A quantitative prediction method for fault fracture zone width based on seismic and well logging fusion

Abstract

This invention relates to a quantitative prediction method for fault fracture zone width based on the fusion of seismic and well logging data. The method obtains fault displacement parameters from seismic data, calibrates the fault fracture zone width using imaging well logging data, and establishes a quantitative relationship model between well profile displacement and fault fracture zone width based on multiple actual drilled well samples, thereby achieving quantitative prediction of fault fracture zone width in unexplored areas. This invention establishes a quantitative relationship model between well profile displacement D and fault fracture zone width W by fusing seismic and imaging well logging data, realizing the transformation from qualitative description to quantitative prediction of fault fracture zone width. This invention utilizes imaging well logging to identify structural fractures and determines the fault fracture zone boundary by the abrupt slope change points in the cumulative structural fracture number-well depth relationship curve, improving the objectivity and repeatability of fracture zone width calibration.

Description

Technical Field

[0001] This invention is based on the technical field of seismic information acquisition of geological structures, specifically relating to a method for quantitative prediction of fault fracture zone width based on the fusion of seismic and well logging. Background Technology

[0002] With the advancement of exploration and development of deep and complex oil and gas reservoirs, volcanic reservoirs and fault-controlled reservoirs have gradually become important exploration targets. Volcanic reservoirs are typically characterized by tightness, strong heterogeneity, and low primary porosity. Tectonic fractures play a crucial role in improving reservoir porosity and permeability, as well as forming effective reservoir space. Fault zones usually consist of fault cores and fracture zones, and their internal structure affects the permeability, sealing, and hydrocarbon migration capacity of the fault zone. Furthermore, there is a scaling relationship between fault displacement and fracture zone width; therefore, the quantitative characterization and prediction of underground fault fracture zone width has significant geological and engineering application value.

[0003] Existing methods for studying fault fracture zone width mainly include field outcrop measurement, single-well fracture statistics, and 3D seismic attribute prediction. Field outcrop measurement can directly observe the fault core, fracture zone, and fracture development characteristics, and establish an empirical relationship between fault displacement and fracture zone width. However, shallow outcrops differ from deep underground reservoirs in terms of temperature and pressure conditions, stress state, and diagenetic environment, making it difficult to directly extend to deep underground fault prediction. Single-well fracture statistics can identify fracture development characteristics near the wellbore using core, wall core, or imaging logging data. The slope of the cumulative fracture count versus distance or depth curve can be used to identify fault fracture zone boundaries. This method has high vertical resolution, but is limited by the number of wells and well location distribution, and can only reflect local information at the well point, making it difficult to predict the fracture zone width in unexplored areas or inter-well regions.

[0004] 3D seismic data offers advantages such as wide coverage and the ability to characterize the spatial distribution of faults. Seismic attributes such as coherence volumes have been widely used to identify faults and stratigraphic discontinuities. However, due to the limited dominant frequency and resolution of seismic data, conventional seismic attribute methods can usually only qualitatively or semi-quantitatively reflect the development range of fault zones, making it difficult to directly output meter-level fracture zone widths with clear physical dimensions. Furthermore, without reliable time-depth conversion and spatial geometric synthesis of seismic time-domain fault displacement, it is difficult to obtain depth-domain fault displacement parameters suitable for quantitative modeling.

[0005] Therefore, existing methods suffer from the problems of "high accuracy of single-well data but weak spatial prediction capability, and wide coverage of seismic data but insufficient quantitative accuracy." This invention combines fault geometric parameter extraction, time-depth conversion, well profile displacement calculation, and cumulative fracture curve slope delimitation methods from 3D seismic data. It uses well logging data at well points to calibrate the width of the fault fracture zone, establishes a quantitative relationship between well profile displacement and fracture zone width, and extends this relationship to prediction in unexplored areas. To verify the rationality of the established relationship, the measured data of the study area can be compared with publicly available global surface fracture databases. Summary of the Invention

[0006] To achieve quantitative prediction of the width of underground fault fracture zones, this invention proposes a method based on the fusion of seismic and well logging. This method obtains fault displacement parameters from seismic data, calibrates the width of the fault fracture zone using imaging well logging data, and establishes a quantitative relationship model between well profile displacement and fault fracture zone width based on multiple actual drilled well samples, thereby enabling quantitative prediction of the width of fault fracture zones in unexplored areas.

