Double-dessert quantitative evaluation method and device for unconventional storage

Abstract

The invention relates to the technical field of oil and gas exploration and development, in particular to a double-dessert quantitative evaluation method and device for unconventional reservoirs, and the method comprises the following steps: selecting m wells for fracturing test, underground television test and fluid production profile analysis at the same development position of a to-be-evaluated area; taking a plurality of characteristic factors with high relevancy between each double-dessert reservoir sample and the logging data information, and obtaining a weight coefficient of each characteristic factor based on a grey correlation method; and determining a double-dessert reservoir quality factor of each double-dessert reservoir sample, preferentially selecting a stratum with a large double-dessert reservoir quality factor for drilling during drilling geosteering, and preferentially selecting a cluster with a large double-dessert reservoir quality factor for fracturing during fracturing clustering design. According to the method, the problem that the productivity deviation of the dessert reservoir and the productivity deviation after fracturing transformation are large due to the fact that the reservoir dessert is directly evaluated by recording and measuring static data is solved, the matching rate of the double-dessert evaluation result and the yield after fracturing is increased, the drilling encounter rate of the reservoir is increased, and effective fracturing of the reservoir is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and is a method and device for quantitatively evaluating double sweet spots of unconventional reservoirs. Background Art

[0002] In recent years, the world has been undergoing a major transformation from traditional oil and gas to unconventional oil and gas development, and the exploration and development of unconventional oil and gas have become a hot topic. China is rich in unconventional oil and gas resources, which are mainly distributed in basins such as Junggar, Ordos, and Sichuan, with great exploration and development potential. The concept of "sweet spot" is widely used in the exploration and development of unconventional oil and gas. The evaluation and prediction of "sweet spot area / section" are the key points and difficulties in the exploration and development of unconventional oil and gas. With the continuous deepening of the exploration and theoretical research of unconventional oil and gas, the evaluation connotation of "sweet spot" has also changed, and engineering parameters have gradually become important evaluation factors. The reservoir is divided into geological and engineering double sweet spots, that is, the formation sections with good reservoir quality and easy fracturing and transformation in unconventional formations. Accurately evaluating and identifying double sweet spot reservoirs, increasing the drilling encounter rate of double sweet spot reservoirs, and improving the fracturing utilization degree of double sweet spot reservoirs are the keys to improving the productivity of a single well and realizing efficient exploration and development.

[0003] At present, mainly using data such as seismic, logging while drilling, and well logging, through parameters such as interpreted porosity, natural fracture development degree, oil saturation, and stress, and using mathematical statistical discrimination methods to identify geological engineering double sweet spots of low-permeability reservoirs. However, due to the characteristics of unconventional reservoirs such as thin reservoirs, low porosity and permeability, and strong lateral heterogeneity, within the same horizon, the double sweet spot reservoirs evaluated by some wells are developed, but the productivity is low after fracturing and transformation; the double sweet spot reservoirs evaluated by some wells are underdeveloped, but they have strong high-yield and stable production capabilities after fracturing and transformation. Therefore, through the production profile test of each cluster (section) of fracturing, it is found that the matching degree between the production level and the double sweet spots evaluated by the existing methods is poor, resulting in ineffective and inefficient drilling encounters of reservoirs and waste of fracturing costs, limited increase in the production of a single well, and affecting the regional efficient development. Summary of the Invention

[0004] The present invention provides a method and device for quantitatively evaluating double sweet spots of unconventional reservoirs, which overcomes the deficiencies of the above-mentioned prior art and can effectively solve the problems of inaccurate identification and large deviation between the sweet spot reservoirs identified and evaluated and the productivity after fracturing and transformation existing in the existing method of directly evaluating and identifying reservoir sweet spots relying on logging and testing static data.

[0005] One of the technical solutions of the present invention is achieved by the following measures: A method for quantitatively evaluating double sweet spots of unconventional reservoirs, comprising:

[0006] In the same development horizon of the area to be evaluated, select m wells for fracturing test, downhole video test and production profile analysis to obtain a double sweet spot reservoir sample set, where the double sweet spot reservoir sample set includes multiple double sweet spot reservoir samples, and each double sweet spot reservoir sample is the quality score of a single cluster of double sweet spot reservoirs;

[0007] Select multiple characteristic factors with a high degree of correlation between each dual sweet spot reservoir sample and logging data, and obtain the weight coefficients of each characteristic factor based on the grey correlation method;

[0008] Determine the dual sweet spot reservoir quality factor of each dual sweet spot reservoir sample. When conducting drilling geological steering, preferentially select the formation with a large dual sweet spot reservoir quality factor to drill through. When designing fracturing clusters, preferentially select the cluster with a large dual sweet spot reservoir quality factor for fracturing.

