Comprehensive evaluation method for reservoir dessert of tight oil reservoir

Abstract

The invention discloses a comprehensive evaluation method for compact oil reservoir desserts, and solves the problem that the existing evaluation method does not dynamically and comprehensively evaluate the reservoir desserts from the aspect of engineering in combination with the aspect of obtaining high productivity potential from the aspect of reservoir transformation longitudinal fracture expansion and fracture network forming capability. The method comprises the steps that S1, dessert influence factors are divided into reservoir static parameters and reservoir dynamic parameters, relevant factor parameters are collected, and an evaluation basic database is established; s2, establishing a static dessert evaluation matrix, calculating a dessert fitting degree, and quantitatively evaluating static desserts; the dispersity between different minerals is calculated, the longitudinal lithologic distribution heterogeneity is combined, the complex potential coefficient of the seam network is defined, and the dynamic dessert is quantitatively evaluated; and S3, comprehensively considering the static and dynamic sweet spot evaluation results of the evaluation reservoir section, calculating the sweet spot index of the evaluation reservoir section, and evaluating the reservoir sweet spot according to the sweet spot index value of the sweet spot index. According to the method, reservoir quality fine division is achieved, and dessert evaluation better conforming to the characteristics of unconventional tight oil and gas is achieved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas engineering technology, and in particular to a comprehensive evaluation method for sweet spots in tight oil reservoirs. Background Technology

[0002] North American unconventional shale oil and gas achieved energy independence through horizontal wells and volumetric fracturing. Subsequent core sampling at hydraulic fracturing test sites confirmed that accurate evaluation and location of reservoir sweet spots were key factors influencing fracturing effectiveness. Finding favorable sweet spots is the primary task of exploration and development. In contrast to North American tight oil and gas reservoirs, Chinese tight reservoirs are even denser, exhibiting strong heterogeneity both vertically and horizontally, with discontinuous sand bodies, well-developed sand-mud interbedded layers, and the presence of natural fractures and faults.

[0003] Based on a review of patent applications and published papers both domestically and internationally, the main evaluation methods for sweet spots in tight reservoirs are as follows:

[0004] (1) Wang Zongjun et al. A method, device and medium for constructing logging quality parameters of low-permeability reservoirs (Patent No.: CN202211703340.1). This method preprocesses and standardizes logging data; selects sensitive logging curves for low-permeability reservoir characteristic parameters from the standardized logging curves; reconstructs the sensitive logging curves for low-permeability reservoir characteristic parameters based on artificial intelligence algorithms; obtains low-permeability reservoir quality parameters based on the reconstructed sensitive curves by pre-constructing empirical formulas for low-permeability reservoir quality parameters; and classifies low-permeability reservoirs based on the low-permeability reservoir quality parameters, thereby achieving the purpose of quantitative evaluation of reservoir quality.

[0005] (2) Xu Jingling et al. Method and system for predicting sweet spots in shale reservoir facies (Patent No.: CN202110232294.0). This method obtains attribute parameters related to oil production per meter based on the intersection relationship between the attribute parameters of shale reservoir facies and the daily oil production per meter, and establishes a comprehensive evaluation model for sweet spots based on the attribute parameters to intuitively evaluate the quality of the reservoir.

[0006] (3) Zhang Shuxia et al. A reservoir quality evaluation method (Patent No.: CN202210571828.7). This method obtains reservoir parameters, including porosity, oil saturation, cementation index, saturation index, clay content, and bound water saturation, through downhole coring. It further quantifies reservoir quality factors and classifies the reservoir into grades, thereby achieving a quantitative evaluation of reservoir quality.

[0007] The three representative methods mentioned above evaluate reservoir sweet spots based on static parameters of the reservoir's physical properties. These are conventional methods, but numerous factors influence reservoir sweet spots, and a comprehensive evaluation method that considers all these factors is currently lacking. Furthermore, unconventional tight oil and gas reservoirs currently require volumetric fracturing to achieve production capacity. Current methods do not dynamically and comprehensively evaluate reservoir sweet spots from an engineering perspective, considering factors such as reservoir stimulation, longitudinal fracture propagation, and fracture network formation capabilities to achieve high production potential. Therefore, there is an urgent need to establish a dynamic evaluation method for sweet spots that aligns with the actual conditions of tight oil reservoirs, providing a basis for the efficient development of tight oil and gas. Summary of the Invention

[0008] This invention addresses the problem in existing evaluation methods that fail to dynamically and comprehensively evaluate reservoir sweet spots from an engineering perspective, considering factors such as the propagation of longitudinal fractures and the formation of fracture networks to achieve high productivity potential. Instead, it provides a comprehensive evaluation method for sweet spots in tight oil reservoirs. This method comprehensively considers multiple static parameters of the reservoir and evaluates both static and dynamic sweet spots from the perspectives of whether the reservoir can form complex fracture networks and achieve high productivity after volumetric fracturing. This results in a more refined classification of reservoir quality and a sweet spot evaluation that better reflects the characteristics of unconventional tight oil and gas.

