A quantitative calculation method for lateral heterogeneity of shale reservoirs based on seismic data

Abstract

The present invention belongs to the field of geophysical exploration technology, and specifically discloses a quantitative calculation method for the lateral heterogeneity of shale reservoirs based on seismic data. The method of the present invention comprises: S1, selecting key geological parameters that affect the heterogeneity of shale reservoirs through intersection analysis; S2, using seismic data to predict the heterogeneity of key geological parameters that characterize the heterogeneity of shale reservoirs, and realizing the quantification of key geological parameters; S3, calculating the variance of any key geological parameter at each research point; performing data standardization on the key geological parameter to obtain the heterogeneity prediction result of the key geological parameter at each research point; S4, repeating step S3 to obtain the heterogeneity prediction result of each key geological parameter at each research point; S5, calculating the lateral heterogeneity of each research point. The present invention can realize multi-parameter comprehensive evaluation and provide effective support for well site deployment and fracturing operations based on heterogeneity evaluation results.

Description

Technical Field

[0001] The present invention belongs to the technical field of geophysical exploration, and in particular relates to a method for quantitatively calculating lateral heterogeneity of shale reservoirs based on seismic data. Background Art

[0002] Shale reservoir heterogeneity is a very important topic in geophysical research and a key factor affecting the effectiveness of shale gas development. Researchers have conducted extensive research on shale reservoir heterogeneity and developed a series of evaluation methods, which can be summarized into two categories:

[0003] The first method is to use drilling data to evaluate reservoir heterogeneity. Well logging data has the advantages of high vertical resolution and a more realistic reflection of reservoir changes. Key geological parameters for shale reservoir evaluation are calculated using well logging data, and reservoir heterogeneity is evaluated by the vertical changes in geological parameters. This method has two main problems that cannot be solved: (1) It can only evaluate the vertical changes in reservoir heterogeneity, but not the horizontal changes; (2) It can only evaluate the changes in heterogeneity at the well point location, but cannot predict the changes in heterogeneity on the horizontal plane.

[0004] The second method is to use seismic data to evaluate reservoir heterogeneity. Seismic data has the advantages of covering a large area and providing extensive lateral sampling. Using seismic data, we can predict lateral variations in geological parameters such as total organic carbon content, total gas content, effective porosity, mineral composition, brittleness index, fracture density, and elastic modulus through prestack inversion, attribute analysis, and anisotropy analysis. This prediction method is currently relatively mature. However, this method has the following problems: (1) After using seismic data to predict the planar changes of reservoir parameters, there is no targeted heterogeneity evaluation method. It can only observe the overall horizontal height changes of geological parameter values, but cannot judge the change law of heterogeneity representing the speed of change between parameter values ​​at different positions; (2) Some geological parameters of shale reservoirs are interrelated. For example, total organic carbon content and total gas content are usually positively correlated. When using seismic prediction results to evaluate heterogeneity, such geological parameters should not appear in the evaluation results at the same time. There is currently no effective method to deal with this problem; (3) During the fracturing process, the impact of the heterogeneity of a single geological parameter on fracturing construction cannot be considered alone. A comprehensive analysis of multiple factors is required. Currently, there is only a qualitative analysis method for a single parameter. It is necessary to form a comprehensive multi-parameter quantitative comprehensive analysis method to effectively support fracturing projects. Summary of the Invention

[0005] In order to address the above-mentioned deficiencies in the known technology, the present invention aims to provide a quantitative calculation method for the lateral heterogeneity of shale reservoirs based on seismic data, so as to achieve the purpose of converting the geological parameter results of seismic prediction into heterogeneity prediction results and realizing quantitative prediction of multi-parameter comprehensive evaluation.

