Well structure optimization method based on three-dimensional formation pressure

Abstract

The invention discloses a well bore structure optimization method based on three-dimensional formation pressure, and belongs to the technical field of oil and gas well engineering. The method comprises the following steps: collecting data required by modeling in a work area, and storing the data into a geological modeling database; the required data comprises single well data and seismic data; based on the data in the geological modeling database, establishing a work area three-dimensional geological model; on the basis of the three-dimensional geological model, completing a three-dimensional geological attribute model of each stratum of the work area; and based on the three-dimensional geological model and the three-dimensional geological attribute model, the well bore structure optimization design is completed by taking the pressure system distribution condition as an evaluation and selection method. Through seismic velocity data processing, seismic stratification information of each layer section is obtained, a three-dimensional geological model of a work area is established, three-dimensional attribute modeling calculation is completed, and three-dimensional attribute modeling of the work area is realized; and finally, the three-dimensional three-pressure calculation profile result of the single well is extracted, reasonable design of the well bore structure can be achieved, and the stability and safety of a well bore are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas well engineering, relates to geological modeling and wellbore structure design, and specifically is a wellbore structure optimization method based on three-dimensional formation pressure. Background Art

[0002] Wellbore structure design is an important part of drilling engineering, which is related to the safety of drilling construction and the economic benefits of drilling and completion. The balance between formation pore pressure, formation collapse pressure and formation fracture pressure is the key parameter of wellbore structure design, but the traditional wellbore structure design method is usually based on one-dimensional formation pressure analysis and design, and fails to fully consider the impact of three-dimensional formation pressure on the wellbore and surrounding formation environment.

[0003] Three-dimensional geological modeling can more accurately reflect the changes in formation pressure and provide new ideas for the optimization design of wellbore structure. Therefore, a geological modeling method based on three-dimensional formation pressure is established, and by comprehensively considering the uneven pressure distribution of the formation, the stress state of the well wall, and the dynamic stress changes during the wellbore construction process, the reasonable design of the wellbore structure can be achieved, the stability and safety of the wellbore can be improved, and the optimization design of the wellbore structure can be achieved. Summary of the invention

[0004] To solve the above problems, the present invention aims to provide a wellbore structure optimization method based on three-dimensional formation pressure, which is based on the geological data of the work area, establishes the geometric constraints of the three-dimensional geological model, adopts the well-seismic correlation method, and obtains the three-dimensional distribution of pore pressure, collapse pressure and fracture pressure of the key layer sections of the work area based on the three-dimensional geological attribute calculation, and applies the three-dimensional three-pressure profile of the work area to actual drilling guidance, realizes the optimization design of the wellbore structure, and reduces the complex underground accidents in the key layers.

[0005] The present invention is achieved through the following technical solutions:

[0006] A wellbore structure optimization method based on three-dimensional formation pressure comprises the following steps:

[0007] S1. Collect the data required for modeling in the work area and store them in the geological modeling database; the required data include single well data and seismic data;

[0008] S2. Establish a three-dimensional geological model of the work area based on the data in the geological modeling database;

[0009] S3. Based on the 3D geological model, complete the 3D geological attribute model of each stratum in the work area;

[0010] S4. Based on the 3D geological model and the 3D geological attribute model, the pressure system distribution is used as the evaluation method to complete the optimization design of the wellbore structure.

[0011] As a specific implementation of the present invention, step S2 includes:

[0012] S201, using the time-depth conversion method to process the seismic data of the work area, forming seismic depth domain data, and obtaining seismic layering information of each layer; identifying geological structures including faults, and constructing a fault model of the work area;

[0013] S202, using single well geological stratification data as hard constraints and the stratigraphic data obtained from seismic interpretation as soft constraints, constructing a stratigraphic model, and combining the fault model and the stratigraphic model to generate a three-dimensional geological model;

[0014] As a specific implementation of the present invention, step S3 includes:

