A three-dimensional pore pressure prediction method, device, storage medium and program based on rock physics analysis

Abstract

The application discloses a three-dimensional pore pressure prediction method and device based on rock physical analysis, a storage medium and a program, belongs to the technical field of oil and gas seismic exploration and drilling, and comprehensively uses logging data and three-dimensional seismic data to determine the content of each mineral component in different lithological strata by adopting a rock physical analysis method, so that a more accurate rock physical elastic modulus range is obtained, a precise data basis is provided for stratum pore pressure prediction, a high-precision pore pressure prediction data body is obtained, a reasonable drilling fluid density is designed, accidents such as blowout and lost circulation are reduced, and important data support is provided for safe and rapid drilling and completion.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas seismic exploration and drilling technology, specifically relating to a three-dimensional pore pressure prediction method, equipment, storage medium and program based on rock physical analysis. Background Technology

[0002] Formation pore pressure and its formation mechanism. Formation pore pressure is the pressure exerted by fluids (oil, gas, water) in the pores and fractures of a formation. In normal geological environments, when the formation is normally compacted, the formation pore pressure equals the hydrostatic pressure at that location; this is called normal formation pore pressure. The magnitude of normal formation pore pressure is related to the properties of the underground fluids. In special geological environments, when the formation pore pressure is higher or lower than the hydrostatic pressure, it is called abnormal formation pore pressure. Formation pore pressure higher than the hydrostatic pressure is called abnormally high pressure or overpressure. Formation pore pressure lower than the hydrostatic pressure is called abnormally low pressure or underpressure. Abnormal formation pore pressure is widely distributed, appearing in metamorphic rocks of the Precambrian to sedimentary rocks of the Cenozoic. Oil and gas exploration practice shows that during drilling, normal formation pore pressure, abnormally high pressure, and abnormally low pressure can all be encountered, but abnormally high pressure occurs more frequently and is more significant for the petroleum industry. Abnormal formation pressure phenomena are widespread in my country's oil and gas basins, with abnormally high-pressure formations existing in the Bohai Bay Basin, Tarim Basin, Junggar Basin, and Qaidam Basin. These unpredictable abnormally high-pressure formations pose significant challenges to oil and gas exploration, drilling, and development.

[0003] The causes of abnormal high pressure are diverse, and the phenomenon may be due to a combination of factors, including geological, physical, chemical, and dynamic factors. In 1994, scholar Ward, from the perspective of stress-strain relationship during sedimentary compaction, classified the formation mechanism of abnormal high pressure into three categories: The first category conforms to the original loading curve, i.e., unbalanced compaction. Unbalanced compaction occurs when, as the deposition and burial depth of overlying sediments increase, the pore drainage capacity weakens or stops, and the load of the continuing to increase overlying sediments is partially or entirely borne by pore fluids. The effective load (vertical effective stress) required for further compaction of sediments decreases or remains unchanged, resulting in under-compacted strata and abnormally high-pressure strata. The second category conforms to the unloading curve, mainly including abnormal high pressure formed by pore fluid expansion (hydrothermal pressurization, hydrocarbon generation, montmorillonite dehydration, hydrocarbon cracking, etc.) and abnormal high pressure formed by strata uplift and erosion caused by tectonic movements. The third category is caused by tectonic compressive stress, fluid density differences, etc., without changes in porosity. In actual drilling operations, unbalanced compaction is the most common mechanism for generating abnormal high pressure, and tectonic compression is also an important pressure-increasing mechanism in basins with intense tectonic activity.

[0004] Significance of Formation Pore Pressure Prediction. Formation pore pressure is crucial for drilling engineering. It serves as fundamental data for calculating in-situ stress, formation collapse pressure, and fracture pressure; it is an important basis for determining casing layers and casing depth; and it is key to rationally designing drilling fluid density and implementing near-balanced, underbalanced, and controlled pressure drilling. Therefore, the accurate determination of formation pore pressure has significant economic value for safe, rapid, and low-cost drilling, as well as preventing and avoiding complex drilling accidents. In petroleum geology, formation pore pressure, or the fluid potential converted from pressure, is one of the main controlling factors in the formation and distribution of oil and gas reservoirs, and serves as the basis for the study of oil and gas reservoir hydrodynamics.

[0005] Current Formation Pore Pressure Prediction Methods and Their Limitations. Pore pressure prediction methods are categorized based on data sources, including measured formation pore pressure methods, drilling data prediction methods, well logging data prediction methods, and seismic data prediction methods. According to their sequence with the drilling process, they can be divided into pre-drilling prediction methods, monitoring while drilling methods, and post-drilling detection methods. Pre-drilling prediction methods process and interpret existing seismic data for the region, combining it with geological data and analysis of adjacent well drilling data to determine the distribution of underground formation pressure in the target area and predict the formations, depths, and magnitudes of abnormally high pressure. Prediction accuracy primarily depends on the quality of the seismic data, the rationality of the prediction model, and the understanding of geological stratification and lithology. Since unbalanced compaction is the most common mechanism for the formation of abnormally high pressure, widely present in sandstone and mudstone formations, commonly used methods include the equivalent depth method, the Eaton method, and the Bowers method. The Eaton method, based on a normal compaction trend line, has become one of the most widely used methods for predicting formation pore pressure using well logging data since its introduction in 1972, offering high prediction accuracy. However, this method is based on post-drilling pore pressure prediction using existing wells. For undrilled areas, well logging data is unavailable, making it impossible to obtain normal compaction trend lines and thus preventing the use of the Eaton method for pore pressure prediction. Currently, the commonly used prediction method is based on empirical models, but this method is highly susceptible to formation velocities and requires setting different rock skeleton velocities and fluid velocities, which can easily lead to significant errors in actual calculations. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a three-dimensional pore pressure prediction method, equipment, storage medium, and program based on rock physical analysis. By integrating well logging data and three-dimensional seismic data, and employing rock physical analysis methods, the content of various mineral components in different lithological strata is determined to obtain a more accurate range of rock elastic modulus. This provides a precise data foundation for formation pore pressure prediction, and offers crucial data support for obtaining high-precision pore pressure prediction data, designing reasonable drilling fluid densities, reducing well blowouts and lost circulation, and ensuring safe and rapid drilling and completion.