[0009] The present invention solves its problem through the following technical solution: a comprehensive evaluation method for sweet spots in tight oil reservoirs, comprising the following steps:

[0010] S1: Divide the factors affecting the sweet spot into two categories: reservoir static parameters and reservoir dynamic parameters, and collect and organize the relevant factors and parameters of reservoir static parameters and reservoir dynamic parameters to establish an evaluation basic database;

[0011] S2: Based on the basic data of static parameters affecting the sweet spot, the optimal sweet spot segment of the target reservoir is obtained, and a static sweet spot evaluation matrix is ​​established. The sweet spot fit is calculated, and the static sweet spot is quantitatively evaluated.

[0012] Based on the evaluation of the rock and mineral composition of the reservoir section, the dispersion between different minerals is calculated. Combined with the heterogeneity of vertical lithological distribution, the fracture network complexity potential coefficient is defined to quantitatively evaluate the dynamic sweet spot.

[0013] S3: Taking into account both the static and dynamic sweet spot evaluation results of the reservoir section, calculate the sweet spot index of the reservoir section, and evaluate the reservoir based on the obtained sweet spot index value.

[0014] Furthermore, the static reservoir parameters mentioned in step S1 are divided into the oil layer thickness, oil saturation, porosity, permeability, and clay content of the reservoir section being evaluated; the dynamic reservoir parameters are the rock and mineral composition and vertical lithological distribution, which are parameters related to the ability to form fracture networks.

[0015] Furthermore, the method for collecting and organizing reservoir static parameters in step S1 includes:

[0016] S101: Divide the reservoir into several evaluation reservoir sections by horizontal wells in the main development layer of the tight oil reservoir along the horizontal direction, and carry out continuous downhole coring in the evaluation reservoir sections.

[0017] S102: Process the downhole rock samples into standard cores with a diameter of 2.5cm and a length of 5cm, and place them in an oven to dry to constant weight;

[0018] S103: Use an automatic helium porosity analyzer and an ultra-low permeability analyzer to test the porosity and permeability of the dried rock sample described in step S102, and at the same time obtain the oil saturation of the rock sample in the evaluation reservoir section.

[0019] S104: Use an X-ray diffractometer to test the mineral composition of the rock sample from step S102;

[0020] S105: The vertical oil layer thickness and mudstone thickness distribution are obtained by using the logging curves of horizontal wells in the same area and layer of the main development layer of tight oil reservoirs.

[0021] Furthermore, the method for calculating the dessert fit in step S2 includes the following steps:

[0022] S201. In order to establish the optimal sweet spot section, obtain the production contribution ratio of each evaluated reservoir section after volumetric fracturing of the cored horizontal well in the main development layer of the tight oil reservoir, i.e., the daily liquid production ratio.

[0023] S202. Perform a correlation analysis between the obtained static parameters of the sweet spot in the evaluated reservoir section and the production capacity contribution ratio of each evaluated unit. The analysis results determine the static parameter values ​​of the reservoir corresponding to the optimal sweet spot section.

[0024] S203. Based on the evaluation reservoir section and sweet spot parameter set, establish the static parameter matrix X of the sweet spot of the tight oil reservoir;

[0025] S204. Based on the static parameters of the optimal dessert segment, establish a static dessert reference column;

[0026] S205. Establish the static sweet spot evaluation matrix Y of tight oil reservoirs using the static sweet spot evaluation matrix X and the static sweet spot reference column X0.

[0027] S206. Standardize each element in the static dessert review matrix Y;

[0028] S207. Calculate the maximum distance between the static parameters of each evaluated reservoir segment and the optimal sweet spot segment using the formula, and further calculate the sweet spot fit of each evaluated reservoir.

[0029] Furthermore, the method for determining the reservoir static parameter values ​​corresponding to the optimal segment of the ideal sweet spot in step S202 is as follows:

[0030] If the static parameters of each rock sample are positively correlated with the production capacity contribution, the maximum value among the static parameters of each rock sample shall be taken.

[0031] If the static parameters of each rock sample are negatively correlated with the productivity contribution, the minimum value among the static parameters of each rock sample shall be taken.