[0015] S301, using single well data as the main variable, collecting and determining the single well data of each well along the longitudinal direction of the wellbore, and using the data as the calibration basis for the lateral distribution prediction of the three-dimensional modeling of the work area; using seismic data as the secondary variable, combined with the seismic interpretation results of the oil and gas exploration department, evaluating and defining the abnormal area in the lateral direction of the work area;

[0016] S302, establishing a three-dimensional target attribute model: using an interpolation algorithm to process attribute modeling parameters related to formation rock characteristics, and completing general modeling of the three-dimensional geological body in the work area; for complex layers in the work area, selecting a random modeling algorithm, and completing random modeling of the three-dimensional geological body in the work area;

[0017] S303. Based on the three-dimensional target attribute model, the stratification information of each layer is obtained, and the seismic attribute assignment calculation is performed on the known area of ​​the three-dimensional geological model; when the three-dimensional target attribute model after interpolation and calibration is consistent with the formation conditions encountered by a single well and the seismic interpretation results, it is used as the three-dimensional attribute model of the work area that meets the modeling requirements of the work area.

[0018] Further, the stochastic modeling method includes:

[0019] S3021, select the point X to be simulated in random order i , based on the conditional probability distribution f(X i )~N(μ(X i ),σ 2 (X i )) Randomly generate a value f Δ (X i );

[0020] S3022, calculate the modified Kriging value f'(X * ), and use it as a known point, and continue to simulate the next point until all grids are simulated;

[0021] Calculate the modified Kriging value f'(X * ) is as follows:

[0022]

[0023] In the formula, X * is the estimated value of the point to be interpolated; X i The actual value of the sample point; n is the amount of sample data; λ i is the Kriging weight coefficient; σ 2 (X i ) is the variance of the Kriging calculation value; μ(X i ) is the mean of the actual values ​​of the sample points; Δh is the calculation step size; N(Δh) is the number of data points with a step size less than Δh; f() is the calculated value of Kriging; r() is the variogram value; C() is the covariance function, dimensionless; λ j (X i ,X j ) represents the sum of the Kriging weighted values ​​corresponding to xi and xj.

[0024] As a specific implementation of the present invention, step S4 includes: firstly obtaining the three-dimensional distribution calculation results of the ground stress and rock physical properties of the work area through the three-dimensional property model established in step S3, then inverting and calculating the pore pressure distribution coefficient of the geological area according to the three-dimensional distribution calculation results of the ground stress and rock physical properties and the single well data associated with the well seismic, and finally obtaining the three-dimensional distribution of the collapse pressure distribution coefficient and the fracture pressure distribution coefficient through the pore pressure distribution coefficient calculation, analyzing the three-dimensional three-pressure distribution around the drilling well, taking the drilling pressure system as the evaluation index, and optimizing the wellbore structure design;

[0025] The calculation formula of pore pressure distribution coefficient is expressed as:

[0026]

[0027] Where P p is the pressure distribution coefficient; C p is the effect coefficient, ranging from 0 to 1; φ p is the porosity of plastic deformation, %; φ0 porosity, %; ε φ is the pore volume strain; H is the formation thickness, m; σ v is the vertical geostress, MPa, calculated from the three-dimensional attributes; f[] is the linear fitting function; R t is the resistivity, Ω·m; DEN is the formation density, g / cm 3 ; Δt p is the longitudinal wave time difference, us / ft.

[0028] The calculation formula of collapse pressure distribution coefficient is:

[0029]

[0030] Where P b is the pressure distribution coefficient; is the internal friction angle of rock, °; μ is the Poisson's ratio of rock.