[0006] To achieve the above object, the technical solutions adopted by the present invention are as follows:

[0007] A method for quantitatively calculating lateral heterogeneity of a shale reservoir based on seismic data comprises the following steps performed in sequence:

[0008] S1. Using the crossplot method, select key geological parameters that affect shale reservoir heterogeneity;

[0009] S2. Use seismic data to predict the heterogeneity of key geological parameters that characterize shale reservoir heterogeneity and quantify the key geological parameters;

[0010] Using the stacked data at different incident angles, the parameters reflecting reservoir geological indicators and rock mechanical parameters are predicted through pre-stack inversion.

[0011] Using prestack time migration seismic data, the density of natural fractures is predicted through attribute analysis and anisotropy analysis.

[0012] S3. Calculate the variance of any key geological parameter at each study point based on the quantified key geological parameters obtained in step S2; perform data standardization on the key geological parameter to obtain a heterogeneity prediction result of the key geological parameter at each study point;

[0013] S4. Repeat step S3 to obtain the heterogeneity prediction results of each key geological parameter at each research point;

[0014] S5. Calculate the lateral heterogeneity at each research point separately. The calculation formula is:

[0015]

[0016] Among them, a1 i,j 、a2 i,j 、a3 i,j 、an i,j are the heterogeneity prediction results of different key geological parameters at position (i, j), where i and j are the x and y coordinates on the plane, a, b, c, and q are the weight values ​​of the heterogeneity of different key geological parameters in the horizontal heterogeneity prediction results, and n is the number of key geological parameters.

[0017] As a limitation, step S1 includes the following steps performed in sequence:

[0018] a1) Collect the parameters of total organic carbon content, total gas content, effective porosity, mineral composition, brittleness index, fracture density, and elastic modulus of the well;

[0019] a2) Through intersection analysis, the correlation between various geological parameters is determined, and key geological parameters that can characterize the heterogeneity of shale reservoirs are selected.

[0020] As a second limitation, the principle for selecting key geological parameters that can characterize the heterogeneity of shale reservoirs in step a2) is that when the correlation between two key geological parameters reaches 80%, either one of them is removed and the other parameter is retained as the key geological parameter to participate in the lateral heterogeneity calculation; if it does not reach 80%, both parameters are involved in the lateral heterogeneity calculation.

[0021] As a third limitation, the parameters of the reservoir geological indicators in step S2 include total organic carbon content, total gas content, effective porosity and reservoir thickness;

[0022] The rock mechanics parameters include rock modulus, Poisson's ratio, brittleness index, horizontal maximum principal stress difference and horizontal minimum principal stress difference.

[0023] As a fourth limitation, the variance of any key geological parameter at all study points in step S3 is determined in the following steps:

[0024] b1) Select the study point (i, j), then select eight points adjacent to the study point on the plane, and calculate the average value of the key geological parameters at these nine points. The formula is as follows:

[0025]

[0026] Among them, v i,j Represents the parameter value of the key geological parameter at the (i, j) coordinate position, represents the average value of the key geological parameters at position (i, j);

[0027] b2) calculating the difference between the parameter value of the key geological parameter at each of the nine points and the average value, and squaring the difference;

[0028] b3) Calculate the variance of key geological parameters at the selected points using the following formula:

[0029]

[0030] Where s represents the square of the difference between the parameter value of the key geological parameter and the average value, va i,j represents the variance of key geological parameters at position (i, j);

[0031] b4) Repeat steps b1 to b3 to calculate the variance of the key geological parameters at all study points.

[0032] As a further limitation, the data normalization method is: based on the maximum and minimum variance values ​​of the calculated key geological parameters in all study points, the heterogeneity of the key geological parameters at any study point is calculated according to the following formula, forming the heterogeneity prediction results of the key geological parameters with a value range between 0 and 1. The calculation formula is as follows:

[0033]

[0034] Among them, a i,j is the heterogeneity of key geological parameters at position (i, j), va max is the maximum variance of the key geological parameters in all study points, va min is the minimum variance value of the key geological parameters among all study points.

[0035] As a fifth limitation, multiple research points are selected in step S3, and they can be selected while performing step S1 or S2, or after completing step S2 and before performing step S3.

