Fault basin sand body distribution prediction method, device and equipment based on fault activity quantitative characterization, storage medium and product

Abstract

The invention provides a fault basin sand body distribution prediction method and device based on fault activity quantitative characterization, equipment, a storage medium and a product, and relates to the technical field of sand body prediction, and the method comprises the steps: obtaining three-dimensional seismic data and logging data of a target region, and carrying out the structural interpretation of the three-dimensional seismic data, and obtaining the structural interpretation data; calculating a thickness difference grid between a fault hanging wall and a fault footwall of the target fault in the target interval based on the structural interpretation data; calculating a fault activity index of the target fault in a target interval deposition period based on the structural interpretation data; based on the structural interpretation data and the logging data, determining a containing space and a supply ratio of the target interval; and generating a sand body thickness distribution diagram of the target interval based on the thickness difference grid, the fault activity index and the containable space and supply ratio. According to the invention, a more accurate sand body distribution prediction result can be obtained.

Description

Technical Field

[0001] This invention relates to the field of sand body prediction technology, and in particular to a method, apparatus, equipment, storage medium and product for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity. Background Technology

[0002] Fault activity is a key factor controlling the tectonic evolution of sedimentary basins and the development of sedimentary systems. The formation, evolution, and differential activity of faults not only control the spatial distribution and thickness variations of strata but also directly affect the migration paths of sedimentary systems, the direction of sediment supply, and the geometry and spatial connectivity of sand bodies. Especially in continental rift basins (such as the Bohai Bay Basin and the Songliao Basin, which are Cenozoic rift areas), faults are densely developed and highly active. Their differential activity often leads to the migration of sedimentary centers and periodic changes in accommodation space, resulting in multi-level, superimposed sedimentary systems. This causes sand bodies to be vertically discontinuous and planarly asymmetrical, significantly enhancing the heterogeneity of the reservoir.

[0003] In recent years, with the development of high-resolution 3D seismic, well logging interpretation, and geological modeling technologies, the accuracy of fault identification and sedimentary system analysis has significantly improved. Existing methods mainly reveal the correlation between fault activity and sand body distribution qualitatively or semi-quantitatively through structural interpretation, seismic attribute analysis, and well logging facies identification. However, these methods are mostly focused on static description or empirical statistics, failing to establish a systematic and quantitative response relationship between fault activity parameters and sand body distribution characteristics. This leads to insufficient accuracy in sand body prediction to meet the needs of refined exploration in areas with low well control or complex geological conditions. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, storage medium, and product for predicting sand body distribution in rift basins based on quantitative characterization of fault activity, in order to address the shortcomings of low accuracy in sand body distribution prediction in existing technologies and achieve highly accurate sand body distribution prediction.

[0005] This invention provides a method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity, comprising the following steps: Acquire three-dimensional seismic data and well logging data of the target area, and perform structural interpretation on the three-dimensional seismic data to obtain structural interpretation data; Based on the constructed interpretation data, the thickness difference grid between the hanging wall and the footwall of the target fault is calculated within the target segment; Based on the structural interpretation data, the fault activity index of the target fault during the deposition period of the target segment is calculated; Based on the structural interpretation data and the well logging data, the allowable space and supply ratio of the target interval are determined; Based on the thickness difference grid, the fault activity index, and the allowable space and supply ratio, a sand body thickness distribution map of the target layer is generated.

[0006] According to the present invention, a method for predicting sand body distribution in rift basins based on quantitative characterization of fault activity, prior to the step of acquiring three-dimensional seismic data and well logging data of the target area, further includes: Acquire initial well logging data for the target area; The initial logging data is subjected to depth correction, same-layer alignment, and curve standardization to obtain standardized logging data; The standardized logging data were lithologically calibrated using core data and thin section analysis data to obtain calibrated logging data. The calibration logging data is standardized to obtain logging data.

