Quantitative evaluation method for spatial evolution of thrust fault based on displacement data analysis

Abstract

The application discloses a quantitative evaluation method of spatial evolution of thrust faults based on displacement data analysis, which comprises the following steps: obtaining three-dimensional geometric data of the thrust faults and extracting an original displacement data set along the strike; constructing a strike-displacement profile, and obtaining a displacement trend line eliminating local noise by using a mathematical fitting method; calculating the difference between the original displacement and the fitted displacement to perform displacement residual analysis, and identifying the fault segmentation connection characteristics; based on the displacement trend line and the residual analysis result, extracting quantitative parameters such as the maximum trend displacement, the total length of the fault, the displacement-length ratio and the end displacement gradient; and finally realizing quantitative evaluation of the spatial evolution mode and the evolution degree of the thrust faults. The application eliminates local measurement errors and secondary structural interference, realizes quantitative and standardized evaluation of the growth and evolution history of the faults, and provides a scientific basis for evaluation of the effectiveness of the fault-related traps and prediction of the oil and gas migration and accumulation channels.

Description

Technical Field

[0001] This invention relates to the field of basin tectonic analysis and hydrocarbon geology research, and in particular to a quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis. Background Technology

[0002] Faults, as one of the most fundamental structural elements in oil and gas basins, are not only important channels for oil and gas migration but also key sealing mechanisms for the formation of fault-related traps. Therefore, the study of fault evolution characteristics has always been a core topic in structural geology and oil and gas exploration. During fault growth and development, the displacement distribution along the fault strike typically exhibits a characteristic of maximum displacement in the middle and gradually decreasing towards the lateral tips. This spatial distribution pattern of displacement is an important window into revealing the spatial growth, merging, and evolution history of faults. Currently, scholars both domestically and internationally mainly use methods such as seismic reflection profile analysis and exploring the relationship between fault length and maximum displacement to conduct qualitative to semi-quantitative analyses of the formation process of faults on two-dimensional profiles or single geometric parameters. This has, to some extent, promoted the understanding of fault growth mechanisms.

[0003] However, existing research on fault evolution still has significant shortcomings. On the one hand, traditional measurement methods often ignore the complex geometric morphology and deformation effects of the fault plane in three-dimensional space when acquiring fault displacement, resulting in displacement data that cannot fully reflect the true kinematic characteristics of the fault. On the other hand, due to limitations in seismic reflection resolution and the ambiguity of human interpretation, discrete displacement measurement data inevitably contain measurement errors. Furthermore, existing technologies lack effective mathematical and physical models to constrain the overall trend of these discrete points, making it difficult to eliminate the interference of local errors on the evaluation results. In addition, existing analytical methods are mostly limited to single linear or nonlinear assumptions, failing to objectively identify the complex trends generated by segmentation or stage transitions during fault spatial evolution. Moreover, there is a lack of standardized evaluation methods for quantitatively calculating key evolution parameters such as the actual length and maximum displacement of the fault based on displacement distribution morphology, which severely restricts the accuracy of fault-related oil and gas trap prediction. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides a quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis, to address the problems raised in the background art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis, comprising: A three-dimensional geological model of the fault was constructed based on seismic reflection profiles, drilling and logging data, and a unified target stratum was selected as the displacement measurement benchmark. Displacement data of the target stratum caused by fault faulting at multiple profile locations along the fault strike are obtained sequentially, and a scatter plot dataset of the relationship between fault displacement and distance along the strike is established. Piecewise linear regression analysis and quadratic polynomial regression analysis were performed on the scatter plot dataset, and one of them was selected as the initial trend model based on the goodness of fit. Calculate the residual between the actual value of each scattered displacement data and the predicted value of the initial trend model, and determine whether the scattered relationship dataset needs to be piecewise fitted based on the residual distribution characteristics, thereby determining the final fault spatial evolution trend model. Based on the fitting equation of the fault spatial evolution trend model, quantitative parameters of fault length and maximum displacement are calculated to achieve a quantitative evaluation of the spatial evolution of thrust faults.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Traditional fault displacement analysis relies directly on raw interpretation data, making it highly susceptible to interference from local noise such as the ambiguity of seismic interpretation, local strata collapse, or the development of secondary faults. This invention constructs a strike-displacement profile and performs global trend fitting to generate a displacement trend line representing the macroscopic evolution of the fault. This effectively filters out local measurement errors and secondary tectonic interference, ensuring the authenticity and reliability of the quantitative evaluation parameters.

