Method and device for identifying dominant channels in a multi-water-drive reservoir

Abstract

The application discloses a kind of multi water drive mode dominant channel identification method and device.The method comprises: selecting the first evaluation index of multi water drive mode dominant channel identification from the engineering index and geological index of well in reservoir;The statistical data of the first evaluation index selected is clustered, and the crossflow grade of well in reservoir is determined according to the clustering result;Whether the well in reservoir exists dominant channel is determined according to crossflow grade.And according to the crossflow grade of well, the historical production data fitting of whole area of reservoir is carried out for second evaluation index and the historical production data fitting of single well is carried out for third evaluation index;According to the fitting result of whole area of reservoir historical production data and the fitting result of each well historical production data, the displacement flux of each region of whole reservoir is determined, so as to determine the crossflow grade of each region of whole reservoir.Can accurately identify whether there is dominant channel in single well and whole reservoir, to provide accurate reference data for reservoir development.

Description

Technical Field

[0001] This invention relates to the field of oil and gas development technology, and in particular to a method and apparatus for identifying the dominant channels of reservoirs in multiple water drive modes. Background Technology

[0002] Water injection development is the most common reservoir development method in sandstone oilfields. With the continuous development of waterflooding, oilfields are entering high and ultra-high water-cut stages. Due to prolonged waterflooding during reservoir development, the reservoir pore structure undergoes significant changes. The imbalance of the hydrodynamic field and the strong heterogeneity of the reservoir cause injected water to continuously flow along dominant channels with higher permeability and water cut, expanding the original pore throat radius and permeability, resulting in inefficient or ineffective water injection circulation and the formation of dominant channels. The existence of these dominant channels puts enormous pressure on oilfield development, leading to reduced recovery rates, increased production costs, and decreased economic benefits. Therefore, research on the identification of dominant channels is urgently needed.

[0003] Currently, the theory of dominant channel identification often focuses on identifying dominant channels between wells. Here, dominant channels between wells generally refer to the dominant channels between injection wells and production wells. Due to long-term water injection scouring, dominant channels are generated between injection wells and production wells. Summary of the Invention

[0004] Due to the presence of natural edge and bottom water in some reservoirs, multiple water drive methods are often employed during water injection development. Current methods for identifying dominant channels between wells are no longer suitable for reservoirs with multiple water drive modes. This is because identifying dominant channels requires calculating the injection volume of injection wells and allocating that volume to each production well through a splitting process. When a reservoir contains strong natural water bodies with strong edge and bottom water, the edge energy is often sufficient, requiring little or no water injection development, resulting in very few injection well data. However, over long-term development, dominant channels can develop between edge and bottom water and production wells. For example, if a production well has a dominant channel and there are nearby injection wells, current methods cannot identify whether the dominant channel is caused by water injection or edge and bottom water. Similarly, if a production well has a dominant channel but no nearby injection wells, the direction of the natural water body cannot be determined. Furthermore, in cases without injection wells, the lack of injection data makes it impossible to split the injection volume. In summary, there is currently a lack of a scientific and reasonable method for identifying the dominant channels in multi-water drive modes to guide the potential tapping and adjustment in the ultra-high water-cut stage, making it impossible to accurately identify the dominant channels of a single well or the entire reservoir.

[0005] In view of the above problems, the present invention is proposed to provide a method and apparatus for identifying the dominant channel of a multi-water-drive reservoir that overcomes or at least partially solves the above problems.

[0006] This invention provides a method for identifying dominant channels in multi-water-drive reservoirs, comprising:

[0007] The first evaluation index for identifying the dominant channels of the multi-water drive mode is selected from the engineering and geological indicators of the wells in the reservoir.

[0008] Cluster the statistical data of the selected first evaluation index, and determine the flow level of the well in the reservoir based on the clustering identification results;

[0009] Based on the aforementioned crossflow level, determine whether there is a dominant channel in the well within the reservoir.

[0010] In some optional embodiments, the first evaluation index for identifying the dominant pathway of the multi-water drive mode from the engineering and geological indicators of the reservoir wells includes:

[0011] The engineering and geological parameters of water injection wells and production wells in the oil reservoir were statistically analyzed.

[0012] Based on the engineering and geological parameters of the injection well, the effective thickness, permeability gradient, permeability variation coefficient, cumulative injection volume, maximum daily injection volume, and injection pressure were selected as the primary evaluation indicators for the multi-water drive mode.

[0013] Based on the engineering and geological parameters of production wells, effective thickness, permeability gradient, permeability variation coefficient, cumulative production, maximum daily production, and water cut were selected as the primary evaluation indicators for identifying the advantageous channels of multi-water drive mode.

[0014] In some optional embodiments, when clustering the statistical data of the selected first evaluation index, the clustering criteria are the maximum inter-class distance and the minimum intra-class distance, and the clustering identification result is obtained through multiple iterations.

[0015] In some optional embodiments, clustering the selected first evaluation index includes:

[0016] Determine the number of clusters, error limits, and initial cluster centers for clustering the first evaluation index;

[0017] The statistical data of the first evaluation index is used as the dataset, and a partition matrix is ​​generated or updated based on the dataset;

[0018] The cluster centers are updated based on the partition matrix. It is then determined whether the difference between the updated cluster centers and the previously obtained cluster centers meets the error limit requirement. If yes, the clustering result for the first evaluation index is obtained. If not, the process returns to continue executing the step of generating or updating the partition matrix based on the dataset.

