional geological modeling, due to its high concentration, the geological statistical results do not conform to the actual situation, resulting in deviations in the analysis of the variance function, making it difficult to establish a geological model that conforms to the actual situation.
By obtaining horizontal well perforation data and vertical well data, we can judge whether there is a geological statistical deviation in the sedimentary phase distribution. If it exists, the horizontal well data will be declustered and adjusted, and three-dimensional geological modeling will be performed using the adjusted data.
The clustering effect of horizontal well data is reduced, the modeling accuracy is improved, and the obtained variation function is better represented by the actual situation, providing a more reliable three-dimensional geological model.
Smart Images
Figure CN119540473A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of mineral and energy exploration and development, and in particular to three-dimensional geological modeling methods, devices, media, equipment and systems. Background Art
[0002] At present, traditional oil and gas reservoirs developed with vertical well networks have relatively mature technologies and methods in the fields of reservoir characterization and three-dimensional geological modeling. However, with the advancement of drilling technology, horizontal wells are becoming more and more commonly used in conventional oil and gas reservoirs, especially unconventional oil and gas reservoirs. The role of horizontal well data in reservoir characterization is becoming more and more important. However, in the field of three-dimensional geological modeling, there is currently little application research.
[0003] Horizontal well data is different from vertical well data. It is often drilled for a specific target layer. Therefore, the horizontal well section extends a longer distance in the target layer section and provides data with higher centralization. This highly centralized data produces a clustering effect in the modeling process, which makes the results of geological statistics inconsistent with the actual geological situation, causes deviations in the analysis of the variogram, and makes it difficult to establish a geological model that conforms to the actual situation. On the other hand, horizontal well targets are often high-quality reservoirs, and the geological information they bring is over-optimized. In the past, areas with fewer horizontal wells often abandoned horizontal well information and only used vertical well information. This is feasible in areas with more uniform and dense vertical wells and fewer horizontal wells, but in areas with more horizontal wells or even areas with completely horizontal wells, horizontal well information cannot be ignored.
[0004] Horizontal well data are very different from vertical well data in terms of data logic results and data spatial distribution. Currently, they are not only increasingly used in conventional oil and gas reservoirs, but also in unconventional reservoirs such as shale gas, shale oil, and natural gas hydrates. Therefore, it is urgent to invent new methods to rationally use horizontal well information to establish an objective three-dimensional geological model that conforms to geological reality. Summary of the invention
[0005] The present invention aims to solve the above-mentioned prior art problem, namely, how to reasonably use horizontal well information to establish an objective three-dimensional geological model that conforms to geological reality.
[0006] In a first aspect, the present invention provides a three-dimensional geological modeling method, the method comprising the following steps:
[0007] Obtain horizontal well penetration data and vertical well data;
[0008] Determine whether the sedimentary facies distribution has geostatistical deviations due to the horizontal well penetration data;
[0009] If the deviation exists, de-clustering adjustment is performed on the horizontal well penetration data; if the deviation does not exist, de-clustering adjustment is not performed on the horizontal well penetration data;
[0010] The horizontal well penetration data or the declustered and adjusted horizontal well penetration data is used to complete three-dimensional geological modeling under horizontal well constraints.
[0011] Preferably, the determining whether the sedimentary facies distribution has a geostatistical deviation due to the horizontal well penetration data includes:
[0012] Counting the distribution ratio of the sedimentary phase after adding the horizontal well penetration data and the distribution ratio of the actual sedimentary phase;
[0013] The difference between the distribution ratio of the sedimentary phase after the horizontal well penetration data is added and the distribution ratio of the actual sedimentary phase is calculated. If the difference exceeds a set threshold, it is determined that the distribution of the sedimentary phase has a geostatistical deviation due to the horizontal well penetration data; otherwise, it is determined that the distribution of the sedimentary phase has no geostatistical deviation.
[0014] Preferably,
[0015] The de-clustering and adjusting the horizontal well penetration data includes:
[0016] The horizontal well is segmented according to the lithological reservoir encountered in the horizontal well section;
[0017] Determine the convergence point of each horizontal well section;
[0018] Calculate the data of the aggregation point of each horizontal well;
[0019] It is determined whether the data of the aggregation point is qualified. If it is qualified, the declustering adjustment is completed; if it is unqualified, the declustering adjustment is performed again until the data of the aggregation point is qualified.
[0020] Preferably, calculating the coordinates of the convergence point of each horizontal well section includes:
[0021] Find the two vertical wells closest to the horizontal wells in this section;
[0022] Connect the two wells to form a connecting line, and make a normal line of the connecting line;
[0023] The aggregation point of the horizontal well section is selected according to whether the normal line intersects with the horizontal well section, and the coordinates of the aggregation point are recorded.
[0024] Preferably, the selecting the aggregation point of the section of horizontal well according to whether the normal line intersects with the section of horizontal well comprises:
[0025] If the normal line has an intersection with the horizontal well section, the intersection is selected as the aggregation point of the horizontal well section;
[0026] If the normal line has no intersection with the horizontal well section, the midpoint of the horizontal well section is selected as the aggregation point of the horizontal well section.
[0027] Preferably, the data for calculating the aggregation point includes:
[0028] The well section core, sedimentary phase, porosity, saturation and permeability of the aggregation point are calculated, and the well section core, sedimentary phase, porosity, saturation and permeability of the aggregation point are used as the data set of the horizontal well of this section.
[0029] Preferably, the determining whether the data of the aggregation point is qualified includes:
[0030] Coarsening the curves of the vertical well data and the data of the aggregation points, and performing variogram analysis;
[0031] It is judged whether the analyzed data is consistent with the geological understanding. If it is consistent with the geological understanding, the data of the aggregation point is qualified, otherwise it is unqualified.