[0007] The method of the present invention includes the step of establishing a prediction model, as follows: S1, acquire basic seismic data and well logging data; S11. Acquire three-dimensional seismic data of the study area and interpret and process the three-dimensional seismic data to extract vertical seismic profiles that pass through the target fault that has been drilled. S12, collect well location coordinates and well trajectory data of relevant drilled wells in the study area, and obtain imaging logging data; In the basic data processing stage, the time-domain seismic profile can be transformed into depth based on the existing time-depth relationship data of the study area to obtain depth-domain parameters for fault displacement calculation.

[0008] S2, Fault displacement calculation based on seismic profiles; based on the vertical seismic profiles, identify the same set of standard reflection interfaces that can be continuously tracked on the hanging wall and footwall of the target fault, obtain the depth difference of the corresponding layers on both sides of the fault at the standard reflection interfaces, and obtain the vertical fault displacement T in the depth domain; simultaneously measure the horizontal fault displacement H of the target fault, and according to... Calculate the well profile displacement D; S3, calibration of fracture zone width based on logging curve; identification of structural fracture data based on imaging logging data, construction of a relationship curve between the cumulative number of structural fractures and well depth; determination of fault fracture zone boundary based on the slope abrupt change point in the relationship curve, and obtaining the total width W of the fault fracture zone at that well. S4, Establishment of the quantitative prediction model; Execute steps S1-S3 to obtain multiple fault samples at different locations. Each sample includes well profile displacement D and the total width W of the fault fracture zone as determined by well logging. Input the well profile displacement D and fault fracture zone width W data of all samples into the statistical software for regression fitting to establish a quantitative relationship model of well profile displacement D and fault fracture zone width W in the target area.

[0009] After the quantitative prediction model is established, the well profile displacement D of the target fault in the un-drilled area is extracted from the three-dimensional seismic data volume. Substituting D into the quantitative prediction model, the predicted value W of the fault fracture zone width of the target fault in the un-drilled area can be obtained.

[0010] Compared with the prior art, the present invention has at least the following beneficial effects: First, this invention establishes a quantitative relationship model between well profile displacement D and fault fracture zone width W by fusing seismic data and imaging logging data, thus realizing the transformation of fault fracture zone width from qualitative description to quantitative prediction.

[0011] Second, this invention utilizes imaging logging to identify structural fractures and determines the boundary of the fault fracture zone by the abrupt change in slope of the cumulative structural fracture number-well depth relationship curve, thereby improving the objectivity and repeatability of fracture zone width calibration.

[0012] Third, when applied in un-drilled areas, this invention only requires extracting the well profile displacement D of the target fault based on three-dimensional seismic data and substituting it into the DW model to obtain the predicted value W of the fault fracture zone width, which has good spatial extension capability and engineering practical value. Attached Figure Description

[0013] Figure 1 This is a graph showing the time-depth transformation relationship of the quadratic polynomial fitting obtained in the first step of Example 2; Figure 2 This is a schematic diagram of displacement extraction and vector synthesis measurement of the well profile in Example 1; Figure 3 This is a schematic diagram of the horizontal displacement read from the Geoframe software in the second step of Example 2; Figure 4 This is a schematic diagram of the vertical displacement read from the Geoframe software in the second step of Example 2; Figure 5 A graph showing the cumulative number of structural fractures in well A versus its depth. Figure 6 A schematic diagram showing the distance measurement from the outer boundary of the fracture zone of well A to the target fault plane; Figure 7 This is a scatter plot of the well profile displacement and fracture zone width obtained from the measured data table in Example 2. Figure 8 This is a comparison and verification diagram between the DW model and the global fault displacement-fracture zone width database in Example 2. Detailed Implementation

[0014] Unless otherwise specified, the data processing methods in the following embodiments are conventional methods in the art, performed according to the techniques or conditions described in the literature or the relevant software manuals. Unless otherwise specified, the data, software, or processing tools used in the following embodiments can be obtained through conventional channels.