[0007] The objective of this invention is achieved through the following technical solution:

[0008] A three-dimensional pore pressure prediction method based on rock physics analysis includes the following steps:

[0009] Step 1: Collect and organize the logging data of drilled wells within a three-dimensional work area;

[0010] Step 2: Use an automatic correction method to calculate the content ratio of different mineral components in the target interval of a single well, and obtain the optimal mineral component content ratio;

[0011] Step 3: Calculate the elastic modulus range of the target layer in each well using rock physics analysis.

[0012] Step 4: Calculate the range of seismic wave propagation velocity within the target layer of a single well, and combine the range of seismic wave propagation velocity within the target layer of a single well with the 3D seismic data to obtain the 3D velocity range volume within the work area;

[0013] Step 5: Combine well logging data and 3D seismic data in the work area to carry out well-seismic joint inversion, obtain elastic parameter volume, and convert the elastic parameter volume and 3D velocity range volume to the depth domain through time-depth conversion;

[0014] Step 6: Combine the three-dimensional velocity range volume with the elastic parameter volume obtained by inversion to calculate the formation pore pressure data volume.

[0015] Preferably, in step 1, for a three-dimensional research area, assuming r wells have been drilled in the area, each well has complete logging data, including sonic transit time data, density data, shear wave velocity data, clay content data, and gamma data. Each type of logging data can be represented by a curve, with the horizontal axis representing the data value, including clay content and gamma values, and the vertical axis representing the depth, indicating the data values ​​measured at different depths. The logging data is collected and organized, and obvious distortions and outliers are removed. The logging data types to be processed are selected. Let n types of logging data be selected, including porosity data, gamma data, P-wave data, S-wave data, density data, permeability data, and resistivity data. Each type of logging data has m data points from shallow to deep. Then the logging data are combined into a matrix G.

[0016]

[0017] Where n represents n types of logging data; m represents m data points for each type of logging data; and gji represents the value at the j-th point in the i-th type of logging data.

[0018] Preferably, in step 2, there are three main strata in the study section: stratum 1, stratum 2, and stratum 3; the points of stratum 1 are 1, 2...m1; the points of stratum 2 are m1+1, m1+2...m2; and the points of stratum 3 are m2+1, m2+2...m.

[0019] Suppose there are p-1 types of minerals in formation 1. Finally, pore type is added as the p-th component type. The value of each mineral in different well logging data is:

[0020]

[0021] Where n represents n types of well logging data; p represents p types of mineral composition; x ji This represents the value of the i-th mineral component in the j-th type of well logging data; the type of well logging data n must be greater than or equal to the type of formation mineral p.

[0022] The proportions of several mineral components in stratum 1 are R = [r1, r2, ..., r] in the rock. p ] T The condition is that the sum of all proportions equals 1, i.e., r1 + r2 + ... + r p =1;

[0023] Construct the mineral composition of data points from the 1st to the m1th:

[0024] G i =X·R;

[0025] G i =[g i1 g i2 , ..., g in ];(3)

[0026] The solution yields the mineral composition content R at point i. i =[r 1i ,r 2i ......r pi ] T Where i = 1, 2, ..., m1;

[0027] For each type of logging data value, an error threshold is set. Let the error threshold for n types of logging data be E = [e1, e2, ..., e]. n ] T The well logging data value G(R) is calculated using the solved mineral component content ratio and mineral component value. ij ) and the actual logging data value G ijError calculation is performed; if the error exceeds the set error threshold, it is judged as unqualified, and the value of the mineral component with the largest proportion needs to be readjusted and recalculated; when the calculation error is less than or equal to the set error threshold, the calculated mineral proportion content is the optimal solution; that is, the criteria for qualified mineral component content are:

[0028] |G(R ij )-G ij |≤E j (4);

[0029] When the calculation error of the j-th type of logging data at data point i exceeds the set error threshold, where i = 1, 2, ..., m1; j = 1, 2, ..., n, it is necessary to adjust the mineral component with the largest calculated mineral content, i.e., to find the maximum (R) value. i Let r be the value of r. ki Here, k represents the mineral with the highest percentage content in the j-th type of logging data at calculation point i, where k = 1, 2, ..., p; then the mineral value x is adjusted according to the following formula:

[0030] If G(R) ij )-G ij If x > 0, then x jk =x jk -0.02x jk (5);

[0031] If G(R) ij )-G ij If x < 0, then x jk =x jk +0.02x jk (6);

[0032] After adjustment, the error is recalculated. If the error is less than the set threshold, the calculation stops. If the error is greater than the set threshold, the mineral composition values ​​need to be further adjusted until the threshold condition is met. After each adjustment calculation is completed, the mineral composition value X is updated synchronously.

