Rock physical model constrained crustal stress prediction method based on digital core

Abstract

The invention discloses a rock physical model constrained ground stress prediction method based on a digital core, and relates to the technical field of exploration geophysics, and the method comprises the steps: scanning a reservoir sample through a digital core technology, obtaining a target reservoir parameter, and calibrating a rock physical model prediction result in combination with logging data; utilizing the rock physical model to predict anisotropy parameters of the reservoir; establishing an inversion initial model by using the obtained anisotropy parameters; and predicting the ground stress distribution of the target layer by using an anisotropic AVO inversion technology. According to the rock physical model constrained crustal stress prediction method based on the digital core, the advantages of sufficient data and high controllability of the digital core are fully played, the prediction of the rock physical model can be more accurate through the high-precision rich data, and after the model is corrected through comparison with the longitudinal wave velocity and the transverse wave velocity obtained through well logging, the rock physical model constrained crustal stress prediction method is more accurate. And ground stress prediction can be carried out on a region in which anisotropic parameters cannot be directly obtained by logging according to the constraint of a rock physical model.

Description

Technical Field

[0001] The invention relates to the field of exploration geophysical technology, and in particular to a rock physics model-constrained geostress prediction method based on digital cores. Background Art

[0002] Many issues related to geostress include oil and gas migration and accumulation during oil and gas exploration and development, wellbore stability during drilling, horizontal well design, reservoir transformation, and well pattern layout in water injection development. Researchers have conducted extensive research on geostress prediction and developed a series of calculation methods, which can be summarized into three categories:

[0003] 1) Estimation of geostress based on well logging data: The principle of this method is to use the longitudinal wave time difference Δt obtained by acoustic logging p , shear wave time difference Δt s The dynamic mechanical parameters of rock are obtained by using the formation density value ρ obtained by density logging. However, a major problem with this method is that it can only obtain layered in-situ stress curves that are continuously distributed along the well depth, but it is impossible to make a comprehensive in-situ stress prediction for the underground medium.

[0004] 2) Numerical simulation method of geostress: This method is mostly based on measured data, taking into account the terrain and geological characteristics, and then establishing a model for inversion, back calculation and simulation. This method is divided into the following three aspects: ① Boundary displacement adjustment method, to determine the strain and geostress prior information obtained by other methods; the best fitting judgment conditions of the simulated value and the measured value need to consider the best fitting of the stress trajectory, the consistency of the stress change law, and the best fitting of the displacement law; ② Displacement back analysis method, using displacement information to infer the initial geostress or boundary load of the underground medium; ③ Stress (displacement) function method, is to use regression analysis method, according to the equilibrium equation, constitutive equation, through the stress (displacement) measurement value, coordinate value and stress (displacement) boundary condition of the measured point to construct a regression equation, and then use the least squares principle to determine the regression coefficient, so as to obtain the stress (strain) function, and finally use the finite element method to calibrate the results of the regression analysis, improve the regression results, and obtain the distribution of the underground stress field. However, each method has corresponding applicable conditions, so when processing actual data, it is necessary to select a suitable method for simulation.

[0005] 3) Seismic prediction method of geostress: This is an emerging geostress prediction method. This method can obtain a continuous geostress profile of a certain area. Even in areas with few wells, the geostress of the area can be calculated. This method overcomes the limitation of location constraints and makes a lateral prediction of the geostress of the entire work area, providing theoretical guidance for well location deployment and hydraulic fracturing development. It has a wide prediction range and the characteristics of continuous distribution of data, which makes this method unique in geostress prediction methods. However, for some unconventional oil and gas reservoirs with strong anisotropy, it is very difficult to collect various anisotropic parameters, and it is impossible to obtain a large amount of anisotropic parameter data. It is difficult to ensure accuracy when calculating the geostress of strongly anisotropic reservoirs.