[0008] 2. Traditional methods mainly rely on the subjective experience of geologists to determine whether faults are segmented and spliced. This invention introduces displacement residual analysis, which quantitatively calculates the deviation between measured displacement and trend displacement, using displacement-deficient areas as the physical basis for identifying fault connection points. This eliminates interference from human experience and improves the objectivity and accuracy of fault segmentation feature identification.

[0009] 3. By combining the geometric shape of the displacement trend line with the characteristics of the displacement residual, this invention can clearly distinguish between isolated growth patterns and segmented connected growth patterns. Furthermore, by comparing the displacement-length ratio and the end displacement gradient with the regional background value, it can quantitatively assess whether the fault is in a free-spreading or restricted growth stage, thereby accurately determining the maturity of the fault's evolution. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a general flowchart illustrating one embodiment of the present invention; Figure 2 This is a planar distribution map of faults established by seismic profiles according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating how to quantitatively obtain fault displacement from a seismic profile according to an embodiment of the present invention; Figure 4 This is a scatter plot and fitting result of the displacement along the strike of fault F1 in a quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis, as described in an embodiment of the present invention. Figure 5 This is a residual distribution diagram of the F1 regression results of the quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis, as described in an embodiment of the present invention. Figure 6 This is a scatter plot and fitting result of the displacement along the strike of fault F2, which is a quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis according to an embodiment of the present invention. Figure 7 This is a residual distribution diagram of the regression results of fault F2 in a quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis, as described in an embodiment of the present invention. Figure 8 This is a scatter plot and fitting result of the displacement along the strike of fault F3, which is a quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis, according to an embodiment of the present invention. Figure 9 This is a residual distribution diagram of the regression results of fault F3 in a quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis, as described in an embodiment of the present invention. Detailed Implementation

[0011] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0012] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0013] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0014] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0015] Example 1

[0016] Reference Figure 1 This is the first embodiment of the present invention, which provides a quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis. This method, through quantitative fitting and residual analysis of the three-dimensional spatial geometry of the fault and the displacement distribution along the strike, can effectively eliminate local measurement errors and accurately extract key geometric parameters of fault evolution, thereby achieving an objective and quantitative evaluation of the spatial evolution characteristics of thrust faults. The method specifically includes the following: S1. Construct a three-dimensional geological model of the fault and select a measurement benchmark.

[0017] Specifically, firstly, high-precision 3D seismic reflection data, drilling data, and well logging data are collected within the study area. Based on the fault characteristics of the reflection axis group on the seismic profile, the spatial location and geometric shape of the fault plane on the profile are accurately identified and tracked. Simultaneously, combined with the extension characteristics of the fault on horizontal or bedding sections, the extent of the fault distribution on the plane is determined. Based on this, a 3D geological model of the fault is constructed using 3D geological modeling software. To ensure the consistency of displacement measurement standards, this invention utilizes drilling and well logging data for detailed stratigraphic correlation, selecting a unified target stratigraphic plane with clear stratigraphic positions, strong regional comparability, and fault fault faulting as the reference surface for fault displacement measurement.

[0018] S2. Obtain displacement data along the strike of the fault.

[0019] Furthermore, in the constructed three-dimensional geological model, multiple seismic analytical profiles perpendicular to the fault strike are sequentially extracted. At each profile location, displacement data of the target strata caused by fault faulting are obtained.

[0020] Furthermore, to fully consider the complex geometric morphology of the fault plane in three-dimensional space (e.g., shovel-shaped, polygonal, etc.) and the influence of fault deformation on displacement, this embodiment abandons the traditional method of only measuring vertical or horizontal fault displacement. Instead, it uses the fault breakpoints on the hanging wall and footwall of the target stratum as endpoints, and measures the curvilinear distance between these two points along the actual bending shape of the fault plane. This curvilinear distance is defined as the actual fault displacement value at that profile location. This measurement method integrates information on vertical fault displacement, horizontal fault displacement, and fault dip angle, and can more realistically reflect the kinematic characteristics of the fault.