[0007] According to the present invention, a method for predicting the distribution of sand bodies in a rift basin based on quantitative characterization of fault activity is provided. The step of calculating a thickness difference grid between the hanging wall and footwall of a target fault within a target segment, based on the tectonic interpretation data, includes: Extract discrete sedimentary thickness data on both sides of the fault within the target segment from the structural interpretation data; Based on the discrete deposition thickness data, the discrete thickness difference between the hanging wall and the footwall of the fault is calculated; Using the strike of the target fault as the main range direction, an anisotropic kriging interpolation algorithm is used to spatially interpolate the discrete thickness differences to generate an initial grid of continuous thickness differences. A construction consistency spatial smoothing constraint is applied to the initial grid of continuous thickness difference to generate a thickness difference grid.

[0008] According to the present invention, a method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity is provided. The method involves calculating the fault activity index of the target fault during the depositional period of the target interval based on the tectonic interpretation data, including: Based on the structural interpretation data, the vertical subsidence rate of the target fault during the deposition of the target segment is obtained through structural inversion technology and stratigraphic restoration technology, and the stratigraphic thickness of the target segment is obtained. Different cutting layers of the target fault are identified from the structural interpretation data, and the number of times the target fault is active is determined based on the number of cutting layers. Extract the late displacement amplitude of the target fault during late tectonic activity from the structural interpretation data; The fault activity index is calculated based on the vertical subsidence rate, the formation thickness, the number of activity events, and the late displacement amplitude.

[0009] According to the present invention, a method for predicting sand body distribution in a rift basin based on quantitative characterization of fault activity is provided. The method involves generating a sand body thickness distribution map of the target segment based on the thickness difference grid, the fault activity index, and the accommodation space and supply ratio. Using the thickness difference grid, the fault activity index, and the allowable space and supply ratio as input variables, a learnable mapping relationship between the thickness of the sand body and the sand body thickness is constructed as a baseline prediction model. The baseline prediction model is trained using a weighted least squares algorithm to obtain the parameter estimates of the baseline prediction model. An initial prediction result is generated based on the parameter estimates, and a Laplace regularization constraint is applied to the initial prediction result. By minimizing the spatial second derivative of the prediction result, a sand body thickness distribution map is generated.

[0010] According to the present invention, a method for predicting sand body distribution in a rift basin based on quantitative characterization of fault activity, after the steps of generating an initial prediction result based on the parameter estimates, applying a Laplace regularization constraint to the initial prediction result, and generating a sand body thickness distribution map by minimizing the spatial second derivative of the prediction result, the method further includes: Based on Monte Carlo and bootstrap sampling methods, multiple new input variables are obtained by perturbing the input variables multiple times. Based on the aforementioned new input variables, multiple sets of sand body thickness distribution maps are generated; Based on the multiple sets of sand body thickness distribution maps, calculate the confidence interval of sand body thickness at each spatial location, and generate a confidence plane map corresponding to each set of sand body thickness distribution maps; A comprehensive prediction report is generated based on the multiple sets of sand body thickness distribution maps and the corresponding confidence level plane maps.

[0011] The present invention also provides a sand body distribution prediction device, comprising the following modules: The acquisition module is used to acquire three-dimensional seismic data and well logging data of the target area, and to perform structural interpretation on the three-dimensional seismic data to obtain structural interpretation data; The calculation module is used to calculate the thickness difference grid between the hanging wall and the footwall of the target fault within the target segment based on the constructed interpretation data. The calculation module is used to calculate the fault activity index of the target fault during the deposition period of the target segment based on the structural interpretation data. The determination module is used to determine the allowable space and supply ratio of the target interval based on the structural interpretation data and the well logging data. The generation module is used to generate a sand body thickness distribution map of the target layer based on the thickness difference grid, the fault activity index, and the allowable space and supply ratio.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity as described above.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity as described above.

[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity as described above.