[0031] The calculation formula of the burst pressure distribution coefficient is:

[0032]

[0033] Where P f is the rupture pressure distribution coefficient; σ h is the minimum horizontal principal stress, MPa; σ H is the maximum horizontal principal stress, MPa; σ t is the tensile strength, MPa.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention is based on a geological modeling platform, obtains seismic stratification information of each layer through seismic velocity data processing, establishes a three-dimensional geological model of the work area, and completes three-dimensional attribute modeling calculations to realize three-dimensional attribute modeling of the work area; finally, the three-dimensional three-pressure calculation profile results of a single well are extracted to complete the wellbore structure optimization design, which can improve the stability and safety of the wellbore. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Loading diagram for boundary conditions;

[0037] Figure 2 It is the seismic velocity spectrum of the work area;

[0038] Figure 3 This is a schematic diagram of the three-dimensional seismic velocity body in the work area;

[0039] Figure 4 This is a schematic diagram of the fault model of the work area;

[0040] Figure 5 This is a diagram showing the roughening model of single well data in the work area;

[0041] Figure 6 This is the three-dimensional geological model map of the work area;

[0042] Figure 7 Schematic diagram of settlement results for attribute modeling;

[0043] Figure 8 This is a schematic diagram of the lateral distribution profile of the formation pressure in Well T108 and the calculation results of the three-dimensional attribute model;

[0044] Fig. 9 This is a schematic diagram of the pressure system encountered during drilling in Well T108;

[0045] Fig.10This is a schematic diagram of the wellbore structure optimization results of Well T108. DETAILED DESCRIPTION

[0046] 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. The described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Example 1

[0048] 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, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without 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 selected embodiments of the present invention.

[0049] Example 1

[0050] The wellbore structure optimization method based on three-dimensional formation pressure in this embodiment uses Petrel software as a geological modeling research tool, and completes geological modeling by inputting modeling data, which specifically includes the following steps:

[0051] S1. Collect the data required for modeling in the work area and store them in the geological modeling database;

[0052] Geological modeling data includes single well data and seismic data, which can be obtained from the on-site data of the work area and stored through storage devices. Among them, single well data refers to the well location data, well logging data, well logging layer data, wellbore trajectory data, etc. at the work area, and seismic data refers to data containing seismic wave transmission time and formation depth information.

[0053] The above modeling data are all imported and exported into the petrel software through the “Online” module.

[0054] S2. Establish a three-dimensional geological model of the work area based on the data in the geological modeling database;

[0055] The geological body is composed of different layers and faults. Therefore, when establishing a three-dimensional geological model, it is necessary to complete the cross-section model and the layer model based on the data in the geological modeling database. The fault model reveals the faults and their intersection and contact relationship in three-dimensional space, and the layer model reflects the change of stratum thickness and their contact relationship in three-dimensional space. Specifically, step S2 includes the following sub-steps:

[0056] S201. Based on the Petrrel geological modeling software platform, the seismic data of the work area are processed by the time-depth conversion method through the "Seisimic" and "Interpretation" modules to form seismic depth domain data and obtain seismic layering information of each layer; then the geological structures such as faults are identified through the "Fault Interpretation" and "Fault Modeling" modules to establish a fault model of the work area;

[0057] S202. Using the single well geological stratification data as hard constraints and the stratigraphic data obtained from seismic interpretation as soft constraints, a layer model with high conformity at the well point and an overall trend in the work area that conforms to geological knowledge is established by comprehensively utilizing soft and hard constraint data. A three-dimensional geological model of the work area including faults is generated through the "Structural Framework" module.

[0058] S3. Based on the 3D geological model, complete the 3D geological attribute model of each stratum in the work area;

[0059] During the geological evolution process, the differences in various geological actions lead to certain anisotropy in different depth segments. However, under stable conditions, the relative positions of each layer segment underground are fixed, and the distribution of its internal anisotropic property characteristics is also relatively constant. Based on this feature, the present invention proposes the idea of ​​general modeling + random modeling to achieve three-dimensional geological attributes and modeling of the work area. Specifically, this step includes the following sub-steps:

[0060] S301, taking single well data as the main variable, collecting and determining the single well data of each well along the longitudinal direction of the wellbore, because it is related to the geological characteristics of the work area and the actual drilling situation, and using it as the calibration basis for the lateral distribution prediction of the three-dimensional modeling of the work area; taking seismic data as the secondary variable, after time-depth conversion, processing seismic velocity and other data, combined with the seismic interpretation results of the oil and gas exploration department, evaluate and define the abnormal area in the lateral direction of the work area;