[0021] S3. Establish a scatter plot dataset showing the relationship between fault displacement and distance along the strike.

[0022] Furthermore, taking a certain endpoint of the fault strike (e.g., the leftmost end) as the starting point, the cumulative distance along the strike of each analytical profile position relative to the starting point is measured. Using the cumulative distance along the fault strike as the abscissa and the actual fault displacement value measured at the corresponding profile as the ordinate, a scatter plot of the displacement distribution along the strike is established, showing the relationship between fault displacement and distance along the strike.

[0023] S4. Perform regression analysis and determine the initial trend model.

[0024] It should be noted that, in order to quantitatively characterize the trend of displacement along the direction, the present invention employs the least squares method to perform piecewise linear regression analysis on the scattered relation dataset. Where y represents fault displacement data, i.e., the vertical axis value; x represents the fault distance along the strike, i.e., the horizontal axis value; a represents the slope of the linear regression equation; b represents the intercept of the linear regression equation) and quadratic polynomial regression analysis ( Where y is the vertical coordinate displacement data, x is the horizontal coordinate distance along the fault strike, and A, B, and C are constants.

[0025] Furthermore, the least squares method in linear regression analysis aims to minimize the sum of squared residuals, specifically the residuals at the i-th data point. Defined as:

[0026] Furthermore, the sum of squared residuals (objective function) in the above linear regression analysis... Defined as:

[0027] Furthermore, regarding the sum of squares of the residuals By taking the partial derivatives with respect to a and b respectively and setting them to zero, we can obtain the least squares estimation formulas for the slope a and the intercept b by solving the system of equations:

[0028]

[0029] Where n is the total number of data points; and These are the measured values ​​of the fault strike distance and fault displacement data for the i-th data point, respectively. and These are the sample mean values ​​of fault strike distance and fault displacement data, respectively.

[0030] Furthermore, the goodness of fit R of the regression results in the linear regression analysis... 2 Used to measure the goodness of fit of a regression equation to measured data, defined as:

[0031] in, Let be the regression predicted value for the i-th data point. The goodness of fit of the regression results ranges from [0,1].

[0032] Furthermore, the least squares method in quadratic polynomial regression analysis aims to minimize the sum of squared residuals. The residuals at the i-th data point... Defined as:

[0033] Furthermore, the sum of squared residuals (objective function) in the above quadratic polynomial regression analysis... Defined as:

[0034] Furthermore, regarding the sum of squares of the residuals Taking the partial derivatives with respect to A, B, and C respectively and setting them to zero, we obtain the following system of normal equations:

[0035]

[0036]

[0037] It should be noted that the estimated values ​​of coefficients A, B, and C can be obtained by solving the above system of equations.

[0038] Furthermore, similarly to the goodness-of-fit analysis of linear regression, the same formula is used to express the results in quadratic polynomial regression analysis, which will not be elaborated upon here. However, it should be noted that in quadratic polynomial regression analysis, .

[0039] Furthermore, for piecewise linear regression analysis, considering that fault displacement typically reaches its maximum in the middle and decreases towards the tips, the dataset is divided into left and right parts (both parts contain this maximum value) using the point with the largest actual displacement value in the scatter plot as the boundary. A linear regression equation is constructed by fitting the data on the left side. Similarly, a linear regression equation is constructed by fitting the data on the right side.

[0040] Furthermore, the overall goodness of fit of piecewise linear regression is defined as the average of the goodness of fits of both sides.

[0041] Furthermore, for quadratic polynomial regression analysis, a quadratic polynomial is fitted to the complete scatter plot dataset to obtain the quadratic polynomial regression equation.

[0042] Furthermore, by comparing the overall goodness of fit of the piecewise linear regression with the goodness of fit of the quadratic polynomial regression equation, the regression analysis result with the highest goodness of fit is selected as the initial trend model.

[0043] S5. Residual calculation and determination of the final spatial evolution trend model.

[0044] Furthermore, in order to verify the rationality of the initial trend model and identify the stage transitions or segment splicing characteristics in the fault development process, residual analysis was performed on the data.