[0019] In some optional embodiments, generating or updating the partition matrix based on the dataset includes: generating or updating the partition matrix U based on the dataset. b =[μ ij ]

[0020]

[0021] Where, x j For a sample of data in dataset x, v i v k μ serves as the cluster center. ij To partition matrix U b In the matrix elements, c and n are the number of samples in each class after partitioning, and m is the membership factor;

[0022] The step of updating the cluster centers based on the partition matrix includes: updating the cluster centers V using the following formula. b+1 :

[0023]

[0024] Determining whether the difference between the updated cluster centers and the previously obtained cluster centers meets the aforementioned error limit requirement includes: based on the currently obtained cluster centers V b+1 And the cluster center V obtained last time b Determine ||V b -V b+1 Does ||≤ε hold true?

[0025] In some optional embodiments, determining whether a well in the reservoir has a dominant channel based on the crossflow level includes:

[0026] If the channeling level of the well in the reservoir is determined to be severe channeling or general channeling, then the well in the reservoir is determined to have a dominant channel. Among them, if the channeling level is severe channeling, the dominant channel of the well in the reservoir is determined to be a large channel.

[0027] If the well in the reservoir is determined to have no crossflow, then it is determined that there is no dominant channel in the well in the reservoir.

[0028] In some optional embodiments, after determining the channeling level of a well in the reservoir, the method further includes:

[0029] Draw a diagram of the well's flow pattern based on the well's flow pattern classification;

[0030] Based on the crossflow diagram, historical production data of the entire reservoir area are fitted for the selected second evaluation index to obtain the historical production data fitting results of the entire reservoir area for each second evaluation index; and historical production data of a single well are fitted for the selected third evaluation index to obtain the historical production data fitting results of each well.

[0031] Based on the fitting results of historical production data for the entire reservoir and the fitting results of historical production data for each well, the displacement flux of each region in the entire reservoir is determined. Based on the displacement flux, the crossflow level of each region in the entire reservoir is determined. Based on the crossflow level of each region, it is determined whether there is a dominant channel in each region.

[0032] In some optional embodiments, the second evaluation index includes at least one of the following: daily oil production, daily liquid production, cumulative oil production, cumulative liquid production, cumulative water production, and water content of the entire region.

[0033] The third evaluation index includes at least one of daily oil production per well and water cut per well.

[0034] In some optional embodiments, the displacement flux PA for each region of the entire reservoir is determined using the following formula:

[0035] Where Q is the volumetric flow rate through the cross section, and Aφ is the pore area of ​​the displacement cross section.

[0036] In some optional embodiments, determining the crossflow level of each region of the entire reservoir based on the displacement flux includes:

[0037] Regions with a displacement flux greater than 500 PV are identified as areas where severe crossflow occurs.

[0038] The region with a displacement flux between 30 PV and 500 PV was identified as the region where general crossflow occurs.

[0039] Regions with a displacement flux of less than 30 PV are identified as regions where crossflow has not occurred.

[0040] This invention provides a multi-water-drive reservoir dominant channel identification device, comprising:

[0041] The selection module is used to select the first evaluation index for identifying the dominant channel of the multi-water drive mode from the engineering and geological indicators of the well in the reservoir.

[0042] The clustering module is used to cluster the statistical data of the selected first evaluation index and determine the flow level of the well in the reservoir based on the clustering identification results.

[0043] The first identification module is used to determine whether there is a dominant channel in the reservoir based on the crossflow level.

[0044] This invention also provides another multi-water drive mode dominant channel identification device, including:

[0045] The drawing module is used to draw a diagram of the well's flow pattern based on the well's flow pattern classification.

[0046] The fitting module is used to fit historical data of the entire reservoir area based on the crossflow situation diagram and the selected second evaluation index to obtain the historical production data fitting results of the entire reservoir area for each second evaluation index; and to fit historical production data of a single well based on the selected third evaluation index to obtain the historical production data fitting results of each well.

[0047] The second identification module is used to determine the displacement flux of each region in the entire reservoir based on the fitting results of the historical production data of the entire reservoir and the fitting results of the historical production data of each well, determine the crossflow level of each region in the entire reservoir based on the displacement flux, and determine whether there is a dominant channel in each region based on the crossflow level of each region.

[0048] This invention also provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for identifying the dominant channel of a multi-water drive mode.

[0049] This invention also provides a computer 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 above-described method for identifying the dominant channel of a multi-water drive mode.

[0050] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0051] The method for identifying dominant channels in reservoirs under multiple water drive modes provided in this invention selects a first evaluation index from the engineering and geological indicators of wells in the reservoir for identification of dominant channels in single wells under multiple water drive modes. The selected first evaluation index is clustered, and the similarity of wells forming dominant water flow channels in dynamic characteristics is explored through fuzzy clustering. Based on the clustering identification results, the crossflow level of wells in the reservoir is determined, thereby determining whether there are dominant channels in the wells of the reservoir. This method accurately identifies single wells with dominant channels by clustering dynamically similar wells, improving the accuracy of dominant channel identification under multiple water drive modes, and providing a basis and adjustment direction for tapping the potential of reservoirs in the ultra-high water-cut stage.

[0052] The multi-water drive mode reservoir dominant channel identification method provided by this invention draws a well crossflow map based on the dominant channel identification results of a single well. Based on the crossflow map, it fits the historical production data of the entire reservoir area with the historical production data of a single well. According to the fitting results, it determines the crossflow level of each region of the entire reservoir, thereby accurately determining whether there are dominant channels in each region of the entire reservoir and the distribution of dominant channels in the reservoir, providing more reliable reference data for reservoir production.