[0032] Furthermore, in step S203, the static parameter matrix X of the sweet spot of the tight oil reservoir is established, and its expression (1) is:

[0033]

[0034] In the formula: X is the static parameter matrix of the dessert; X i (j) represents the evaluation matrix element; m represents the number of evaluation reservoir segments; i represents the corresponding number of the evaluation reservoir segment; n represents the number of static parameters of the sweet spot affected by the evaluation reservoir segment; j represents the number of static parameters of the sweet spot affected by the evaluation reservoir segment.

[0035] And / or,

[0036] Step S204, based on the static parameters of the optimal dessert segment, establishes the expression for the static dessert reference column as follows:

[0037] X0=(X0(1),X0(2),...,X0(n)) (2)

[0038] In the formula: X0 is the static dessert reference column; X0(n) is the element of the static dessert reference column;

[0039] And / or,

[0040] Step S205: Using the tight oil reservoir static sweet spot evaluation matrix X and the static sweet spot reference column X0, establish the tight oil reservoir static sweet spot evaluation matrix Y, whose expression (3) is:

[0041]

[0042] In the formula: Y is the static sweet spot evaluation matrix of tight oil reservoir.

[0043] And / or,

[0044] Step S206 uses formula (4) to standardize each element in the static dessert review matrix Y, and its expression (5) is:

[0045]

[0046] in: Y is a standardized element of the static sweet spot evaluation matrix for tight oil reservoirs. *A standardized evaluation matrix for static sweet spots in tight oil reservoirs.

[0047] And / or,

[0048] Step S207 uses formula (6) to calculate the maximum distance between the static parameters of each evaluated reservoir segment and the optimal sweet spot segment, and further calculates the sweet spot fit of each evaluated reservoir, the expression of which is (7):

[0049]

[0050]

[0051] In the formula: E represents the sweet spot fit of the reservoir segment.

[0052] Furthermore, the method for quantitatively evaluating dynamic desserts in step S2 includes:

[0053] Calculate the dispersion of different minerals based on the evaluation of the mineral composition of the reservoir section;

[0054] The vertical oil layer and mudstone thickness distribution of the evaluation reservoir section is obtained by interpreting the logging curves, the fracture network complexity potential coefficient is defined, and the dynamic sweet spot is quantitatively evaluated.

[0055] Furthermore, the method for calculating the dispersion between different minerals is as follows:

[0056] Based on the results of the mineral composition test of the rock samples in the evaluation reservoir section, the dispersion between different minerals is calculated using formula (8), as shown in the following expression:

[0057]

[0058] In the formula: U is the dispersion degree between different minerals, %; M k represents the percentage of mineral composition in the rock sample, %; k is the mineral type number, and l is the number of mineral types.

[0059] Furthermore, the method for defining the complexity potential coefficient of the sewing mesh is as follows:

[0060] The vertical oil layer and mudstone thickness distribution of the evaluation reservoir section is obtained by interpreting well logging curves. Combined with the dispersion of different minerals in the rock sample, a fracture network complexity potential coefficient is defined. The larger the value, the more complex the fracture network after volumetric fracturing, and the better the dynamic sweet spot of the reservoir. Its expression is as follows:

[0061]

[0062] In the formula: F is the complex potential coefficient of the fracture network, which is dimensionless; H is the longitudinal oil layer thickness of the evaluated reservoir section, in meters; h is the longitudinal mudstone thickness of the evaluated reservoir section, in meters.

[0063] Furthermore, the expression for calculating the sweet spot index of the reservoir section in step S3 is as follows:

[0064]

[0065] In the formula: R is the sweet spot index for evaluating the reservoir section, which is dimensionless.

[0066] Furthermore, the method for evaluating the reservoir in step S3 is as follows: the larger the sweet spot index value, the better the reservoir quality and the greater the potential for high production capacity after volumetric fracturing.

[0067] Compared with the above-mentioned background technology, the present invention has the following beneficial effects:

[0068] This invention provides a comprehensive evaluation method for sweet spots in tight oil reservoirs. This method comprehensively considers multiple static parameters of the reservoir and evaluates the static and dynamic sweet spots from the perspective of whether the reservoir can form a complex fracture network and obtain high productivity after volumetric fracturing. This achieves fine classification of reservoir quality and represents a qualitative improvement compared to previous static parameter evaluation methods.

[0069] Furthermore, this method is more consistent with the sweet spot evaluation of unconventional tight oil and gas reservoirs. From an economic perspective, it eliminates the need for extensive field testing, significantly reducing costs and time. The method is highly practical and can provide important evidence and direction for evaluating sweet spots in similar reservoirs. Attached Figure Description

[0070] Figure 1 This is a graph showing the correlation between the static parameter of oil layer thickness and the percentage of daily oil production in dessert production according to an embodiment of the present invention.