[0009] The following is a further optimization or / and improvement of the above-mentioned inventive technical solution:

[0010] In the same development horizon of the area to be evaluated, select m wells for fracturing testing, downhole video testing, and liquid production profile analysis to obtain a dual sweet spot reservoir sample set, including:

[0011] In the same development horizon of the area to be evaluated, select m wells. For each well, use perforating-bridge plug integrated cluster fracturing, and after fracturing, use coiled tubing to drill out the bridge plugs for fracturing, and flush the wellbore clean. Among them, each well has a total of n clusters, and the fracturing process of each cluster is the same;

[0012] Connect the coiled tubing to the downhole video tool to conduct downhole video testing on each cluster, measure the perforation hole images of each cluster, obtain the abrasion area of each hole, and use the following formula to score the fracturing effect of each cluster;

[0013]

[0014] Among them, X n is the fracturing effect scoring value; E n is the abrasion area of the holes in the nth cluster; E min is the minimum abrasion area of the holes in each cluster; E max is the maximum abrasion area of the holes in each cluster;

[0015] For each well, open the well to produce fluid and test the production. After the oil and gas production is stable, measure the liquid production profile to obtain the production contribution rate C n of each cluster; use the following formula to obtain the dual sweet spot reservoir quality score Q n of each cluster, and form a dual sweet spot reservoir sample set;

[0016] Q n = C n × X n .

[0017] The above-mentioned selection of multiple characteristic factors with a high degree of correlation between each dual sweet spot reservoir sample and logging data, and obtaining the weight coefficients of each characteristic factor based on the grey correlation method, includes:

[0018] Perform a correlation analysis on the double-sweet spot reservoir quality scores of each cluster and the corresponding logging and well testing data of each cluster to obtain a set of characteristic factors with a high degree of correlation. The logging and well testing data include logging data, well logging data, and core experiment data. The set of characteristic factors includes core experiment permeability, nuclear magnetic effective porosity, oil saturation, shale content, brittleness index, and thickness;

[0019] Take each characteristic factor as an independent variable and the double-sweet spot reservoir quality score of each cluster as a dependent variable, and perform normalization processing on the independent variable and the dependent variable;

[0020] Use the grey correlation method to perform a correlation analysis on the dependent variable and each independent variable to obtain the weight coefficients of each characteristic factor affecting the double-sweet spot reservoir quality score.

[0021] The above-mentioned determination of the double-sweet spot reservoir quality factors of each double-sweet spot reservoir sample includes multiplying the normalized independent variable by each weight coefficient and adding the obtained products to obtain the double-sweet spot reservoir quality factor of this cluster, that is, the double-sweet spot reservoir quality factor of the double-sweet spot reservoir sample.

[0022] The second technical solution of the present invention is achieved through the following measures: A double-sweet spot quantitative evaluation device for unconventional reservoirs, including:

[0023] A sample set acquisition unit, in the same development horizon of the area to be evaluated, select m wells for fracturing test, downhole video test and liquid production profile analysis to obtain a double-sweet spot reservoir sample set, where the double-sweet spot reservoir sample set includes multiple double-sweet spot reservoir samples, and each double-sweet spot reservoir sample is the double-sweet spot reservoir quality score of a single cluster;

[0024] A weight coefficient analysis unit, take multiple characteristic factors with a high degree of correlation between each double-sweet spot reservoir sample and the logging and well testing data, and obtain the weight coefficients of each characteristic factor based on the grey correlation method;

[0025] An evaluation unit, determine the double-sweet spot reservoir quality factors of each double-sweet spot reservoir sample, and preferentially select the formation with a large double-sweet spot reservoir quality factor during drilling geological steering. When designing fracturing clusters, preferentially select the cluster with a large double-sweet spot reservoir quality factor for fracturing.