[0045] Specifically, firstly, the residual between the actual measured displacement value and the predicted value of the initial trend model for each scatter data point is calculated. Then, using the left-to-right sequence number of the data points (or the independent variable) as the x-axis and the residual values ​​as the y-axis, a scatter plot of the residual distribution is drawn, and judgments are made based on its distribution characteristics: Scenario 1: If the residual scatter points exhibit a random and irregular discrete distribution around the zero line, it indicates that the initial trend model has fully captured the characteristics of displacement changes and there are no unidentified systematic biases. In this case, the initial trend model is directly determined as the final fault spatial evolution trend model.

[0046] Scenario 2: If the residual scatter plots exhibit a clear systematic broken line shape along the data point sequence (e.g., a regular fluctuation like a "V" shape or an inverted "V"), it indicates that the fault displacement exhibits staged transformations or segmented growth characteristics in space, and a single overall fit cannot meet the accuracy requirements. In this case, segmented fitting is necessary.

[0047] Furthermore, when piecewise fitting is deemed necessary, the original data points corresponding to the inflection points (turning points) of the residual polygonal line shape are used as boundary points to divide the original scattered relation dataset into multiple sub-segments (each adjacent sub-segment contains this boundary point). For the data in each sub-segment, independent regression analysis is performed using the optimal fitting method (linear or quadratic polynomial) selected in S4 to obtain the fitting equation for each segment. Combining the fitting equations for all segments constitutes the final fault spatial evolution trend model.

[0048] S6. Calculate the quantitative parameters of fault spatial evolution.

[0049] Furthermore, based on the fitting equation of the finally determined fault spatial evolution trend model, the key geometric parameters reflecting the fault evolution characteristics, including fault length and maximum displacement, are quantitatively calculated.

[0050] Specifically, fault length is defined as the strike distance interval corresponding to the final trend model when the displacement is zero.

[0051] If the final trend model is a linear model (the leftmost regression equation is...), then... The rightmost regression equation is , , The slope of the regression equation; , (where the intercept of the regression equation is the fault length) can then be defined as:

[0052] If the final trend model is a quadratic term model ( ; , and If the fault length is constant, then the fault length can be defined as:

[0053] Specifically, the calculation of the maximum displacement is divided into the following two types: If the final trend model is a piecewise linear model, find the two adjacent linear fitting equations on either side of the actual maximum displacement point (the regression equation to the left of the maximum point is...). The regression equation to the right of the maximum point is: ; , The slope of the regression equation; , The intersection point of the regression equation (and its intercept) is represented as:

[0054] If the final trend model is a quadratic polynomial model ( ; , and (where is a constant), the maximum displacement corresponds to the ordinate value of the vertex of the parabola, expressed as:

[0055] S7. Quantitative evaluation of the spatial evolution of thrust faults.

[0056] Furthermore, using the morphological characteristics (linear, curvilinear, or segmented combination morphology) of the finally determined fault spatial evolution trend model, combined with the fault length, maximum displacement, and the ratio of maximum displacement to length calculated in S6, the spatial evolution pattern and degree of evolution of the thrust fault are quantitatively evaluated: Evolutionary pattern identification: If the trend model presents a smooth quadratic polynomial curve, it indicates that the fault is in a mature monolithic growth stage. If it presents a piecewise linear or complex turning line shape, it indicates that the fault has undergone a process of splicing and merging of multiple fault segments (i.e., a piecewise connection growth pattern).

[0057] Evolutionary degree evaluation: By measuring the ratio of maximum displacement to length and the displacement gradient along the strike, the activity intensity and maturity of faults are quantitatively assessed, providing a scientific basis for evaluating the effectiveness of fault-related traps and analyzing hydrocarbon migration and accumulation channels.

[0058] Example 2

[0059] Reference Figures 2 to 9 This is the second embodiment of the present invention. In this embodiment, the thrust faults F1, F2 and F3 below the salt layer in the Kuqa Depression of the Tarim Basin are selected as the analysis objects to realize the quantitative evaluation method of the spatial evolution of thrust faults based on displacement data analysis described in Embodiment 1.