[0015] Example 1

[0016] This embodiment provides a method for quantitatively predicting the width of fault fracture zones based on the fusion of seismic and well logging data. The method includes the following steps: Step 1: Acquiring Basic Data a. First, acquire 3D seismic data for the study area. This 3D seismic data can be in SEG-Y format or a pre-loaded 3D seismic interpretation data volume into seismic interpretation software. In this embodiment, Geoframe software is used to interpret the 3D seismic data and extract vertical seismic profiles traversing the drilled target fault. The same effect can be achieved using software such as Petrel and Landmark. This profile includes the well trajectory of the actual drilled well, facilitating subsequent reading of the reflection interface time of the fault's hanging wall and footwall and measurement of fault geometric parameters.

[0017] b. Well Trajectory and Fracture Data Acquisition: Simultaneously, collect the well location coordinates and well trajectory data of all relevant drilled wells within the study area. More importantly, acquire image logging data for these wells, such as FMI (Formation Micro-Imager) or UBI (Ultra-Borehole Imager). FMI can record wellbore images at high resolution (up to centimeter level), clearly revealing information such as fracture morphology, orientation, and opening degree. If image logging is unavailable, conventional fracture logging data (such as dual lateral, dual induction, and sonic variable density logging) will be used for auxiliary analysis.

[0018] c. Establishing time-depth conversion relationships: In the basic data processing stage, a time-depth transformation relationship can be established based on existing TD relationship data for the study area to transform time-domain seismic interpretation results to the depth domain. The time-depth transformation formula can be: y = ax 2 + bx + c Where x is the one-way travel time (OWT) in milliseconds (ms); y is the transformation depth in meters (m); and a, b, and c are empirical coefficients obtained by fitting the TD relationship data of the study area.

[0019] It should be noted that the time-depth conversion is a basic seismic data processing step used to obtain the depth domain parameters required for subsequent fault displacement calculation.

[0020] Step 2: Fault displacement calculation based on seismic profiles The task at this stage is to accurately calculate the displacement parameters of the fault in the depth domain, which will serve as key independent variables for subsequent prediction models.

[0021] d. On the extracted vertical seismic profile, seismic interpreters interpret the target fault plane and identify the same set of standard reflection interfaces that can be continuously tracked in the hanging and footwalls of the fault. These standard reflection interfaces ensure that the readings from the hanging and footwalls correspond to the same geological stratum. By reading the time-domain values ​​of these standard reflection interfaces on both sides of the fault and combining them with the time-depth conversion relationship from step c, the depth difference between corresponding strata in the hanging and footwalls can be calculated, thereby obtaining the depth-domain vertical fault displacement T.

[0022] e. Time Reading and Depth Conversion: At the intersection of the actual drilled well trajectory and the fault plane, the seismic reflection time of the standard reflection interfaces of the hanging wall and footwall of the fault is read, in milliseconds (ms). If the original seismic interpretation result is a two-way travel time (TWT), then TWT is divided by 2 to convert it to a one-way travel time, and then substituted into the time-depth conversion formula to calculate the corresponding depth value.

[0023] f. Vertical Threshold and Time-Depth Conversion: The difference between the depth of the hanging wall and the depth of the footwall is the vertical throw (Throw, T) in the depth domain.

[0024] g. Calculation of horizontal displacement and well profile displacement: (e.g.) Figure 2 As shown, on the seismic profile, the horizontal distance between corresponding points on the hanging wall and footwall of the fault is measured along a direction perpendicular to the fault strike to obtain the horizontal fault displacement (Heave, H). Based on the spatial geometric composition relationship, the well profile displacement at the actual drilled well profile is calculated using the following formula. Where T is the vertical displacement in the depth domain, H is the horizontal displacement, and D is the well profile displacement, all in meters.

[0025] Step 3: Calibration of fracture zone width based on well logging curves The core of this stage is to use high-resolution logging data to establish an objective and repeatable method for defining and calibrating the width of the fracture zone.

[0026] h. Fracture data screening and processing: Import all structural fracture data (including depth, dip, dip angle, aperture, type, etc.) extracted from imaging logging into Excel software.