[0006] Therefore, in order to solve the problem of calculating the geostress in strongly anisotropic reservoirs, it is necessary to establish a model that can not only accurately predict the anisotropic parameters but also improve the accuracy of the anisotropic parameters, give full play to the advantage of the seismic prediction method of geostress that is not restricted by location constraints, and provide theoretical guidance for well location deployment and hydraulic fracturing development. Summary of the invention

[0007] For some areas where anisotropic parameter detection is impossible, the present invention discloses a rock physics model-constrained geostress prediction method based on digital cores. The target layer is analyzed in detail through digital core technology, the rock physics model is used to predict the anisotropic parameters of the target layer, and AVO inversion is performed to calculate the geostress based on the prediction results, and finally the geostress distribution of the target layer is predicted.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] A rock physics model-constrained geostress prediction method based on digital cores comprises the following steps:

[0010] S1. Scan reservoir samples through digital core technology to obtain target layer parameters, build corresponding rock physics models, and calibrate and correct the rock physics model prediction results in combination with logging data;

[0011] S2, using the rock physics model obtained in step S1, predicting anisotropic parameters of the reservoir;

[0012] S3, using the predicted anisotropic parameters and the well logging curves to establish an initial inversion model;

[0013] S4. Based on the approximate formula of anisotropic medium reflection coefficient, anisotropic AVO inversion is performed to calculate the horizontal stress distribution of HTI medium and predict the ground stress distribution of the target layer.

[0014] Optionally, in step S1, the specific steps are as follows:

[0015] S11. Obtaining calibration points using digital cores: Obtain mineral type, mineral content, and mineral morphology information at a certain depth through MaipScan, obtain fracture information at the same depth through full-diameter cores, obtain pore type, diameter, flatness ratio, and surface ratio at the same depth through scanning electron microscopy, and obtain pore connectivity information at the same depth through focused ion beam scanning electron microscopy FEB-SEM. All information is calculated using a rock physics model to obtain calibration points;

[0016] S12. Compare the P- and S-wave velocities predicted by the rock physics model with those in the logging data, project the calibration points onto the logging curve, and the calibration results are consistent with the logging curve, indicating that the model prediction is accurate.

[0017] Optionally, in step S4, the specific steps include:

[0018] S41. Calculate the reflection coefficient of the target layer by combining the HTI medium anisotropic medium reflection coefficient approximate equation. The formula is as follows:

[0019]

[0020] Where α, β and ρ are P-wave velocity, S-wave velocity and density respectively, θ is the incident angle, Δα / α, Δβ / β and Δρ / ρ are the P-wave velocity reflection coefficient, S-wave velocity reflection coefficient and density reflection coefficient respectively, Δδ (V) , Δε (V) and Δγ (V) is the difference of anisotropic parameters between the upper and lower media, k is the ratio of shear wave velocity to longitudinal wave velocity, R PP (θ) is the reflection coefficient;

[0021] S42. Based on the results of the anisotropic medium reflection coefficient, AVO inversion is performed. Combined with the inversion results, the horizontal stress difference ratio of the HTI medium is calculated, and the distribution of the ground stress on the profile is predicted. The ground stress calculation formula is:

[0022]

[0023] The calculation formula for the HTI medium horizontal stress difference ratio is:

[0024]

[0025] Where V P0 、V S0 are the longitudinal wave velocity and the shear wave velocity, σ x , σ y , σ z represent the minimum horizontal stress, maximum horizontal stress and vertical stress respectively.

[0026] Based on the maximum horizontal geostress, the minimum horizontal geostress and the horizontal stress difference ratio, the geostress distribution map of the target layer area is obtained.

[0027] The beneficial effect of the present invention is that the rock physics model-constrained geostress prediction method based on digital cores gives full play to the advantages of sufficient digital core data and strong controllability. The high-precision and rich data can make the prediction of the rock physics model more accurate. After comparing and correcting the model with the P- and S-wave velocities obtained by well logging, geostress prediction can be performed based on the constraints of the rock physics model for areas where anisotropic parameters cannot be directly obtained by well logging. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Comparison chart of rock physics model and well log curve;

[0029] Figure 2 Digital core calibration map;

[0030] Figure 3 Anisotropy parameter prediction map;

[0031] Figure 4 Distribution diagram of maximum horizontal principal stress in section x955;

[0032] Figure 5 Distribution diagram of maximum horizontal principal stress in L1150 section;

[0033] Figure 6 Distribution diagram of minimum horizontal principal stress in the x955 section;

[0034] Figure 7 Distribution diagram of minimum horizontal principal stress in L1150 section;

[0035] Figure 8 Distribution diagram of horizontal principal stress difference ratio in the x955 section;

[0036] Fig. 9 Distribution of horizontal principal stress difference ratio in L1150 profile. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0038] A rock physics model-constrained geostress prediction method based on digital cores comprises the following steps:

