Dual sweet spot quantitative evaluation method and device for unconventional reservoirs

Abstract

The present application relates to a kind of oil and gas exploration and development technical field, a kind of unconventional reservoir double sweet spot quantitative evaluation method, device, including in the same development horizon of the region to be evaluated, select m well to carry out fracturing test, downhole television test and production fluid profile analysis;Take the correlation degree of each double sweet spot reservoir sample with the multiple characteristic factors of logging data information is high, based on the weight coefficient of each characteristic factor obtained by grey correlation method;Determine the double sweet spot reservoir quality factor of each double sweet spot reservoir sample, when drilling geosteering, preferentially select the formation drilled by double sweet spot reservoir quality factor, when fracturing cluster design, preferentially select the cluster by double sweet spot reservoir quality factor for fracturing.The present application solves the problem that the sweet spot reservoir is directly evaluated by relying on logging static data, leading to the large deviation of the capacity of sweet spot reservoir and fracturing reconstruction, improves the matching rate of double sweet spot evaluation result and production after fracturing, improves the drilling rate of this kind of reservoir, realizes the effective fracturing of this kind of reservoir.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, specifically a method and apparatus for quantitative evaluation of double sweet spots in unconventional reservoirs. Background Technology

[0002] In recent years, the world has been undergoing a major transformation from conventional oil and gas to unconventional oil and gas development, making unconventional oil and gas exploration and development a hot topic. my country is rich in unconventional oil and gas resources, mainly distributed in the Junggar, Ordos, and Sichuan basins, with enormous exploration and development potential. The concept of "sweet spots" is widely used in unconventional oil and gas exploration and development, and the evaluation and prediction of "sweet spot zones / sections" are both key and challenging aspects. With the deepening of unconventional oil and gas exploration and theoretical research, the evaluation connotation of "sweet spots" has also changed. Engineering parameters have gradually become important evaluation factors, dividing reservoirs into geological and engineering "sweet spots," which are unconventional formations with good reservoir quality and easy fracturing stimulation. Accurate evaluation and identification of double-sweet spot reservoirs, improving their drilling rate, and increasing their fracturing utilization are crucial for improving single-well productivity and achieving efficient exploration and development.

[0003] Currently, seismic, logging, and well logging data are mainly used to identify double-sweet spots in low-permeability reservoirs by interpreting parameters such as porosity, natural fracture development, oil saturation, and stress, and employing mathematical statistical discrimination methods. However, due to the characteristics of unconventional reservoirs, such as thin reservoirs, low porosity and permeability, and strong lateral heterogeneity, within the same stratigraphic level, some wells are evaluated as having well-developed double-sweet spots, but their production capacity is low after fracturing. Conversely, some wells are evaluated as having underdeveloped double-sweet spots, but their production capacity is high and stable after fracturing. Therefore, through production profile testing of each fracturing cluster (section), it has been found that the production level does not match the double-sweet spots evaluated by existing methods, resulting in ineffective and inefficient reservoir encounters and wasted fracturing costs. This limits the production increase of individual wells and affects the regional economic development. Summary of the Invention

[0004] This invention provides a method and apparatus for quantitative evaluation of double sweet spots in unconventional reservoirs, overcoming the shortcomings of the prior art. It can effectively solve the problems of inaccurate identification and large deviation between the identified sweet spot reservoir and the production capacity after fracturing stimulation in existing methods that rely on static data recording to directly evaluate and identify sweet spots in reservoirs.

[0005] One of the technical solutions of this invention is achieved through the following measures: a method for quantitative evaluation of double sweet spots in unconventional reservoirs, comprising:

[0006] In the same development layer of the area to be evaluated, m wells were selected for fracturing tests, downhole television tests and production profile analysis to obtain a double sweet spot reservoir sample set. The double sweet spot reservoir sample set includes multiple double sweet spot reservoir samples, and each double sweet spot reservoir sample is a single cluster double sweet spot reservoir quality score.

[0007] We selected several characteristic factors that were highly correlated with logging data for each double sweet spot reservoir sample, and obtained the weight coefficients of each characteristic factor based on the grey relational analysis method.