[0053] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0054] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 This is a flowchart of the method for identifying the dominant channels of a reservoir in a multi-water-drive mode according to Embodiment 1 of the present invention;

[0057] Figure 2 This is a flowchart of the method for identifying the dominant channels of a reservoir in a multi-water-drive mode according to Embodiment 2 of the present invention;

[0058] Figure 3 This is an example diagram of the fuzzy clustering recognition results of water injection wells in Embodiment 2 of the present invention;

[0059] Figure 4 This is an example diagram of the fuzzy clustering identification results of production wells in Embodiment 2 of the present invention;

[0060] Figure 5 This is a flowchart of the method for identifying the dominant channels of a reservoir in a multi-water-drive mode according to Embodiment 3 of the present invention;

[0061] Figure 6a This is an example diagram of the fitting results of the daily oil production of the entire region in Embodiment 3 of the present invention;

[0062] Figure 6b This is an example diagram of the fitting results of the daily liquid production of the entire region in Embodiment 3 of the present invention;

[0063] Figure 6c This is an example diagram of the fitting results of the total cumulative liquid production in the whole region in Embodiment 3 of the present invention;

[0064] Figure 6d This is an example diagram of the fitting results of the cumulative oil production of the entire region in Embodiment 3 of the present invention;

[0065] Figure 6e This is an example diagram of the fitting results of the total cumulative water production in the whole region in Embodiment 3 of the present invention;

[0066] Figure 6f This is an example diagram of the fitting results for the total moisture content in Embodiment 3 of the present invention;

[0067] Figure 7a This is an example diagram of the historical fitting results of the production dynamics of well 7 in Embodiment 3 of the present invention;

[0068] Figure 7b This is an example diagram of the historical fitting results of the production dynamics of well 16 in Embodiment 3 of the present invention;

[0069] Figure 7c This is an example diagram of the historical fitting results of the production dynamics of well 23 in Embodiment 3 of the present invention;

[0070] Figure 7d This is an example diagram of the historical production dynamics fitting results of well 38 in Embodiment 3 of the present invention;

[0071] Figure 7e This is an example diagram of the historical fitting results of the production dynamics of well 42 in Embodiment 3 of the present invention;

[0072] Figure 8a This is an example diagram showing the identification results of the dominant channels in the SK-1 layer of the reservoir in Embodiment 3 of the present invention;

[0073] Figure 8b This is an example diagram showing the identification results of the dominant channels in the SK-2 layer of the reservoir in Embodiment 3 of the present invention;

[0074] Figure 8c This is an example diagram showing the identification results of the dominant channels in the SK-3 reservoir in Embodiment 3 of the present invention;

[0075] Figure 9 This is a schematic diagram of the structure of the first multi-water-drive reservoir dominant channel identification device in an embodiment of the present invention.

[0076] Figure 10 This is a schematic diagram of the structure of the second type of reservoir advantage channel identification device in the embodiment of the present invention. Detailed Implementation

[0077] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0078] To address the problem of existing technologies failing to accurately identify dominant channels in single wells or the entire reservoir, this invention provides a method for identifying dominant channels in multi-water drive reservoirs. This method includes identifying dominant channels in a single well and identifying dominant channels throughout the entire reservoir. Based on determined evaluation indicators for the multi-water drive model, the flow level of a single well is judged. Historical production data is fitted using auxiliary numerical simulation, and the displacement flux (PA) is statistically analyzed to identify dominant channels throughout the entire reservoir. This method can accurately identify dominant channels in single wells or the entire reservoir, thereby better guiding oil and gas production.

[0079] Example 1

[0080] Embodiment 1 of the present invention provides a method for identifying the dominant channel in multiple water drive modes, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0081] Step S101: Select the first evaluation index for identifying the dominant channel from the engineering and geological indicators of the well in the reservoir.

[0082] The engineering and geological parameters of water injection wells and production wells in the reservoir were statistically analyzed, and the primary evaluation index for identifying the dominant pathways in the multi-water drive mode was selected based on the statistical results. For example, effective thickness, permeability gradient, permeability coefficient of variation, cumulative water injection volume, maximum daily water injection volume, and water injection pressure were selected as the primary evaluation index for the multi-water drive mode from the engineering and geological parameters of water injection wells; and effective thickness, permeability gradient, permeability coefficient of variation, cumulative production volume, maximum daily production volume, and water cut were selected as the primary evaluation index for the multi-water drive mode from the engineering and geological parameters of production wells.

[0083] The evaluation indicators for water injection wells and production wells can be selected as needed, and are not limited to the indicators listed above. The number of indicators is also not limited to six, and the number of selected evaluation indicators can be changed as needed.

[0084] Step S102: Cluster the statistical data of the selected first evaluation index, and determine the flow level of the well in the reservoir based on the clustering results.

[0085] When clustering the statistical data of the selected first evaluation indicator, the clustering criteria are the maximum inter-cluster distance and the minimum intra-cluster distance. The clustering results are obtained through multiple iterations. Clustering calculations are performed on the relevant data of the selected first evaluation indicator, and the clustering results are obtained through multiple iterations. The channeling level of wells in the reservoir can be categorized as: severe channeling, moderate channeling, and no channeling.