[0033] Preferably, the above steps are repeated to calculate the mineral content percentage at all points in strata 2 and strata 3, to obtain the optimal mineral content percentage calculation result R for the target stratum:

[0034]

[0035] Where p represents p kinds of mineral components, and m represents m data points for each type of logging data.

[0036] Preferably, in step 3, the elastic modulus range of the target layer in a single well is calculated. By using the content ratio of different mineral components and the known modulus values ​​of each mineral component, the maximum and minimum values ​​of the elastic modulus of the comprehensive lithology of each layer are obtained.

[0037] Preferably, the elastic modulus includes bulk modulus V and shear modulus Q.

[0038] Preferably, for point i to be calculated, the mineral component content ratio R obtained through step 2 is... i =[r 1i r 2i ......r pi ] T , i = 1, 2, ..., m; The bulk modulus and shear modulus of each mineral component were obtained from literature review as V0 = [V1, V2, ..., V...]. p ] T and Q0 = [Q1, Q2, ..., Q p ] T Let the maximum and minimum values ​​of the bulk modulus and shear modulus of each mineral component be:

[0039] V max =MAX(V0) V min =MIN(V0) Q max =MAX(Q0) Q min =MIN(Q0) (8);

[0040] The minimum bulk modulus of the comprehensive lithology at calculation point i is V. N :

[0041]

[0042] The maximum bulk modulus of the comprehensive lithology at calculation point i is V. M At this point, it is necessary to remove the proportion of porosity from the mineral component content percentage, and then re-normalize the proportions of the remaining mineral components; the porosity proportion is r. pi Then the total percentage of the remaining mineral components is 1-r. pi The proportions of the remaining mineral components are renormalized, and the new proportions of mineral components are R′. i =[r′ 1i , r′ 2i ......r p-1i ], i = 1, 2, ..., m;

[0043] in:

[0044]

[0045] The maximum bulk modulus of the comprehensive lithology at point i is V. M :

[0046]

[0047] The minimum shear modulus of the comprehensive lithology at calculation point i is Q. N :

[0048]

[0049] Minimum shear modulus Q M The calculation also requires the use of the new mineral component content ratio R′ i :

[0050]

[0051] Where T max and T min They are respectively:

[0052]

[0053]

[0054] Preferably, in step 4, the range of seismic wave propagation velocity within the target layer of a single well is calculated; the maximum and minimum values ​​of seismic wave propagation velocity in the target layer are calculated based on the relationship between bulk modulus, shear modulus, and seismic wave propagation velocity.

[0055] Preferably, the maximum value of the seismic wave propagation velocity V max and minimum value V min :

[0056]

[0057]

[0058] Where: D represents the density data in the well logging data.

[0059] Preferably, the maximum and minimum values ​​of seismic wave propagation within the target layer of a single well are combined with three-dimensional seismic data, and the maximum and minimum three-dimensional velocity volumes within the work area are obtained by inversion.

[0060] Preferably, in step 5, the elastic data volume includes a P-wave velocity volume, a S-wave velocity volume, and a density volume; the elastic data volume obtained by well-seismic joint inversion is the basic data for pore pressure prediction, and the inversion accuracy of the P-wave velocity volume directly affects the prediction accuracy of the final pore pressure; the P-wave velocity volume, density volume, maximum velocity volume, and minimum velocity volume obtained by inversion and calculation are converted to time-depth, and the time-domain seismic data volume is converted to the depth domain.

[0061] Preferably, in step 6, the formation pore pressure data volume is calculated by combining the three-dimensional maximum velocity volume and minimum velocity volume, as well as the elastic parameter volume obtained by inversion:

[0062]

[0063] Where: V t For the inversion of the P-wave velocity volume at the calculation point; D h It is the average density of the overlying strata, expressed in g / cm³. 3 g is the acceleration due to gravity, with units of m / s². 2 h is the vertical depth from the Earth's surface to the target stratum, measured in meters (m).

[0064] Preferably, the inverted P-wave velocity volume V at each calculation point needs to be determined before calculation. t Is it at maximum speed body V? max Minimum velocity body V min Within the range; that is, whether V is satisfied. min <V t <V max If the value is within the acceptable range, normal calculations are performed. If the value is outside the acceptable range, there may be anomalies in the velocity. In this case, the median value of nine spatially adjacent points is used to replace the original value to form a new longitudinal wave velocity volume V. t ', Maximum speed body V m ' ax and minimum velocity body V m ' in The formation pore pressure data volume is as follows:

[0065]

[0066] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0067] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described above.

[0068] A computer program product includes a computer program that, when executed by a processor, implements the steps of the method described above.

[0069] The beneficial effects of this technical solution are as follows:

[0070] I. This invention provides a three-dimensional pore pressure prediction method based on rock physical analysis. By integrating well logging data and three-dimensional seismic data, it uses rock physical analysis to determine the content of various mineral components in different lithological strata, obtaining a more accurate range of rock elastic modulus and the ranges of maximum and minimum velocities. This provides precise basic data for formation pore pressure prediction, and is crucial for obtaining high-precision full-section pore pressure prediction data, designing reasonable drilling fluid densities, reducing well blowouts and lost circulation, and ensuring safe and rapid drilling and completion. It has achieved good application results in practical applications and represents a significant technical improvement over traditional methods.