[0008] Determine the double sweet spot reservoir quality factor for each double sweet spot reservoir sample. When drilling geological steering, prioritize drilling into formations with a large double sweet spot reservoir quality factor. When designing fracturing clusters, prioritize fracturing clusters with a large double sweet spot reservoir quality factor.

[0009] The following are further optimizations and / or improvements to the above-mentioned technical solution:

[0010] In the same development stratum within the area to be evaluated, m wells were selected for fracturing tests, downhole television tests, and production profile analysis to obtain a double-sweet spot reservoir sample set, including:

[0011] In the same development stratum of the area to be evaluated, m wells are selected. For each well, integrated perforation bridge plug fracturing is used in clusters. After fracturing, the bridge plugs for fracturing are drilled using coiled tubing, and the wellbore is flushed clean. Each well has a total of n clusters, and the fracturing process for each cluster is the same.

[0012] The coiled tubing is connected to the downhole television tool to conduct downhole television tests on each cluster, and the perforation images of each cluster are measured to obtain the abrasion area of ​​each perforation. The fracturing effect of each cluster is scored using the following formula.

[0013]

[0014] in, Assign scores to the fracturing effect; Let be the area of ​​the pores in the nth cluster. This represents the minimum abrasion area of ​​each cluster of pores; This represents the maximum abrasion area of ​​each cluster of pores;

[0015] After each well was opened and the fluid was withdrawn for trial production, and the oil and gas production stabilized, the production profile was measured to obtain the production contribution rate of each cluster. ;

[0016] The following formula is used to obtain the quality score of each cluster of double sweet spot reservoirs. This forms a double sweet spot reservoir sample set;

[0017] .

[0018] The above-mentioned features with high correlation between each double-sweet reservoir sample and logging data are used to obtain the weight coefficients of each feature factor based on the grey relational analysis method, including:

[0019] Correlation analysis was performed between the quality scores of the double sweet spot reservoirs in each cluster and the corresponding logging data for each cluster to obtain a set of characteristic factors with high correlation. The logging data included logging data, well logging data, and core experimental data. The characteristic factor set included core experimental permeability, nuclear magnetic resonance effective porosity, oil saturation, clay content, brittleness index, and thickness.

[0020] Each characteristic factor was used as the independent variable, and the quality score of each cluster of double sweet spot reservoirs was used as the dependent variable. The independent and dependent variables were normalized.

[0021] The grey relational analysis method was used to analyze the correlation between the dependent variable and its independent variables, and the weight coefficients of each characteristic factor affecting the quality score of the double sweet spot reservoir were obtained.

[0022] The above-mentioned determination of the double sweet spot reservoir quality factor for each double sweet spot reservoir sample includes multiplying the normalized independent variable by each weight coefficient, and adding the resulting products to obtain the double sweet spot reservoir quality factor of the cluster, that is, the double sweet spot reservoir quality factor of the double sweet spot reservoir sample.

[0023] The second technical solution of the present invention is achieved through the following measures: a quantitative evaluation device for double sweet spots in unconventional reservoirs, comprising:

[0024] The sample set acquisition unit selects m wells in the same development layer of the area to be evaluated to conduct fracturing tests, downhole television tests and production profile analysis to obtain a double sweet spot reservoir sample set. The double sweet spot reservoir sample set includes multiple double sweet spot reservoir samples, and each double sweet spot reservoir sample is a single cluster double sweet spot reservoir quality score.

[0025] The weighting coefficient analysis unit takes multiple characteristic factors that are highly correlated with logging data for each double sweet spot reservoir sample, and obtains the weighting coefficients of each characteristic factor based on the grey relational analysis method.

[0026] The evaluation unit determines the double sweet spot reservoir quality factor for each double sweet spot reservoir sample. When drilling geological steering, priority is given to drilling into formations with a large double sweet spot reservoir quality factor. When designing fracturing clusters, clusters with a large double sweet spot reservoir quality factor are preferred for fracturing.