[0061] S302, establishing a three-dimensional target attribute model: using an interpolation algorithm to process the modeling parameters of attributes related to formation rock characteristics (including but not limited to formation Poisson's ratio, Young's modulus, cohesion, which can be selected by the user according to applicable conditions) to complete the general modeling of the three-dimensional geological body in the work area; for complex layers in the work area, selecting a random modeling algorithm to complete the random modeling of the three-dimensional geological body in the work area;

[0062] Based on previous literature research, Kriging is a commonly used method in geological 3D modeling. This method performs weighted summation of the attribute values ​​of the sampled data points, and then estimates the unsampled area, and uses Kriging covariance to quantify the smoothness of the estimated results. Therefore, the interpolation algorithm described in this step is to apply Kriging to calculate the attribute interpolation of the stratum based on the existing geological research data of the work area. Kriging Algorithm

[0063] Furthermore, the Kriging method is expressed as:

[0064]

[0065] i=1,…,n;j=1,…,n

[0066] In the formula, X * is the estimated value of the point to be interpolated; X i The actual value of the sample point can be obtained from the logging data; i is the Kriging weight coefficient, dimensionless; C() is the covariance function, dimensionless; n is the number of sample data; f(X * ) is the Kriging interpolation result.

[0067] The random modeling method is sequential Gaussian simulation, which is a random method that combines the sequential simulation algorithm of Gaussian probability theory to generate the spatial distribution of continuous variables. This method applies the previous geological information to unknown areas in a random simulation manner, thereby better reflecting the overall nature of regional attributes and can effectively handle the complex layers of geological modeling.

[0068] Furthermore, sequential Gaussian simulation needs to use the variogram to reflect the spatial relationship between the sampling points and adjacent points, which can be expressed as:

[0069]

[0070] In the formula, Δh is the calculation step size, which is determined by the actual calculation situation; N(Δh) is the number of data points with a step size less than Δh; f() is the calculated value of Kriging;

[0071] The uncertainty is expressed in terms of variance:

[0072]

[0073] In the formula, σ 2 (X i ) is the variance of the Kriging calculated values.

[0074] Therefore, the Kriging calculation method for stochastic modeling is modified as follows:

[0075]

[0076] In the formula, μ(X i ) is the mean of the actual values ​​of the sample points, f Δ (X i ) is based on f(X i )~N(μ(X i ),σ 2 (X i ))’s conditional probability distribution.

[0077] Therefore, the steps of random modeling are as follows: select the points to be simulated X in random order i , randomly generate a value f based on conditional probability Δ (X i ), and calculate the modified Kriging value f'(X * ) and use it as a known point to continue simulating the next point until all meshes are simulated.

[0078] This type of operation can be written in the "Property Modeling" module built into the petrel software, and then combined with the input data for calculation to complete the calculation of various three-dimensional properties of the work area. It should also be noted that in the three-dimensional geological model, the single well data involved in the coupling of well-seismic correlation attributes comes from the calculation of well logging data; in order to meet the attribute constraints corresponding to the layer velocity of different layer intervals, the single well data needs to be coarsened, and the coarsened single well data is assigned to each geological model grid through which the well trajectory passes. After the single well data is successfully coarsened, sequential Gaussian simulation can be selected as the interpolation method for unknown areas between wells in complex layers, and related attribute data of other types of parameters can be added to the area to supplement the constraints as the inter-well interpolation trend, to ensure that the modeling results are in a convergence state, and finally a three-dimensional target attribute model based on the entire work area is obtained.

[0079] S303, based on the three-dimensional target attribute model in the petrel software, the layering information of each layer is obtained, and the seismic attribute assignment calculation is performed on the known area of ​​the three-dimensional geological model; the accuracy of the three-dimensional attribute model is adjusted by modifying the calculation step length Δh in step S302, and then the seismic data of the work area are interpreted using the "Well-SeismicTying" module of the petrel software. When the three-dimensional target attribute model after interpolation and calibration is consistent with the formation conditions encountered by a single well and the seismic interpretation results, it is used as the three-dimensional attribute model of the work area that meets the modeling requirements of the work area;

[0080] S4. Based on the 3D geological model and the 3D geological attribute model, the pressure system distribution is used as the evaluation method to complete the optimization design of the wellbore structure.