[0071] Figure 2 This is a graph showing the correlation between the static parameter of oil saturation in desserts and the percentage of daily oil production in an embodiment of the present invention.

[0072] Figure 3 This is a graph showing the correlation between porosity, a static parameter of desserts, and the percentage of daily oil production in an embodiment of the present invention.

[0073] Figure 4 This is a graph showing the correlation between the static parameter penetration rate of desserts and the proportion of daily oil production in an embodiment of the present invention.

[0074] Figure 5 This is a graph showing the correlation between the static parameter of clay content in desserts and the percentage of daily oil production in an embodiment of the present invention.

[0075] Figure 6 This is a comparison chart of the sweet spot fit of different evaluation reservoir segments in embodiments of the present invention;

[0076] Figure 7 This is a comparison chart of the dispersion of different minerals in an embodiment of the present invention;

[0077] Figure 8 This is a comparison chart of the fracture network complexity potential coefficients of different reservoir evaluation sections in embodiments of the present invention;

[0078] Figure 9 This is a comparison chart of sweet spot indices for different reservoir segments in embodiments of the present invention;

[0079] Figure 10 This is a flowchart of the comprehensive evaluation method for sweet spots in tight oil reservoirs according to the present invention. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0081] like Figure 10 As shown, a comprehensive evaluation method for sweet spots in tight oil reservoirs includes the following steps:

[0082] S1: The factors influencing the sweet spot reservoir are divided into two main categories: reservoir static parameters and reservoir dynamic parameters. Relevant factor parameters are collected and organized to establish a basic evaluation database. Specific methods include the following steps:

[0083] S101: The factors affecting sweet spots are divided into two main categories: reservoir static parameters and reservoir dynamic parameters. The reservoir static parameters are further divided into the thickness of the oil layer in the reservoir section, oil saturation, porosity, permeability, and clay content. The reservoir dynamic parameters are the parameters of the ability to form fracture networks, namely rock and mineral composition and vertical lithological distribution.

[0084] S102: Divide the reservoir into several evaluation reservoir sections by horizontal wells in the main development layer of the tight oil reservoir along the horizontal direction, and carry out continuous downhole coring work in the evaluation reservoir sections.

[0085] S103: Process the downhole rock sample into a standard core with a diameter of 2.5 cm and a length of 5 cm, and place it in an oven to dry to constant weight.

[0086] S104: Use an automatic helium porosity analyzer and an ultra-low permeability analyzer to test the porosity and permeability of the dried rock sample described in step 103, and at the same time obtain the oil saturation of the rock sample in the evaluation reservoir section.

[0087] S105: Use an X-ray diffractometer to test the mineral composition of the rock sample from step 103.

[0088] S106: The vertical oil layer thickness and mudstone thickness distribution are obtained by using the logging curves of vertical wells in the same area and layer of the main development layer of the tight oil reservoir.

[0089] S2: Based on the basic data of static parameters affecting the sweet spot, the optimal sweet spot segment of the target reservoir is obtained, and a static sweet spot evaluation matrix is ​​established. The sweet spot fit is calculated, and the static sweet spot is quantitatively evaluated.

[0090] Based on this, according to the evaluation of the rock and mineral composition of the reservoir section, the dispersion between different minerals is calculated. Combined with the heterogeneity of vertical lithological distribution, the fracture network complexity potential coefficient is defined to quantitatively evaluate the dynamic sweet spot.

[0091] The method for calculating the dessert fit in step S2 includes the following steps:

[0092] S201. In order to establish the optimal sweet spot section, obtain the production contribution ratio of each evaluated reservoir section after volumetric fracturing of the cored horizontal well in the main development layer of the tight oil reservoir, i.e., the daily liquid production ratio.

[0093] S202. Perform a correlation analysis between the obtained static parameters of the sweet spot in the evaluated reservoir section and the production capacity contribution ratio of each evaluated unit. The analysis results determine the static parameter values ​​of the reservoir corresponding to the optimal sweet spot section.

[0094] If the static parameters of each rock sample are positively correlated with the production capacity contribution, the maximum value among the static parameters of each rock sample shall be taken.

[0095] If the static parameters of each rock sample are negatively correlated with the productivity contribution, the minimum value among the static parameters of each rock sample shall be taken.