[0027] i. Plotting the relationship curve between the cumulative number of structural fractures and well depth: Statistically analyze the structural fracture data according to well depth, calculate the cumulative number of structural fractures corresponding to different well depths, and construct a relationship curve between the cumulative number of structural fractures and well depth.

[0028] j. Boundary delineation of the fracture zone: First derivative (i.e., slope) analysis is performed on the above curves.

[0029] In an ideal fracture development profile, the curve will exhibit three typical stages: The fracture zone is divided into three sections: the initial section (sparse cracks, low slope), the main development section (dense cracks, slope initially increases sharply and then stabilizes), and the tail section (cracks decrease or disappear again, slope drops back). The top boundary of the fracture zone corresponds to the first inflection point where the curve slope suddenly increases, while the bottom boundary corresponds to the second inflection point where the slope drops back to the background value. The projection points of these two inflection points onto the depth axis are the top and bottom depths of the fracture zone.

[0030] k. Calculation of fracture zone width: The fracture zone boundary is determined based on the abrupt change in slope in the cumulative structural fracture number-well depth curve. For cases where the well trajectory completely traverses both boundaries of the fault fracture zone, the depth values ​​corresponding to the two inflection points at the top and bottom boundaries of the fracture zone can be extracted, and the difference in well depth between the two, or the spatial distance corrected by the well trajectory, can be used as the total width W of the fault fracture zone at that well point. For cases where the well trajectory only identifies the outer boundary of the fracture zone on one side of the fault, the location of this outer boundary can be projected onto the through-well seismic profile, and the vertical distance r from this outer boundary to the target fault plane can be measured. 2r can be used as the total width W of the fault fracture zone at that well point. The unit of W is meters (m).

[0031] Step 4: Establishment and validation of quantitative prediction models The task at this stage is to use the large amount of sample data obtained in the preceding steps to establish a reliable mathematical model and to conduct preliminary verification of it.

[0032] 1. Sample Data Summary: Within the study area, the system executes steps a to k to obtain fault samples from multiple locations. It is recommended that the number of samples be no less than 15-20. Each sample includes well profile displacement and well logging calibration fracture zone width W.

[0033] m. Fitting: The (D, W) data of all samples are input into statistical software such as Excel for regression fitting to establish a quantitative relationship model of well profile displacement D-fault fracture zone width W in the target area, namely the DW model.

[0034] n. Model Validation: The DW model established in this region is compared with fault displacement-fracture zone width data in the publicly available global surface fracture database. If the distribution trend of the measured sample points in this region is consistent with the trend of the fault displacement-fracture zone width relationship in the publicly available database, and the regression parameters or prediction results of the DW model in this region are within the confidence interval of the corresponding regression relationship in the publicly available database, then the established model has good applicability. If the model parameters, prediction results, or sample trends deviate, steps S1-S3 are reviewed and samples are added before refitting. In the review of step S3, errors caused by non-tectonic fractures, wellbore collapse, or abnormal data quality are excluded.

[0035] The constructed DW model can be used to predict the width of the fault fracture zone in the un-drilled area. The specific steps of the prediction are as follows: (1) Seismic parameter extraction: For the un-drilled blocks in the study area, extract the well profile displacement D of the target fault on the three-dimensional seismic data volume.

[0036] (2) Width prediction and output: Substitute the extracted well profile displacement D into the DW model to obtain the predicted value W of the fault fracture zone width. Finally, the prediction results can be superimposed on the geological map in the form of a raster file or vector fault segment to form a fault fracture zone width prediction distribution map.

[0037] Example 2

[0038] Based on the method in Example 1, this example illustrates the situation using the target fault encountered by Well A in the study area as an example: Step 1: Acquiring Basic Data a. Obtain 3D seismic data for the study area. The 3D seismic data used in this embodiment is in SEG-Y format and has been loaded into Geoframe seismic interpretation software. Geoframe software is used to extract seismic profiles perpendicular to the target fault strike and passing through the trajectory of well A.

[0039] b. Collect the well location coordinates, well trajectory data, and imaging logging fracture interpretation results for Well A. The fracture interpretation results include the structural fracture depths, as shown in Table 1.