[0036] As a sixth limitation, multiple key geological parameters are selected in step S1.

[0037] Due to the adoption of the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0038] (1) The method of the present invention realizes a quantitative evaluation method for the heterogeneity of multiple geological parameters on a plane, reducing the limitations and one-sidedness of the two existing methods for predicting heterogeneity; it can convert the geological parameter results of earthquake prediction into heterogeneity prediction results, and realize quantitative prediction of multi-parameter comprehensive evaluation;

[0039] (2) The method of the present invention selects interrelated geological parameters, which solves the problem that interrelated geological parameters appear simultaneously in the evaluation results;

[0040] (3) The present invention fully utilizes the advantage of high lateral resolution of seismic data to provide effective support for well location deployment and fracturing operations based on heterogeneity evaluation results;

[0041] (4) The method of the present invention predicts heterogeneity, which is of great significance for reducing engineering accidents, increasing single well production, and achieving efficient development.

[0042] In summary, the present invention can convert earthquake prediction results into heterogeneity prediction results, and realize quantitative prediction of multi-parameter comprehensive evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0044] Figure 1 A cross-plot of geological parameters related to reservoir mechanical properties according to an embodiment of the present invention;

[0045] Figure 2 This is a cross-plot of geological parameters related to reservoir indicators in an embodiment of the present invention;

[0046] Figure 3 This is a total organic carbon content prediction plane diagram of an embodiment of the present invention;

[0047] Figure 4 This is a brittleness index prediction plane diagram according to an embodiment of the present invention;

[0048] Figure 5 This is a natural fracture prediction plan diagram according to an embodiment of the present invention;

[0049] Figure 6 This is a plane diagram for predicting the heterogeneity of total organic carbon content according to an embodiment of the present invention;

[0050] Figure 7 This is a brittleness index heterogeneity prediction plane diagram of an embodiment of the present invention;

[0051] Figure 8 This is a natural fracture heterogeneity prediction plane diagram according to an embodiment of the present invention;

[0052] Figure 9 This is a multi-parameter heterogeneity comprehensive prediction plane diagram according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to better explain the present invention and facilitate understanding, preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings through specific implementation methods.

[0054] Example Quantitative calculation method of lateral heterogeneity of shale reservoir based on seismic data

[0055] This embodiment provides a quantitative calculation method for lateral heterogeneity of shale reservoirs based on seismic data. This technical method has been applied to the prediction of shale gas horizontal well faults in the Yunnan-Guizhou region in the southern margin of the Sichuan Basin.

[0056] The study area of ​​this embodiment is located in the Sun Anticline Belt in the Yunnan-Guizhou region. The reservoir index parameters (total organic carbon content, total gas content, effective porosity, and reservoir thickness) in the area have relatively stable lateral variations. However, due to the influence of the first and second phases of tectonic movements in the Yanshan Mountains, the degree of development of natural fractures in the area has changed significantly, resulting in strong reservoir heterogeneity. During the horizontal well fracturing process, engineering problems such as casing deformation, pressure channeling, and the inability to form a network of fractures are prone to occur.

[0057] The quantitative calculation method of lateral heterogeneity of shale reservoirs based on seismic data includes the following steps:

[0058] S1. Using the crossplot method, select key geological parameters that affect shale reservoir heterogeneity;

[0059] S2. Use seismic data to predict the heterogeneity of key geological parameters that characterize shale reservoir heterogeneity and quantify the key geological parameters;

[0060] Using the stacked data at different incident angles, the parameters reflecting reservoir geological indicators and rock mechanical parameters are predicted through pre-stack inversion.

[0061] Using prestack time migration seismic data, the density of natural fractures is predicted through attribute analysis and anisotropy analysis.