[0026] The following is a further optimization or / and improvement of the above-mentioned invention technical solution:

[0027] The above-mentioned sample set acquisition unit includes:

[0028] A fracturing test module, in the same development horizon of the area to be evaluated, select m wells, perform perforation-bridge plug integrated cluster fracturing on each well, and use coiled tubing to drill out the bridge plugs for fracturing after fracturing, and flush the wellbore clean. Among them, each well has a total of n clusters, and the fracturing process of each cluster is the same;

[0029] Downhole TV test module: Connect the coiled tubing to the downhole TV tool to conduct downhole TV tests on each cluster, measure the perforation hole images of each cluster, obtain the abrasion area of each hole, and score the fracturing effect of each cluster using the following formula;

[0030]

[0031] Among them, X n is the scoring value of the fracturing effect; E n is the abrasion area of the holes in the nth cluster; E min is the minimum value of the abrasion area of the holes in each cluster; E max is the maximum value of the abrasion area of the holes in each cluster;

[0032] Production contribution rate acquisition module: Conduct well-opening liquid withdrawal and trial production for each well. After the oil and gas production stabilizes, measure the liquid production profile to obtain the production contribution rate C n ;

[0033] Use the following formula to obtain the dual sweet spot reservoir quality score Q n ;

[0034] Q n = C n × X n .

[0035] The above-mentioned weight coefficient analysis unit includes:

[0036] Feature factor acquisition module: Conduct a correlation analysis between the dual sweet spot reservoir quality scores of each cluster and the corresponding logging data of each cluster to obtain a set of feature factors with high correlation. The logging data includes logging data, well logging data, and core experiment data. The set of feature factors includes core experiment permeability, nuclear magnetic effective porosity, oil saturation, shale content, brittleness index, and thickness;

[0037] Normalization module: Take each feature factor as an independent variable and the dual sweet spot reservoir quality score of each cluster as a dependent variable, and perform normalization processing on the independent variable and the dependent variable;

[0038] Weight analysis module: Use the grey relational analysis method to conduct a correlation analysis between the dependent variable and each independent variable to obtain the weight coefficients of each feature factor affecting the dual sweet spot reservoir quality score.

[0039] In the present invention, multiple wells are selected in the same development horizon of the area to be evaluated for fracturing tests, downhole video tests, and liquid production profile analysis, that is, the productivity after fracturing and the fracturing effect are analyzed to obtain the dual sweet spot reservoir quality scores of each cluster in each well, forming a dual sweet spot reservoir sample set. The correlation analysis is carried out on the dual sweet spot reservoir sample set and the logging data, and multiple characteristic factors with high correlation are obtained. Based on the grey correlation method, the weight coefficients of each characteristic factor are obtained, and the dual sweet spot reservoir quality factors of each dual sweet spot reservoir sample are determined by combining the weight coefficients of each characteristic factor, so as to evaluate the dual sweet spot reservoir in the area to be evaluated. Thus, the problem that the current direct evaluation of reservoir sweet spots relying on static logging data leads to a large deviation between the sweet spot reservoir and the productivity after fracturing transformation is solved, the matching rate of the dual sweet spot evaluation result and the production after fracturing is improved, the drilling encounter rate of this type of reservoir is increased, the effective fracturing of this type of reservoir is realized, and the single well productivity is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Attached Figure 1 is a schematic flow chart of the method of the present invention.

[0041] Attached Figure 2 is a schematic flow chart of the method for obtaining the dual sweet spot reservoir sample set in the present invention.

[0042] Attached Figure 3 is a schematic flow chart of the method for obtaining the weight coefficients of each characteristic factor in the present invention.

[0043] Attached Figure 4 is a schematic structural diagram of the device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The present invention is not limited by the following embodiments, and the specific implementation manners can be determined according to the technical solutions of the present invention and the actual situation.

[0045] The present invention will be further described below in conjunction with the embodiments and the drawings:

[0046] Embodiment 1: As shown in the attached Figure 1 figure, the embodiment of the present invention discloses a method for quantitatively evaluating the dual sweet spots of unconventional reservoirs, including:

[0047] Step S110, in the same development horizon of the area to be evaluated, select m wells for fracturing tests, downhole video tests, and liquid production profile analysis to obtain a dual sweet spot reservoir sample set, where the dual sweet spot reservoir sample set includes multiple dual sweet spot reservoir samples, and each dual sweet spot reservoir sample is the single-cluster dual sweet spot reservoir quality score;

[0048] Step S120, select multiple characteristic factors with high correlation between each dual sweet spot reservoir sample and the logging data, and obtain the weight coefficients of each characteristic factor based on the grey correlation method;

[0049] Take multiple characteristic factors with high correlation between the above-mentioned double sweet spot reservoir samples and logging data, that is, analyze the characteristic laws of the high and low quality scores of single-cluster double sweet spot reservoirs in logging data, and select multiple characteristic factors with high correlation for correlation analysis.