[0015] This invention provides a method, apparatus, equipment, storage medium, and product for predicting sand body distribution in rift basins based on quantitative characterization of fault activity. The method involves acquiring 3D seismic data and well logging data of a target area, performing structural interpretation on the 3D seismic data to obtain structural interpretation data, calculating the thickness difference grid between the hanging wall and footwall of the target fault within the target interval based on the structural interpretation data, calculating the fault activity index of the target fault during the depositional period of the target interval based on the structural interpretation data, determining the accommodation space and supply ratio of the target interval based on the structural interpretation data and the well logging data, and generating a sand body thickness distribution map of the target interval based on the thickness difference grid, the fault activity index, and the accommodation space and supply ratio. This invention addresses the technical problem of low accuracy in predicting sand body distribution. Compared to existing technologies, it couples three key parameters—the thickness difference grid between the hanging wall and footwall of the target segment, the fault activity index, and the capacity and supply ratio—into a multivariate manner. This allows the generated sand body thickness distribution map to fully inherit the lateral resolution capability of seismic data while accurately calibrating and constraining the capacity and supply ratio using well logging data. This effectively overcomes the inherent ambiguity problem of traditional methods that rely on single seismic attributes or empirical statistical models, significantly improving the accuracy of predicting the spatial distribution of sand bodies in complex rift basins. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is one of the flowcharts of the method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity provided by the present invention.

[0018] Figure 2 This is the second flowchart of the method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity provided by the present invention.

[0019] Figure 3 This is a schematic diagram of the structure of the device for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity provided by the present invention.

[0020] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] The following is combined with Figures 1-4 This invention describes a method for predicting sand body distribution in rift basins based on quantitative characterization of fault activity. This method is applicable to the prediction of any sand body distribution. The main body executing this method can be an electronic device or a sand body distribution prediction device installed in the electronic device. The sand body distribution prediction device can be implemented through software, hardware, or a combination of both.

[0023] Figure 1 This is one of the flowcharts illustrating the method for predicting sand body distribution in rift basins based on quantitative characterization of fault activity provided by this invention. Figure 1 As shown, the method includes the following: Step 101: Obtain three-dimensional seismic data and well logging data of the target area, and perform structural interpretation on the three-dimensional seismic data to obtain structural interpretation data; It should be noted that 3D seismic data refers to geophysical data volumes formed after field acquisition and migration repositioning, using a 3D spatial grid as the carrier. It typically includes main survey lines (Inline), connecting survey lines (Crossline), and time / depth domain coordinates. Its voxel attribute values ​​characterize the reflection intensity of seismic waves or specific geophysical properties of the subsurface medium wave impedance interface.

[0024] It should be noted that well logging data refers to a one-dimensional or multi-dimensional data sequence continuously collected vertically along the drilled wellbore, reflecting the physical properties and lithological characteristics of the surrounding strata. By performing environmental correction, standardization, and comprehensive interpretation on well logging data, accurate lithological profiles, physical parameters, and key interface depths can be obtained. This provides direct wellpoint constraints for determining the sand content of a single well, calibrating the geological age and lithological significance of seismic reflection phase axes, and establishing a quantitative relationship between accommodation space and sediment supply.

[0025] It should be noted that the structural interpretation results are based on the three-dimensional seismic data volume and are digital models of geological structures identified by automatic tracking algorithms or human-computer interaction methods. They mainly include stratigraphic interpretation data describing the undulation of strata in three-dimensional space and three-dimensional fault models depicting discontinuous features such as strata fracture and faulting.

[0026] In practice, before determining the structural interpretation data based on the 3D seismic data, conventional processing such as noise suppression, deconvolution, velocity analysis, and migration imaging can be performed on the 3D seismic data. Furthermore, attributes such as coherence volume and root mean square amplitude can be extracted to identify fault geometry, stratigraphic interfaces, and sedimentary structures.

[0027] In practical implementation, after acquiring the 3D seismic and well logging data of the target area, preprocessing can be performed on the structural interpretation and well logging data. This preprocessing mainly includes consistency checks, outlier removal, and structural surface smoothing. Furthermore, to eliminate the impact of differences in the distribution range of different data sources on model training, z-score normalization is used to map the structural interpretation and well logging data to a uniform scale interval, ensuring they play an equal weight role in subsequent calculations. Specifically, the calculation formula for z-score normalization is as follows: In the formula, This refers to the construction and interpretation of data or well logging data. This represents the mean. Indicates standard deviation; This represents the standardized result.