[0071] II. This invention provides a three-dimensional pore pressure prediction method based on rock physical analysis. First, it obtains the content of various mineral components in different formations using well logging data from drilled wells within the work area. Second, it obtains the range of elastic moduli of different mineral components in each formation within a single well. Third, it calculates the range of seismic wave propagation velocity in a single well based on the range of elastic moduli of different mineral components. Fourth, it combines the seismic wave propagation velocity range of the single well with three-dimensional seismic data to obtain a three-dimensional seismic wave propagation velocity range volume. Finally, it combines the P-wave velocity and density data obtained from the seismic inversion with the three-dimensional seismic wave propagation velocity range data volume to comprehensively predict formation pore pressure. This method is not constrained by lithology and clarifies the content proportion of each mineral component in different lithological formations through rock physical analysis. It avoids the problem of large calculation errors caused by inaccurate settings of rock skeleton velocity and fluid velocity in traditional pore pressure prediction methods based on empirical models, providing a new method for predicting pore pressure across the entire formation. Attached Figure Description

[0072] Figure 1 This is a flowchart of the present invention;

[0073] Figure 2 This is a schematic diagram illustrating the solution of the optimal mineral component content ratio using the automatic correction method in this invention;

[0074] Figure 3 This is a diagram showing the percentage of mineral components in a single well in Example 4 of this invention.

[0075] Figure 4 This is a seismic data profile diagram of Embodiment 4 in this invention;

[0076] Figure 5 This is a cross-sectional view of the inversion velocity through the well in Example 4 of this invention;

[0077] Figure 6 This is a formation pore pressure diagram for Example 4 of the present invention. Detailed Implementation

[0078] The present invention will be further described in detail below with reference to embodiments, but the implementation of the present invention is not limited thereto.

[0079] Example 1

[0080] like Figure 1 and Figure 2 As shown, a three-dimensional pore pressure prediction method based on rock physical analysis includes the following steps:

[0081] Step 1: Collect and organize the logging data of drilled wells within a three-dimensional work area;

[0082] Step 2: Use an automatic correction method to calculate the content ratio of different mineral components in the target interval of a single well, and obtain the optimal mineral component content ratio;

[0083] Step 3: Calculate the elastic modulus range of the target layer in each well using rock physics analysis.

[0084] Step 4: Calculate the range of seismic wave propagation velocity within the target layer of a single well, and combine the range of seismic wave propagation velocity within the target layer of a single well with the 3D seismic data to obtain the 3D velocity range volume within the work area;

[0085] Step 5: Combine well logging data and 3D seismic data in the work area to carry out well-seismic joint inversion, obtain elastic parameter volume, and convert the elastic parameter volume and 3D velocity range volume to the depth domain through time-depth conversion;

[0086] Step 6: Combine the three-dimensional velocity range volume with the elastic parameter volume obtained by inversion to calculate the formation pore pressure data volume.

[0087] Example 2

[0088] A three-dimensional pore pressure prediction method based on rock physics analysis includes the following steps:

[0089] Step 1: Collect and organize the logging data of drilled wells within a three-dimensional work area;

[0090] Step 2: Use an automatic correction method to calculate the content ratio of different mineral components in the target interval of a single well, and obtain the optimal mineral component content ratio;

[0091] Step 3: Calculate the elastic modulus range of the target layer in each well using rock physics analysis.

[0092] Step 4: Calculate the range of seismic wave propagation velocity within the target layer of a single well, and combine the range of seismic wave propagation velocity within the target layer of a single well with the 3D seismic data to obtain the 3D velocity range volume within the work area;

[0093] Step 5: Combine well logging data and 3D seismic data in the work area to carry out well-seismic joint inversion, obtain elastic parameter volume, and convert the elastic parameter volume and 3D velocity range volume to the depth domain through time-depth conversion;

[0094] Step 6: Combine the three-dimensional velocity range volume with the elastic parameter volume obtained by inversion to calculate the formation pore pressure data volume.

[0095] In step 1, for a three-dimensional research area, assuming r wells have been drilled, each well has complete logging data, including sonic transit time data, density data, shear wave velocity data, clay content data, and gamma ray data. Each type of logging data can be represented by a curve, with the horizontal axis representing the data value (including clay content and gamma ray values) and the vertical axis representing the depth, indicating the data values ​​measured at different depths. The logging data is collected and organized, and obvious distortions and outliers are removed. The method for removing outliers from logging data is relatively mature and will not be described in detail here. The logging data types to be processed are selected. Let n types of logging data be selected, including porosity data, gamma ray data, P-wave data, S-wave data, density data, permeability data, and resistivity data. Each type of logging data has m data points from shallow to deep. The logging data are then combined into a matrix G.

[0096]

[0097] Where n represents n types of logging data; m represents m data points for each type of logging data; and gji represents the value at the j-th point in the i-th type of logging data.

[0098] In step 2, it is assumed that there are three main strata in the study section: stratum 1, stratum 2, and stratum 3; the points of stratum 1 are 1, 2...m1; the points of stratum 2 are m1+1, m1+2...m2; and the points of stratum 3 are m2+1, m2+2...m.