[0027] The following are further optimizations and / or improvements to the above-mentioned technical solution:

[0028] The aforementioned sample set acquisition unit includes:

[0029] The fracturing test module selects m wells in the same development layer of the area to be evaluated. For each well, the integrated perforation bridge plug cluster fracturing is used. After fracturing, the bridge plug for fracturing is drilled out using coiled tubing, and the wellbore is flushed clean. Each well has a total of n clusters, and the fracturing process of each cluster is the same.

[0030] The downhole television testing module uses a coiled tubing to connect to a downhole television tool to perform downhole television testing on each cluster, measuring the perforation images of each cluster, obtaining the abrasion area of ​​each perforation, and using the following formula to score the fracturing effect of each cluster.

[0031]

[0032] in, Assign scores to the fracturing effect; Let be the area of ​​the pores in the nth cluster. This represents the minimum abrasion area of ​​each cluster of pores; This represents the maximum abrasion area of ​​each cluster of pores;

[0033] The production contribution rate acquisition module measures the production profile of each cluster after each well has undergone trial production with fluid withdrawal and the oil and gas production has stabilized. ;

[0034] The following formula is used to obtain the quality score of each cluster of double sweet spot reservoirs. ;

[0035] .

[0036] The aforementioned weighting coefficient analysis unit includes:

[0037] The feature factor acquisition module performs correlation analysis between the quality scores of each cluster of double sweet spot reservoirs and the corresponding logging and logging data of each cluster to obtain a set of feature factors with high correlation. The logging and logging data includes logging data, logging data, and core experimental data. The feature factor set includes core experimental permeability, nuclear magnetic resonance effective porosity, oil saturation, clay content, brittleness index, and thickness.

[0038] The normalization module takes each characteristic factor as the independent variable and the quality score of each cluster of double sweet spot reservoirs as the dependent variable, and performs normalization processing on the independent and dependent variables.

[0039] The weighting analysis module uses the grey relational analysis method to analyze the correlation between the dependent variable and its independent variables, and obtains the weight coefficients of each characteristic factor that affects the quality score of the double sweet spot reservoir.

[0040] This invention selects multiple wells in the same development layer of the evaluation area for fracturing tests, downhole television tests, and production profile analysis. This involves analyzing the production capacity and fracturing effect after fracturing, obtaining the double-sweet spot reservoir quality score for each cluster in each well, forming a double-sweet spot reservoir sample set. Correlation analysis is performed between the double-sweet spot reservoir sample set and logging data to identify several highly correlated characteristic factors. Weight coefficients for each characteristic factor are obtained using grey relational analysis. These weight coefficients are then combined to determine the double-sweet spot reservoir quality factor for each sample, thus evaluating the double-sweet spot reservoirs in the evaluation area. This solves the problem of large discrepancies between sweet spot reservoirs and post-fracturing production capacity caused by directly evaluating reservoir sweet spots based on static logging data. It improves the matching rate between double-sweet spot evaluation results and post-fracturing production, increases the drilling rate of this type of reservoir, achieves effective fracturing of this type of reservoir, and improves single-well production capacity. Attached Figure Description

[0041] Appendix Figure 1 This is a schematic diagram of the method flow of the present invention.

[0042] Appendix Figure 2 This is a schematic diagram of the method for obtaining a double sweet spot reservoir sample set according to the present invention.

[0043] Appendix Figure 3 This is a schematic diagram of the method for obtaining the weight coefficients of each feature factor in this invention.

[0044] Appendix Figure 4 This is a schematic diagram of the device structure of the present invention. Detailed Implementation

[0045] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0046] The present invention will be further described below with reference to embodiments and accompanying drawings:

[0047] Example 1: As shown in the attached document Figure 1 As shown, this embodiment of the invention discloses a method for quantitative evaluation of double sweet spots in unconventional reservoirs, including:

[0048] Step S110: In the same development layer of the area to be evaluated, select m wells to conduct fracturing tests, downhole television tests and production profile analysis to obtain a double sweet spot reservoir sample set, wherein the double sweet spot reservoir sample set includes multiple double sweet spot reservoir samples, and each double sweet spot reservoir sample is a single cluster double sweet spot reservoir quality score.