[0028] In one feasible implementation, before the steps of acquiring the three-dimensional seismic data and well logging data of the target area, the method includes: acquiring initial well logging data of the target area; performing depth correction processing, same-layer alignment processing, and curve standardization processing on the initial well logging data to obtain standardized well logging data; using core data and thin section analysis data to perform lithological calibration on the standardized well logging data to obtain calibrated well logging data; and performing standardization processing on the calibrated well logging data to obtain well logging data.

[0029] It should be noted that initial logging data can undergo depth correction, layer alignment, and curve standardization. Simultaneously, lithological calibration is performed using logging curves from core data and thin section analysis data, extracting parameters such as logging thickness, sand-slurry ratio, and sand content. This effectively improves the accuracy of sand body identification. Standardizing the calibrated logging data maps it to a uniform scale range, ensuring it plays an equal role in subsequent calculations.

[0030] In this embodiment, depth correction, layer alignment, and curve standardization of well logging data effectively eliminate systematic errors and scale differences in multi-well data, establishing a regionally consistent data benchmark. Furthermore, lithological calibration based on cores and thin sections reliably transforms geophysical curves into geological information, significantly improving the geological reliability and accuracy of extracting key parameters such as sandstone thickness and sand content. Finally, data standardization ensures that all well logging variables play an equal role in the model, thereby enhancing the stability of model training.

[0031] Step 102: Based on the constructed interpretation data, calculate the thickness difference grid between the hanging wall and the footwall of the target fault within the target segment; It should be noted that the target interval refers to the three-dimensional geological unit defined by two isochronous interfaces (top and bottom interfaces) in the aforementioned 3D seismic and well logging data, and its internal sedimentary response is the direct object of this prediction; the target fault refers to the syn-sedimentary fault that was continuously active during the deposition of the target interval, and its activity is the core tectonic factor controlling the development of contemporaneous paleogeography and accommodation space; the hanging wall is the side of the fault located above the fault plane when the fault moves; the footwall is the side of the fault located below the fault plane when the fault moves. In the context of normal faults, the downthrown wall often exhibits subsidence or basins, which can form accumulation sites for thick sand bodies, while the hanging wall is relatively uplifted or subsided more slowly; the thickness difference grid is a spatial gridded data generated based on the difference in stratigraphic thickness between the hanging wall and footwall of the fault within the target interval.

[0032] In one feasible implementation, the step of calculating the thickness difference grid between the hanging wall and footwall of the target fault within the target segment based on the structural interpretation data includes: extracting discrete sedimentary thickness data on both sides of the fault within the target segment from the structural interpretation data; calculating discretely distributed thickness differences between the hanging wall and footwall based on the discrete sedimentary thickness data; spatially interpolating the discretely distributed thickness differences using an anisotropic kriging interpolation algorithm with the strike of the target fault as the main range direction to generate a continuous thickness difference initial grid; and applying a structural consistency spatial smoothing constraint to the continuous thickness difference initial grid to generate a thickness difference grid.

[0033] It should be noted that the discrete thickness difference between the hanging wall and footwall of a fault can be calculated by statistically analyzing and comparing the strata thicknesses on both sides of the main fault. Specifically, the formula for calculating the thickness difference is as follows: In the formula, Indicates the thickness difference; H up Indicates the thickness of the hanging wall of the fault; H down This indicates the thickness of the hanging wall of the fault.

[0034] Understandably, thickness differences can reflect the differences in stratigraphic subsidence caused by fault activity, and are a direct response of fault activity in the stratigraphic record. The stronger the fault activity, the more significant the subsidence of its hanging wall, the larger the sedimentary space, and the thicker the sand body. In contrast, the thickness difference between the two sides of a less active fault is smaller, and the development of sand bodies is limited.