[0050] Step S130, determine the double sweet spot reservoir quality factors of each double sweet spot reservoir sample. When conducting drilling geological steering, preferentially drill through the formation with a large double sweet spot reservoir quality factor. When designing fracturing clusters, preferentially fracture the cluster with a large double sweet spot reservoir quality factor.

[0051] The larger the value of the above-mentioned double sweet spot reservoir quality factor, the more developed the double sweet spot reservoir is. When conducting drilling geological steering, preferentially drill through the formation with a large value of the double sweet spot reservoir quality factor. When designing fracturing clusters, preferentially fracture the section with a large value of the double sweet spot reservoir quality factor.

[0052] The double sweet spot quantitative evaluation method for unconventional reservoirs disclosed by the present invention selects multiple wells in the same development horizon of the area to be evaluated for fracturing testing, downhole video testing and liquid production profile analysis, that is, analyzes the productivity and fracturing effect after fracturing, obtains the double sweet spot reservoir quality scores of each cluster in each well, forms a double sweet spot reservoir sample set, conducts a correlation analysis on the double sweet spot reservoir sample set and logging data, obtains multiple characteristic factors with high correlation, obtains the weight coefficients of each characteristic factor based on the grey correlation method, determines the double sweet spot reservoir quality factors of each double sweet spot reservoir sample in combination with the weight coefficients of each characteristic factor, evaluates the double sweet spot reservoirs in the area to be evaluated, thereby solving the problem that the current direct evaluation of reservoir sweet spots relying on static logging data leads to a large deviation between sweet spot reservoirs and productivity after fracturing transformation, improving the matching rate of the double sweet spot evaluation result and the production after fracturing, increasing the drilling encounter rate of this type of reservoir, realizing effective fracturing of this type of reservoir, and increasing the productivity of a single well.

[0053] Embodiment 2: The embodiment of the present invention discloses a double sweet spot quantitative evaluation method for unconventional reservoirs, including:

[0054] Step S210, in the same development horizon of the area to be evaluated, select m wells for fracturing testing, downhole video testing and liquid production profile analysis to obtain a double sweet spot reservoir sample set, where the double sweet spot reservoir sample set includes multiple double sweet spot reservoir samples, and each double sweet spot reservoir sample is the quality score of a single-cluster double sweet spot reservoir.

[0055] As shown in the appendix Figure 2 The above steps specifically include:

[0056] Step S211: Select m wells at the same development horizon in the area to be evaluated. For each well, perforating-bridge plug integrated cluster fracturing is carried out. After fracturing, coiled tubing is used to drill out the bridge plugs for fracturing, and the wellbore is flushed clean. Each well has a total of n clusters, and the fracturing process for each cluster is the same.

[0057] Step S212: Connect the coiled tubing to the downhole video tool to conduct downhole video tests on each cluster, measure the perforation hole images of each cluster, obtain the abrasion area of each hole, and use the following formula to score the fracturing effect of each cluster.

[0058]

[0059] Among them, X n is the scoring value of the fracturing effect; E n is the abrasion area of the holes in the nth cluster; E min is the minimum value of the abrasion areas of the holes in each cluster; E max is the maximum value of the abrasion areas of the holes in each cluster.

[0060] If it is the maximum value of the above-mentioned hole abrasion area, the fracturing effect score is 100 points; if it is the minimum value of the hole abrasion area, the fracturing effect score is 0 points.

[0061] Step S213: Open the wells of each well for liquid withdrawal and trial production. After the oil and gas production is stable, measure the liquid production profile to obtain the production contribution rate C n ;

[0062] Step S214: Use the following formula to obtain the dual sweet spot reservoir quality score Q n of each cluster to form a dual sweet spot reservoir sample set.

[0063] Q n = C n × X n .

[0064] The dual sweet spot reservoir quality score Q n of the above single cluster is equal to the product of the production contribution rate C n of this cluster and the hole abrasion area X n , that is, the greater the production contribution rate of the reservoir, the greater the hole abrasion area (the more proppant enters), indicating that the dual sweet spot reservoir is more developed.