[0032] In a second aspect, the present invention provides a three-dimensional geological modeling device, comprising:
[0033] A data acquisition unit, which is used to acquire horizontal well penetration data;
[0034] a de-clustering adjustment unit, which is used to select whether to perform de-clustering adjustment according to whether the sedimentary phase distribution has a geological statistical deviation due to the horizontal well penetration data; if the deviation exists, the de-clustering adjustment is performed on the horizontal well penetration data, and if the deviation does not exist, the de-clustering adjustment is not performed on the horizontal well penetration data;
[0035] The three-dimensional geological modeling unit enables the use of the horizontal well penetration data or the horizontal well penetration data after de-clustering adjustment to complete the three-dimensional geological modeling under the horizontal well constraint.
[0036] In a third aspect, the present invention provides a storage medium, wherein a computer program is stored in the storage medium, and when the computer program is executed, a processor implements the method described in the first aspect.
[0037] In a fourth aspect, the present invention provides an electronic device, comprising: a memory and a processor connected to the memory, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.
[0038] In a fifth aspect, the present invention provides a three-dimensional geological modeling system, characterized in that it includes the electronic device as described in the fourth aspect.
[0039] Through the above technical solution, the present invention has the following beneficial effects:
[0040] Starting from the overall geological concept, data are merged according to the lithofacies types encountered by horizontal wells to reduce the clustering effect of horizontal well data caused by large-scale data concentration; the misunderstanding of the variogram obtained due to the clustering effect is solved, so that the obtained variogram can better represent the actual situation of this work area.
[0041] In the early stage of modeling, there is no need to make other changes to the logging data, just treat it as a separate well for calculation; it has strong applicability and can be used in all work areas with horizontal wells; it uses horizontal well data aggregated based on the normal of adjacent wells, which has intuitive physical and geostatistical significance, maximizes the use of horizontal well data, eliminates the clustering effect, improves modeling accuracy, and provides a more reliable model guarantee for the effective development of work areas with horizontal wells. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] Figure 1 It is a schematic diagram of the main steps of the three-dimensional geological modeling method;
[0043] Figure 2 It is a schematic diagram of the main steps of de-clustering adjustment;
[0044] Figure 3 This is a schematic diagram of the main steps for calculating the coordinates of the aggregation points of each horizontal well:
[0045] Figure 4 It is a schematic diagram for determining the aggregation point when the normal line of the adjacent well intersects with the horizontal well;
[0046] Figure 5 It is a schematic diagram for determining the aggregation point when the normal line of the adjacent well has no intersection with the horizontal well;
[0047] Figure 6 It is a schematic diagram of the main steps to determine whether the data of the aggregation point is qualified;
[0048] Figure 7 It is a schematic diagram of the main flow of the method of this embodiment;
[0049] Figure 8 It is a schematic diagram of the horizontal well correction three-dimensional structural model;
[0050] Fig. 9 It is a schematic diagram of the deviation in geostatistics;
[0051] Fig.10 It is a schematic diagram of horizontal well segmentation;
[0052] Fig.11 It is a schematic diagram of the determined aggregation point of well NP22 in the study area;
[0053] Fig.12 This is the profile of the aggregation point of well NP22 in the study area;
[0054] Fig.13 It is a schematic diagram of the determined aggregation point of well NP5 in the study area;
[0055] Fig.14 It is a schematic diagram of the variogram analysis of horizontal well data and the variogram analysis after aggregation;
[0056] Fig.15 It is a comparison chart of modeling using vertical well data plus unaggregated horizontal well data, modeling using vertical well data, and modeling using vertical well data plus aggregated horizontal well data. DETAILED DESCRIPTION
[0057] At present, traditional oil and gas reservoirs developed with vertical well networks have relatively mature technologies and methods in the fields of reservoir characterization and three-dimensional geological modeling. However, with the advancement of drilling technology, horizontal wells are becoming more and more commonly used in conventional oil and gas reservoirs, especially unconventional oil and gas reservoirs. The role of horizontal well data in reservoir characterization is becoming more and more important. However, in the field of three-dimensional geological modeling, there is currently little application research.
[0058] Horizontal well data is different from vertical well data. It is often drilled for a specific target layer. Therefore, the horizontal well section extends a longer distance in the target layer section and provides data with higher centralization. This highly centralized data produces a clustering effect in the modeling process, which makes the results of geological statistics inconsistent with the actual geological situation, causes deviations in the analysis of the variogram, and makes it difficult to establish a geological model that conforms to the actual situation. On the other hand, horizontal well targets are often high-quality reservoirs, and the geological information they bring is over-optimized. In the past, areas with fewer horizontal wells often abandoned horizontal well information and only used vertical well information. This is feasible in areas with more uniform and dense vertical wells and fewer horizontal wells, but in areas with more horizontal wells or even areas with completely horizontal wells, horizontal well information cannot be ignored.
[0059] Conventional geological modeling is carried out in the following steps: first, a three-dimensional structural model is established based on vertical well information and seismic information; second, the vertical well logging curve is discretized based on the structural model. In this step, the horizontal well information is filtered out to ensure that the data conforms to the laws of geostatistics; third, the required three-dimensional lithofacies model and attribute model are established through appropriate methods based on the discrete information in the grid.
[0060] At present, there are few patents and literatures on horizontal well modeling, which can be roughly divided into three categories. One category uses the formation information encountered by the horizontal well to correct the structure, that is, to establish a more accurate three-dimensional structural model through the horizontal well information; the other category models the horizontal well study area but rarely considers the horizontal well data, such as directly roughening the horizontal well without removing the clustering effect, or directly removing the horizontal well data to cause data loss; the last category explores the horizontal well phase model and attribute model methods, but there are their own limitations, such as only applicable to the area of the horizontal well group in a single direction. At present, there is no relatively mature attribute modeling method for horizontal wells, and the existing various methods for horizontal well modeling have various shortcomings.