[0081] Specifically, in this step, the three-dimensional property model established in step S3 is first used to obtain the three-dimensional distribution calculation results of the ground stress and rock physical properties (Poisson's ratio, internal friction angle, porosity, etc.) in the work area, and then the pore pressure distribution coefficient of the work area geology is inverted and calculated based on the three-dimensional distribution calculation results of the ground stress and rock physical properties and the single well data associated with the well seismic. Finally, the three-dimensional distribution of the collapse pressure distribution coefficient and the fracture pressure distribution coefficient is obtained by calculating the pore pressure distribution coefficient, and the three-dimensional three-pressure distribution around the drilling well is analyzed. The drilling pressure system is used as an evaluation index to optimize the wellbore structure design.

[0082] Specifically, the calculation formula of the pore pressure distribution coefficient is expressed as:

[0083]

[0084] Where P p is the pressure distribution coefficient; C p is the effect coefficient, ranging from 0 to 1; φ p is the porosity of plastic deformation, %; φ0 is the porosity, %; ε φ is the pore volume strain; H is the formation thickness, m; σ v is the vertical geostress, MPa, calculated from the three-dimensional attributes; f[] is the linear fitting function; R t is the resistivity, Ω·m; DEN is the formation density, g / cm 3 ; Δt p is the longitudinal wave time difference, us / ft; Dε φ / DH is the differential expression of pore volume strain with respect to formation thickness.

[0085] The calculation formula of collapse pressure distribution coefficient is:

[0086]

[0087] Where P b is the collapse pressure distribution coefficient; is the internal friction angle of rock, °; μ is the Poisson's ratio of rock.

[0088] The calculation formula of the burst pressure distribution coefficient is:

[0089]

[0090] Where P f is the rupture pressure distribution coefficient; σ h is the minimum horizontal principal stress, MPa; σ H is the maximum horizontal principal stress, MPa; σ t is the tensile strength, MPa, and α is the effective stress coefficient, which depends on the formation properties.

[0091] The calculation formulas for pore pressure, collapse pressure and fracture pressure can all be defined in the "Simulation" module of petrel to realize the calculation of three-dimensional three-pressure distribution.

[0092] To further illustrate the operability and applicability of this method, the scenario of this invention is the ZJ block, with a total development area of ​​750km 2 The complex strata encountered during drilling are the Qiongzhusi, Maidiping and Dengying formations. The planned boundary of the public area construction design is loaded as the boundary condition for geological modeling, such as Figure 1 shown.

[0093] Then, the well coordinates, well inclination azimuth, single well data based on logging interpretation (including rock mechanics parameters, geostress parameters, three pressures, etc.), target layer interpretation results, stratigraphic comparison well stratification data, fault line data and geological layer structural contour data of the 7 completed wells and 10 drilling wells in the entire work area are input into the petrel software through the "Online" module.

[0094] like Figure 2 As shown in the figure, it is the seismic velocity map of the work area. Through the "Seisimic" and "Interpretation" modules, the time-depth conversion method is used to process the three-dimensional seismic data of the work area. Figure 3 Then the “Structural Framework” module generates a three-dimensional structural model of the work area including the fault as shown in Figure 4 shown.

[0095] The top surface of the Qiongzhusi Formation and the bottom surface of the Dengying Formation are used as the layer model to establish the basic geometric data; the contact surface of the Qiongzhusi Formation-Maidiping Formation and the contact surface of the Maidiping Formation-Dengying Formation are generated under soft and hard constraint data, totaling 4 layers. The plane grid spacing of the 3D geological modeling is set to 50m×50m, the vertical grid size is set to 5m, and the calculation step is 0.15m, that is, the overall work area is set to a 50m×50m×5m grid division standard, and the single well data coarsening model of the work area is established. Figure 5 As shown in the figure, the established three-dimensional geological model of the work area is as follows Figure 6 Some of the calculation results are shown in Figure 7 shown.