[0039] S1. Scan reservoir samples through digital core technology to obtain target layer parameters, build corresponding rock physics models, and calibrate and correct the rock physics model prediction results in combination with logging data;

[0040] The specific steps are as follows:

[0041] S11. Obtaining calibration points using digital cores: Obtain mineral type, mineral content, and mineral morphology information at a certain depth through MaipScan, obtain fracture information at the same depth through full-diameter cores, obtain pore type, diameter, flatness ratio, and surface ratio at the same depth through scanning electron microscopy, and obtain pore connectivity information at the same depth through FEB-SEM. All information is calculated using a rock physics model to obtain calibration points;

[0042] S12. Compare the P-wave and S-wave velocities predicted by the rock physics model with those in the logging data, project the calibration points onto the logging curve, and the calibration results are consistent with the logging curve. The model prediction is accurate, such as Figure 1 As shown in Figure 2, the P-wave and S-wave velocity results (red dashed lines) obtained by simulating with the rock physics model are highly similar to the measured data (blue solid lines), and the prediction of the rock physics model is relatively accurate. Figure 2 As shown in the figure, after the model calculation was performed using calibration points 1 and 2 with detailed digital core data and projected onto the logging curve, it was found that the prediction results of the calibration points were consistent with the logging results, verifying the accuracy of the model.

[0043] S2. Using the rock physics model obtained in step S1, predict the anisotropic parameters of the reservoir, such as Figure 3As shown, the above rock physics model is used to predict the anisotropic parameters epsilon (ε), delta (δ), and gamma (γ) of the target layer section of the target well.

[0044] S3, using the predicted anisotropic parameters and the well logging curves to establish an initial inversion model;

[0045] S4. Based on the approximate formula 0 of the anisotropic medium reflection coefficient, anisotropic AVO inversion is performed to calculate the horizontal stress distribution of the HTI medium and predict the ground stress distribution of the target layer;

[0046] The specific steps include:

[0047] S41. Calculate the reflection coefficient of the target layer by combining the HTI medium anisotropic medium reflection coefficient approximate equation. The formula is as follows:

[0048]

[0049] Where α, β and ρ are P-wave velocity, S-wave velocity and density respectively, θ is the incident angle, Δα / α, Δβ / β and Δρ / ρ are the P-wave velocity reflection coefficient, S-wave velocity reflection coefficient and density reflection coefficient respectively, Δδ (V) , Δε (V) and Δγ (V) is the difference of anisotropic parameters between the upper and lower media, k is the ratio of shear wave velocity to longitudinal wave velocity, R PP (θ) is the reflection coefficient;

[0050] S42. Based on the results of the anisotropic medium reflection coefficient, AVO inversion is performed. Combined with the inversion results, the horizontal stress difference ratio of the HTI medium is calculated, and the distribution of the ground stress on the profile is predicted. The ground stress calculation formula is:

[0051]

[0052] The calculation formula for the HTI medium horizontal stress difference ratio is:

[0053]

[0054] Where V P0 、V S0 are the longitudinal wave velocity and the shear wave velocity, σ x , σ y , σ z represent the minimum horizontal stress, maximum horizontal stress and vertical stress respectively.

[0055] Based on the maximum horizontal geostress, the minimum horizontal geostress and the horizontal stress difference ratio, the geostress distribution map of the target layer area is obtained.

[0056] Figure 4 and Figure 5 They are the maximum horizontal geostress distributions of the x955 and L1100 sections in the study area, Figure 6 and Figure 7 In particular, the distribution of minimum horizontal geostress applied to the x955 and L1100 sections in the study area. Figure 8 and Fig. 9 They are the horizontal geostress difference ratios applied to the x955 section and the L1100 section in the study area, respectively, and the target layer is the Wufeng Formation-Longmaxi Formation. DHSR is an important indicator of reservoir geostress, which determines the effect of reservoir fracturing. When the DHSR is small, the stress is released more evenly during the reservoir fracturing process, and it is often easier to form a network of fracture groups; when the DHSR is large, when the reservoir is fracturing, the stress is often released in one direction, resulting in a relatively single development of reservoir fractures, and the effect of improving the reservoir permeability is not obvious. Therefore, the smaller the DHSR of the shale reservoir, the more favorable the fracturing area of ​​the corresponding reservoir. Figure 4 , Figure 6 and Figure 8 It can be seen that in the x955 direction, the trend of the minimum horizontal principal stress of the target layer is consistent with the maximum horizontal principal stress. The blue and green colors are lower values ​​of DHSR, and the horizontal stress difference of the target layer is smaller than that in the Longmaxi Formation. Figure 5 , Figure 7 and Fig. 9 It can be seen that the minimum horizontal principal stress in the L1150 direction is consistent with the maximum horizontal principal stress trend, and the horizontal stress difference in the Longmaxi Formation is small, which is conducive to the deployment of dense horizontal wells or well groups and the fracturing of dense wells in the later stage. It is worth noting that DHSR reflects local characteristics, and its results are relatively specific to this study block.