[0086] The clustering process includes: determining the number of clusters, error bounds, and initial cluster centers for the statistical data of the first evaluation indicator; using the obtained statistical data of the first evaluation indicator as the dataset, generating or updating the partition matrix based on the dataset; updating the cluster centers based on the partition matrix, and determining whether the difference between the updated cluster centers and the previously obtained cluster centers meets the error bounds requirement; if yes, obtaining the clustering result for the first evaluation indicator; otherwise, returning to continue executing the step of generating or updating the partition matrix based on the dataset. Wherein:

[0087] Generating or updating partition matrices based on datasets includes: generating or updating partition matrix U based on datasets. b =[μ ij ]

[0088]

[0089] Where, x j For a sample of data in dataset x, v i v k μ serves as the cluster center. ij To partition matrix U b In the matrix elements, c and n are the number of samples in each class after partitioning, and m is the membership factor, which is usually set to 2 by default, but can be set as needed.

[0090] Updating cluster centers based on the partition matrix includes updating cluster centers V using the following formula. b+1 :

[0091]

[0092] Determine whether the difference between the updated cluster centers and the previously obtained cluster centers meets the error limit requirements, including: based on the currently obtained cluster center V b+1 And the cluster center V obtained last time b Determine ||V b -V b+1 If ||≤ε is true, the difference is considered to meet the error limit requirement; otherwise, it is considered not true.

[0093] Step S103: Determine whether there is a dominant channel in the wells of the reservoir based on the crossflow level of the wells in the reservoir.

[0094] The presence of a dominant channel in a single well can be determined by the level of crossflow in that well. If the level of crossflow in a well in the reservoir is determined to be severe crossflow or general crossflow, then the presence of a dominant channel in the well in the reservoir is determined. Specifically, if the level of crossflow is severe crossflow, then the dominant channel in the well in the reservoir is determined to be a large channel. If the level of crossflow in a well in the reservoir is determined to be no crossflow, then the presence of a dominant channel in the well in the reservoir is determined.

[0095] The method described in this embodiment is applicable to the identification of dominant channels in a single well. Based on the selected evaluation index, the flow level of a single well in the reservoir is determined. Based on the flow level of a single well, it is determined whether a dominant channel has been formed in the single well. This method can accurately identify the dominant channels of a single well. By guiding oil and gas production through the identification results, the reservoir recovery rate can be improved and the production cost can be reduced.

[0096] "Birds of a feather flock together" is a fundamental principle of nature, and the same applies to oil and gas field development. When oil and water wells form dominant water flow channels, their dynamics exhibit similarity. With the introduction of the concept of fuzzy clustering, dynamic similarity can be addressed using fuzzy set methods. The aforementioned method employs the fuzzy c-means (FCM) algorithm, using the statistical data of the first evaluation index as the dataset, also known as the sample set. The sample set contains multiple samples, and the specific processing flow is as follows:

[0097] Initialization: First, determine how many classes the sample set will be divided into, i.e., determine the number of classes C. Obviously, C should be between 2 and the maximum number of samples, i.e., the minimum number of classes is 2, and the maximum number of classes is the number of samples in the sample set. In addition to determining the number of classes, the maximum error bound ε and the initial cluster centers V0 should also be determined, and the iteration counter b = 0 should be set.

[0098] According to the formula Calculate or update the partition matrix U b =[μ ij ]:

[0099] According to the formula Update cluster center V b+1 :

[0100] According to the updated cluster center V b+1 Compared to the last updated cluster center V b Make a judgment if the following relationship is satisfied: ||V b -V b+1 If ||≤ε, then the accuracy requirement is considered met; otherwise, let b=b+1, return to step 1, and perform another iteration to obtain the optimal value.

[0101] As can be seen from the calculation process of fuzzy clustering analysis, it is a gradual process. Using the maximum inter-cluster distance and minimum intra-cluster distance as clustering criteria, it iterative calculations are performed continuously until the final optimal solution is obtained. This iterative solution process places very high demands on the expression of distance; different sets of indicators and different data formats require different optimal distance expressions. During the clustering process, different distance expressions will identify different patterns, mainly depending on the distribution of the data. In practical applications, the appropriate distance expression can be selected for the clustering process as needed. The selectable distance expressions include, but are not limited to, the following:

[0102] Minkowski distance:

[0103]

[0104] Distance to Manhattan:

[0105]

[0106] Weighted Euclidean distance:

[0107]

[0108] Chebyshev distance:

[0109]

[0110] Where, x i x j Let d be a sample in dataset x, d be the distance between two samples, i, j, k be the labels between samples of different types, p be the number of samples, w be the weight, and q be an arbitrary constant.

[0111] Due to inconsistent data completeness across different oilfields, varying well types, and different artificial lift methods, the corresponding development indicators differ, making it more difficult to guarantee the stability of the corresponding dominant channel sensitivity indicators. In the dominant channel identification model, data analysis is necessary. For example, Euclidean distance is suitable for identifying radial circular patterns, while Manhattan distance is suitable for identifying triangular patterns, thus improving identification accuracy.

[0112] Based on the above theory, a computer program was written to identify the dominant water flow channels.

[0113] Example 2

[0114] Embodiment 2 of the present invention provides a specific implementation process of the method for identifying dominant channels in multiple water drive modes, and describes the process of identifying dominant channels in a single well as follows: Figure 2 As shown, it includes the following steps:

[0115] Step S201: Select the first evaluation index for identifying the dominant channel of the multi-water drive mode from the engineering and geological indicators of the well in the reservoir.