[0096] The "Property Modeling" module was used to complete the three-dimensional geological property modeling of the work area, and the density, Young's modulus and Poisson's ratio of the work area were three-dimensional. Furthermore, data was extracted from the three-dimensional property modeling calculation results of the petrel software, and a three-dimensional distribution analysis was carried out for the complex layers encountered. The three-dimensional pore pressure distribution of the Qiongzhusi, Maidiping and Dengying formations is shown in the table. The property body is shown in Figure 8The lateral distribution range of the pore pressure coefficient in the southeast of the Qiongzhusi Formation is 1.86-2.00, and the lateral distribution range of the pore pressure coefficient in the northwest is 1.90-2.10; the lateral distribution range of the pore pressure coefficient in the southern area of ​​the Maidiping Formation in the Maidiping Formation work area is 1.85-2.02, and the lateral distribution range of the pore pressure coefficient in the northern area is 1.98-2.10; the lateral distribution range of the pore pressure coefficient in the southeast of the Dengying Formation is 1.18-1.26, and the lateral distribution range of the pore pressure coefficient in the northwest is 1.20-1.40.

[0097] Based on the three-dimensional distribution of pore pressure, the "Well-Seismic Tying" module of petrel is used to calculate the distribution of fracture pressure and collapse pressure based on the well to be optimized, and the wellbore structure is optimized based on the calculation results. Taking the T108 well in the work area as an example, the pore pressure coefficient of the Dengying Formation in the target layer of the well area is calculated to be 1.21-1.40, the collapse pressure coefficient is 1.27-1.42, and the fracture pressure coefficient is 2.49-2.60. Fig. 9 As shown in the figure, along the vertical direction of the wellbore, the formation pressure system of the lower carbonate rock section of Well T108 is relatively complex, and there are abnormally high-pressure sections in both the Qiongzhusi Formation and the Maidiping Formation. Combined with the results of seismic interpretation, faults are developed in the Dengying Formation, and the fault distribution is close to the designed wellbore of Well T108, resulting in a high risk of well leakage in the lower carbonate rock section dominated by the Qiongzhusi-Dengying Formation. Therefore, the wellbore structure should be adjusted from level 5 to level 6 to separate the high-pressure section of the Qiongzhusi-Maidiping Formation from the low-pressure layer of the Dengying Formation, and to avoid high-density drilling fluid from entering the Dengying Formation with insufficient pressure-bearing capacity, such as Fig.10 shown.

[0098] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the embodiments of the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing wellbore structure based on three-dimensional formation pressure, characterized in that: The following steps are involved: S1. Collect the data required for modeling in the work area and store them in the geological modeling database; the required data include single well data and seismic data; S2. Establish a three-dimensional geological model of the work area based on the data in the geological modeling database; S3. Based on the 3D geological model, complete the 3D geological attribute model of each stratum in the work area; S4. Based on the 3D geological model and the 3D geological attribute model, the pressure system distribution is used as the evaluation method to complete the optimization design of the wellbore structure.

2. A wellbore structure optimization method based on three-dimensional formation pressure according to claim 1, characterized in that: Step S2 includes: S201, using the time-depth conversion method to process the seismic data of the work area, forming seismic depth domain data, and obtaining seismic layering information of each layer; identifying geological structures including faults, and constructing a fault model of the work area; S202, using the single well geological stratification data as hard constraints and the stratigraphic data obtained from seismic interpretation as soft constraints, construct a layer model, and generate a three-dimensional geological model by combining the fault model and the layer model.