[0061] Horizontal well data is very different from vertical well data in terms of data logic results and data spatial distribution. At present, it is not only increasingly used in conventional oil and gas reservoirs, but also in unconventional reservoirs such as shale gas, shale oil, and natural gas hydrates. Therefore, it is urgent to invent new methods to reasonably use horizontal well information to establish an objective three-dimensional geological model that conforms to geological reality. In view of the above problems, this embodiment provides a three-dimensional geological modeling method, device, medium, equipment and system.
[0062] The present disclosure is further described below in conjunction with the embodiments shown in the accompanying drawings.
[0063] Example 1
[0064] Reference Figure 1 In a first aspect, this embodiment provides a three-dimensional geological modeling method, the method comprising the following steps:
[0065] S1. Obtain horizontal well penetration data and vertical well data.
[0066] S2. Determine whether there is a geostatistical deviation in sedimentary facies distribution due to the horizontal well penetration data; if there is a deviation, perform de-clustering adjustment on the horizontal well penetration data; if there is no deviation, do not perform de-clustering adjustment on the horizontal well penetration data.
[0067] In step S2, whether to perform de-clustering adjustment is selected according to whether the sedimentary facies distribution has a geological statistical deviation due to the horizontal well penetration data, including:
[0068] Counting the distribution ratio of the sedimentary phase after adding the horizontal well penetration data and the distribution ratio of the actual sedimentary phase;
[0069] The difference between the distribution ratio of the sedimentary phase after the horizontal well penetration data is added and the distribution ratio of the actual sedimentary phase is calculated. If the difference exceeds a set threshold, it is determined that the distribution of the sedimentary phase has a geostatistical deviation due to the horizontal well penetration data; otherwise, it is determined that the distribution of the sedimentary phase has no geostatistical deviation.
[0070] S3. Use the horizontal well penetration data or the horizontal well penetration data after de-clustering adjustment to complete the three-dimensional geological modeling under the horizontal well constraint.
[0071] Starting from the overall geological concept, data are merged according to the lithofacies types encountered by horizontal wells to reduce the clustering effect of horizontal well data caused by large-scale data concentration; the misunderstanding of the variogram obtained due to the clustering effect is solved, so that the obtained variogram can better represent the actual situation of this work area.
[0072] In the early stage of modeling, there is no need to make other changes to the logging data, just treat it as a separate well for calculation; it has strong applicability and can be used in all work areas with horizontal wells; it uses horizontal well data aggregated based on the normal of adjacent wells, which has intuitive physical and geostatistical significance, maximizes the use of horizontal well data, eliminates the clustering effect, improves modeling accuracy, and provides a more reliable model guarantee for the effective development of work areas with horizontal wells.
[0073] Example 2
[0074] Reference Figure 1 This embodiment provides a three-dimensional geological modeling method, which includes the following steps:
[0075] S1. Obtain horizontal well penetration data and vertical well data.
[0076] S2. Determine whether there is a geostatistical deviation in sedimentary facies distribution due to the horizontal well penetration data; if there is a deviation, perform de-clustering adjustment on the horizontal well penetration data; if there is no deviation, do not perform de-clustering adjustment on the horizontal well penetration data.
[0077] In step S2, whether to perform de-clustering adjustment is selected according to whether the sedimentary facies distribution has a geological statistical deviation due to the horizontal well penetration data, including:
[0078] Counting the distribution ratio of the sedimentary phase after adding the horizontal well penetration data and the distribution ratio of the actual sedimentary phase;
[0079] The difference between the distribution ratio of the sedimentary phase after the horizontal well penetration data is added and the distribution ratio of the actual sedimentary phase is calculated. If the difference exceeds a set threshold, it is determined that the distribution of the sedimentary phase has a geostatistical deviation due to the horizontal well penetration data; otherwise, it is determined that the distribution of the sedimentary phase has no geostatistical deviation.
[0080] Reference Figure 2 The de-clustering adjustment in step S2 includes the following steps:
[0081] S21. The horizontal well is segmented according to the lithologic reservoirs encountered in the horizontal well section.
[0082] Determine whether the horizontal well section of the horizontal well encounters the same lithology reservoir. If so, the horizontal well section of the horizontal well is not segmented, that is, the horizontal well section of the entire horizontal well is one section; otherwise, the horizontal well section is segmented according to the lithology entered by the horizontal well section of the horizontal well, and each lithology is divided into one section, usually divided into 3 sections at most, and each section is regarded as a separate horizontal well section.
[0083] S22. Determine the convergence point of each horizontal well segment.
[0084] S23. Calculate the data of the aggregation point of each horizontal well.
[0085] The data to be calculated in step S23 include: well section core, sedimentary phase, porosity, saturation and permeability. The well section core and sedimentary phase data are calculated using the most of method, while the porosity, saturation and other data are calculated using the arithmetic mean algorithm, and the permeability data are calculated using the harmonic mean or geometric mean method. The well section core, sedimentary phase, porosity, saturation and permeability of the aggregation point are taken as the data set of the horizontal well of this section.
[0086] S24. Determine whether the data of the aggregation point is qualified. If it is qualified, complete the declustering adjustment; if it is unqualified, perform the declustering adjustment again until the data of the aggregation point is qualified.
[0087] S3. Use the horizontal well penetration data or the horizontal well penetration data after de-clustering adjustment to complete the three-dimensional geological modeling under the horizontal well constraint.