[0099] Suppose there are p-1 types of minerals in formation 1. Finally, pore type is added as the p-th component type. The value of each mineral in different well logging data is:

[0100]

[0101] Where n represents n types of well logging data; p represents p types of mineral composition; x ji This represents the value of the i-th mineral component in the j-th type of well logging data; the type of well logging data n must be greater than or equal to the type of formation mineral p.

[0102] The proportions of several mineral components in stratum 1 are R = [r1, r2, ..., r] in the rock. p ] TThe condition is that the sum of all proportions equals 1, i.e., r1 + r2 + ... + r p =1;

[0103] Construct the mineral composition of data points from the 1st to the m1th:

[0104] G i =X·R;

[0105] G i =[g i1 g i2 , ..., g in (3)

[0106] The solution yields the mineral composition content R at point i. i =[r 1i ,r 2i ......r pi ] T Where i = 1, 2, ..., m1;

[0107] For each type of logging data value, an error threshold is set. Let the error threshold for n types of logging data be E = [e1, e2, ..., e]. n ] T The well logging data value G(R) is calculated using the solved mineral component content ratio and mineral component value. ij ) and the actual logging data value G ij Error calculation is performed; if the error exceeds the set error threshold, it is judged as unqualified, and the value of the mineral component with the largest proportion needs to be readjusted and recalculated; when the calculation error is less than or equal to the set error threshold, the calculated mineral proportion content is the optimal solution; that is, the criteria for qualified mineral component content are:

[0108] |G(R ij )-G ij |≤E j (4);

[0109] When the calculation error of the j-th type of logging data at data point i exceeds the set error threshold, where i = 1, 2, ..., m1; j = 1, 2, ..., n, it is necessary to adjust the mineral component with the largest calculated mineral content, i.e., to find the maximum (R) value. i Let ), be rk i Here, k represents the mineral with the highest percentage content in the j-th type of logging data at calculation point i, where k = 1, 2, ..., p; then the mineral value x is adjusted according to the following formula:

[0110] If G(R) ij )-G ij If x > 0, then x jk =x jk-0.02x jk (5);

[0111] If G(R) ij )-G ij If x < 0, then x jk =x jk +0.02x jk (6);

[0112] After adjustment, the error is recalculated. If the error is less than the set threshold, the calculation stops. If the error is greater than the set threshold, the mineral composition values ​​need to be further adjusted until the threshold condition is met. After each adjustment calculation is completed, the mineral composition value X is updated synchronously.

[0113] The above steps are repeated to calculate the mineral content percentage at all points in strata 2 and strata 3, obtaining the optimal mineral content percentage calculation result R for the target stratum:

[0114]

[0115] Where p represents p kinds of mineral components, and m represents m data points for each type of logging data.

[0116] In step 3, the elastic modulus range of the target layer in a single well is calculated. By using the content ratio of different mineral components and the known modulus values ​​of each mineral component, the maximum and minimum values ​​of the elastic modulus of the comprehensive lithology of each layer are obtained.

[0117] The elastic modulus includes bulk modulus V and shear modulus Q.

[0118] For point i to be calculated, the mineral component content ratio R obtained through step 2 is... i =[r 1i r 2i ......r pi ] T , i = 1, 2, ..., m; The bulk modulus and shear modulus of each mineral component were obtained from literature review as V0 = [V1, V2, ..., V...]. p ] T and Q0 = [Q1, Q2, ..., Q p ] T Let the maximum and minimum values ​​of the bulk modulus and shear modulus of each mineral component be:

[0119] V max =MAX(V0) V min =MIN(V0) Q max =MAX(Q0) Q min =MIN(Q0) (8);

[0120] The minimum bulk modulus of the comprehensive lithology at calculation point i is V. N :

[0121]

[0122] The maximum bulk modulus of the comprehensive lithology at calculation point i is V. M At this point, it is necessary to remove the proportion of porosity from the mineral component content percentage, and then re-normalize the proportions of the remaining mineral components; the porosity proportion is r. pi Then the total percentage of the remaining mineral components is 1-r. pi The proportions of the remaining mineral components are renormalized, and the new proportions of mineral components are R′. i =[r′ 1i , r′ 2i ......r′ p-1i ], i = 1, 2, ..., m;

[0123] in:

[0124]

[0125] The maximum bulk modulus of the comprehensive lithology at point i is V. M :

[0126]

[0127] The minimum shear modulus of the comprehensive lithology at calculation point i is Q. N :

[0128]

[0129] Minimum shear modulus Q M The calculation also requires the use of the new mineral component content ratio R. i ':

[0130]

[0131] Where T max and T min They are respectively:

[0132]

[0133]

[0134] In step 4, the range of seismic wave propagation velocity within the target layer of a single well is calculated; the maximum and minimum values ​​of seismic wave propagation velocity in the target layer are calculated based on the relationship between bulk modulus, shear modulus and seismic wave propagation velocity.

[0135] Preferably, the maximum value of the seismic wave propagation velocity V max and minimum value V min :

[0136]

[0137]

[0138] Where: D represents the density data in the well logging data.

[0139] Among them, the maximum and minimum values ​​of seismic wave propagation within the target layer of a single well are combined with three-dimensional seismic data, and the three-dimensional maximum and minimum velocity volumes within the work area are obtained by inversion.

[0140] In step 5, the elastic data volume includes the P-wave velocity volume, the S-wave velocity volume, and the density volume. The elastic data volume obtained by well-seismic joint inversion is the basic data for pore pressure prediction. The inversion accuracy of the P-wave velocity volume directly affects the prediction accuracy of the final pore pressure. The P-wave velocity volume, density volume, maximum velocity volume, and minimum velocity volume obtained by inversion and calculation are converted to time-depth, and the time-domain seismic data volume is converted to the depth domain.