[0116] Taking the South Kumkol oilfield as an example, there are currently 32 production wells and 12 water injection wells. Based on the selection criteria, three geological indicators and three engineering indicators were chosen.

[0117] Step S202: Collect relevant data for the first evaluation index for each well to obtain statistical data.

[0118] Basic data related to oil and water wells were collected separately, and the corresponding evaluation indicators needed to identify the dominant channels were calculated. The relevant indicators for water injection wells and production wells are shown in Tables 1 and 2. Table 1 is a statistical table of the calculation results for water injection well indicators, including the current effective thickness of the injection layer, permeability gradient, permeability coefficient of variation, cumulative water injection volume, maximum daily water injection volume, and injection pressure. Table 2 is a statistical table of the calculation results for production well indicators, including the current effective thickness of the production layer, permeability gradient, permeability coefficient of variation, cumulative fluid production, maximum daily fluid production, and water cut.

[0119] Table 1. Statistical Table of Calculation Results of Relevant Indicators for Water Injection Wells

[0120]

[0121] Table 2 Statistical Table of Calculation Results of Relevant Indicators for Production Wells

[0122]

[0123]

[0124] Step S203: Cluster the statistical data of the evaluation indicators of the selected multi-water drive mode to obtain the cluster identification results of the wells in the reservoir, and determine the crossflow level of the wells in the reservoir based on the cluster identification results.

[0125] When clustering the evaluation indicators of the selected multi-water drive modes, the relevant data of the evaluation indicators can be represented in the form of a feature index matrix. By performing clustering calculations on the feature index matrix, the fuzzy clustering identification results for each well are obtained. The clustering calculation process may involve multiple iterations until the clustering criteria are met, such as distance requirements. For example, the water injection well data shown in Table 1 converged after multiple iterations, such as 53 iterations, while the production well data shown in Table 2 converged after multiple iterations, such as 32 iterations. Spatial distribution maps of different data can be obtained, such as... Figure 3 The image shows the fuzzy clustering identification results for water injection wells, as follows: Figure 4 The image shows the fuzzy clustering identification results for production wells. Star-shaped points represent severe crossflow, circular points represent moderate crossflow, and triangular points represent no crossflow.

[0126] After clustering the selected primary evaluation indicator, it can be used to determine whether a dominant channel has been formed, such as... Figure 3 The results shown include fuzzy clustering identification results for indicators such as effective thickness, permeability gradient, and permeability variation coefficient for injection wells, as well as fuzzy clustering identification results for indicators such as cumulative injection volume, maximum daily injection volume, and wellhead injection pressure. Taking the three types of data—cumulative injection volume, maximum daily injection volume, and wellhead injection pressure—as examples, given a higher injection pressure and a higher permeability variation coefficient, the easier it is to form a dominant channel. Therefore, in the final clustering result, if the probability of forming a dominant channel is high, then the well has formed a dominant channel.

[0127] like Figure 3 The results shown include fuzzy clustering identification results for indicators such as effective thickness, permeability gradient, and permeability variation coefficient for water injection wells, as well as fuzzy clustering identification results for indicators such as cumulative production, maximum daily production, and water cut.

[0128] Step S204: Determine whether there is a dominant channel in the wells of the reservoir based on the crossflow level of the wells in the reservoir.

[0129] Using the example above, after determining the crossflow level based on the clustering identification results, the identification results of the dominant water flow channels in the South Kumkol oilfield (e.g., June 2019) can be obtained. The fuzzy clustering identification results are summarized in Table 3. Among them, general crossflow (or crossflow exists) and severe crossflow are considered to form dominant channels. Severe crossflow is considered to be the dominant channel of the well as a large channel. If the crossflow level is no crossflow (or normal water injection and normal production), it is considered that the well does not have a dominant channel.

[0130] Table 3 Summary of Fuzzy Clustering Recognition Results

[0131]

[0132]

[0133] Example 3

[0134] Embodiment 3 of the present invention provides a method for identifying dominant channels in multiple water drive modes, and describes the process of identifying dominant channels throughout the entire reservoir as follows: Figure 5 As shown, it includes the following steps:

[0135] Step S301: Draw a flow diagram of the well based on the flow classification of the well.

[0136] Based on the statistical results of the crossflow level of individual wells, a crossflow diagram of each well in the reservoir is drawn to better statistically analyze the distribution of dominant channels in the entire reservoir.

[0137] Step S302: Based on the crossflow diagram, fit the historical production data of the entire reservoir area for the selected second evaluation index to obtain the fitting results of the historical production data of the entire reservoir area for each second evaluation index.

[0138] Historical production data for the entire reservoir can be statistically analyzed based on the selected second evaluation indicator. The second evaluation indicator can be selected as needed and can be wholly or partially the same as the first evaluation indicator. For example, the second evaluation indicator may include at least one of the following: daily oil production, daily liquid production, cumulative oil production, cumulative liquid production, cumulative water production, and water cut. Historical production data statistics for the entire reservoir can be performed on a specified time period, the length of which can be selected as needed.

[0139] Step S303: Based on the crossflow diagram, fit the historical production data of a single well to the selected third evaluation index to obtain the historical production data fitting results for each well.

[0140] Historical production data for individual wells can be statistically analyzed based on the selected third evaluation indicator. The third evaluation indicator can be selected as needed and can be all or part of the same as the first and second evaluation indicators. The third evaluation indicator includes at least one of the following: daily oil production per well and water cut per well. Historical production data statistics for individual wells can be performed within a specified time period, the length of which can be selected as needed.