[0065] Step S220: Select multiple characteristic factors with a high degree of correlation between each dual sweet spot reservoir sample and logging data. Based on the grey correlation method, obtain the weight coefficients of each characteristic factor.

[0066] As shown in the appendix Figure 3 , the above steps specifically include:

[0067] Step S221: Perform a correlation analysis on the reservoir quality scores of each cluster of dual sweet spots and the corresponding logging data for each cluster to obtain a set of characteristic factors with high correlation. The logging data includes logging data, well logging data, and core experiment data. The set of characteristic factors includes core experiment permeability, nuclear magnetic resonance effective porosity, oil saturation, shale content, brittleness index, and thickness.

[0068] The above-mentioned correlation analysis of the reservoir quality scores of each cluster of dual sweet spots and the corresponding logging data for each cluster is to analyze the characteristic laws of the high and low values of the single-cluster dual sweet spot reservoir quality score Q n in the logging data. Select the characteristic factors with high correlation from these laws and place them in the set of characteristic factors. Therefore, the characteristic factors in the set of characteristic factors are not fixed. Generally, the characteristic factors related to the single-cluster dual sweet spot reservoir quality score include core experiment permeability, nuclear magnetic resonance effective porosity, oil saturation, shale content, brittleness index, thickness, etc.

[0069] Step S222: Use each characteristic factor as an independent variable and the reservoir quality scores of each cluster of dual sweet spots as a dependent variable, and perform normalization processing on the independent variable and the dependent variable.

[0070] In this embodiment, the normalization processing can adopt the maximum value normalization method, that is, divide the single parameter by the maximum value of the same type of parameter to make each evaluation score range from 0 to 1. For parameters whose larger values reflect better reservoir quality, such as core experiment permeability and nuclear magnetic resonance effective porosity, directly divide by the maximum value of this parameter; for parameters whose smaller values reflect better reservoir quality, such as shale content, use the difference between the maximum value of this parameter and the single parameter and then divide by the maximum value to make them comparable.

[0071] Step S223: Use the grey relational analysis method to perform a correlation degree analysis on the dependent variable and each independent variable to obtain the weight coefficients of each characteristic factor affecting the reservoir quality scores of the dual sweet spots.

[0072] The basic principle of the grey relational analysis method is to judge the closeness of the relationship according to the similarity degree of the geometric shapes of the sequence curves. The closer the curves are, the greater the correlation degree between the corresponding sequences, and vice versa. Therefore, in this step, the grey relational analysis method is used to calculate the grey correlation degree and determine the correlation degree between the dependent variable (single-cluster dual sweet spot reservoir quality score) and each independent variable (core experiment permeability, nuclear magnetic resonance effective porosity, oil saturation, shale content, brittleness index, thickness, etc.).

[0073] After the correlation degree calculation is completed, the weight coefficients of each characteristic factor affecting the reservoir quality scores of the dual sweet spots are obtained based on the following formula;

[0074]

[0075] where i is the number of independent variables, r iis the correlation degree, a i is the weight coefficient.

[0076] Step S230: Determine the dual sweet spot reservoir quality factor Q n ′ of each dual sweet spot reservoir sample. When conducting drilling geological steering, preferentially drill through the formation with a large dual sweet spot reservoir quality factor Q n ′. When designing fracturing clusters, preferentially select the cluster with a large dual sweet spot reservoir quality factor Q n ′ for fracturing.

[0077] Conduct dual sweet spot reservoir evaluation based on the dual sweet spot reservoir quality factors of each dual sweet spot reservoir sample, that is, the dual sweet spot reservoir quality factors of each cluster. That is, when conducting drilling geological steering, preferentially drill through the formation with a large Q n ′ value. When designing fracturing clusters, preferentially select the section with a large Q n ′ value for fracturing.

[0078] Example 3: Combine with a specific horizontal well and use the method disclosed in the present invention to conduct dual sweet spot reservoir evaluation, which specifically includes:

[0079] The first step: For the horizontal well MaHW12, adopt integrated perforating and bridge plug fracturing in clusters, with a total of 10 clusters. The fracturing process of each cluster (section) is the same, and the fracturing construction parameters (displacement, sand addition amount, sand ratio, etc.) are basically the same. After fracturing, use coiled tubing to drill out the bridge plugs for fracturing and flush the wellbore clean.