[0141] In step 6, the formation pore pressure data volume is calculated by combining the three-dimensional maximum velocity volume and minimum velocity volume, as well as the elastic parameter volume obtained by inversion:

[0142]

[0143] Where: V t For the inversion of the P-wave velocity volume at the calculation point; D h It is the average density of the overlying strata, expressed in g / cm³. 3 g is the acceleration due to gravity, with units of m / s². 2 h is the vertical depth from the Earth's surface to the target stratum, measured in meters (m).

[0144] Before calculation, it is necessary to determine the inverted P-wave velocity volume V at each calculation point. t Is it at maximum speed body V? max Minimum velocity body V min Within the range; that is, whether V is satisfied. min <V t <V max If the value is within the acceptable range, normal calculations are performed. If the value is outside the acceptable range, there may be anomalies in the velocity. In this case, the median value of nine spatially adjacent points is used to replace the original value to form a new longitudinal wave velocity volume V. t ', Maximum speed body V m ' ax and minimum velocity body V m ' inThe formation pore pressure data volume is as follows:

[0145]

[0146] Example 3

[0147] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, it can: obtain the content of various mineral components in different formations through well logging data of drilled wells in the work area; secondly, obtain the range of elastic modulus of different mineral components in each formation in a single well; thirdly, calculate the range of seismic wave propagation velocity of a single well based on the range of elastic modulus of different mineral components; fourthly, combine the range of seismic wave propagation velocity of a single well with three-dimensional seismic data to obtain a three-dimensional seismic wave propagation velocity range volume; finally, combine the P-wave velocity and density data volume obtained by seismic inversion, as well as the three-dimensional seismic wave propagation velocity range data volume, to comprehensively predict the formation pore pressure.

[0148] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored. The computer program, when executed by a processor, can: first, obtain the content of various mineral components in different formations using logging data from drilled wells within the work area; second, obtain the range of elastic moduli of different mineral components in each formation within a single well; third, calculate the range of seismic wave propagation velocity in a single well using the range of elastic moduli of different mineral components; fourth, combine the seismic wave propagation velocity range of the single well with three-dimensional seismic data to obtain a three-dimensional seismic wave propagation velocity range volume; and finally, combine the P-wave velocity and density data volume obtained from the seismic inversion with the three-dimensional seismic wave propagation velocity range data volume to comprehensively predict formation pore pressure.

[0149] In some embodiments of this example, a computer program product is provided, including a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0150] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0151] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disk, floppy disk, solid-state drive, hard disk drive, removable disk, Blu-ray disc, etc.).

[0152] Computer-readable storage media may also store at least one computer-executable program, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0153] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0154] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0155] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0156] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0157] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0158] Example 4

[0159] This embodiment employs the three-dimensional pore pressure prediction method based on rock physical analysis described in Embodiment 2. It utilizes well logging and seismic data obtained from a specific work area to predict formation pore pressure within the area based on rock physical analysis. An experiment is now conducted according to the invention's process, as follows:

[0160] The logging data from the eight wells drilled in the work area were analyzed and organized. Abnormal distortion values ​​in the logging data were optimized and removed to ensure that all logging data values ​​were within a reasonable range, providing accurate basic data for subsequent rock physics analysis and mineral composition content calculation. The logging data included seven types of logging data: P-wave velocity, S-wave velocity, density, gamma, resistivity, permeability, and compensation seed, which formed the data matrix G in formula (1).

[0161] like Figures 3-5 As shown, the target strata for the study area are clearly defined as the Triassic and Permian systems, which transition from shallow sandstone and mudstone strata to deep carbonate rock strata. Therefore, the target strata are divided into two parts: the sandstone and mudstone strata above Xujiahe, and the carbonate rock strata below the bottom of Xujiahe. The mineral composition of the strata above Xujiahe includes clay, quartz, feldspar, mica, and kaolinite; the mineral composition of the strata below Xujiahe includes calcite, dolomite, quartz, clay, and gypsum. Porosity is added to the above two strata, and each mineral in the two strata is assigned a value for each well logging data. The specific value range is based on the rock physical values ​​of common minerals in relevant rock physics literature, resulting in matrix X in formula (2). Based on formula (3), a formula for the proportion of mineral components in the Xujiahe and above strata is constructed and the proportion vector R1 is obtained. The well logging data composed of R1 and mineral component values ​​is compared with the actual values ​​of the well logging data. If the difference between the two is less than the error threshold, the optimal solution is obtained. If the difference between the two is greater than the error threshold, it is adjusted by formula (5) or formula (6), and the adjusted mineral component values ​​are used to reconstruct X and substituted into formula (3) to obtain a new mineral component proportion R1. The above steps are repeated to compare and analyze whether the interpolation between the synthesized well logging data and the actual well logging data is greater than the threshold and to perform corresponding processing until all errors are less than the set threshold range. The optimal mineral component proportion R1 of this segment is obtained. Similarly, the optimal mineral component proportion R2 of the carbonate rock segment below Xujiahe is calculated.