[0062] S3. Calculate the variance of any key geological parameter at each study point based on the quantified key geological parameters obtained in step S2; perform data standardization on the key geological parameter to obtain a heterogeneity prediction result of the key geological parameter at each study point;

[0063] S4. Repeat step S3 to obtain the heterogeneity prediction results of each key geological parameter at each research point;

[0064] S5. Calculate the lateral heterogeneity at each research point separately. The calculation formula is:

[0065]

[0066] Among them, a1 i,j 、a2 i,j 、a3 i,j 、an i,j are the heterogeneity prediction results of different key geological parameters at position (i, j), where i and j are the x and y coordinates on the plane, a, b, c, and q are the weight values ​​of the heterogeneity of different key geological parameters in the horizontal heterogeneity prediction results, and n is the number of key geological parameters.

[0067] Wherein, step S1 includes the following steps performed in sequence:

[0068] a1) Collect the parameters of total organic carbon content, total gas content, effective porosity, mineral composition, brittleness index, fracture density, and elastic modulus of the well;

[0069] a2) Through intersection analysis, the correlation between various geological parameters is determined, and key geological parameters that can characterize the heterogeneity of shale reservoirs are selected.

[0070] During selection, when the correlation between two key geological parameters reaches 80%, any one of them is removed and the other parameter is retained as the key geological parameter to participate in the lateral heterogeneity calculation. When the correlation between the two key geological parameters does not reach 80%, both parameters need to participate in the lateral heterogeneity calculation.

[0071] The parameters of the reservoir geological indicators in step S2 include total organic carbon content, total gas content, effective porosity and reservoir thickness; the rock mechanical parameters include rock modulus, Poisson's ratio, brittleness index, horizontal maximum principal stress difference and horizontal minimum principal stress difference.

[0072] The incident angle stacking data in step S2 is obtained by stacking the pre-stack gather data at different incident angles. The pre-stack time migration seismic data is a data that must be provided for seismic data processing.

[0073] like Figure 1 and Figure 2 As shown, Figure 1 The medium brittleness index has a significant correlation with Young's modulus and Poisson's ratio, with the correlation coefficient reaching more than 80%; Figure 2 The three parameters of total organic carbon content, total gas content, and effective porosity have obvious correlation, and the correlation coefficient reaches more than 80%. At the same time, considering the rapid lateral change characteristics of natural fracture development, total organic carbon content, brittleness index, and natural fractures are selected as key geological parameters in this example to carry out heterogeneity prediction work.

[0074] Total organic carbon content and brittleness index are elastic parameters calculated using the prestack inversion method. They are predicted based on the quantitative relationship between the elastic parameters determined by logging data and the total organic carbon content and brittleness index. Natural fractures are predicted using ant body properties through seismic data.

[0075] get Figure 3 、 Figure 4 、 Figure 5 The plan view of total organic carbon content, brittleness index, and natural fracture seismic prediction results for the high-quality shale section of the Longmaxi Formation in the Taiyang anticline is shown. Figures 3 to 5 The black line in the middle represents the trajectory of the actual drilled horizontal well. In the background color, the darker the color, the higher the total organic carbon content and brittleness index, and the more developed the natural fractures; conversely, the brighter the color, the lower the total organic carbon content and brittleness index, and the less developed the natural fractures.

[0076] The study points can be selected while performing step S1 or S2, or after completing step S2 and before performing step S3. In this embodiment, the study points are selected before the start of step S3. The variance of any key geological parameter at all study points in step S3 is calculated in the following steps:

[0077] b1) Select the study point (i, j), then select eight points adjacent to the study point on the plane, and calculate the average value of the key geological parameters at these nine points. The formula is as follows:

[0078]

[0079] Among them, v i,j Represents the parameter value of the key geological parameter at the (i, j) coordinate position, represents the average value of the key geological parameters at position (i, j);

[0080] b2) calculating the difference between the parameter value of the key geological parameter at each of the nine points and the average value, and squaring the difference;

[0081] b3) Calculate the variance of key geological parameters at the selected points using the following formula:

[0082]

[0083] Where s represents the square of the difference between the parameter value of the key geological parameter and the average value, va i,j represents the variance of key geological parameters at position (i, j);

[0084] b4) Repeat steps b1 to b3 to calculate the variance of the key geological parameters at all study points.