[0080] The second step: Connect the coiled tubing to the downhole TV tool for testing, measure the perforation hole images of each cluster (section), calculate the abrasion area of each hole, the maximum abrasion area of the hole is 3965 mm 2 , and the minimum abrasion area of the hole is 1215 mm 2 . The fracturing effect scores of each cluster are shown in Table 1;

[0081] Table 1 Calculation table of scores for each cluster of Well MaHW12

[0082] Cluster number Number of holes <![CDATA[Total abrasion area E n / mm 2 > <![CDATA[X n fraction]]> 1 24 2543 48 2 24 1215 0 3 24 1980 28 4 24 1368 6 5 24 1821 22 6 24 3140 70 7 24 2534 48 8 24 3965 100 9 24 1900 25 10 24 3560 85

[0083] The third step: After MaHW12 is opened for fluid withdrawal and production testing, when the oil and gas production is stable, measure the liquid production profile to obtain the production contribution rate C n of each cluster, as shown in Table 2;

[0084] The fourth step: Calculate the dual sweet spot reservoir quality score Q n of each cluster, as shown in Table 2; and in the same development horizon of this area, select 5 wells for liquid production profile and downhole TV testing to obtain 120 samples of the single-cluster dual sweet spot reservoir quality score Q n ;

[0085] Table 2 Calculation table of scores for each cluster of Well MaHW12

[0086]

[0087]

[0088] Step 5: Correlate the double-sweet-spot reservoir quality score Q of each cluster (section) n with data such as logging and core experiments. In this area, Q n has good correlations with core-experiment permeability, nuclear magnetic resonance effective porosity, oil saturation, shale content, brittleness index, and thickness. Take these six parameters as independent variables and the double-sweet-spot reservoir quality score Q n as the dependent variable. Normalize the original data of the independent and dependent variables. In this case, the maximum normalization method can be used.

[0089] Step 6: Use the grey correlation method to calculate the grey correlation degree. The correlation degrees r between the dependent variable and each independent variable (core-experiment permeability, nuclear magnetic resonance effective porosity, oil saturation, shale content, brittleness index, thickness) are r = (1, 0.785, 0.713, 0.535, 0.632, 0.733); the weight coefficients of each independent variable are a = (0.23, 0.18, 0.16, 0.12, 0.14, 0.17);

[0090] Step 7: Multiply the normalized independent variables by the weight coefficients of this category, and then sum up the products to obtain the double-sweet-spot reservoir quality factor Q n ' of each single cluster, as shown in Table 3. Combining the double-sweet-spot reservoir quality factors of each single cluster, during drilling geological steering, preferentially drill through formations with large Q n ' values. During fracturing cluster design, preferentially fracture sections with large Q n ' values.

[0091] Table 3 Double-sweet-spot reservoir quality factors of single clusters (sections) (partial sample clusters (sections))

[0092]

[0093] Example 4: As shown in Figure 4, this example discloses a double-sweet-spot quantitative evaluation device for unconventional reservoirs, including:

[0094] A sample set acquisition unit selects m wells in the same development horizon of the area to be evaluated for fracturing tests, downhole video tests, and liquid production profile analysis to obtain a double-sweet-spot reservoir sample set, where the double-sweet-spot reservoir sample set includes multiple double-sweet-spot reservoir samples, and each double-sweet-spot reservoir sample is the double-sweet-spot reservoir quality score of a single cluster;

[0095] A weight coefficient analysis unit selects multiple characteristic factors with high correlations between each double-sweet-spot reservoir sample and logging data, and obtains the weight coefficients of each characteristic factor based on the grey correlation method;

[0096] Evaluation unit, determine the dual sweet spot reservoir quality factor of each dual sweet spot reservoir sample, and preferentially select the formation with a large dual sweet spot reservoir quality factor when drilling geological steering. During fracturing cluster design, preferentially select the cluster with a large dual sweet spot reservoir quality factor for fracturing.