[0060] It should be noted that the Kuqa Depression selected in this example has abundant oil and gas resources. A three-dimensional geological model of the faults was established using high-precision seismic data. Based on the fault plane analysis of the seismic reflection axis group, the morphology of faults F1, F2, and F3 on the cross-section was determined: as follows... Figure 2 As shown, the distribution of faults on the plane is then determined based on their spatial extension, such as... Figure 3 As shown.

[0061] Furthermore, the top surface of the Mesozoic strata was selected as the target formation, and the corresponding reflection axis position on the seismic profile was determined using drilling and logging data. Using the top surface of the Mesozoic strata with fault faulting as the two endpoints, a curve was drawn along the fault plane to obtain the displacement data of faults F1, F2, and F3 on the profile.

[0062] Furthermore, multiple profiles are selected along the strike on the horizontal plane, and the displacement of the fault at different profile positions is measured repeatedly according to this step to obtain the displacement datasets of faults F1, F2 and F3.

[0063] Furthermore, a scatter plot was plotted for fault F1 with the distance along the strike of the fault as the abscissa and the displacement as the ordinate. Linear regression analysis and quadratic regression analysis were performed on the scatter data of the two faults using the least squares method, as follows: Figure 4 As shown. The fault F1 linear regression analysis, with the point of maximum displacement as the boundary, yields the left-hand linear regression equation y = 0.1286x + 464.95, R0. 2 =0.9595, the right-hand linear regression equation is y=-0.1549x+15565, R0 2 =0.9808, the goodness of fit (R²) between the two is 0.9808. 2 The average value is 0.9702. The regression equation for the quadratic F1 regression analysis of the fault is y = -0.000003x. 2 +0.3085x-1426.7, R 2 =0.9424.

[0064] It should be noted that the goodness of fit of linear regression analysis is greater than that of multi-term regression analysis. The scatter plot of the displacement of fault F1 along the strike is better fitted by linear regression analysis, showing a linear trend.

[0065] Furthermore, using the linear fitting equations y=0.1286x+464.95 and y=-0.1549x+15565 with higher goodness of fit, the residuals of the fault displacement scatter points were calculated. Substituting the actual fault displacement value minus the abscissa of the fault displacement scatter points into the fitting equation yielded a displacement fitting value equal to the residual of each scatter point. Then, a scatter plot of the residual distribution of the fitting equation was plotted, as shown below. Figure 5 As shown, the results exhibit random distribution characteristics, indicating that the fitting results are reasonable.

[0066] Furthermore, a scatter plot was plotted for fault F2 with the distance along the strike of the fault as the abscissa and the displacement as the ordinate. Linear regression analysis and quadratic regression analysis were performed on the scatter data of the two faults using the least squares method, as follows: Figure 6 As shown. The fault F2 linear regression analysis, with the point of maximum displacement as the boundary, yields the left-hand linear regression equation: y = 0.0545x + 2212.3, R0. 2 =0.7999, the right-hand linear regression equation is y = -0.0762x + 6245.2, R0 2 =0.9400, the goodness of fit (R²) between the two is 0.9400. 2 The average value is 0.8700. The regression equation for the quadratic F2 regression analysis of the fault is y = -0.000002x. 2+0.1207x+1794.9, R 2 =0.9639.

[0067] It should be noted that the goodness of fit of the multiple term regression analysis is greater than that of the linear regression analysis. The scatter plot of the displacement change of fault F2 along the strike is better fitted by the multiple term regression analysis, which reflects that there is curvature in the trend of fault displacement change.

[0068] Furthermore, a quadratic term with a higher good fit is used to fit the equation y = -0.000002x. 2 The residuals of the fault displacement scatter points are calculated using +0.1207x+1794.9. The actual fault displacement value minus the abscissa of the fault displacement scatter points is substituted into the fitting equation to obtain the displacement fitting value, which equals the residual of each scatter point. Then, a scatter plot of the residual distribution of the fitting equation is drawn, as shown below. Figure 7 As shown, the results exhibit random distribution characteristics, indicating that the fitting results are reasonable.