[0096] S203. Based on the evaluation reservoir section and sweet spot parameter set, establish the static parameter matrix X of the sweet spot of the tight oil reservoir; its expression (1) is:

[0097]

[0098] In the formula: X is the static parameter matrix of the dessert; X i (j) represents the evaluation matrix element; m represents the number of evaluation reservoir segments; i represents the corresponding number of the evaluation reservoir segment; n represents the number of static parameters of the sweet spot affected by the evaluation reservoir segment; j represents the number of static parameters of the sweet spot affected by the evaluation reservoir segment.

[0099] S204. Based on the static parameters of the optimal dessert segment, establish a static dessert reference column;

[0100] The expression for the static dessert reference column is:

[0101] X0=(X0(1),X0(2),...,X0(n)) (2)

[0102] In the formula: X0 is the static dessert reference column; X0(n) is the element of the static dessert reference column;

[0103] S205. Establish the static sweet spot evaluation matrix Y of tight oil reservoirs using the static sweet spot evaluation matrix X and the static sweet spot reference column X0.

[0104] The static sweet spot evaluation matrix Y of tight oil reservoirs, expressed as (3), is:

[0105]

[0106] In the formula: Y is the static sweet spot evaluation matrix of tight oil reservoir.

[0107] S206. Standardize each element in the static sweetness rating matrix Y; its expression (5) is:

[0108]

[0109] in: Y is a standardized element of the static sweet spot evaluation matrix for tight oil reservoirs. * A standardized evaluation matrix for static sweet spots in tight oil reservoirs.

[0110] S207. Calculate the maximum distance between the static parameters of each evaluated reservoir segment and the optimal sweet spot segment using the formula, and further calculate the sweet spot fit of each evaluated reservoir.

[0111] Its expression (7) is:

[0112]

[0113] In the formula: E represents the sweet spot fit of the reservoir segment.

[0114] The method for quantitatively evaluating dynamic desserts in step S2 includes:

[0115] Based on the evaluation of the reservoir rock mineral composition, the dispersion among different minerals is calculated; the method is as follows:

[0116] Based on the results of the mineral composition test of the rock samples in the evaluation reservoir section, the dispersion between different minerals is calculated using formula (8), as shown in the following expression:

[0117]

[0118] In the formula: U is the dispersion degree between different minerals, %; M k represents the percentage of mineral composition in the rock sample, %; k is the mineral type number, and l is the number of mineral types.

[0119] The vertical oil layer and mudstone thickness distribution of the evaluation reservoir section is obtained by interpreting the logging curves, the fracture network complexity potential coefficient is defined, and the dynamic sweet spot is quantitatively evaluated.

[0120] The method for defining the complexity potential coefficient of the sewing mesh is as follows:

[0121] The vertical oil layer and mudstone thickness distribution of the evaluation reservoir section is obtained by interpreting well logging curves. Combined with the dispersion of different minerals in the rock sample, a fracture network complexity potential coefficient is defined. The larger the value, the more complex the fracture network after volumetric fracturing, and the better the dynamic sweet spot of the reservoir. Its expression is as follows:

[0122]

[0123] In the formula: F is the complex potential coefficient of the fracture network, which is dimensionless; H is the longitudinal oil layer thickness of the evaluated reservoir section, in meters; h is the longitudinal mudstone thickness of the evaluated reservoir section, in meters.

[0124] S3: Taking into account the static and dynamic sweet spot evaluation results of the reservoir section, calculate the sweet spot index. The larger the value, the better the reservoir quality and the greater the potential for high production capacity after volumetric fracturing.

[0125] The expression for calculating the sweet spot index of the reservoir is as follows:

[0126]

[0127] In the formula: R is the sweet spot index for evaluating the reservoir section, which is dimensionless.

[0128] Example 1

[0129] This example provides a comprehensive evaluation method for sweet spots in tight oil reservoirs, as detailed below:

[0130] Taking the YH1 well, a horizontal well in the main development zone of tight oil reservoir, as an example, the horizontal section of the well is 1500m long and the reservoir encounter rate is 80.6%. The reservoir in this area is highly heterogeneous with discontinuous sand bodies. After volumetric fracturing on the same platform, the production of single wells varies greatly. It is urgent to carry out quantitative evaluation of sweet spots to provide a basis for the optimized design of volumetric fracturing.

[0131] Step 1: Divide the influencing factors of the sweet spot into two main categories: reservoir static parameters and reservoir dynamic parameters, and collect and organize relevant factor parameters to establish an evaluation basic database; specifically including the following:

[0132] (1) The volumetric fracturing design of well YH1 consisted of 21 sections, of which downhole coring was carried out on the first 6 sections. These 6 sections were used as evaluation reservoir sections and were numbered YH1-1 to YH1-6. One sample was taken from each evaluation reservoir section, for a total of 6 samples, numbered C1 to C6.