[0162] The bulk modulus and shear modulus ranges of the two layers are calculated sequentially. For the sandstone and mudstone layer above Xujiahe, the maximum and minimum bulk modulus are calculated using formulas (8)-(11); the maximum and minimum shear modulus are calculated using formulas (12)-(15). When calculating the maximum bulk modulus and maximum shear modulus, the percentage of porosity needs to be removed, and the percentage of the remaining minerals needs to be normalized before calculation.

[0163] Based on the obtained ranges of bulk modulus and shear modulus, as well as the logging density data, the maximum velocity V is calculated using formulas (16) and (17). max and minimum speed V minThe maximum and minimum seismic velocities within the target layer of a single well are combined with 3D seismic data, and the 3D maximum and minimum velocity volumes within the work area are obtained through inversion.

[0164] By combining existing well logging and seismic data within the work area, a high-precision three-dimensional velocity and density volumes were obtained using a combined well-seismic inversion method. Then, the inverted and calculated P-wave velocity, density, maximum velocity, and minimum velocity volumes were converted to a time-depth model, transforming the time-domain seismic data volume into the depth domain.

[0165] Combining the three-dimensional maximum and minimum velocity volumes, and the inverted elastic parameter volume, the formation pore pressure data volume is calculated according to formula (18). Before calculation, the inverted P-wave velocity volume V at each calculation point needs to be determined. t Is it at maximum speed body V? max Minimum velocity body V min Within the range. That is, whether V is satisfied. min <V t <V max If the value is within the acceptable range, normal calculations are performed. If the value is outside the acceptable range, there may be anomalies in the velocity. In this case, the median value of nine spatially adjacent points is used to replace the original value to form a new longitudinal wave velocity volume V. t '、Maximum velocity body V′ max and minimum velocity body Vv min Then, the formation pore pressure data volume is calculated using formula (19). The formation pore pressure data volume is as follows: Figure 6 As shown.

[0166] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A three-dimensional pore pressure prediction method based on rock physical analysis, characterized in that, Includes the following steps: A three-dimensional pore pressure prediction method based on rock physics analysis includes the following steps: Step 1: Collect and organize the logging data of drilled wells within a three-dimensional work area; Step 2: Use an automatic correction method to calculate the content ratio of different mineral components in the target interval of a single well, and obtain the optimal mineral component content ratio; Step 3: Calculate the elastic modulus range of the target layer in each well using rock physics analysis. Step 4: Calculate the range of seismic wave propagation velocity within the target layer of a single well, and combine the range of seismic wave propagation velocity within the target layer of a single well with the 3D seismic data to obtain the 3D velocity range volume within the work area; Step 5: Combine well logging data and 3D seismic data in the work area to carry out well-seismic joint inversion, obtain elastic parameter volume, and convert the elastic parameter volume and 3D velocity range volume to the depth domain through time-depth conversion; Step 6: Combine the three-dimensional velocity range volume with the elastic parameter volume obtained by inversion to calculate the formation pore pressure data volume.

2. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 1, characterized in that: In step 1, for a three-dimensional research area, assuming r wells have been drilled, each well has complete logging data, including sonic transit time data, density data, shear wave velocity data, clay content data, and gamma ray data. Each type of logging data can be represented by a curve, with the horizontal axis representing the data value (including clay content and gamma ray values) and the vertical axis representing the depth, indicating the data values ​​measured at different depths. The logging data is collected and organized, and obvious distortions and outliers are removed. The logging data types to be processed are selected. Let n types of logging data be selected: porosity data, gamma ray data, P-wave data, S-wave data, density data, permeability data, and resistivity data. Each type of logging data has m data points from shallow to deep. Then, the logging data are combined into a matrix G. Where n represents n types of logging data; m represents m data points for each type of logging data; g ji This represents the value at the j-th point in the i-th type of well logging data.

3. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 2, characterized in that: In step 2, it is assumed that there are three main strata in the study section: stratum 1, stratum 2, and stratum 3; the points of stratum 1 are 1, 2...m1; the points of stratum 2 are m1+1, m1+2...m2; and the points of stratum 3 are m2+1, m2+2...m. Suppose there are p-1 types of minerals in formation 1. Finally, pore type is added as the p-th component type. The value of each mineral in different well logging data is: Where n represents n types of well logging data; p represents p types of mineral composition; x ji This represents the value of the i-th mineral component in the j-th type of well logging data; the type of well logging data n must be greater than or equal to the type of formation mineral p. The proportions of several mineral components in stratum 1 are R = [r1, r2, ..., r] in the rock. p ] T The condition is that the sum of all proportions equals 1, i.e., r1 + r2 + ... + r p =1; Construct the mineral composition of data points from the 1st to the m1th: G i =X·R; G i =[g i1 ,g i2 ,...,g in ]; (3) The solution yields the mineral composition content R at point i. i =[r 1i ,r 2i ......r pi ] T Where i = 1, 2, ..., m1; For each type of logging data value, an error threshold is set. Let the error threshold for n types of logging data be E = [e1, e2, ..., e]. n ] T The well logging data value G(R) is calculated using the solved mineral component content ratio and mineral component value. ij ) and the actual logging data value G ij Error calculation is performed; if the error exceeds the set error threshold, it is judged as unqualified, and the value of the mineral component with the largest proportion needs to be readjusted and recalculated; when the calculation error is less than or equal to the set error threshold, the calculated mineral proportion content is the optimal solution; that is, the criteria for qualified mineral component content are: |G(R ij )-G ij |≤E j (4); When the calculation error of the j-th type of logging data at data point i exceeds the set error threshold, where i = 1, 2, ..., m1; j = 1, 2, ..., n, it is necessary to adjust the mineral component with the largest calculated mineral content, i.e., to find the maximum (R) value. i Let r be the value of r. ki Here, k represents the mineral with the highest percentage content in the j-th type of logging data at calculation point i, where k = 1, 2, ..., p; then the mineral value x is adjusted according to the following formula: If G(R) ij )-G ij If x > 0, then x jk =x jk -0.02x jk (5); If G(R) ij )-G ij If x < 0, then x jk =x jk +0.02x jk (6); After adjustment, the error is recalculated. If the error is less than the set threshold, the calculation stops. If the error is greater than the set threshold, the mineral composition values ​​need to be further adjusted until the threshold condition is met. After each adjustment calculation is completed, the mineral composition value X is updated synchronously.

4. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 3, characterized in that: Repeat the above steps to calculate the mineral content percentage at all points in strata 2 and strata 3, and obtain the optimal mineral content percentage calculation result R for the target interval: Where p represents p kinds of mineral components, and m represents m data points for each type of logging data.

5. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 4, characterized in that: In step 3, the elastic modulus range of the target layer in a single well is calculated. By using the content ratio of different mineral components and the known modulus values ​​of each mineral component, the maximum and minimum values ​​of the elastic modulus of the comprehensive lithology of each layer are obtained.

6. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 5, characterized in that: The elastic modulus includes the bulk modulus V and the shear modulus Q.

7. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 6, characterized in that: For point i to be calculated, the mineral component content percentage R obtained through step 2 is... i =[r 1i ,r 2i ......r pi ] T , i = 1, 2, ..., m; the bulk modulus and shear modulus of each mineral component were obtained from literature review, and were V O =[V1,V2......V p ] T and Q O =[Q1,Q2......Q p ] T Let the maximum and minimum values ​​of the bulk modulus and shear modulus of each mineral component be: V max =MAX(V0) V min =MIN(V0) Q max =MAX(Q0) Q min =MIN(Q0) (8); The minimum bulk modulus of the comprehensive lithology at calculation point i is V. N : The maximum bulk modulus of the comprehensive lithology at calculation point i is V. M At this point, it is necessary to remove the proportion of porosity from the mineral component content percentage, and then re-normalize the proportions of the remaining mineral components; the porosity proportion is r. pi Then the total percentage of the remaining mineral components is 1-r. pi The proportions of the remaining mineral components are renormalized, and the new proportions of mineral components are R'. i =[r' 1i ,r' 2i ......r' p-1i ], i = 1, 2, ..., m; in: The maximum bulk modulus of the comprehensive lithology at point i is V. M : The minimum shear modulus of the comprehensive lithology at calculation point i is Q. N : Minimum shear modulus Q M The calculation also requires the use of the new mineral component content ratio R. i ': Where T max and T min They are respectively:

8. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 7, characterized in that: In step 4, the range of seismic wave propagation velocity within the target layer of a single well is calculated; the maximum and minimum values ​​of seismic wave propagation velocity in the target layer are calculated based on the relationship between bulk modulus, shear modulus and seismic wave propagation velocity.

9. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 8, characterized in that: The maximum velocity of seismic waves, V max and minimum value V min : Where: D represents the density data in the well logging data.

10. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 9, characterized in that: By combining the maximum and minimum values ​​of seismic wave propagation within the target layer of a single well with 3D seismic data, the maximum and minimum 3D velocity volumes within the work area are obtained through inversion.

11. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 10, characterized in that: In step 5, the elastic data volume includes the P-wave velocity volume, the S-wave velocity volume, and the density volume. The elastic data volume obtained by well-seismic joint inversion is the basic data for pore pressure prediction. The inversion accuracy of the P-wave velocity volume directly affects the prediction accuracy of the final pore pressure. The P-wave velocity volume, density volume, maximum velocity volume, and minimum velocity volume obtained by inversion and calculation are converted to time-depth, and the time-domain seismic data volume is converted to the depth domain.

12. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 11, characterized in that: In step 6, the formation pore pressure data volume is calculated by combining the three-dimensional maximum velocity volume and minimum velocity volume, as well as the elastic parameter volume obtained by inversion: Where: V t For the inversion of the P-wave velocity volume at the calculation point; D h It is the average density of the overlying strata, expressed in g / cm³. 3 g is the acceleration due to gravity, with units of m / s². 2 h is the vertical depth from the Earth's surface to the target stratum, measured in meters (m).

13. The three-dimensional pore pressure prediction method based on rock physical analysis according to claim 12, characterized in that: Before calculation, it is necessary to determine the inverted P-wave velocity V at each calculation point. t Is it at maximum speed body V? max Minimum velocity body V min Within the range; that is, whether V is satisfied. min <V t <V max If the value is within the acceptable range, normal calculations are performed. If the value is outside the acceptable range, there may be anomalies in the velocity. In this case, the median value of nine spatially adjacent points is used to replace the original value to form a new longitudinal wave velocity volume V. t ', Maximum speed body V m ' ax and minimum velocity body V m ' in The formation pore pressure data volume is as follows:

14. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1-13.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program performs the steps of the method described in any one of claims 1-13.

16. A computer program product, comprising a computer program, characterized in that: When executed by a processor, the computer program performs the steps of the method described in any one of claims 1-13.

Images