[0049] Step S120: Select multiple feature factors that are highly correlated with logging data for each double sweet spot reservoir sample, and obtain the weight coefficients of each feature factor based on the grey relational analysis method.

[0050] The above-mentioned features that are highly correlated with the logging data of each double sweet spot reservoir sample are analyzed to examine the characteristic patterns of the quality scores of single cluster double sweet spot reservoirs in the logging data, and to select several highly correlated features for correlation analysis.

[0051] Step S130: Determine the double sweet spot reservoir quality factor of each double sweet spot reservoir sample. When drilling geological steering, prioritize drilling into formations with large double sweet spot reservoir quality factors. When designing fracturing clusters, prioritize fracturing clusters with large double sweet spot reservoir quality factors.

[0052] The higher the double-sweet spot reservoir quality factor value, the more developed the double-sweet spot reservoir. When conducting well geological steering, it is preferable to drill into formations with a high double-sweet spot reservoir quality factor value. When designing fracturing clusters, it is preferable to fracture sections with a high double-sweet spot reservoir quality factor value.

[0053] This invention discloses a quantitative evaluation method for double-sweet spots in unconventional reservoirs. Multiple wells in the same development layer of the evaluation area are selected for fracturing tests, downhole television tests, and production profile analysis. This involves analyzing the post-fracturing productivity and fracturing effect to obtain the double-sweet spot reservoir quality score for each cluster in each well, forming a double-sweet spot reservoir sample set. Correlation analysis is performed between the double-sweet spot reservoir sample set and logging data to identify several highly correlated characteristic factors. Weight coefficients for each characteristic factor are obtained using grey relational analysis. These weight coefficients are then combined to determine the double-sweet spot reservoir quality factor for each sample, thus evaluating the double-sweet spot reservoirs in the evaluation area. This method solves the problem of large discrepancies between sweet spot reservoirs and post-fracturing productivity caused by directly evaluating reservoir sweet spots based on static logging data. It improves the matching rate between double-sweet spot evaluation results and post-fracturing production, increases the drilling encounter rate of this type of reservoir, achieves effective fracturing of this type of reservoir, and improves single-well productivity.

[0054] Example 2: This embodiment of the invention discloses a method for quantitative evaluation of double sweet spots in unconventional reservoirs, including:

[0055] Step S210: In the same development layer of the area to be evaluated, select m wells to conduct fracturing tests, downhole television tests and production profile analysis to obtain a double sweet spot reservoir sample set. The double sweet spot reservoir sample set includes multiple double sweet spot reservoir samples, and each double sweet spot reservoir sample is a single cluster double sweet spot reservoir quality score.

[0056] As attached Figure 2 As shown, the above steps specifically include:

[0057] Step S211: In the same development layer of the area to be evaluated, select m wells, and perform integrated perforation bridge plug fracturing on each well. After fracturing, use coiled tubing to drill for fracturing bridge plugs and flush the wellbore clean. Each well has a total of n clusters, and the fracturing process for each cluster is the same.

[0058] Step S212: Connect the downhole television tool to the coiled tubing to perform downhole television testing on each cluster, measure the perforation images of each cluster, obtain the abrasion area of ​​each perforation, and score the fracturing effect of each cluster using the following formula.

[0059]

[0060] in, Assign scores to the fracturing effect; Let be the area of ​​the pores in the nth cluster. This represents the minimum abrasion area of ​​each cluster of pores; This represents the maximum abrasion area of ​​each cluster of pores;

[0061] The maximum value of the pore abrasion area results in a fracturing effect score of 100 points, while the minimum value results in a fracturing effect score of 0 points.

[0062] Step S213: Perform trial production by withdrawing fluid from each well. After the oil and gas production stabilizes, measure the production profile and obtain the production contribution rate of each cluster. ;

[0063] Step S214: Obtain the quality score of each cluster of double sweet spot reservoirs using the following formula. This forms a double sweet spot reservoir sample set;

[0064] .