[0133] (2) The six rock samples from the well were processed into standard rock cores with a diameter of 2.5 cm and a length of 5 cm, and then dried in an oven until constant weight.

[0134] (3) The porosity and permeability of the dried rock sample described in step (2) were tested using an automatic helium porosity analyzer and an ultra-low permeability analyzer, respectively. At the same time, the oil saturation of the rock sample in the evaluation reservoir section was obtained by finely interpreting the logging curves, as shown in Table 1.

[0135] Table 1. Test table of basic physical property parameters of rock samples in the reservoir evaluation section.

[0136]

[0137]

[0138] (4) Use an X-ray diffractometer to test the mineral composition of the rock sample from step (2), see Table 2.

[0139] Table 2. Results of mineral composition analysis of rock samples

[0140]

[0141] (5) The vertical oil layer thickness and mudstone thickness distribution were obtained by using the logging curves of the horizontal wells in the same area and layer of the main development layer of the tight oil reservoir, as shown in Table 3.

[0142] Table 3. Statistical results of the vertical basic parameters and daily oil production share of the reservoir section under evaluation.

[0143]

[0144]

[0145] Step 2: Based on the basic data of static parameters affecting the sweet spot, obtain the optimal sweet spot segment of the target reservoir, establish a static sweet spot evaluation matrix, calculate the sweet spot fit degree, and quantitatively evaluate the static sweet spot. On this basis, according to the rock and mineral composition of the evaluated reservoir segment, calculate the dispersion between different minerals, and combine the heterogeneity of vertical lithological distribution to define the fracture network complexity potential coefficient, and quantitatively evaluate the dynamic sweet spot.

[0146] (1) Based on the basic data of static parameters affecting the sweet spot, the optimal sweet spot segment of the target reservoir is identified. Combined with the basic data of the evaluated reservoir segments, a static sweet spot evaluation model is established, the sweet spot fit is calculated, and the static sweet spot is quantitatively evaluated. Specifically, this includes the following:

[0147] ① In order to establish the optimal sweet spot segment and obtain the main development layer of tight oil reservoir, after the core horizontal well volumetric fracturing in step 1, the production contribution ratio of each evaluated reservoir segment YH1-1~YH1-6, i.e. the daily oil production ratio, is shown in Table 3.

[0148] ② Perform a correlation analysis between the static parameters of the sweet spot section of the evaluation reservoir obtained in step 1 and the production contribution ratio of each evaluation unit. The analysis results will determine the static parameter values ​​corresponding to the optimal sweet spot section of the reservoir. (See appendix) Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown in the figure. Oil layer thickness, oil saturation, porosity, and permeability are positively correlated with productivity contribution, with the maximum values ​​of the static parameters for each rock sample taken (Tables 1 and 3). Clay content is negatively correlated with productivity contribution (see Appendix). Figure 5 The minimum value of the static parameters of each rock sample was taken (Table 2).

[0149] ③ Based on the value analysis results of step ②, the optimal static parameter values ​​for the sweet spot segment are obtained. The static parameters include oil layer thickness, oil saturation, porosity, permeability, and clay content, as shown in Table 4.

[0150] Table 4. Determination of Static Parameters of the Optimal Sweet Spot Reservoir

[0151]

[0152] ④ Based on the evaluation reservoir section and sweet spot parameter set, establish the static parameter matrix X of the sweet spot of the tight oil reservoir, as shown in expression (11).

[0153]

[0154] ⑤ Based on the static parameters of the optimal dessert segment, establish a static dessert reference column, as shown in expression (12):

[0155] X0=(12.4,68.7,12.3,0.41,7.8) (12)

[0156] ⑥ By using the static sweet spot evaluation matrix X of the tight oil reservoir and the static sweet spot reference column X0, the static sweet spot evaluation matrix Y of the tight oil reservoir is established, as shown in expression (13):

[0157]

[0158] ⑦ Use formula (4) to standardize each element in the static dessert review matrix Y.

[0159] ⑧ Calculate the maximum distance between the static parameters of each evaluated reservoir segment and the optimal sweet spot segment using formula (6), and further calculate the sweet spot fit of each evaluated reservoir, see Appendix. Figure 6 .

[0160] (2) Based on the rock and mineral composition of the evaluated reservoir section, calculate the dispersion between different minerals, use the logging curve interpretation results to obtain the vertical oil layer and mudstone thickness distribution of the evaluated reservoir section, define the fracture network complexity potential coefficient, and quantitatively evaluate the dynamic sweet spot.