[0088] Starting from the overall geological concept, data are merged according to the lithofacies types encountered by horizontal wells to reduce the clustering effect of horizontal well data caused by large-scale data concentration; the misunderstanding of the variogram obtained due to the clustering effect is solved, so that the obtained variogram can better represent the actual situation of this work area.
[0089] In the early stage of modeling, there is no need to make other changes to the logging data, just treat it as a separate well for calculation; it has strong applicability and can be used in all work areas with horizontal wells; it uses horizontal well data aggregated based on the normal of adjacent wells, which has intuitive physical and geostatistical significance, maximizes the use of horizontal well data, eliminates the clustering effect, improves modeling accuracy, and provides a more reliable model guarantee for the effective development of work areas with horizontal wells.
[0090] Example 3
[0091] Reference Figure 1 This embodiment provides a three-dimensional geological modeling method, which includes the following steps:
[0092] S1. Obtain horizontal well penetration data and vertical well data.
[0093] S2. Determine whether there is a geostatistical deviation in sedimentary facies distribution due to the horizontal well penetration data; if there is a deviation, perform de-clustering adjustment on the horizontal well penetration data; if there is no deviation, do not perform de-clustering adjustment on the horizontal well penetration data.
[0094] In step S2, whether to perform de-clustering adjustment is selected according to whether the sedimentary facies distribution has a geological statistical deviation due to the horizontal well penetration data, including:
[0095] Counting the distribution ratio of the sedimentary phase after adding the horizontal well penetration data and the distribution ratio of the actual sedimentary phase;
[0096] The difference between the distribution ratio of the sedimentary phase after the horizontal well penetration data is added and the distribution ratio of the actual sedimentary phase is calculated. If the difference exceeds a set threshold, it is determined that the distribution of the sedimentary phase has a geostatistical deviation due to the horizontal well penetration data; otherwise, it is determined that the distribution of the sedimentary phase has no geostatistical deviation.
[0097] Reference Figure 2 The de-clustering adjustment in step S2 includes the following steps:
[0098] S21. The horizontal well is segmented according to the lithologic reservoirs encountered in the horizontal well section.
[0099] Determine whether the horizontal well section of the horizontal well encounters the same lithology reservoir. If so, the horizontal well section of the horizontal well is not segmented, that is, the horizontal well section of the entire horizontal well is one section; otherwise, the horizontal well section is segmented according to the lithology entered by the horizontal well section of the horizontal well, and each lithology is divided into one section, usually divided into 3 sections at most, and each section is regarded as a separate horizontal well section.
[0100] S22. Determine the convergence point of each horizontal well segment.
[0101] Reference Figure 3 Step S22 specifically includes the following steps:
[0102] S221. Find the two vertical wells closest to the horizontal well. Take the target point as the starting point and the bottom of the horizontal well as the end point, find the midpoint O of the horizontal well, and take O as the center of the circle to find the two nearest wells nearby.
[0103] S222. Connect two wells to make a connecting line and make a normal line of the connecting line.
[0104] S223. Select the aggregation point of the horizontal well section according to whether the normal line intersects with the horizontal well section.
[0105] S23. Calculate the data of the aggregation point of each horizontal well.
[0106] The data to be calculated in step S23 include: well section core, sedimentary phase, porosity, saturation and permeability. The well section core and sedimentary phase data are calculated using the most of method, while the porosity, saturation and other data are calculated using the arithmetic mean algorithm, and the permeability data are calculated using the harmonic mean or geometric mean method. The well section core, sedimentary phase, porosity, saturation and permeability of the aggregation point are taken as the data set of the horizontal well of this section.
[0107] S24. Determine whether the data of the aggregation point is qualified. If it is qualified, complete the declustering adjustment; if it is unqualified, perform the declustering adjustment again until the data of the aggregation point is qualified.
[0108] S3. Use the horizontal well penetration data or the horizontal well penetration data after de-clustering adjustment to complete the three-dimensional geological modeling under the horizontal well constraint.
[0109] Starting from the overall geological concept, data are merged according to the lithofacies types encountered by horizontal wells to reduce the clustering effect of horizontal well data caused by large-scale data concentration; the misunderstanding of the variogram obtained due to the clustering effect is solved, so that the obtained variogram can better represent the actual situation of this work area.
[0110] In the early stage of modeling, there is no need to make other changes to the logging data, just treat it as a separate well for calculation; it has strong applicability and can be used in all work areas with horizontal wells; it uses horizontal well data aggregated based on the normal of adjacent wells, which has intuitive physical and geostatistical significance, maximizes the use of horizontal well data, eliminates the clustering effect, improves modeling accuracy, and provides a more reliable model guarantee for the effective development of work areas with horizontal wells.
[0111] Example 4
[0112] Reference Figure 1This embodiment provides a three-dimensional geological modeling method, which includes the following steps:
[0113] S1. Obtain horizontal well penetration data and vertical well data.
[0114] S2. Determine whether there is a geostatistical deviation in sedimentary facies distribution due to the horizontal well penetration data; if there is a deviation, perform de-clustering adjustment on the horizontal well penetration data; if there is no deviation, do not perform de-clustering adjustment on the horizontal well penetration data.
[0115] In step S2, whether to perform de-clustering adjustment is selected according to whether the sedimentary facies distribution has a geological statistical deviation due to the horizontal well penetration data, including:
[0116] Counting the distribution ratio of the sedimentary phase after adding the horizontal well penetration data and the distribution ratio of the actual sedimentary phase;
[0117] The difference between the distribution ratio of the sedimentary phase after the horizontal well penetration data is added and the distribution ratio of the actual sedimentary phase is calculated. If the difference exceeds a set threshold, it is determined that the distribution of the sedimentary phase has a geostatistical deviation due to the horizontal well penetration data; otherwise, it is determined that the distribution of the sedimentary phase has no geostatistical deviation.