[0069] Furthermore, a scatter plot was plotted for fault F3 with the distance along the strike as the abscissa and the displacement as the ordinate. Linear regression analysis and quadratic regression analysis were performed on the scatter data of the two faults using the least squares method, as follows: Figure 8 As shown. The linear regression analysis of fault F3, with the point of maximum displacement as the boundary, yields the left-hand linear regression equation: y = 0.1637x - 1483.6, R0. 2 =0.9018, the right-hand linear regression equation is y=-0.1972x+16150, R0 2 =0.9403, and the average goodness of fit between the two is 0.9211. The regression equation for the quadratic term regression analysis of the fault F3 is y = -0.000004x 2 +0.3312x-2794.2, R 2 =0.7162. The goodness of fit of linear regression analysis is greater than that of multi-term regression analysis. The scatter plot of the displacement change of fault F3 along the strike is better fitted by linear regression analysis, showing a linear trend.

[0070] Furthermore, using the linear fitting equations y=0.1637x-1483.6 and y=-0.1972x+16150 with higher goodness of fit, the residuals of the fault displacement scatter points were calculated. Substituting the actual fault displacement value minus the abscissa of the fault displacement scatter points into the fitting equation yielded a displacement fitting value equal to the residual of each scatter point. Then, a scatter plot of the residual distribution of the fitting equation was plotted, as shown below. Figure 9 As shown, the fitting equations on both sides are presented as "V"-shaped broken lines, indicating that the trend of the scatter points on both sides is segmented with the maximum displacement as the boundary.

[0071] Furthermore, the inflection point of the broken line of the residual scatter distribution on the left side of fault F3 is the fifth point. Therefore, using this as the boundary, the left-side displacement scatter points are divided into two parts, both of which include the fifth scatter point. Then, linear fitting is performed, resulting in two regression equations: y = 0.0718x - 70.592, R0. 2 =0.9679 and y=0.2809x-6039.3, R 2 =0.9754; The inflection point of the broken line of the residual scatter distribution on the right side of fault F3 is the fifth point to the right from the maximum displacement point of the fault. Therefore, the right-side displacement scatter points are divided into two parts based on this boundary, with both parts including the fifth scatter point. Linear fitting is then performed, yielding two regression equations: y = -0.2569x + 19542, R0. 2 =0.9898 and y=-0.0434x+4145.3, R 2 =0.9972. The average goodness of fit after piecewise linear fitting is 0.9826, which is significantly higher than the average goodness of fit of linear fitting without piecewise fitting, indicating that the fitting result after piecewise fitting is more reasonable.

[0072] Furthermore, the predicted length and maximum displacement of the fault are calculated using the optimal fitting results. The predicted length of fault F1 is approximately 104.10 km, calculated by subtracting the intersection of the right-hand fitted curve (y=-0.1549x+15565) and the abscissa from the intersection of the left-hand fitted curve (y=0.1286x+464.95) and the abscissa. The predicted maximum displacement is approximately 7.31 km, calculated by the ordinate of the intersection of the two fitted curves. The predicted length of fault F2 is approximately 104.10 km, calculated by subtracting the intersection of the right-hand fitted curve (y=-0.1549x+15565) and the abscissa. 2 The predicted length of fault F3 is approximately 85.04 km, calculated by subtracting the value of the intersection of the rightmost fitted curve (y=-0.1207x+1794.9) with the leftmost fitted curve (y=0.0718x-70.592) from the value of the intersection of the rightmost fitted curve (y=-0.1207x+1794.9) with the leftmost fitted curve (y=0.1207x+1794.9) with the leftmost fitted curve (y=0.1207x+1794.9). The predicted maximum displacement is approximately 7.32 km, calculated by subtracting the value of the intersection of the fitted equations (y=0.2809x-6039.3 and y=-0.2569x+19542) with the leftmost fitted curve (y=0.2809x-6039.3 and y=-0.2569x+19542) with the leftmost fitted curve (y=0.1207x+1794.9) with the rightmost fitted curve.

[0073] Furthermore, the characteristics and degree of fault spatial evolution are evaluated using the shape of the final fitted curve and fault-related parameters (length, maximum displacement).