[0035] In practical implementation, to improve spatial continuity and structural consistency, anisotropic kriging interpolation is used for the discrete thickness differences, with the principal axis of the range along the fault strike, and the semi-variogram can be a spherical / exponential model; subsequently, a structural consistency spatial smoothing constraint (Laplacian smoothing) is applied. The specific calculation formula is as follows: In the formula, Indicates sample index ( ); This represents the thickness difference at the i-th sample point. This represents the estimated value calculated by Kriging at the i-th sample point; Represents the smoothing weights for constructing a consistent space smoothing constraint; This represents the Laplace operator.

[0036] In this embodiment, the thickness difference grid between the hanging wall and footwall of the fault is accurately calculated by constructing interpretation data, and this thickness difference grid is used as a key input factor for predicting sand body distribution. This provides quantitative constraints on the migration of sedimentary centers and the enrichment patterns of sand bodies, thereby improving the accuracy and reliability of the prediction model.

[0037] Step 103: Based on the structural interpretation data, calculate the fault activity index of the target fault during the deposition period of the target segment; It should be noted that the vertical subsidence rate can be obtained based on the structural interpretation data through structural inversion technology and stratigraphic restoration technology. At the same time, the number of fault activities can be determined by combining the number of fault-cutting strata in the seismic interpretation. The stratum thickness H is used as a normalization scale to construct a fault activity index. This index can reflect the multi-stage superposition of fault activities and provide a quantitative driving factor for the trend of sand body thickness variation.

[0038] In one feasible implementation, calculating the fault activity index of the target fault during the depositional period of the target segment based on the structural interpretation data includes: obtaining the vertical subsidence rate of the target fault during the depositional period of the target segment using structural inversion and stratigraphic reconstruction techniques, and obtaining the stratigraphic thickness of the target segment; identifying different cutting strata of the target fault from the structural interpretation data, and determining the number of times the target fault has been active based on the number of cutting strata; extracting the late displacement amplitude of the target fault during late tectonic activity from the structural interpretation data; and calculating the fault activity index based on the vertical subsidence rate, the stratigraphic thickness, the number of times the fault has been active, and the late displacement amplitude.

[0039] Understandably, the vertical subsidence rate (D) directly reflects the efficiency with which fault activity creates accommodative space (A). A higher rate means a faster increase in space available for sediment accumulation, which is more conducive to the formation of thick sand bodies. Normalization using stratigraphic thickness (H) (e.g., D / H) can eliminate differences in background sedimentary thickness across different regions, making the activity index more regionally comparable. The number of activity events (N_act) characterizes the persistence and stages of fault activity. Long-term, multi-stage faults (e.g., Type I faults) exert continuous and complex control over sedimentary patterns, affecting the multi-stage superposition patterns and connectivity of sedimentary bodies, while the impact of faults with only one stage of activity is relatively simple. Late-stage displacement amplitude... In practical implementation, the vertical subsidence rate (D) of the target fault during the depositional period of the target segment can be calculated by using "tectonic inversion technology" and "stratigraphic restoration technology" based on structural interpretation data. Simultaneously, the stratigraphic thickness (H) of the target segment can be obtained from the structural interpretation data. Next, the stratigraphic interfaces (cutting strata) of different ages cut by the target fault can be identified from the structural interpretation data. The number of different strata cut (N_act) determines the frequency of fault activity. Then, the late displacement amplitude (ΔD_late) generated by the target fault during late tectonic activity (such as since the Neogene) is extracted from the target structural interpretation data. Finally, the fault activity index is calculated based on the vertical subsidence rate, stratigraphic thickness, activity frequency, and late displacement amplitude. Specifically, the specific formula for calculating the fault activity index is as follows: In the formula, I FA The fault activity index is represented by α, β, and γ, which are weighting coefficients. D represents the fault subsidence rate, H represents the formation thickness, and ΔD represents the fault activity index. late N represents the magnitude of the late-stage displacement. act Indicates the number of times the activity was conducted.