[0118] Reference Figure 2 The de-clustering adjustment in step S2 includes the following steps:
[0119] S21. The horizontal well is segmented according to the lithologic reservoirs encountered in the horizontal well section.
[0120] Determine whether the horizontal well section of the horizontal well encounters the same lithology reservoir. If so, the horizontal well section of the horizontal well is not segmented, that is, the horizontal well section of the entire horizontal well is one section; otherwise, the horizontal well section is segmented according to the lithology entered by the horizontal well section of the horizontal well, and each lithology is divided into one section, usually divided into 3 sections at most, and each section is regarded as a separate horizontal well section.
[0121] S22. Determine the convergence point of each horizontal well segment.
[0122] Reference Figure 3 Step S22 specifically includes the following steps:
[0123] S221. Find the two vertical wells closest to the horizontal well. Take the target point as the starting point and the bottom of the horizontal well as the end point, find the midpoint O of the horizontal well, and take O as the center of the circle to find the two nearest wells nearby.
[0124] S222. Connect two wells to make a connecting line and make a normal line of the connecting line.
[0125] S223. Select the aggregation point of the horizontal well section according to whether the normal line intersects with the horizontal well section.
[0126] Reference Figure 4 If the normal line intersects with the horizontal well section, the intersection point is selected as the aggregation point of the horizontal well section.
[0127] Reference Figure 5 If the normal line has no intersection with the horizontal well section, the midpoint of the horizontal well section is selected as the aggregation point of the horizontal well section.
[0128] S23. Calculate the data of the aggregation point of each horizontal well.
[0129] The data to be calculated in step S23 include: well section core, sedimentary phase, porosity, saturation and permeability. The well section core and sedimentary phase data are calculated using the most of method, while the porosity, saturation and other data are calculated using the arithmetic mean algorithm, and the permeability data are calculated using the harmonic mean or geometric mean method. The well section core, sedimentary phase, porosity, saturation and permeability of the aggregation point are taken as the data set of the horizontal well of this section.
[0130] S24. Determine whether the data of the aggregation point is qualified. If it is qualified, complete the declustering adjustment; if it is unqualified, perform the declustering adjustment again until the data of the aggregation point is qualified.
[0131] S3. Use the horizontal well penetration data or the horizontal well penetration data after de-clustering adjustment to complete the three-dimensional geological modeling under the horizontal well constraint.
[0132] Starting from the overall geological concept, data are merged according to the lithofacies types encountered by horizontal wells to reduce the clustering effect of horizontal well data caused by large-scale data concentration; the misunderstanding of the variogram obtained due to the clustering effect is solved, so that the obtained variogram can better represent the actual situation of this work area.
[0133] In the early stage of modeling, there is no need to make other changes to the logging data, just treat it as a separate well for calculation; it has strong applicability and can be used in all work areas with horizontal wells; it uses horizontal well data aggregated based on the normal of adjacent wells, which has intuitive physical and geostatistical significance, maximizes the use of horizontal well data, eliminates the clustering effect, improves modeling accuracy, and provides a more reliable model guarantee for the effective development of work areas with horizontal wells.
[0134] Example 5
[0135] Reference Figure 1 This embodiment provides a three-dimensional geological modeling method, which includes the following steps:
[0136] S1. Obtain horizontal well penetration data and vertical well data.
[0137] S2. Determine whether there is a geostatistical deviation in sedimentary facies distribution due to the horizontal well penetration data; if there is a deviation, perform de-clustering adjustment on the horizontal well penetration data; if there is no deviation, do not perform de-clustering adjustment on the horizontal well penetration data.
[0138] In step S2, whether to perform de-clustering adjustment is selected according to whether the sedimentary facies distribution has a geological statistical deviation due to the horizontal well penetration data, including:
[0139] Counting the distribution ratio of the sedimentary phase after adding the horizontal well penetration data and the distribution ratio of the actual sedimentary phase;
[0140] The difference between the distribution ratio of the sedimentary phase after the horizontal well penetration data is added and the distribution ratio of the actual sedimentary phase is calculated. If the difference exceeds a set threshold, it is determined that the distribution of the sedimentary phase has a geostatistical deviation due to the horizontal well penetration data; otherwise, it is determined that the distribution of the sedimentary phase has no geostatistical deviation.
[0141] Reference Figure 2 The de-clustering adjustment in step S2 includes the following steps:
[0142] S21. The horizontal well is segmented according to the lithologic reservoirs encountered in the horizontal well section.
[0143] Determine whether the horizontal well section of the horizontal well encounters the same lithology reservoir. If so, the horizontal well section of the horizontal well is not segmented, that is, the horizontal well section of the entire horizontal well is one section; otherwise, the horizontal well section is segmented according to the lithology entered by the horizontal well section of the horizontal well, and each lithology is divided into one section, usually divided into 3 sections at most, and each section is regarded as a separate horizontal well section.
[0144] S22. Determine the convergence point of each horizontal well segment.
[0145] Reference Figure 3 Step S22 specifically includes the following steps:
[0146] S221. Find the two vertical wells closest to the horizontal well. Take the target point as the starting point and the bottom of the horizontal well as the end point, find the midpoint O of the horizontal well, and take O as the center of the circle to find the two nearest wells nearby.
[0147] S222. Connect two wells to make a connecting line and make a normal line of the connecting line.
[0148] S223. Select the aggregation point of the horizontal well section according to whether the normal line intersects with the horizontal well section.
[0149] Reference Figure 4If the normal line intersects with the horizontal well section, the intersection point is selected as the aggregation point of the horizontal well section.