[0040] Table 1 Depth of structural cracks c. Establish the time-depth conversion relationship for the study area. This embodiment uses the existing time-depth conversion formula (TD) table for the study area. The TD table is provided by the data provider and includes depth, round-trip travel time (TWT), and one-way travel time (OWT) converted from TWT, where OWT = TWT / 2. Partial data for depth and round-trip travel time (TWT) are shown in Table 2.

[0041] In Excel, using the one-way travel time (OWT) as the x-axis and the corresponding depth (Depth) as the y-axis, a quadratic polynomial fit was performed on the TD relationship data. The result is as follows: Figure 1 As shown, the empirical formula for time-depth conversion used in this embodiment is: y = 0.0009x 2 + 0.7473x + 463.3 The fitting correlation coefficient is: R 2 = 0.9965 Where x is the one-way travel time OWT in milliseconds (ms), and y is the conversion depth in meters (m). The coefficients 0.0009, 0.7473, and 463.3 in the formula are obtained by fitting the TD relationship data mentioned above. If the original seismic data reading result is a two-way travel time TWT, then TWT is first divided by 2 to convert it to a one-way travel time OWT before being substituted into the time-depth conversion formula above.

[0042] Table 2 shows partial data on depth and round-trip travel time (TWT) for the study area. Step 2: Fault displacement calculation based on seismic profiles In Geoframe software, the large-scale fault encountered in well A was used as the target fault, and the target fault plane was interpreted. On the seismic profile, the target fault plane exhibits discontinuity of the reflection phase axis, reduced reflection continuity, and significant displacement of the standard reflection interfaces on both sides of the fault. A seismic profile passing through the well trajectory of well A and perpendicular to the strike of the target fault was selected as the well-through interpretation profile. On this profile, the same set of standard reflection interfaces on the hanging wall and footwall of the fault were identified and marked. Figure 4 (The purple axis in the diagram). A schematic diagram of displacement extraction and vector synthesis measurement across the well profile is shown below. Figure 2 As shown, an example of reading the horizontal and vertical fault displacement values ​​of the target fault in well A is as follows. Figure 3 and Figure 4 As shown.

[0043] Vertical displacement measurement T: The seismic reflection times of the standard reflection interfaces of the hanging wall and footwall of the target fault are read in Geoframe software. When the original reading time is the two-way travel time TWT, it is first divided by 2 to convert it to the one-way travel time OWT. In this embodiment, the two-way travel time of the standard reflection interface of the hanging wall is 1522.4 ms, corresponding to a one-way travel time of 761.2 ms; the two-way travel time of the standard reflection interface of the footwall is 3410.6 ms, corresponding to a one-way travel time of 1705.3 ms. Therefore, the vertical time difference between the hanging wall and footwall in the time domain is 1705.3 - 761.2 = 944.1 ms.

[0044] Since the time-depth conversion formula is based on fitting the one-way travel time OWT to the depth, the one-way travel time of the upper scale (761.2 ms) and the one-way travel time of the lower scale (1705.3 ms) are substituted into the time-depth conversion formula respectively: y = 0.0009x 2 + 0.7473x + 463.3 The conversion depths of the upper and lower standard reflective interfaces are calculated, and then the vertical displacement T in the depth domain is obtained by subtracting the conversion depth of the upper interface from the conversion depth of the lower interface.

[0045] In this embodiment, the calculated vertical displacement T in the depth domain is 2801.392 m. The relevant reading and calculation results are shown in Table 3.

[0046] Substituting the horizontal displacement H = 3296.5 and the vertical displacement T = 2801.392 into the equation... Vector synthesis was performed, and the local fault displacement D at the wellhead profile of well A was calculated to be 4326.0 m. Thus, the displacement D was obtained through measurements of the vertical and horizontal fault displacements.