[0141] Steps S302 and S303 can be performed in any order and can be performed simultaneously.

[0142] Step S304: Based on the fitting results of historical production data for the entire reservoir and the fitting results of historical production data for each well, determine the displacement flux for each region of the entire reservoir.

[0143] Displacement flux is defined as the ratio of the cumulative volume of displaced fluid to the pore area of ​​the displacement cross section. It can be statistically calculated based on the fitting results of historical production data for the entire reservoir and the fitting results of historical production data for each well. Statistics can be performed on specific regions, which can be represented by a grid on a statistical chart, with different regions corresponding to different grids.

[0144] The displacement flux PA in each region of the entire reservoir is determined using the following formula: Where Q is the volumetric flow rate through the cross-section, and the unit can be m³ / s. 3 / s, Aφ is the pore area of ​​the displacement cross section, and the unit can be m. 2 The units used for each parameter can be selected as needed.

[0145] Among them, PA is an index used to identify the dominant channels in the reservoir, and it is an important index for realizing the identification of dominant channels in the entire reservoir in this invention; Q refers to the volumetric flow rate through the cross section; the pore area of ​​the displacement cross section is statistically analyzed by numerical simulation grid.

[0146] Step S305: Determine the crossflow level of each region in the entire reservoir based on the displacement flux, and determine whether there is a dominant channel in each region based on the crossflow level of each region.

[0147] Displacement flux range thresholds can be preset for different crossflow levels. Based on the threshold range of the displacement flux in each region, the crossflow level of each region can be determined. For example, regions with a displacement flux greater than 500 PV are defined as regions with severe crossflow; regions with a displacement flux between 30 PV and 500 PV are defined as regions with moderate crossflow; and regions with a displacement flux less than 30 PV are defined as regions without crossflow.

[0148] Then, based on the crossflow level of each area, it can be determined whether there is a dominant channel in each area. Areas with severe crossflow and moderate crossflow are considered to have a dominant channel, while areas without crossflow are considered not to have a dominant channel.

[0149] The method described in this embodiment first identifies dominant channels in a single well based on fuzzy clustering for the identification of dominant channels across the entire reservoir. This clarifies which wells possess dominant channels. However, identifying dominant channels in a single well cannot determine their spatial distribution and direction. Therefore, numerical simulation is used to fit the production dynamics history. For wells with dominant channels, high-permeability strips are added to ensure a 100% fitting rate for wells with dominant channels, and a fitting rate of over 60% for other wells. After the history fitting is completed, the numerical model uses computer software to statistically analyze the displacement flux of each grid (i.e., each region) using a formula. Grids with a displacement flux greater than 500 Pa are considered to have experienced severe crossflow, i.e., formed large channels (strong dominant channels). Grids with a flux between 30 and 500 Pa are considered to have experienced general crossflow, i.e., formed dominant channels. Grids with a flux less than 30 Pa are considered to have not experienced crossflow.

[0150] Based on a thorough understanding of the reservoir geology, accurate model verification and correction, and consistent reserve data, pressure fitting was conducted across the entire region. The pressure fitting process primarily corrected for local permeability and edge water energy to ensure consistency between the model's pressure and actual pressure test data. Therefore, static pressure test data from South Kumkol from 2001 to 2018 were statistically analyzed to perform pressure fitting on the refined model, demonstrating its reliability. Based on the accurate pressure fitting, historical data fitting was performed across the entire model region. The region-wide fitting employed a constant-liquidity fitting method, with an error of less than 3%. The fitting results are as follows: Figures 6a-6f As shown, where:

[0151] Figure 6a This is an example graph showing the fitting results of daily oil production for the entire region. ○ represents daily oil production data at different times, and the curve is the curve after data fitting.

[0152] Figure 6b The following is an example graph showing the fitting results of the daily liquid production in the entire region. □ represents the daily liquid production data at different times, and the curve is the curve after data fitting.

[0153] Figure 6c This is an example graph showing the fitting results of the cumulative liquid production in the entire region. × represents the cumulative liquid production data at different times, and the curve is the curve after data fitting.

[0154] Figure 6d This is an example graph showing the fitting results of the cumulative oil production in the entire region. ○ represents the cumulative oil production data at different times, and the curve is the curve after data fitting. Due to the dense data, multiple ○ overlap in the graph and appear as a relatively thick line.

[0155] Figure 6e This is an example graph showing the fitting results of the cumulative water production in the entire region. □ represents the cumulative water production data at different times, and the curve is the curve after data fitting. Due to the dense data, multiple □ overlap in the graph and appear as a relatively thick line.

[0156] Figure 6f This is an example graph showing the fitting results for the moisture content of the entire region. × represents moisture content data at different times, and the curve is the curve after data fitting. In some areas of the graph, due to the dense data, multiple × overlaps and appear as a relatively thick line.

[0157] The fitting results of historical production data for a single well are as follows: Figures 7a-7e As shown, where: Figure 7a This is an example figure showing the fitting results of the production dynamics history of Well 7. Figure 7b This is an example graph showing the historical fitting results of the production dynamics of Well 16. Figure 7c This is an example figure showing the fitting results of the production dynamics history of Well 23. Figure 7d This is an example graph showing the historical fitting results of the production dynamics of Well 38. Figure 7eThis is an example graph showing the historical production dynamics fitting results for Well 42. In the graph, ○ represents the daily oil production data of the well at different times, and the corresponding curve is the fitted curve. △ represents the water cut data of the well at different times, and the corresponding curve is the fitted curve. Due to the dense data, the symbols indicating the data may appear as a relatively thick line due to overlap.