[0161] ①Based on the results of the mineral composition test of the rock samples in the evaluation reservoir section, the dispersion between different minerals was calculated using formula (8), see Appendix. Figure 7 ;

[0162] ②Use the logging curve interpretation results to obtain the vertical oil layer and mudstone distribution of the evaluation reservoir section, and combine the dispersion between different minerals in the rock sample to calculate the fracture network complexity potential coefficient of different evaluation reservoir sections using formula (9), see Appendix Figure 8 .

[0163] 3. Taking into account the static and dynamic sweet spot evaluation results of the reservoir section, calculate the sweet spot index. The larger the value, the better the reservoir quality and the greater the potential for high production capacity after volumetric fracturing. Use formula (10) to calculate the sweet spot index corresponding to different evaluation reservoir sections, see Appendix. Figure 9 The calculation results show that the quality of the reservoir sweet spots is ranked as follows: YH1-4>YH1-3>YH1-2>YH1-5>YH1-1>YH1-6.

[0164] The present invention has been specifically described above through embodiments. It should be noted that these embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way, nor are they limited to the forms disclosed herein, and should not be construed as excluding other embodiments. Modifications and simple variations made by those skilled in the art that do not depart from the technical concept and scope of the present invention are all within the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for sweet spots in tight oil reservoirs, characterized in that: Includes the following steps: S1: Divide the factors affecting the sweet spot into two categories: reservoir static parameters and reservoir dynamic parameters, and collect and organize the relevant factors and parameters of reservoir static parameters and reservoir dynamic parameters to establish an evaluation basic database; S2: Based on the basic data of static parameters affecting the sweet spot, the optimal sweet spot segment of the target reservoir is obtained, and a static sweet spot evaluation matrix is ​​established. The sweet spot fit is calculated, and the static sweet spot is quantitatively evaluated. Based on the evaluation of the rock and mineral composition of the reservoir section, the dispersion between different minerals is calculated. Combined with the heterogeneity of vertical lithological distribution, the fracture network complexity potential coefficient is defined to quantitatively evaluate the dynamic sweet spot. S3: Taking into account both the static and dynamic sweet spot evaluation results of the reservoir section, calculate the sweet spot index of the reservoir section, and evaluate the reservoir based on the obtained sweet spot index value.

2. The comprehensive evaluation method for sweet spots in tight oil reservoirs according to claim 1, characterized in that: The static reservoir parameters mentioned in step S1 are divided into the thickness of the oil layer in the evaluation reservoir section, oil saturation, porosity, permeability, and clay content; the dynamic reservoir parameters are the parameters of fracture network formation ability, rock and mineral composition, and vertical lithological distribution.

3. A comprehensive evaluation method for sweet spots in tight oil reservoirs according to claim 1 or 2, characterized in that: The method for collecting and organizing reservoir static parameters in step S1 includes: S101: Divide the reservoir into several evaluation reservoir sections by horizontal wells in the main development layer of the tight oil reservoir along the horizontal direction, and carry out continuous downhole coring in the evaluation reservoir sections. S102: Process the downhole rock samples into standard cores with a diameter of 2.5cm and a length of 5cm, and place them in an oven to dry to constant weight; S103: Use an automatic helium porosity analyzer and an ultra-low permeability analyzer to test the porosity and permeability of the dried rock sample described in step S102, and at the same time obtain the oil saturation of the rock sample in the evaluation reservoir section. S104: Use an X-ray diffractometer to test the mineral composition of the rock sample from step S102; S105: The vertical oil layer thickness and mudstone thickness distribution are obtained by using the logging curves of horizontal wells in the same area and layer of the main development layer of tight oil reservoirs.

4. The comprehensive evaluation method for sweet spots in tight oil reservoirs according to claim 1, characterized in that: The method for calculating the dessert fit in step S2 includes the following steps: S201. In order to establish the optimal sweet spot section, obtain the production contribution ratio of each evaluated reservoir section after volumetric fracturing of the cored horizontal well in the main development layer of the tight oil reservoir, i.e., the daily liquid production ratio. S202. Perform a correlation analysis between the obtained static parameters of the sweet spot in the evaluated reservoir section and the production capacity contribution ratio of each evaluated unit. The analysis results determine the static parameter values ​​of the reservoir corresponding to the optimal sweet spot section. S203. Based on the evaluation reservoir section and sweet spot parameter set, establish the static parameter matrix X of the sweet spot of the tight oil reservoir; S204. Based on the static parameters of the optimal dessert segment, establish a static dessert reference column; S205. Establish the static sweet spot evaluation matrix Y of tight oil reservoirs using the static sweet spot evaluation matrix X and the static sweet spot reference column X0. S206. Standardize each element in the static dessert review matrix Y; S207. Calculate the maximum distance between the static parameters of each evaluated reservoir segment and the optimal sweet spot segment using the formula, and further calculate the sweet spot fit of each evaluated reservoir.