[0065] The above-mentioned single-cluster double sweet spot reservoir quality score Equal to the yield contribution rate of this cluster abrasion area of ​​the hole The greater the contribution of reservoir production, the larger the porosity abrasion area (the more proppant enters), reflecting the more developed the double sweet spot reservoir.

[0066] Step S220: Select multiple feature factors that are highly correlated with logging data for each double sweet spot reservoir sample, and obtain the weight coefficients of each feature factor based on the grey relational analysis method.

[0067] As attached Figure 3 As shown, the above steps specifically include:

[0068] Step S221: Correlation analysis is performed on the quality scores of the double sweet spot reservoirs of each cluster and the corresponding logging data of each cluster to obtain a set of characteristic factors with high correlation. The logging data includes logging data, well logging data, and core experimental data. The set of characteristic factors includes core experimental permeability, nuclear magnetic resonance effective porosity, oil saturation, clay content, brittleness index, and thickness.

[0069] The above analysis correlates the quality scores of each cluster of double-sweet reservoirs with the corresponding logging data of each cluster, i.e., it analyzes the quality scores of single-cluster double-sweet reservoirs. The characteristics of high and low well logging data are analyzed. Among these characteristics, highly correlated features are selected and placed in the feature factor set. Therefore, the features in the feature factor set are not fixed. Generally, the features related to the quality score of single-cluster double sweet spot reservoirs include core test permeability, nuclear magnetic resonance effective porosity, oil saturation, clay content, brittleness index, thickness, etc.

[0070] Step S222: Take each characteristic factor as the independent variable and the quality score of each cluster of double sweet spot reservoirs as the dependent variable, and normalize the independent and dependent variables.

[0071] In this embodiment, the normalization process can adopt the maximum value standardization method, that is, dividing each individual parameter by the maximum value of the same type of parameters to make each evaluation score 0-1. For parameters whose larger values ​​reflect better reservoir quality, such as core test permeability and nuclear magnetic resonance effective porosity, they are directly divided by the maximum value of the parameter; for parameters whose smaller values ​​reflect better reservoir quality, such as clay content, the maximum value of the parameter is subtracted from the difference of the individual parameters and then divided by the maximum value to make them comparable.

[0072] Step S223: Use grey relational analysis to analyze the correlation between the dependent variable and its independent variables to obtain the weight coefficients of each characteristic factor affecting the quality score of the double sweet spot reservoir.

[0073] The basic principle of grey relational analysis is to determine the strength of the relationship between sequence curves based on the similarity of their geometric shapes. The closer the curves are, the greater the correlation between the corresponding sequences, and vice versa. Therefore, in this step, grey relational analysis is used to calculate the grey relational degree to determine the correlation between the dependent variable (single-cluster double-sweet reservoir quality score) and its independent variables (core test permeability, NMR effective porosity, oil saturation, clay content, brittleness index, thickness, etc.).

[0074] After the correlation calculation is completed, the weight coefficients of each characteristic factor affecting the quality score of the double sweet spot reservoir are obtained based on the following formula;

[0075]

[0076] in, i The number of independent variables. For relevance, These are the weighting coefficients.

[0077] Step S230: Determine the double sweet spot reservoir quality factor for each double sweet spot reservoir sample. When conducting well geological steering, double sweet spot reservoir quality factors should be prioritized. When drilling large formations and designing fracturing clusters, double sweet spot reservoir quality factors are preferred. The clusters were subjected to fracturing.

[0078] Double-sweet reservoirs are evaluated based on the double-sweet reservoir quality factors of each double-sweet reservoir sample, i.e., the double-sweet reservoir quality factors of each cluster. This is used for drilling geological steering, where the best double-sweet reservoirs are selected. When encountering formations with high values, the preferred fracturing cluster design should be... Fracturing of sections with high values.

[0079] Example 3, using the method disclosed in this invention to evaluate double-sweet reservoirs in conjunction with a specific horizontal well, specifically includes:

[0080] The first step is to use integrated perforation bridge plug fracturing for horizontal well MaHW12, with a total of 10 clusters. Each cluster (segment) has the same fracturing process and the fracturing construction parameters (displacement, sand addition, sand ratio, etc.) are basically the same. After fracturing, the bridge plugs for fracturing are drilled from the coiled tubing and the wellbore is flushed clean.