[0158] In step S304, the displacement flux PA refers to the sum of the cumulative volumes of injected water and natural water passing through a unit pore area in the porous medium, reflecting the intensity of the cumulative water flow and the physical and chemical interactions between the reservoir. The larger the displacement flux, the stronger the fluid scouring and the stronger the flow field; the stronger the flow field, the more dominant the flow path.

[0159] Based on historical data fitting, numerical simulations were used to statistically analyze the displacement flux in each grid. Displacement fluxes exceeding 30 PA and 500 PA were used as cutoffs: fluxes above 500 PA were considered severe crossflows, fluxes between 30 PA and 500 PA were considered moderate crossflows, and fluxes below 30 PA were considered non-crossflows. The final identification results are as follows: Figures 8a-8c As shown. Wherein: Figure 8a This is an example image showing the identification results of dominant channels in the entire reservoir of the SK-1 layer. Figure 8b This is an example image showing the identification results of dominant channels in the entire SK-2 reservoir. Figure 8c This is an example image showing the identification results of dominant channels in the SK-3 reservoir. In the image, black represents primary dominant channels, i.e., large channels formed by severe crossflow; the gray area next to black represents secondary dominant channels, i.e., channels formed by general crossflow; the areas with progressively lighter gray areas represent the oil-bearing zone and the edge water zone, respectively.

[0160] Based on the same inventive concept, embodiments of the present invention also provide a multi-water drive mode dominant channel identification device. This device can be installed in a computer device with computing power for identifying the dominant channel of a single well. The structure of the device is as follows: Figure 9 As shown, it includes:

[0161] Module 11 is selected to select the first evaluation index for identifying the dominant channel of the multi-water drive mode from the engineering and geological indicators of the well in the reservoir.

[0162] Clustering module 12 is used to cluster the statistical data of the selected first evaluation index and determine the crossflow level of the well in the reservoir based on the clustering identification results.

[0163] The first identification module 13 is used to determine whether there is a dominant channel in the reservoir based on the crossflow level.

[0164] Based on the same inventive concept, embodiments of the present invention also provide another multi-water drive mode dominant channel identification device. This device can be installed in a computer device with computing power for identifying dominant channels throughout the entire reservoir. The structure of this device is as follows: Figure 10 As shown, it includes:

[0165] The drawing module 21 is used to draw a diagram of the well's flow pattern according to the well's flow pattern level.

[0166] The fitting module 22 is used to fit the historical data of the entire reservoir area to the selected second evaluation index based on the crossflow situation diagram, and to obtain the historical production data fitting results of the entire reservoir area for each second evaluation index; and to fit the historical production data of a single well to the selected third evaluation index, and to obtain the historical production data fitting results of each well.

[0167] The second identification module 23 is used to determine the displacement flux of each region in the entire reservoir based on the fitting results of historical production data of the entire reservoir area and the fitting results of historical production data of each well, determine the crossflow level of each region in the entire reservoir based on the displacement flux, and determine whether there is a dominant channel in each region based on the crossflow level of each region.

[0168] Figure 9 and Figure 10 The aforementioned devices can be installed individually or together; that is, embodiments of the present invention can also provide a multi-water drive mode dominant channel identification device, including... Figure 9 and Figure 10 The various modules in the aforementioned device.

[0169] This invention also provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for identifying the dominant channel of a multi-water drive mode.

[0170] This invention also provides a computer 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 above-described method for identifying the dominant channel of a multi-water drive mode.

[0171] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0172] This invention provides a method for identifying dominant channels in multi-water-drive reservoirs, including single-well dominant channel identification and reservoir-wide dominant channel identification. Single-well dominant channel identification mainly includes: determining evaluation indicators for the multi-water-drive mode, selecting three indicators from both geological and engineering indicators, with parameters primarily including effective thickness, permeability range, permeability variation coefficient, cumulative injection volume, maximum daily injection volume, and injection pressure; using fuzzy clustering to determine the single-well crossflow level, classifying crossflow levels into severe crossflow, general crossflow, and no crossflow, and plotting a single-well crossflow map of the reservoir; based on the single-well crossflow status, assisting with historical fitting through numerical simulation; introducing displacement flux (PA), statistically analyzing PA values ​​across the entire reservoir, and achieving spatiotemporal discrimination of dominant channels across the entire reservoir. This invention enables the discrimination of dominant channels in reservoirs with mixed water-drive modes including natural energy water bodies and artificial water injection, providing a basis and adjustment direction for tapping the potential of such reservoirs in the ultra-high water-cut stage.