[0097] Among them, the sample set acquisition unit includes:

[0098] Fracturing test module, in the same development horizon of the area to be evaluated, select m wells, use perforating-bridge plug integrated cluster fracturing for each well, and after fracturing, use coiled tubing to drill out the bridge plugs for fracturing and wash the wellbore clean. Among them, each well has a total of n clusters, and the fracturing process for each cluster is the same;

[0099] Downhole video test module, use coiled tubing to connect the downhole video tool to conduct downhole video tests on each cluster, measure the perforation hole images of each cluster, obtain the abrasion area of each hole, and score the fracturing effect of each cluster using the following formula;

[0100]

[0101] Among them, X n is the fracturing effect scoring value; E n is the abrasion area of the holes in the nth cluster; E min is the minimum abrasion area of the holes in each cluster; E max is the maximum abrasion area of the holes in each cluster;

[0102] Production contribution rate acquisition module, conduct fluid production test after opening the well for each well, measure the fluid production profile after the oil and gas production stabilizes, and obtain the production contribution rate C n ;

[0103] Use the following formula to obtain the dual sweet spot reservoir quality score Q n ;

[0104] Q n = C n × X n .

[0105] Among them, the weight coefficient analysis unit includes:

[0106] Characteristic factor acquisition module, conduct correlation analysis on the dual sweet spot reservoir quality scores of each cluster and the corresponding logging data of each cluster to obtain a set of characteristic factors with high correlation. Among them, the logging data includes logging data, well logging data, and core experiment data. The set of characteristic factors includes core experiment permeability, nuclear magnetic effective porosity, oil saturation, shale content, brittleness index, and thickness;

[0107] A normalization module that takes each characteristic factor as an independent variable and the reservoir quality scores of each cluster of double sweet spots as the dependent variable, and performs normalization processing on the independent and dependent variables;

[0108] A weight analysis module that uses the grey relational analysis method to analyze the correlation degree between the dependent variable and each independent variable, and obtains the weight coefficients of the characteristic factors affecting the reservoir quality scores of the double sweet spots.

[0109] The above technical features constitute the best embodiment of the present invention, which has strong adaptability and the best implementation effect. Non-essential technical features can be added or subtracted according to actual needs to meet the requirements of different situations.

Claims

1. A method for quantitatively evaluating double sweet spots of unconventional reservoirs, characterized in that, it includes: In the same development horizon of the area to be evaluated, select m wells for fracturing tests, downhole video tests and liquid production profile analysis to obtain a double sweet spot reservoir sample set, where the double sweet spot reservoir sample set includes multiple double sweet spot reservoir samples, and each double sweet spot reservoir sample is the quality score of a single cluster of double sweet spot reservoirs; Select multiple characteristic factors with high correlation between each double sweet spot reservoir sample and logging data, and obtain the weight coefficients of each characteristic factor based on the grey correlation method; Determine the double sweet spot reservoir quality factors of each double sweet spot reservoir sample. When drilling geological steering, preferentially select the formation with a large double sweet spot reservoir quality factor to drill through. When designing fracturing clusters, preferentially select the cluster with a large double sweet spot reservoir quality factor for fracturing.

2. The method for quantitatively evaluating double sweet spots of unconventional reservoirs according to claim 1, characterized in that, The step of selecting m wells in the same development horizon of the area to be evaluated for fracturing tests, downhole video tests and liquid production profile analysis to obtain a double sweet spot reservoir sample set includes: In the same development horizon of the area to be evaluated, select m wells. For each well, use perforating bridge plug integrated cluster fracturing, and after fracturing, use coiled tubing to drill out the bridge plugs for fracturing, and flush the wellbore clean. Among them, each well has a total of n clusters, and the fracturing process of each cluster is the same; Connect the coiled tubing to the downhole video tool to conduct downhole video tests on each cluster, measure the perforation hole images of each cluster, obtain the abrasion area of each hole, and use the following formula to score the fracturing effect of each cluster; Among them, X n is the score value of the fracturing effect; E n is the hole erosion area of the nth cluster; E min is the minimum value of the hole erosion area of each cluster; E max is the maximum value of the hole erosion area of each cluster; For each well, the well is opened for liquid withdrawal and trial production. After the oil and gas production stabilizes, the liquid production profile is measured to obtain the production contribution rate C of each cluster. n ; Obtain the quality score Q of the double sweet spot reservoirs in each cluster using the following formula n , and form a sample set of double sweet spot reservoirs; Q n = C n × X n .