[0081] The second step involves connecting a downhole television tool to the coiled tubing for testing. Images of each perforation cluster (segment) are captured, and the erosion area of ​​each perforation is calculated. The maximum erosion area is 3965 mm². 2 The minimum area of ​​pit abrasion is 1215 mm. 2 The fracturing effect scores for each cluster are shown in Table 1.

[0082]

[0083] The third step involved well opening and de-fluidizing trial production at MaHW12. After the oil and gas production stabilized, the production profile was measured to obtain the production contribution rate of each cluster. As shown in Table 2;

[0084] The fourth step is to calculate the double sweet spot reservoir quality score for each cluster. As shown in Table 2; and in the same development stratum in this area, 5 wells were selected for production profile and downhole television testing to obtain the quality scores of single-cluster double sweet spot reservoirs. 120 samples;

[0085]

[0086] The fifth step is to assign the double sweet spot reservoir quality score Q to each cluster (segment). nCorrelation analysis was performed with data from recording, core experiments, etc., and in this region, Q n The six parameters showed good correlation with core test permeability, nuclear magnetic resonance effective porosity, oil saturation, clay content, brittleness index, and thickness. Using these six parameters as independent variables, the double-sweet reservoir quality score Q was determined. n As the dependent variable, the original data of both the independent and dependent variables are normalized; in this case, the maximum standardization method can be used.

[0087] The sixth step is to use the grey relational analysis method to calculate the grey relational degree. The correlation degree between the dependent variable and its independent variables (core test permeability, NMR effective porosity, oil saturation, clay content, brittleness index, and thickness) is r = (1, 0.785, 0.713, 0.535, 0.632, 0.733); the weight coefficients of the independent variables are a = (0.23, 0.18, 0.16, 0.12, 0.14, 0.17).

[0088] The seventh step is to multiply the normalized independent variable by the weight coefficient of the class, and then sum the products to obtain the quality factor of each single-cluster double sweet spot reservoir. As shown in Table 3, considering the quality factors of each single-cluster double-sweet reservoir, during drilling geological steering, the preferred method is... When encountering formations with high values, the preferred fracturing cluster design should be... Fracturing of sections with high values.

[0089]

[0090] Example 4, as shown in the appendix Figure 4 As shown, this embodiment discloses a dual sweet spot quantitative evaluation device for unconventional reservoirs, comprising:

[0091] The sample set acquisition unit selects m wells in the same development layer of the area to be evaluated to conduct fracturing tests, downhole television tests and production profile analysis to obtain a double sweet spot reservoir sample set. The double sweet spot reservoir sample set includes multiple double sweet spot reservoir samples, and each double sweet spot reservoir sample is a single cluster double sweet spot reservoir quality score.

[0092] The weighting coefficient analysis unit takes multiple characteristic factors that are highly correlated with logging data for each double sweet spot reservoir sample, and obtains the weighting coefficients of each characteristic factor based on the grey relational analysis method.

[0093] The evaluation unit determines the double sweet spot reservoir quality factor for each double sweet spot reservoir sample. When drilling geological steering, priority is given to drilling into formations with a large double sweet spot reservoir quality factor. When designing fracturing clusters, clusters with a large double sweet spot reservoir quality factor are preferred for fracturing.

[0094] The sample set acquisition unit includes:

[0095] The fracturing test module selects m wells in the same development layer of the area to be evaluated. For each well, the integrated perforation bridge plug cluster fracturing is used. After fracturing, the bridge plug for fracturing is drilled out using coiled tubing, and the wellbore is flushed clean. Each well has a total of n clusters, and the fracturing process of each cluster is the same.

[0096] The downhole television testing module uses a coiled tubing to connect to a downhole television tool to perform downhole television testing on each cluster, measuring the perforation images of each cluster, obtaining the abrasion area of ​​each perforation, and using the following formula to score the fracturing effect of each cluster.