[0040] Step 104: Based on the structural interpretation data and the well logging data, determine the allowable space and supply ratio of the target interval; It should be noted that the accommodation space to supply ratio (A / S ratio) is the ratio of accommodation space to sediment supply intensity, and is a core dynamic equilibrium indicator controlling the type of sedimentary system and the degree of sand body development. Accommodation space (A) can be obtained from structural interpretation data, specifically the isochronous stratigraphic thickness of the target interval. This thickness directly reflects the total amount of space available for sediment filling created by tectonic subsidence (including target fault activity) during the depositional period. Sediment supply intensity (S) can be obtained from well logging data, specifically the sand content (cumulative sandstone thickness / total stratigraphic thickness) obtained based on lithological interpretation statistics. This parameter, after being corrected for provenance direction and intensity by paleogeographic features reflected by the thickness difference grid, can more accurately characterize the input efficiency of sandy sediments. The A / S ratio is obtained by dividing the accommodation space by the sediment supply intensity grid by one grid.

[0041] It should be noted that coupled analysis of fault activity index, thickness difference grid, and accommodation space and supply with sand body thickness within the same mathematical framework (such as sedimentary facies analysis, i.e., identifying braided river sand bodies, core bars, and fine-grained mud deposits through seismic attribute profiles and well logging responses (GR box / bell features), and comparing sedimentary facies on both sides of the fault) can reveal the core control mechanism of fault activity on sand body development: (1) Tectonic control of sedimentary slope → affects the direction of river migration; (2) Settlement rate controls the accommodative space A → affects the thickness of sand accumulation; (3) The sequence of activity phases controls the superposition of sedimentary systems → determines the connectivity of sand bodies; (4) Difference between the upper and lower plates controls the redistribution of material sources → affects the distribution range of sand bodies.

[0042] Studies show that in areas of intense fault activity, the hanging wall subsides significantly, forming sedimentary centers with thicker, more widespread, and more connected sand bodies. Conversely, in areas of weaker fault activity, sedimentary space is limited, resulting in scattered and thinner sand bodies. To avoid the inadequacy of a single regression model in explaining the segmented activity characteristics of complex faults, a tectonic zoning regression strategy can be adopted. This involves classifying faults into Type I, II, and III faults based on their attributes, establishing regression relationships for each, and adding an interaction term to capture nonlinear superposition effects. This ensures the model maintains good predictive ability in complex tectonic regions by capturing these nonlinear superposition effects. The specific regression relationships are as follows: Step 105: Based on the thickness difference grid, the fault activity index, and the allowable space and supply ratio, generate a sand body thickness distribution map of the target layer.

[0043] In practice, sand body thickness prediction volumes, fault activity response volumes, and sedimentary facies zones can be overlaid to generate planar distribution maps, profile connectivity maps, and 3D sand body models. All final outputs can be displayed on a visualization platform, allowing these sand body thickness distribution maps to be used for well location optimization, reservoir modeling, and oil and gas development scheme design, providing quantitative data for actual exploration and development.

[0044] This invention acquires 3D seismic data and well logging data of a target area, and performs structural interpretation on the 3D seismic data to obtain structural interpretation data. Based on the structural interpretation data, a thickness difference grid between the hanging wall and footwall of the target fault is calculated within the target interval. Based on the structural interpretation data, the fault activity index of the target fault during the depositional period of the target interval is calculated. Based on the structural interpretation data and the well logging data, the allowable space and supply ratio of the target interval are determined. Based on the thickness difference grid, the fault activity index, and the allowable space and supply ratio, a sand body thickness distribution map of the target interval is generated. This invention addresses the technical problem of low accuracy in predicting sand body distribution. Compared to existing technologies, it couples three key parameters—the thickness difference grid between the hanging wall and footwall of the target segment, the fault activity index, and the capacity and supply ratio—into a multivariate manner. This allows the generated sand body thickness distribution map to fully inherit the lateral resolution capability of seismic data while accurately calibrating and constraining the capacity and supply ratio using well logging data. This effectively overcomes the inherent ambiguity problem of traditional methods that rely on single seismic attributes or empirical statistical models, significantly improving the accuracy of predicting the spatial distribution of sand bodies in complex rift basins.