[0085] The data of the total organic carbon content parameter is normalized. The data normalization method is as follows: based on the maximum variance value and the minimum variance value of the calculated key geological parameters in all research points, the heterogeneity of the key geological parameters at any research point is calculated according to the following formula, and the heterogeneity prediction results of the key geological parameters are formed with the value range between 0 and 1. The calculation formula is as follows:

[0086]

[0087] Among them, a i,j is the heterogeneity of total organic carbon content at position (i, j), va max is the maximum variance of total organic carbon content in all study points, va min is the minimum variance value of total organic carbon content in all study points.

[0088] According to the above steps, the variance of brittleness index and natural fractures at all study points were calculated in turn, and the data of brittleness index and natural fractures were standardized respectively. Figure 6 、 Figure 7 、 Figure 8 Total organic carbon content, brittleness index, and natural fracture heterogeneity plan views are shown. Figures 6 to 8The black lines in the middle are the trajectories of the actual drilled horizontal wells. In the background color, the darker the color, the stronger the heterogeneity caused by the total organic carbon content, brittleness index, and natural fracture plane changes; conversely, the brighter the color, the weaker the heterogeneity caused by the total organic carbon content, brittleness index, and natural fracture plane changes.

[0089] It should be noted that when calculating the variance of the brittleness index at all research points, v i,j Represents the brittle index parameter value at the (i, j) coordinate position, represents the average value of the parameter value of the brittleness index at the position (i, j); when the brittleness index is normalized, a i,j is the heterogeneity of the brittleness index at position (i, j), va max is the maximum variance of the brittleness index in all study points, va min is the minimum variance value of the brittleness index at all study points; when calculating the variance of natural cracks at all study points, v i,j Represents the natural fracture parameter value at the (i, j) coordinate position, represents the average value of the parameter value of the natural fracture at the position (i, j); when the data of the natural fracture is normalized, a i,j is the heterogeneity of the natural fracture at position (i, j), va max is the maximum variance value of natural fractures in all study points, va min is the minimum variance value of natural fractures among all study points.

[0090] When calculating the lateral heterogeneity at each study point, the calculation formula is:

[0091]

[0092] In this embodiment, a1 i,j Represents the prediction result of total organic carbon content heterogeneity, a2 i,j Represents the brittleness index heterogeneity prediction result, a3 i,j represents the prediction result of natural fracture heterogeneity; a, b, and c are the weight values ​​of total organic carbon content, brittleness index, and natural fracture heterogeneity in the horizontal heterogeneity prediction result, respectively; n is the number of key geological parameters, and the value of n in this embodiment is 3.

[0093] The reservoir indicators in the Sun Anticline are relatively stable. The total organic carbon content has little effect on heterogeneity, and the weight is assigned to 0.5. The brittleness index changes relatively quickly laterally, and the weight is assigned to 1.0. The natural fracture weight is assigned to 1.5. Figure 6-Figure 8 The content in is calculated and obtained Figure 9The figure shows a comprehensive prediction plan of multi-parameter heterogeneity in the Sun Anticline region. The black lines are the trajectories of the actual drilled horizontal wells. In the background color, the darker the color, the stronger the multi-parameter heterogeneity; the brighter the color, the weaker the multi-parameter heterogeneity.