3. The method for optimizing wellbore structure based on three-dimensional formation pressure according to claim 1, characterized in that: Step S3 includes: S301, using single well data as the main variable, collecting and determining the single well data of each well along the longitudinal direction of the wellbore, and using the data as the calibration basis for the lateral distribution prediction of the three-dimensional modeling of the work area; using seismic data as the secondary variable, combined with the seismic interpretation results of the oil and gas exploration department, evaluating and defining the abnormal area in the lateral direction of the work area; S302, establishing a three-dimensional target attribute model: using an interpolation algorithm to process attribute modeling parameters related to formation rock characteristics, and completing general modeling of the three-dimensional geological body in the work area; for complex layers in the work area, selecting a random modeling algorithm, and completing random modeling of the three-dimensional geological body in the work area; S303. Based on the three-dimensional target attribute model, the stratification information of each layer is obtained, and the seismic attribute assignment calculation is performed on the known area of ​​the three-dimensional geological model; when the three-dimensional target attribute model after interpolation and calibration is consistent with the formation conditions encountered by a single well and the seismic interpretation results, it is used as the three-dimensional attribute model of the work area that meets the modeling requirements of the work area.

4. A method for optimizing wellbore structure based on three-dimensional formation pressure according to claim 3, characterized in that: The stochastic modeling method includes: S3021, select the point X to be simulated in random order i , based on the conditional probability distribution f(X i )~N(μ(X i ),σ 2 (X i )) Randomly generate a value f Δ (X i ); S3022, calculate the modified Kriging value f'(X * ), and use it as a known point, and continue to simulate the next point until all grids are simulated; Calculate the modified Kriging value f'(X * ) is as follows: Where, X * is the estimated value of the point to be interpolated; X i The actual value of the sample point; n is the amount of sample data; λ i is the Kriging weight coefficient; σ 2 (X i ) is the variance of the Kriging calculation value; μ(X i ) is the mean of the actual values ​​of the sample points; Δh is the calculation step size; N(Δh) is the number of data points with a step size less than Δh; f() is the calculated value of Kriging; r() is the variogram value; C() is the covariance function, dimensionless; λ j (X i ,X j ) represents the sum of the Kriging weighted values ​​corresponding to xi and xj.

5. The method for optimizing wellbore structure based on three-dimensional formation pressure according to claim 1, characterized in that: Step S4 includes: firstly obtaining the three-dimensional distribution calculation results of the geostress and rock physical properties of the work area through the three-dimensional attribute model established in step S3, then inverting and calculating the pore pressure distribution coefficient of the geology of the work area based on the three-dimensional distribution results of the geostress and rock physical properties and the single well data associated with the well seismic, and finally obtaining the three-dimensional distribution of the collapse pressure distribution coefficient and the fracture pressure distribution coefficient through the calculation of the pore pressure distribution coefficient, analyzing the three-dimensional three-pressure distribution around the drilling well, taking the drilling pressure system as the evaluation index, and optimizing the wellbore structure design.

6. A method for optimizing wellbore structure based on three-dimensional formation pressure according to claim 5, characterized in that: The calculation formula of pore pressure distribution coefficient is expressed as: Where P p is the pressure distribution coefficient; C p is the effect coefficient, ranging from 0 to 1; φ p is the porosity of plastic deformation, %; φ0 is porosity, %; ε φ is the pore volume strain; H is the formation thickness, m; σ v is the vertical geostress, MPa, calculated from the three-dimensional attributes; f[] is the linear fitting function; R t is the resistivity, Ω·m; DEN is the formation density, g / cm 3 ; Δt p is the longitudinal wave time difference, us / ft; Dε φ / DH is the differential expression of pore volume strain with respect to formation thickness.

7. A method for optimizing wellbore structure based on three-dimensional formation pressure according to claim 6, characterized in that: The calculation formula of collapse pressure distribution coefficient is: Where P b is the collapse pressure distribution coefficient; is the internal friction angle of rock, °; μ is the Poisson's ratio of rock.

8. A method for optimizing wellbore structure based on three-dimensional formation pressure according to claim 7, characterized in that: The calculation formula of collapse pressure distribution coefficient is: The calculation formula of the burst pressure distribution coefficient is: Where P f is the rupture pressure distribution coefficient; σ h is the minimum horizontal principal stress, MPa; σ H is the maximum horizontal principal stress, MPa; σ t is the tensile strength, MPa, and α is the effective stress coefficient.

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