[0047] Table 3 Data read and calculated from the target fault. In this embodiment, Figure 4 The purple-marked reflection interface is not an arbitrarily selected reflection horizon, but rather the horizon corresponding to the maximum displacement of the target fault on the seismic profile passing through well A. By comparing the fault displacement of multiple comparable reflection interfaces near the target fault, it was found that the fault displacement in the hanging wall and footwall is the largest at the purple reflection interface. Therefore, the purple reflection interface was selected as the measurement benchmark for the vertical fault displacement T and the horizontal fault displacement H. Since this invention establishes a quantitative relationship model between well profile displacement D and fault fracture zone width W, using the maximum displacement as the input parameter of the DW model can better characterize the development scale of the target fault and the control intensity of the fracture zone, avoiding underestimation of the fault fracture zone width due to the use of locally smaller displacement horizons. It should be noted that the purple reflection interface is the horizon corresponding to the maximum displacement in this embodiment. In other wells or other target faults, other reflection interfaces corresponding to the actual maximum displacement should also be used as the measurement benchmark.

[0048] Step 3: Calibration of fracture zone width based on well logging curves The fracture depths identified in Table 1 were categorized by strike and imported into Excel. The horizontal axis represents the cumulative number of structural fractures, and the vertical axis represents the depth interval. Using the number of structural fractures in Well A, a curve showing the relationship between "cumulative number of structural fractures" and "well depth (m)" was plotted (results are shown below). Figure 5 (As shown).

[0049] Analysis of the vertical curve revealed that the slope suddenly increases from a certain depth, marking the entry into the upper boundary of the fracture zone; extending downwards to the lower boundary where the slope clearly decreases. Figure 5 Extract the well depth value corresponding to the lower inflection point, and use this as the location of the outer boundary of the NE-oriented fractured zone at that well measuring point. Figure 5 The NE-trending fracture zone bottom boundary is 2080 m.

[0050] Fracture zone width W: Slope analysis of the vertical curve revealed that the slope of the NE-direction cumulative fracture count curve drops significantly near a well depth of 2080 m, corresponding to the outer boundary of the NE-direction fracture zone. Since well trajectory and logging interpretation data have been loaded into Well A, the 2080 m well depth location can be projected onto the through-well seismic profile in Geoframe software, and the vertical distance from this point to the target fault plane can be measured. Figure 6 ).

[0051] In this embodiment, the vertical distance from the outer boundary of the NE-trending fracture zone to the target fault plane was measured to be 675.9 m. Considering that fault fracture zones are usually distributed on both sides of the fault plane, this embodiment estimates the total width W of the fault fracture zone at this well based on the assumption that the fracture zones on both sides of the fault are symmetrically developed. 总 for: W 总 = 2 × 675.9 = 1351.8 m This yields the width W of the target fault fracture zone in well A. 总 The calibration value is 1351.8 m.

[0052] Step 4: Establishment and validation of quantitative prediction models Following the methods described in steps one through three, the measured parameters of underground fault samples from 16 wells were summarized. Regression fitting was then performed on the well profile displacement (D) extracted from seismic data and the fracture zone width (W) calibrated for each well. A quantitative scaling model suitable for this target area was established (e.g., Figure 7 ): The relationship between well profile displacement and total width of the fractured zone is: W = 55.5 × D 0.44 n. Projecting underground measured data points into a global fault database, where the data distribution trends of both are consistent, such as... Figure 8 As shown.

[0053] Based on the constructed DW model, this invention can predict the total width of the fault fracture zone in the un-drilled area. The specific steps are as follows: (1) For unexplored areas, measure the well profile displacement D of the target fault based on three-dimensional seismic data.

[0054] (2) Substitute the measured well profile displacement D into the DW model to calculate the predicted value W of the fault fracture zone width.

[0055] In this embodiment, the established DW model is: W = 55.5 × D 0.44 Where D is the well profile displacement in meters (m), and W is the predicted width of the fault fracture zone in meters (m).

[0056] For example, for a target fault in an unexplored area, the measured well profile displacement of the fault section is D=2801m. Substituting D into the DW model: W = 55.5 × 2801 0.44 Calculation yields: W ≈ 1824.6m.

[0057] Therefore, when the well profile displacement of the target fault in the un-drilled area is 2801m, the predicted width of the fault fracture zone obtained using the DW model is approximately 1824.6m. This prediction result can serve as a reference for predicting the spatial distribution range of the fault fracture zone in the un-drilled area and for subsequent exploration deployment.