[0097]

[0098] in, Assign scores to the fracturing effect; Let be the area of ​​the pores in the nth cluster. This represents the minimum abrasion area of ​​each cluster of pores; This represents the maximum abrasion area of ​​each cluster of pores;

[0099] The production contribution rate acquisition module measures the production profile of each cluster after each well has undergone trial production with fluid withdrawal and the oil and gas production has stabilized. ;

[0100] The following formula is used to obtain the quality score of each cluster of double sweet spot reservoirs. ;

[0101] .

[0102] The weighting coefficient analysis unit includes:

[0103] The feature factor acquisition module performs correlation analysis between the quality scores of each cluster of double sweet spot reservoirs and the corresponding logging and logging data of each cluster to obtain a set of feature factors with high correlation. The logging and logging data includes logging data, logging data, and core experimental data. The feature factor set includes core experimental permeability, nuclear magnetic resonance effective porosity, oil saturation, clay content, brittleness index, and thickness.

[0104] The normalization module takes each characteristic factor as the independent variable and the quality score of each cluster of double sweet spot reservoirs as the dependent variable, and performs normalization processing on the independent and dependent variables.

[0105] The weighting analysis module uses the grey relational analysis method to analyze the correlation between the dependent variable and its independent variables, and obtains the weight coefficients of each characteristic factor that affects the quality score of the double sweet spot reservoir.

[0106] The above technical features constitute the preferred embodiment of the present invention, which has strong adaptability and optimal implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the requirements of different situations.

Claims

1. A method for quantitative evaluation of dual sweet spots of unconventional reservoirs, characterized in that, The method comprises the following steps: In the same development layer of the region to be evaluated, m wells are selected to perform fracturing tests, downhole television tests and production fluid profile analyses, and a double sweet spot reservoir sample set is obtained, wherein the double sweet spot reservoir sample set comprises a plurality of double sweet spot reservoir samples, and each double sweet spot reservoir sample is a single-cluster double sweet spot reservoir quality score; A plurality of characteristic factors with high correlation degrees between each double sweet spot reservoir sample and logging data are taken, and weight coefficients of each characteristic factor are obtained based on a grey correlation method; A double sweet spot reservoir quality factor of each double sweet spot reservoir sample is determined, a formation with a large double sweet spot reservoir quality factor is selected when drilling geosteering, and a cluster with a large double sweet spot reservoir quality factor is selected when designing fracturing clustering; In the same development layer of the region to be evaluated, m wells are selected to perform fracturing tests, downhole television tests and production fluid profile analyses, and a double sweet spot reservoir sample set is obtained, comprising: In the same development layer of the region to be evaluated, m wells are selected, and for each well, a perforation bridge plug integrated fracturing clustering is performed, and after fracturing, a coiled tubing is used to drill a fracturing bridge plug, and the wellbore is cleaned, wherein each well has n clusters, and the fracturing processes of the clusters are the same; A downhole television tool connected to the coiled tubing is used to perform downhole television tests on the clusters, and each cluster is measured to obtain a perforation hole image, and each perforation hole erosion area is obtained, and the fracturing effect of each cluster is scored by using the following formula: wherein, score for fracture effectiveness; is the erosion area for the nth cluster of perforations; is the minimum erosion area for each cluster of perforations; is the maximum erosion area for each cluster of perforations; The wells are opened and the liquid is withdrawn for trial production, and the liquid production profile is measured after the oil and gas production is stable to obtain the yield contribution rate of each cluster ; The quality score for each cluster of dual sweet spot reservoirs is obtained using the following equation , forming a sample set of dual sweet spot reservoirs; 。 2. The method for dual sweet spot quantitative evaluation of unconventional reservoirs according to claim 1, characterized in that, The plurality of characteristic factors with high correlation degrees between each double sweet spot reservoir sample and logging data are taken, and weight coefficients of each characteristic factor are obtained based on a grey correlation method, comprising: Correlation analysis is performed on the double sweet spot reservoir quality scores of the clusters and corresponding logging data, and a characteristic factor set with a high correlation degree is obtained, wherein the logging data comprises logging data, well logging data and core experiment data, and the characteristic factor set comprises core experiment permeability, nuclear magnetic effective porosity, oil saturation, shale content, brittleness index and thickness; Each characteristic factor is taken as an independent variable, and the double sweet spot reservoir quality scores of the clusters are taken as dependent variables, and the independent variables and the dependent variables are normalized; The grey correlation method is used to analyze the correlation degrees between the dependent variables and the independent variables, and weight coefficients of each characteristic factor affecting the double sweet spot reservoir quality scores are obtained.