5. The comprehensive evaluation method for sweet spots in tight oil reservoirs according to claim 4, characterized in that: The method for determining the reservoir static parameter values ​​corresponding to the optimal segment of the ideal sweet spot in step S202 is as follows: If the static parameters of each rock sample are positively correlated with the production capacity contribution, the maximum value among the static parameters of each rock sample shall be taken. If the static parameters of each rock sample are negatively correlated with the productivity contribution, the minimum value among the static parameters of each rock sample shall be taken.

6. The comprehensive evaluation method for sweet spots in tight oil reservoirs according to claim 4, characterized in that: In step S203, the static parameter matrix X of the sweet spot of the tight oil reservoir is established, and its expression (1) is: In the formula: X is the static parameter matrix of the dessert; X i (j) represents the evaluation matrix element; m represents the number of evaluation reservoir segments; i represents the corresponding number of the evaluation reservoir segment; n represents the number of static parameters of the sweet spot affected by the evaluation reservoir segment; j represents the number of static parameters of the sweet spot affected by the evaluation reservoir segment. And / or, Step S204, based on the static parameters of the optimal dessert segment, establishes the expression for the static dessert reference column as follows: X0=(X0(1),X0(2),...,X0(n)) (2) In the formula: X0 is the static dessert reference column; X0(n) is the element of the static dessert reference column; And / or, Step S205: Using the tight oil reservoir static sweet spot evaluation matrix X and the static sweet spot reference column X0, establish the tight oil reservoir static sweet spot evaluation matrix Y, whose expression (3) is: In the formula: Y is the static sweet spot evaluation matrix of tight oil reservoir. And / or, Step S206 uses formula (4) to standardize each element in the static dessert review matrix Y, and its expression (5) is: in: Y is a standardized element of the static sweet spot evaluation matrix for tight oil reservoirs. * A standardized evaluation matrix for static sweet spots in tight oil reservoirs. And / or, Step S207 uses formula (6) to calculate the maximum distance between the static parameters of each evaluated reservoir segment and the optimal sweet spot segment, and further calculates the sweet spot fit of each evaluated reservoir, as shown in expression (7): In the formula: E represents the sweet spot fit of the reservoir segment.

7. The comprehensive evaluation method for sweet spots in tight oil reservoirs according to claim 1, characterized in that: The method for quantitatively evaluating dynamic desserts in step S2 includes: Calculate the dispersion of different minerals based on the evaluation of the mineral composition of the reservoir section; The vertical oil layer and mudstone thickness distribution of the evaluation reservoir section is obtained by interpreting the logging curves, the fracture network complexity potential coefficient is defined, and the dynamic sweet spot is quantitatively evaluated.

8. The comprehensive evaluation method for sweet spots in tight oil reservoirs according to claim 7, characterized in that: The method for calculating the dispersion between different minerals is as follows: Based on the results of the mineral composition test of the rock samples in the evaluation reservoir section, the dispersion between different minerals is calculated using formula (8), as shown in the following expression: In the formula: U is the dispersion degree between different minerals, %; M k represents the percentage of mineral composition in the rock sample, %; k is the mineral type number, and l is the number of mineral types.

9. The comprehensive evaluation method for sweet spots in tight oil reservoirs according to claim 7, characterized in that: The method for defining the complexity potential coefficient of the sewing mesh is as follows: The vertical oil layer and mudstone thickness distribution of the evaluation reservoir section is obtained by interpreting well logging curves. Combined with the dispersion of different minerals in the rock sample, a fracture network complexity potential coefficient is defined. The larger the value, the more complex the fracture network after volumetric fracturing, and the better the dynamic sweet spot of the reservoir. Its expression is as follows: In the formula: F is the complex potential coefficient of the fracture network, which is dimensionless; H is the longitudinal oil layer thickness of the evaluated reservoir section, in meters; h is the longitudinal mudstone thickness of the evaluated reservoir section, in meters.

10. The comprehensive evaluation method for sweet spots in tight oil reservoirs according to claim 1, characterized in that: The expression for calculating the sweet spot index of the reservoir segment in step S3 is as follows: In the formula: R is the sweet spot index for evaluating the reservoir section, which is dimensionless.

11. The comprehensive evaluation method for sweet spots in tight oil reservoirs according to claim 1, characterized in that: The method for evaluating the reservoir in step S3 is as follows: the larger the sweet spot index value, the better the reservoir quality and the greater the potential for high production capacity after volumetric fracturing.

Images