[0150] Reference Figure 5 If the normal line has no intersection with the horizontal well section, the midpoint of the horizontal well section is selected as the aggregation point of the horizontal well section.
[0151] S23. Calculate the data of the aggregation point of each horizontal well.
[0152] The data to be calculated in step S23 include: well section core, sedimentary phase, porosity, saturation and permeability. The well section core and sedimentary phase data are calculated using the most of method, while the porosity, saturation and other data are calculated using the arithmetic mean algorithm, and the permeability data are calculated using the harmonic mean or geometric mean method. The well section core, sedimentary phase, porosity, saturation and permeability of the aggregation point are taken as the data set of the horizontal well of this section.
[0153] S24. Determine whether the data of the aggregation point is qualified. If it is qualified, complete the declustering adjustment; if it is unqualified, perform the declustering adjustment again until the data of the aggregation point is qualified.
[0154] Reference Figure 6 , step S24 comprises the following steps:
[0155] S241. Curve coarsening is performed on the data of all vertical wells and horizontal well aggregation points, and variogram analysis is performed;
[0156] S242. Determine whether the analyzed data is consistent with geological knowledge. If it is consistent with geological knowledge, the data of the aggregation point is qualified, otherwise it is unqualified.
[0157] S3. Use the horizontal well penetration data or the horizontal well penetration data after de-clustering adjustment to complete the three-dimensional geological modeling under the horizontal well constraint.
[0158] Starting from the overall geological concept, data are merged according to the lithofacies types encountered by horizontal wells to reduce the clustering effect of horizontal well data caused by large-scale data concentration; the misunderstanding of the variogram obtained due to the clustering effect is solved, so that the obtained variogram can better represent the actual situation of this work area.
[0159] In the early stage of modeling, there is no need to make other changes to the logging data, just treat it as a separate well for calculation; it has strong applicability and can be used in all work areas with horizontal wells; it uses horizontal well data aggregated based on the normal of adjacent wells, which has intuitive physical and geostatistical significance, maximizes the use of horizontal well data, eliminates the clustering effect, improves modeling accuracy, and provides a more reliable model guarantee for the effective development of work areas with horizontal wells.
[0160] Reference Figure 7 The main process of the method provided in this embodiment during the specific implementation process is as follows:
[0161] It is preferred to obtain horizontal well penetration data.
[0162] The structural model is established based on seismic and vertical well information, and the structural model is corrected through horizontal well penetration data. The "virtual well local optimization" obtains a more refined three-dimensional structural model with horizontal well correction. The horizontal well penetration data used in the construction model process is retrieved.
[0163] Then determine whether the sedimentary phase distribution will cause geological statistical deviation due to the existence of horizontal well penetration data. Calculate the difference between the sedimentary phase distribution ratio after statistical horizontal well penetration data and the actual sedimentary phase distribution ratio. If the difference between the two does not exceed the set threshold, it means that there is no deviation, and the horizontal well penetration data can be directly used to complete the three-dimensional geological modeling under the horizontal well constraint; if the difference between the two exceeds the set threshold, it means that there is a deviation, and the horizontal well penetration data needs to be declustered.
[0164] The de-clustering adjustments include:
[0165] Determine whether the horizontal well section of the horizontal well encounters the same lithology reservoir. If so, the horizontal well section of the horizontal well is not divided, that is, the horizontal well section of the horizontal well is one section. Otherwise, the horizontal well is divided into several sections according to the situation (based on the lithology it enters, usually 2 to 3 sections at most), and each section is regarded as a separate horizontal well section.
[0166] Taking the target point as the starting point and the bottom of the horizontal well as the end point, find the center point O of the horizontal well section, and use O as the center of the circle to find the two nearest wells nearby; draw a straight line from the two nearest wells, and at the same time draw the normal of this straight line, and intersect the normal line and the horizontal well trajectory. The intersection point is the aggregation point M of the horizontal well data. This point is equidistant from the two adjacent wells, and the coordinates (x, y, z) of this aggregation point M are recorded.
[0167] When the normal lines of two adjacent wells have no focus with the horizontal well trajectory, the center point of the horizontal well section is used as the aggregation point M of the horizontal well data.
[0168] The core and sedimentary phase data of this horizontal well section are calculated using the most of method, while the porosity, saturation and other data are calculated using the arithmetic average algorithm, and the permeability data are calculated using the harmonic mean or geometric mean method. The data of the aggregation point M is used as the data set of the horizontal well section.
[0169] Determine whether the data of the above-mentioned aggregation point M is qualified. Curve coarsening is performed on all vertical well and horizontal well aggregation point data, and variogram analysis is performed, including major and minor ranges, and analysis of the proportion of each lithofacies. Comparative inspection includes horizontal well aggregation points and direct analysis using horizontal well data, including sedimentary facies proportion histograms, quality credibility attribute graphs, etc. If the analysis results are consistent with the actual understanding, the data of the aggregation point M is qualified, otherwise, de-clustering adjustment is performed again.
[0170] According to the parameter results of the condensation point variation function analysis of vertical wells and horizontal wells or according to the penetration data of vertical wells and horizontal wells, subsequent phase and attribute modeling is carried out to complete the three-dimensional geological model under the constraints of horizontal wells.
[0171] The following is an explanation with reference to specific cases.
[0172] An oil field in northwest my country is a low-permeability reservoir with developed braided river delta deposits. The main part is the braided main channel, and the side is the side edge of the braided channel and the braided river delta plain. The reservoir has large lateral heterogeneity. It is divided into two sets of strata, namely Y522-1 and Y522-2. At the top of the Y522-1 formation is a coal seam covering the entire study area.