[0173] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0174] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0175] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0176] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Skilled individuals can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0177] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0178] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0179] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method for identifying the dominant channel in multiple water drive modes, characterized in that, include: The primary evaluation index for identifying the dominant channels of the multi-water drive model is selected from the engineering and geological indicators of wells in the reservoir. This includes: statistically analyzing the engineering and geological indicators of water injection wells and production wells in the reservoir; selecting effective thickness, permeability gradient, permeability variation coefficient, cumulative water injection volume, maximum daily water injection volume, and water injection pressure as the primary evaluation index for the multi-water drive model from the engineering and geological indicators of water injection wells; and selecting effective thickness, permeability gradient, permeability variation coefficient, cumulative production volume, maximum daily production volume, and water cut as the primary evaluation index for identifying the dominant channels of the multi-water drive model from the engineering and geological indicators of production wells. Cluster the statistical data of the selected first evaluation index, and determine the flow level of the well in the reservoir based on the clustering identification results; Based on the aforementioned crossflow level, determine whether there is a dominant channel in the well within the reservoir; and A flow path diagram is drawn based on the flow path level of each well. Based on the flow path diagram, historical production data for the entire reservoir area is fitted to the selected second evaluation index to obtain the historical production data fitting results for each second evaluation index. Historical production data for individual wells is also fitted to the selected third evaluation index to obtain the historical production data fitting results for each well. Based on the historical production data fitting results for the entire reservoir area and the historical production data fitting results for each well, the displacement flux in each region of the entire reservoir is determined. The flow path level in each region is determined based on the displacement flux. Finally, the existence of a dominant channel in each region is determined based on the flow path level.

2. The method as described in claim 1, characterized in that, When clustering the statistical data of the selected first evaluation index, the clustering criteria are the maximum inter-cluster distance and the minimum intra-cluster distance, and the clustering identification results are obtained through multiple iterations.

3. The method as described in claim 1, characterized in that, The clustering of the selected first evaluation index includes: Determine the number of clusters, error limits, and initial cluster centers for clustering the first evaluation index; The statistical data of the first evaluation index is used as the dataset, and a partition matrix is ​​generated or updated based on the dataset; The cluster centers are updated based on the partition matrix. It is then determined whether the difference between the updated cluster centers and the previously obtained cluster centers meets the error limit requirement. If yes, the clustering result for the first evaluation index is obtained. If not, the process returns to continue executing the step of generating or updating the partition matrix based on the dataset.

4. The method as described in claim 3, characterized in that, The process of generating or updating the partition matrix based on the dataset includes: generating or updating the partition matrix U based on the dataset. b =[μ ij ] , 1 ≤ i ≤ c, 1 ≤ j ≤ n; in, For a sample of data in dataset x, , As cluster center, To partition matrix U b In the matrix elements, c and n are the number of samples in each class after partitioning, and m is the membership factor; The step of updating the cluster centers based on the partition matrix includes: updating the cluster centers V using the following formula. b+1 : , 1 ≤ i ≤ c; Determining whether the difference between the updated cluster centers and the previously obtained cluster centers meets the aforementioned error limit requirement includes: based on the currently obtained cluster centers V b+1 And the cluster center V obtained last time b Determine || V b -V b+1 Does || ≤ε hold true? 5. The method as described in claim 1, characterized in that, Based on the aforementioned crossflow level, determine whether there are dominant channels in the wells of the reservoir, including: If the channeling level of the well in the reservoir is determined to be severe channeling or general channeling, then the well in the reservoir is determined to have a dominant channel. Among them, if the channeling level is severe channeling, the dominant channel of the well in the reservoir is determined to be a large channel. If the well in the reservoir is determined to have no crossflow, then it is determined that there is no dominant channel in the well in the reservoir.

6. The method as described in claim 1, characterized in that, The second evaluation index includes at least one of the following: daily oil production, daily liquid production, cumulative oil production, cumulative liquid production, cumulative water production, and water content of the entire region. The third evaluation index includes at least one of daily oil production per well and water cut per well.

7. The method as described in claim 1, characterized in that, The displacement flux PA in each region of the entire reservoir is determined using the following formula: ; Where Q is the volumetric flow rate through the cross-section. It is the pore area of ​​the displacement cross section.

8. The method according to any one of claims 1-7, characterized in that, The channeling levels in each region of the entire reservoir are determined based on the displacement flux, including: Regions with a displacement flux greater than 500 PV are identified as areas where severe crossflow occurs. The region with a displacement flux between 30 PV and 500 PV was identified as the region where general crossflow occurs. Regions with a displacement flux of less than 30 PV are identified as regions where crossflow has not occurred.

9. A multi-water-drive mode dominant channel identification device, characterized in that, For implementing the multi-water drive mode dominant channel identification method according to any one of claims 1-8, the apparatus comprises: The selection module is used to select the first evaluation index for identifying the dominant channel of the multi-water drive mode from the engineering and geological indicators of the well in the reservoir. The clustering module is used to cluster the statistical data of the selected first evaluation index and determine the flow level of the well in the reservoir based on the clustering identification results. The first identification module is used to determine whether there is a dominant channel in the reservoir based on the crossflow level.

10. A multi-water-drive mode dominant channel identification device, characterized in that, For implementing the multi-water drive mode dominant channel identification method according to any one of claims 1-8, the apparatus comprises: The drawing module is used to draw a diagram of the well's flow pattern based on the well's flow pattern classification. The fitting module is used to fit historical data of the entire reservoir area based on the crossflow situation diagram and the selected second evaluation index to obtain the historical production data fitting results of the entire reservoir area for each second evaluation index; and to fit historical production data of a single well based on the selected third evaluation index to obtain the historical production data fitting results of each well. The second identification module is used to determine the displacement flux of each region in the entire reservoir based on the fitting results of the historical production data of the entire reservoir and the fitting results of the historical production data of each well, determine the crossflow level of each region in the entire reservoir based on the displacement flux, and determine whether there is a dominant channel in each region based on the crossflow level of each region.

11. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the multi-water drive mode dominant channel identification method according to any one of claims 1-8.

12. A computer device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the multi-water drive mode dominant channel identification method according to any one of claims 1-8.

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