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A quantitative evaluation method for the spatial evolution of thrust faults based on displacement data analysis, characterized in that, include: A three-dimensional geological model of the fault was constructed based on seismic reflection profiles, drilling and logging data, and a unified target stratum was selected as the displacement measurement benchmark. Displacement data of the target stratum caused by fault faulting at multiple profile locations along the fault strike are obtained sequentially, and a scatter plot dataset of the relationship between fault displacement and distance along the strike is established. Piecewise linear regression analysis and quadratic polynomial regression analysis were performed on the scattered relationship dataset, and one of them was selected as the initial trend model based on the goodness of fit. Calculate the residual between the actual value of each scattered displacement data and the predicted value of the initial trend model, and determine whether the scattered relationship dataset needs to be piecewise fitted based on the residual distribution characteristics, thereby determining the final fault spatial evolution trend model. Based on the fitting equation of the fault spatial evolution trend model, quantitative parameters of fault length and maximum displacement are calculated to achieve a quantitative evaluation of the spatial evolution of thrust faults.

2. The method for quantitative evaluation of the spatial evolution of thrust faults based on displacement data analysis as described in claim 1, characterized in that, The acquisition of the displacement data includes: Using the fault points on both sides of the target stratum as endpoints, the curved distance between the two endpoints is measured along the fault plane, and the distance is used as the fault displacement value.

3. The method for quantitative evaluation of the spatial evolution of thrust faults based on displacement data analysis as described in claim 1, characterized in that, The piecewise linear regression analysis includes: The dataset is divided into two parts, left and right, with the maximum displacement point in the scatter data as the boundary. Linear regression is then performed on the two parts containing the maximum displacement point, and the average goodness of fit of the two regression equations is used as the overall goodness of fit of the piecewise linear regression.

4. The method for quantitative evaluation of the spatial evolution of thrust faults based on displacement data analysis as described in claim 1 or 3, characterized in that, The regression analysis result with the highest goodness of fit is selected as the initial trend model.

5. The method for quantitative evaluation of the spatial evolution of thrust faults based on displacement data analysis as described in claim 1, characterized in that, The judgment method based on residual distribution characteristics includes: If the residuals are randomly discretely distributed along the data point indices, then the initial trend model will be directly determined as the final fault spatial evolution trend model. If the residuals exhibit a systematic broken line shape along the data point indices, then piecewise fitting is required.

6. The method for quantitative evaluation of the spatial evolution of thrust faults based on displacement data analysis as described in claim 5, characterized in that, When it is determined that piecewise fitting is necessary, this includes: Using the original data points corresponding to the inflection points of the residual broken line shape as the dividing points, the original scattered relation dataset is segmented, and the selected regression analysis is used to fit the data of each segment separately. The fitting results of all segments are combined to determine the final fault spatial evolution trend model.

7. The method for quantitative evaluation of the spatial evolution of thrust faults based on displacement data analysis as described in claim 1, characterized in that, The calculation of fault length includes: The distance between the intersection of the fitting equation of the leftmost segment and the horizontal axis where the displacement is zero, and the intersection of the fitting equation of the rightmost segment and the horizontal axis where the displacement is zero, is the fault length.

8. The method for quantitative evaluation of the spatial evolution of thrust faults based on displacement data analysis as described in claim 1, characterized in that, When the fault spatial evolution trend model is a piecewise linear model, the method for calculating its maximum displacement includes: Find the intersection point of the two linear fitting equations on both sides of the point where the displacement is at its maximum. The ordinate value of the intersection point is the maximum displacement.

9. The method for quantitative evaluation of the spatial evolution of thrust faults based on displacement data analysis as described in claim 1, characterized in that, When the fault spatial evolution trend model is a quadratic polynomial model, the method for calculating its maximum displacement includes: Find the vertex of the quadratic polynomial fitting equation containing the data points with the maximum displacement, and the ordinate value of the vertex is the maximum displacement.

10. The method for quantitative evaluation of the spatial evolution of thrust faults based on displacement data analysis as described in any one of claims 1 to 9, characterized in that, Finally, the spatial evolution pattern and degree of the thrust fault are evaluated by using the morphology of the fault spatial evolution trend model, i.e., linear, curved, or segmented combination morphology, combined with the calculated quantitative parameters of fault length and maximum displacement.

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