Claims

1. A quantitative calculation method for lateral heterogeneity of shale reservoirs based on seismic data, characterized in that: The method comprises the following steps performed in sequence: S1. Using the crossplot method, select key geological parameters that affect shale reservoir heterogeneity; S2. Use seismic data to predict the heterogeneity of key geological parameters that characterize shale reservoir heterogeneity and quantify the key geological parameters; Using the stacked data at different incident angles, the parameters reflecting reservoir geological indicators and rock mechanical parameters are predicted through pre-stack inversion. Using prestack time migration seismic data, the density of natural fractures is predicted through attribute analysis and anisotropy analysis. S3. Calculate the variance of any key geological parameter at each study point based on the quantified key geological parameters obtained in step S2; perform data standardization on the key geological parameter to obtain a heterogeneity prediction result of the key geological parameter at each study point; S4. Repeat step S3 to obtain the heterogeneity prediction results of each key geological parameter at each research point; S5. Calculate the lateral heterogeneity at each research point separately. The calculation formula is: Among them, a1 i,j 、a2 i,j 、a3 i,j 、an i,j are the heterogeneity prediction results of different key geological parameters at position (i, j), where i and j are the x and y coordinates on the plane, a, b, c, and q are the weight values ​​of the heterogeneity of different key geological parameters in the horizontal heterogeneity prediction results, and n is the number of key geological parameters.

2. The method for quantitatively calculating lateral heterogeneity of shale reservoirs based on seismic data according to claim 1, characterized in that: The step S1 includes the following steps performed in sequence: a1) Collect the parameters of total organic carbon content, total gas content, effective porosity, mineral composition, brittleness index, fracture density, and elastic modulus of the well; a2) Through intersection analysis, the correlation between various geological parameters is determined, and key geological parameters that can characterize the heterogeneity of shale reservoirs are selected.

3. The method for quantitatively calculating lateral heterogeneity of shale reservoirs based on seismic data according to claim 2, characterized in that: The principle for selecting key geological parameters that can characterize the heterogeneity of shale reservoirs in step a2) is that when the correlation between two geological parameters reaches 80%, either one of them is removed and the other parameter is retained as the key geological parameter to participate in the lateral heterogeneity calculation; if it does not reach 80%, both parameters are used in the lateral heterogeneity calculation.

4. The method for quantitatively calculating lateral heterogeneity of shale reservoirs based on seismic data according to claim 1, characterized in that: The parameters of the reaction reservoir geological indicators in step S2 include total organic carbon content, total gas content, effective porosity and reservoir thickness; The rock mechanics parameters include rock modulus, Poisson's ratio, brittleness index, horizontal maximum principal stress difference and horizontal minimum principal stress difference.

5. The method for quantitatively calculating lateral heterogeneity of shale reservoirs based on seismic data according to claim 1, characterized in that: The variance of any key geological parameter at all study points in step S3 is calculated in the following steps: b1) Select the study point (i, j), then select eight points adjacent to the study point on the plane, and calculate the average value of the key geological parameters at these nine points. The formula is as follows: Among them, v i,j Represents the parameter value of the key geological parameter at the (i, j) coordinate position, represents the average value of the key geological parameters at position (i, j); b2) calculating the difference between the parameter value of the key geological parameter at each of the nine points and the average value, and squaring the difference; b3) Calculate the variance of key geological parameters at the selected points using the following formula: Where s represents the square of the difference between the parameter value of the key geological parameter and the average value, va i,j represents the variance of key geological parameters at position (i, j); b4) Repeat steps b1 to b3 to calculate the variance of the key geological parameters at all study points.

6. The method for quantitatively calculating lateral heterogeneity of shale reservoirs based on seismic data according to claim 5, characterized in that: The data standardization method is: based on the maximum variance and minimum variance of the calculated key geological parameters in all study points, the heterogeneity of the key geological parameters at any study point is calculated according to the following formula, forming the heterogeneity prediction results of the key geological parameters with a value range between 0 and 1. The calculation formula is as follows: Among them, a i,j is the heterogeneity of key geological parameters at position (i, j), va max is the maximum variance of the key geological parameters in all study points, va min is the minimum variance value of the key geological parameters among all study points.

7. The method for quantitatively calculating lateral heterogeneity of shale reservoirs based on seismic data according to claim 1, characterized in that: In step S3, multiple research points are selected, and they are selected while performing step S1 or S2, or after completing step S2 and before performing step S3.

8. The method for quantitatively calculating lateral heterogeneity of shale reservoirs based on seismic data according to claim 1, characterized in that: In step S1, multiple key geological parameters are selected.

Images