3. The method for quantitatively evaluating double sweet spots of unconventional reservoirs according to claim 1 or 2, characterized in that, The step of selecting multiple characteristic factors with high correlation between each double sweet spot reservoir sample and logging data and obtaining the weight coefficients of each characteristic factor based on the grey correlation method includes: Conduct a correlation analysis between the quality scores of each cluster of double sweet spot reservoirs 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 experiment data. The set of characteristic factors includes core experiment permeability, nuclear magnetic effective porosity, oil saturation, shale content, brittleness index, and thickness; Take each characteristic factor as an independent variable and the quality scores of each cluster of double sweet spot reservoirs as a dependent variable, and perform normalization processing on the independent variable and the dependent variable; Use the grey correlation method to conduct a correlation analysis between the dependent variable and each independent variable to obtain the weight coefficients of each characteristic factor affecting the quality scores of double sweet spot reservoirs.

4. The method for quantitatively evaluating double sweet spots of unconventional reservoirs according to claim 1 or 2, characterized in that, The step of determining the double sweet spot reservoir quality factors of each double sweet spot reservoir sample includes multiplying the normalized independent variable by each weight coefficient, and adding the obtained products to obtain the double sweet spot reservoir quality factor of this cluster, that is, the double sweet spot reservoir quality factor of the double sweet spot reservoir sample.

5. The method for quantitatively evaluating double sweet spots of unconventional reservoirs according to claim 3, characterized in that, Determining the dual sweet spot reservoir quality factor of each dual sweet spot reservoir sample includes multiplying the normalized independent variable by each weight coefficient, and adding the obtained products to obtain the dual sweet spot reservoir quality factor of this cluster, that is, the dual sweet spot reservoir quality factor of the dual sweet spot reservoir sample.

6. A dual sweet spot quantitative evaluation device for unconventional reservoirs applying the method according to any one of claims 1 to 5, characterized in that it includes: A sample set acquisition unit selects m wells in the same development horizon of the area to be evaluated for fracturing tests, downhole video tests, and liquid production profile analysis to obtain a dual sweet spot reservoir sample set, where the dual sweet spot reservoir sample set includes multiple dual sweet spot reservoir samples, and each dual sweet spot reservoir sample is a single cluster dual sweet spot reservoir quality score; A weight coefficient analysis unit selects multiple characteristic factors with high correlation between each dual sweet spot reservoir sample and logging data, and obtains the weight coefficients of each characteristic factor based on the grey correlation method; An evaluation unit determines the dual sweet spot reservoir quality factor of each dual sweet spot reservoir sample, and preferentially selects the formation with a large dual sweet spot reservoir quality factor during drilling geological steering. During fracturing cluster design, the cluster with a large dual sweet spot reservoir quality factor is preferably selected for fracturing.

7. The dual sweet spot quantitative evaluation device for unconventional reservoirs according to claim 6, characterized in that the sample set acquisition unit includes: A fracturing test module selects m wells in the same development horizon of the area to be evaluated, performs perforation-bridge plug integrated cluster fracturing on each well, drills out the fracturing bridge plugs with coiled tubing after fracturing, and flushes the wellbore clean. Among them, each well has a total of n clusters, and the fracturing process of each cluster is the same; A downhole video test module connects the coiled tubing to the downhole video tool to perform downhole video tests on each cluster, measures the perforation hole images of each cluster, obtains the abrasion area of each hole, and scores the fracturing effect of each cluster using the following formula; Among them, X n is the score value for the fracturing effect; E n is the erosion area of the perforations in the nth cluster; E min is the minimum value of the erosion areas of the perforations in each cluster; E max is the maximum value of the erosion areas of the perforations in each cluster; Production contribution rate acquisition module: Each well is opened for liquid withdrawal and trial production. After the oil and gas production stabilizes, the liquid production profile is measured to obtain the production contribution rate C of each cluster. n ; Obtain the quality score Q of the double sweet spot reservoirs in each cluster using the following formula n ; Q n = C n × X n .

8. The dual sweet spot quantitative evaluation device for unconventional reservoirs according to claim 6 or 7, characterized in that the weight coefficient analysis unit includes: A characteristic factor acquisition module performs correlation analysis between the dual sweet spot reservoir quality scores 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 experiment data. The set of characteristic factors includes core experiment permeability, nuclear magnetic effective porosity, oil saturation, shale content, brittleness index, and thickness; A normalization module takes each characteristic factor as an independent variable and the dual sweet spot reservoir quality scores of each cluster as a dependent variable, and performs normalization processing on the independent variable and the dependent variable; A weight analysis module uses the grey correlation method to perform correlation analysis between the dependent variable and each independent variable to obtain the weight coefficients of each characteristic factor affecting the dual sweet spot reservoir quality score.

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