[0057] Due to the advantages of sufficient data and strong controllability of digital core technology, three-dimensional digital core construction and numerical simulation analysis are carried out on almost all rock samples, including cuttings, loose sandstone, old core, damaged core and wellbore core, with relatively few rock samples, and the microscopic parameters of digital core are adjusted as needed, which is conducive to improving the data accuracy of rock physics modeling and making the prediction of rock physics model more accurate. By comparing the longitudinal and transverse wave velocities obtained by logging, and calibrating and correcting the model with the rock physics model prediction results of digital core data and logging results, the rock physics model can be more in line with reality and enhance the rationality of model prediction. The rock physics model is then used to predict the anisotropic parameters in areas where the anisotropic parameters cannot be directly obtained by logging, and the initial model is calculated and inverted in combination with the reflection coefficient equation of the anisotropic medium, and AVO inversion is performed. Combined with the inversion results, the horizontal stress difference ratio of the HTI medium is calculated to predict the distribution of ground stress on the profile.

[0058] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A rock physics model-constrained geostress prediction method based on digital core, characterized in that: The steps include: S1. Scan reservoir samples through digital core technology to obtain target layer parameters, build corresponding rock physics models, and calibrate and correct the rock physics model prediction results in combination with logging data; S2, using the rock physics model obtained in step S1, predicting anisotropic parameters of the reservoir; S3, using the predicted anisotropic parameters and the well logging curves to establish an initial inversion model; S4. Based on the approximate formula of anisotropic medium reflection coefficient, anisotropic AVO inversion is performed to calculate the horizontal stress distribution of HTI medium and predict the ground stress distribution of the target layer.

2. A method for predicting geostress based on rock physics model constraints of digital core according to claim 1, characterized in that: In step S1, the specific steps are as follows: S11. Obtaining calibration points using digital cores: Obtain mineral type, mineral content, and mineral morphology information at a certain depth through MaipScan, obtain fracture information at the same depth through full-diameter cores, obtain pore type, diameter, flatness ratio, and surface ratio at the same depth through scanning electron microscopy, and obtain pore connectivity information at the same depth through focused ion beam scanning electron microscopy. All information is calculated using a rock physics model to obtain calibration points; S12. Compare the P- and S-wave velocities predicted by the rock physics model with those in the logging data, project the calibration points onto the logging curve, and the calibration results are consistent with the logging curve, indicating that the model prediction is accurate.

3. The method for predicting geostress based on rock physics model constraints of digital core according to claim 1, characterized in that: In step S4, the specific steps include: S41. Calculate the reflection coefficient of the target layer by combining the HTI medium anisotropic medium reflection coefficient approximate equation. The formula is as follows: Where α, β and ρ are P-wave velocity, S-wave velocity and density respectively, θ is the incident angle, Δα / α, Δβ / β and Δρ / ρ are the P-wave velocity reflection coefficient, S-wave velocity reflection coefficient and density reflection coefficient respectively, Δδ (V) , Δε (V) and Δγ (V) is the difference of anisotropic parameters between the upper and lower media, k is the ratio of shear wave velocity to longitudinal wave velocity, R PP (θ) is the reflection coefficient; S42. Based on the results of the anisotropic medium reflection coefficient, anisotropic AVO inversion is performed. Combined with the inversion results, the horizontal stress difference ratio of the HTI medium is calculated, and the distribution of the ground stress on the profile is predicted. The ground stress calculation formula is: The calculation formula for the HTI medium horizontal stress difference ratio is: Where V P0 、V S0 are the longitudinal wave velocity and the shear wave velocity, σ x , σ y , σ z represent the minimum horizontal geostress, maximum horizontal geostress and vertical geostress respectively. Based on the maximum horizontal geostress, the minimum horizontal geostress and the horizontal stress difference ratio, the geostress distribution map of the target layer area is obtained.

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