[0058] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A quantitative prediction method for fault fracture zone width based on seismic and well logging fusion, characterized in that, Includes the following steps: (1) Establish a quantitative prediction model for well profile displacement D-fault fracture zone width W in the target area based on basic seismic data and well logging data; S1, acquire basic seismic data and well logging data; S11. Acquire three-dimensional seismic data of the study area and interpret and process the three-dimensional seismic data to extract vertical seismic profiles that pass through the target fault that has been drilled. S12, collect well location coordinates and well trajectory data of relevant drilled wells in the study area, and obtain imaging logging data; S2, Fault displacement calculation based on seismic profile; Based on the vertical seismic profile obtained in step S11, the same set of standard reflection interfaces that can be continuously tracked on the hanging wall and footwall of the target fault are identified. The depth difference between the corresponding layers on both sides of the fault at these standard reflection interfaces is obtained to determine the vertical fault displacement T in the depth domain. Simultaneously, the horizontal fault displacement H of the target fault is measured and... Calculate the well profile displacement D; S3, calibration of fracture zone width based on well logging curves; Based on the data and information from step S12, structural fracture data are identified, and a curve relating the cumulative number of structural fractures to the well depth is constructed. The boundary of the fault fracture zone is determined based on the abrupt change in the slope of the curve, and the total width W of the fault fracture zone at that well is obtained. S4, Establishment of a quantitative prediction model; Perform steps S1-S3 to obtain multiple fault samples at different locations. Each sample includes the well profile displacement D and the total width W of the fault fracture zone as determined by well logging. The well profile displacement D-total fault fracture zone width W of all samples were input into statistical software for regression fitting to establish a quantitative prediction model of well profile displacement D-fault fracture zone width W in the target area. In step S3, the method for obtaining the total width W of the fault fracture zone is as follows: The boundary of the fracture zone is determined by the abrupt change in slope of the cumulative structural fracture number-well depth curve. For cases where the well trajectory completely passes through both sides of the fault fracture zone, the depth values ​​corresponding to the two inflection points at the top and bottom boundaries of the fracture zone are extracted, and the difference between the two well depths is taken as the total width W of the fault fracture zone at that well point. For cases where the well trajectory only identifies the outer boundary of the fracture zone on one side of the fault, the location of the outer boundary is projected onto the seismic profile through the well, the vertical distance r from the outer boundary to the target fault plane is measured, and 2r is taken as the total width W of the fracture zone at that well point. (2) Using the well profile displacement D of the target fault in the un-drilled area extracted from the three-dimensional seismic data volume, substitute D into the quantitative prediction model obtained in step (1) to obtain the predicted value W of the fault fracture zone width of the target fault in the un-drilled area.

2. The method for quantitative prediction of fault fracture zone width based on seismic and well logging fusion as described in claim 1, characterized in that, In step S2, the specific steps for obtaining the depth domain vertical displacement T are as follows: interpret the extracted vertical seismic profile, identify the same set of standard reflection interfaces that can be continuously tracked in the hanging wall and footwall of the fault, and obtain the depth difference of the standard reflection interface at the corresponding layer on both sides of the fault, thereby obtaining the depth domain vertical displacement T.

3. The method for quantitative prediction of fault fracture zone width based on seismic and well logging fusion as described in claim 1, characterized in that, The 3D seismic data of the study area obtained in step S11 is a seismic data volume in SEG-Y format; Geoframe software, Petrel software, or Landmark software are used for interpretation and processing of the 3D seismic data.

4. The method for quantitative prediction of fault fracture zone width based on seismic and well logging fusion as described in claim 1, characterized in that, In step S4, after the model is built, the established quantitative relationship model of well profile displacement D-fault fracture zone width W is compared and verified with the fault displacement-fracture zone width data in the public database.

5. The method for quantitative prediction of fault fracture zone width based on seismic and well logging fusion as described in claim 4, characterized in that, If the parameters, prediction results, or sample trends of the quantitative relationship model deviate from the corresponding relationships in the public database, then review steps S1-S3 and supplement the samples before refitting; in the review of step S3, exclude errors caused by non-structural cracks, wellbore collapse, or abnormal data quality.

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