3. The method for dual sweet spot quantitative evaluation of unconventional reservoirs according to claim 1 or 2, characterized in that, The double sweet spot reservoir quality factor of each double sweet spot reservoir sample is determined by multiplying the normalized independent variables by each weight coefficient, and adding each product to obtain the double sweet spot reservoir quality factor of the cluster, that is, the double sweet spot reservoir quality factor of the double sweet spot reservoir sample.

4. A device for quantitative evaluation of dual sweet spots of unconventional reservoirs using the method according to any one of claims 1 to 3, characterized in that, The method comprises the following steps: A sample set acquisition unit is configured to, in the same development layer of the region to be evaluated, select m wells to perform fracturing tests, downhole television tests and production fluid profile analyses, and obtain a double sweet spot reservoir sample set, wherein the double sweet spot reservoir sample set comprises a plurality of double sweet spot reservoir samples, and each double sweet spot reservoir sample is a single-cluster double sweet spot reservoir quality score; A weight coefficient analysis unit is configured to take a plurality of characteristic factors with high correlation degrees between each double sweet spot reservoir sample and logging data, and obtain weight coefficients of each characteristic factor based on a grey correlation method; A weight coefficient analysis unit is configured to take a plurality of characteristic factors with high correlation degrees between each double sweet spot reservoir sample and logging data, and obtain weight coefficients of each characteristic factor based on a grey correlation method; The evaluation unit determines the dual sweet spot reservoir quality factor of each dual sweet spot reservoir sample, selects a formation drilled in drilling geosteering when the dual sweet spot reservoir quality factor is large, and selects a cluster with a large dual sweet spot reservoir quality factor for fracturing in fracturing cluster design.

5. The apparatus for dual sweet spot quantitative evaluation of unconventional reservoirs of claim 4, wherein, The sample set acquisition unit comprises: The fracturing test module selects m wells in the same development layer of the to-be-evaluated area, adopts integrated cluster fracturing of perforating and bridge plug for each well, uses coiled tubing to drill a fracturing bridge plug after fracturing, and cleans the wellbore, wherein each well has n clusters, and the cluster fracturing processes are the same; The downhole television test module connects a downhole television tool to each cluster for downhole television testing, measures the perforation hole image of each cluster, obtains the abrasion area of each hole, and scores the fracturing effect of each cluster by using the following formula: wherein, score for fracture effectiveness; is the n-th cluster of hole erosion area; is the minimum value of hole erosion area for each cluster; is the maximum value of hole erosion area for each cluster; The yield contribution rate acquisition module measures the liquid production profile after the test production of opening and liquid withdrawal of each well, and obtains the yield contribution rate of each cluster ; The cluster dual sweet spot reservoir quality score is obtained using the following equation ; 。 6. The apparatus for dual sweet spot quantitative evaluation of unconventional reservoirs according to claim 4 or 5, characterized in that, The weight coefficient analysis unit comprises: The characteristic factor acquisition module performs correlation analysis on the dual sweet spot reservoir quality score of each cluster and the corresponding logging data of each cluster, obtains a characteristic factor set with high correlation, wherein the logging data includes logging data, well logging data, and core experiment data, and the characteristic factor set includes core experiment permeability, nuclear magnetic effective porosity, oil saturation, shale content, brittleness index, and thickness; The normalization module takes each characteristic factor as an independent variable and takes the dual sweet spot reservoir quality score of each cluster as a dependent variable, and performs normalization processing on the independent variable and the dependent variable; The weight analysis module uses the grey correlation method to analyze the correlation degree of the dependent variable and each independent variable, and obtains the weight coefficient of each characteristic factor affecting the dual sweet spot reservoir quality score.

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