[0173] There are 54 wells in the study area (since the pilot well can also provide geological information, the pilot well is also calculated as a separate well when modeling), of which 7 wells are vertical wells, 20 pilot wells, and 27 wells are horizontal wells, and most of the horizontal wells are distributed within the main river channel; the overall well spacing is 400 meters on average, and the average length of the horizontal well section is 700 meters. The direction of most horizontal well groups is roughly east-west and perpendicular to the main river channel. Since horizontal wells account for a large proportion of 50% and the amount of data in the horizontal well direction is large, using horizontal wells for variogram analysis will bring a lot of data errors, resulting in a larger proportion of sand bodies in the overall model.
[0174] Horizontal well structural modeling should focus on fine stratification to ensure that horizontal wells do not penetrate into other layers. The conventional modeling method is to use only seismic structural interpretation, vertical well stratification and small layer thickness data to establish a three-dimensional structural model. Since this method can only rely on seismic interpretation trends for inter-well structural surfaces, it is easy to cause mismatches between structural layers and horizontal well trajectories. For example, in the study area, the conventional method was used to establish the structural model, and the horizontal section of the NP25 well entered the coal seam. However, the production condition of the horizontal section of this well was good, which was inconsistent with the actual production data.
[0175] Reference Figure 8In order to overcome the problem of mismatch between structure and well trajectory and adjacent well stratification, the drilling geological conditions, logging curves and adjacent well data were analyzed. After repeated demonstrations, the position of the horizontal well trajectory in the formation was clarified, and the "virtual well local optimization" scheme was used to correct the three-dimensional structural model, ultimately achieving matching of the structure with the horizontal well trajectory and attribute model.
[0176] Reference Fig. 9 According to previous geological knowledge, the study area mainly develops braided river delta phase, and 18 of the 27 horizontal wells are distributed in the main part of the braided river channel, and the other horizontal wells are also distributed on the side edge of the braided river channel. In addition, the horizontal well data are concentrated along the well trajectory, that is, sandstone is distributed. From the logging data statistics, the proportion of sandstone using vertical wells is 57%, and after adding the horizontal well data, the proportion of sandstone is as high as 89.5%. According to previous geological analysis, the range of braided river channels is only one-third to one-half of the study area. Therefore, after adding the horizontal well data statistics, the proportion of sandstone is significantly higher than the actual situation. The clustering effect is more prominent and is a transitional estimate. Corresponding de-clustering adjustments should be made, otherwise it will seriously affect the modeling quality.
[0177] Reference Fig.10 This time, the horizontal well information aggregation method is used, that is, the corresponding horizontal well information is merged, so the horizontal wells need to be segmented to prevent information loss caused by the merging of horizontal wells; according to the location of the horizontal well trajectory, if the horizontal well passes through two or more sets of strata with large lithofacies changes, it is divided into several sections according to the changes in lithofacies; the horizontal wells in the center of the braided river channel in the study area are generally divided into one section, while a few horizontal wells at the edge may penetrate the main body of the braided river channel and enter the mudstone of the delta plain, so they need to be divided into two sections for analysis.
[0178] Reference Fig.11 , take the horizontal well NP22 as an example. This horizontal well is located in the northern part of the study area. According to the previous geological background and logging phase research and analysis, it is within the main river channel and the lithology of the horizontal well section does not change much. It mainly develops sandstone and can be divided into one section as a whole. The horizontal well trajectory of NP22 well is used as a circle. The nearest vertical wells are ND14 and ND24. These two wells are used as a straight line and the normal of this straight line is also used. The intersection point with the normal line is the aggregation point M of NP22 well. The coordinates of this point M are recorded.
[0179] Reference Fig.12, the horizontal well trajectory data are merged according to different data types. The lithofacies data adopts the most of method. The merged data is sandstone. From the horizontal well logging interpretation analysis, the interpretation is basically sandstone, so the data merger is more reasonable; the porosity and oil saturation adopt the arithmetic mean method. The porosity horizontal section range is 0.11-0.15, and it is 0.136 after merging. The oil saturation range is 0.74-0.85, and it is 0.79 after merging. The permeability range varies slightly from 10.9mD to 32.8mD, and it is 20.6mD after merging. The permeability is not simulated in the later stage, and the permeability model is directly calculated using the calculation formula.
[0180] Reference Fig.13 For example, if the normal line of the adjacent well near the horizontal well does not intersect with the horizontal well trajectory, the well is relatively independent from the surrounding area, and the aggregation point is the midpoint of the horizontal well trajectory; taking NP5 well as an example, ND26 well and ND37 well are its nearest adjacent wells, and the vertical line of the normal line of the two vertical wells only intersects with the extension line of the horizontal well trajectory. At this time, the aggregation point M is the midpoint of target points A and B; the porosity range of the horizontal well section of NP5 well is 11.3% to 15.6%, which is 12.4% after merging, and the oil saturation range is 0.62 to 0.79, which is 0.71 after merging.
[0181] Reference Fig.14 , the variogram analysis of the well data was performed. The main range direction of the channel sand body variogram obtained by the data of the aggregation points was -10°, which was consistent with the source direction, and the main range size was 820m. The main range of the channel sand body variogram obtained by the horizontal well data was 512 meters. Due to the concentrated distribution of a large amount of data in the horizontal well section, the number of variogram point pairs increased by 10 times, and the main range direction became -90°, which was consistent with the horizontal well trajectory direction. This result was a misunderstanding caused by the dense distribution of horizontal well data in the well trajectory direction of -90° to -120°; the final result of the variogram analysis was a main range size of 820 meters, a secondary range of 324 meters, and a main direction of -10°.
[0182] After data aggregation of all horizontal wells, statistical analysis was performed. The sandstone proportion of vertical wells alone was 57%, and the sandstone proportion of vertical and horizontal wells together was 89.5%. After aggregation, the sandstone proportion of vertical and horizontal wells was 61.7%. Since the horizontal wells are all within the main river channel, it is believed that the increased sandstone proportion is consistent with geological understanding and can be carried out to the next step of analysis.
[0183] Reference Fig.15, the aggregated horizontal well data and vertical well data were used for lithofacies modeling, and the parameters came from the results of variogram analysis. In order to verify the accuracy of the results, vertical well data and original horizontal well data without aggregation were used for modeling respectively; it can be seen from the model that the three-dimensional geological model established by using vertical well data + unaggregated horizontal well data has a relatively high sand body ratio and very good continuity, especially the northern sand body contains almost no mudstone, which is mainly related to the sandstone data collected by the horizontal well in the river channel, resulting in an increase in the sand body content in the overall model, which is inconsistent with the actual situation; and the lithofacies model established by using the vertical well model, as the sandstone ratio in the overall model drops to a reasonable level, but due to the lack of well control in many areas, the sandstone ratio in the river channel is too small; and the three-dimensional geological model established by vertical well + aggregated horizontal well has good sand body continuity in the river channel direction, a moderate sandstone ratio, and mudstone is partially developed in the river channel sand body, which is more consistent with the actual situation. In summary, it is believed that the three-dimensional geological model established by the horizontal well aggregation method is more reliable.
[0184] The various embodiments in the present disclosure are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.
[0185] The protection scope of the present disclosure is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and their equivalents, the intention of the present disclosure also includes these changes and modifications.
Claims
1. A three-dimensional geological modeling method, characterized in that: The following steps are involved: Obtain horizontal well penetration data and vertical well data; Determine whether the sedimentary facies distribution has geostatistical deviations due to the horizontal well penetration data; If the deviation exists, de-clustering adjustment is performed on the horizontal well penetration data; if the deviation does not exist, de-clustering adjustment is not performed on the horizontal well penetration data; The horizontal well penetration data or the declustered and adjusted horizontal well penetration data is used to complete three-dimensional geological modeling under horizontal well constraints.
2. The three-dimensional geological modeling method according to claim 1, characterized in that: The determining whether the sedimentary facies distribution has a geostatistical deviation due to the horizontal well penetration data includes: Counting the distribution ratio of the sedimentary phase after adding the horizontal well penetration data and the distribution ratio of the actual sedimentary phase; The difference between the distribution ratio of the sedimentary phase after the horizontal well penetration data is added and the distribution ratio of the actual sedimentary phase is calculated. If the difference exceeds a set threshold, it is determined that the distribution of the sedimentary phase has a geostatistical deviation due to the horizontal well penetration data; otherwise, it is determined that the distribution of the sedimentary phase has no geostatistical deviation.
3. The three-dimensional geological modeling method according to claim 1, characterized in that: The de-clustering and adjusting the horizontal well penetration data includes: The horizontal well is segmented according to the lithological reservoir encountered in the horizontal well section; Determine the convergence point of each horizontal well section; Calculate the data of the aggregation point of each horizontal well; It is determined whether the data of the aggregation point is qualified. If it is qualified, the declustering adjustment is completed; if it is unqualified, the declustering adjustment is performed again until the data of the aggregation point is qualified.
4. The three-dimensional geological modeling method according to claim 3, characterized in that: Calculate the coordinates of the convergence point of each horizontal well, including: Find the two vertical wells closest to the horizontal wells in this section; Connect the two wells to form a connecting line, and make a normal line of the connecting line; The aggregation point of the horizontal well section is selected according to whether the normal line intersects with the horizontal well section, and the coordinates of the aggregation point are recorded.
5. The three-dimensional geological modeling method according to claim 4, characterized in that: The step of selecting the aggregation point of the horizontal well section according to whether the normal line intersects the horizontal well section comprises: If the normal line has an intersection with the horizontal well section, the intersection is selected as the aggregation point of the horizontal well section; If the normal line has no intersection with the horizontal well section, the midpoint of the horizontal well section is selected as the aggregation point of the horizontal well section.
6. The three-dimensional geological modeling method according to claim 3, characterized in that: The data for calculating the aggregation point includes: The well section core, sedimentary phase, porosity, saturation and permeability of the aggregation point are calculated, and the well section core, sedimentary phase, porosity, saturation and permeability of the aggregation point are used as the data set of the horizontal well of this section.
7. The three-dimensional geological modeling method according to claim 6, characterized in that: The determining whether the data of the aggregation point is qualified includes: Coarsening the curves of the vertical well data and the data of the aggregation points, and performing variogram analysis; It is judged whether the analyzed data is consistent with the geological understanding. If it is consistent with the geological understanding, the data of the aggregation point is qualified, otherwise it is unqualified.
8. A three-dimensional geological modeling device, characterized in that: include: A data acquisition unit, which is used to acquire horizontal well penetration data; a de-clustering adjustment unit, which is used to select whether to perform de-clustering adjustment according to whether the sedimentary phase distribution has a geological statistical deviation due to the horizontal well penetration data; if the deviation exists, the de-clustering adjustment is performed on the horizontal well penetration data, and if the deviation does not exist, the de-clustering adjustment is not performed on the horizontal well penetration data; The three-dimensional geological modeling unit enables the use of the horizontal well penetration data or the horizontal well penetration data after de-clustering adjustment to complete the three-dimensional geological modeling under the horizontal well constraint.
9. A storage medium, characterized in that: The storage device stores a computer program, which enables a processor to implement the method according to any one of claims 1 to 7 when executed.
10. An electronic device, characterized in that: include: A memory and a processor connected to the memory, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.
11. A three-dimensional geological modeling system, characterized in that: Comprising the electronic device as claimed in claim 10.