[0045] Figure 2 This is the second flowchart of the method for predicting sand body distribution in rift basins based on quantitative characterization of fault activity provided by this invention. Figure 2 As shown, step 105 also includes steps 1051 to 1053: Step 1051: Using the thickness difference grid, the fault activity index, and the allowable space and supply ratio as input variables, construct a learnable mapping relationship between the thickness of the sand body and the sand body as a baseline prediction model. It should be noted that the learnable mapping relationship can be represented as: In the formula, The ratio of available space to supply ( Proximated by settlement rate or isochronous thickness, (Obtained by combining sand content / sand-mud ratio and source conditions), θ is the set of parameters to be calibrated (i.e., parameter estimates), and ε is the error term.

[0046] Understandably, the baseline linear model, as a first-level optimization strategy, is not primarily used for final prediction, but rather as a crucial tool for "explanatory analysis." This model establishes a multiple linear regression relationship between the standardized three core geological control parameters—fault activity index (I_FA), paleogeographic thickness difference (Δh), and accommodation space to supply ratio (A / S*)—and sand body thickness (T_s). Its mathematical expression is as follows: Step 1052: The baseline prediction model is trained using the weighted least squares algorithm to obtain the parameter estimates of the baseline prediction model; It should be noted that the weighted least squares algorithm is used to train the baseline prediction model mainly to solve for the parameter estimates in the baseline prediction model. The specific calculation formula is as follows: In the formula, α represents the estimated optimal parameter value to be found (e.g., α0, αj); T S,k x represents the actual sand body thickness observation value of the k-th known sample (e.g., well point); jk The input variable corresponding to this sample is represented by (i.e., IFA, Δh, A / S); w k It is the weight assigned to the k-th known sample; Var(T) S,k ) represents the observed sand body thickness T in the k-th sample. S,k The variance.

[0047] Step 1053: Generate an initial prediction result based on the parameter estimate, and apply a Laplace regularization constraint to the initial prediction result. By minimizing the spatial second derivative of the prediction result, generate a sand body thickness distribution map.

[0048] It should be noted that applying Laplace regularization to the initial prediction results is primarily aimed at addressing the spatial discontinuity of the prediction results caused by data noise or model limitations, thereby forcing a spatially smooth distribution of the output that conforms to sedimentary geological patterns. Specifically, this process is achieved by solving the following optimization problem: Understandably, by minimizing the above objective function, it is possible to automatically identify and smooth out those local drastic abrupt changes or isolated anomalies (high spatial curvature regions) caused by random noise that do not conform to the distribution law of the sedimentary system without significantly changing the initial prediction results. This results in a natural spatial transition of the final sand body thickness distribution map (Ts), which is consistent with the regional tectonic background and the inherent continuity of the sedimentary body, and significantly improves the geological rationality and visualization effect of the prediction results.

[0049] In one feasible implementation, after the step of applying Laplace regularization constraints to the initial prediction result and generating a sand body thickness distribution map by minimizing the spatial second derivative of the prediction result, the method further includes: obtaining multiple new input variables by perturbing the input variables multiple times based on Monte Carlo and bootstrap sampling methods; generating multiple sets of sand body thickness distribution maps based on the multiple new input variables; calculating the confidence interval of sand body thickness at each spatial location based on the multiple sets of sand body thickness distribution maps, and generating a confidence level plane map corresponding to each set of sand body thickness distribution maps; and generating a comprehensive prediction report based on the multiple sets of sand body thickness distribution maps and the corresponding confidence level plane maps.

[0050] It should be noted that perturbation sampling can be performed on the input variables, and repeated training can be performed using the bootstrap / Monte Carlo method. The confidence interval is obtained the following time: In the formula, CI 95% (i) represents the 95% confidence interval of the sand body thickness Ts at spatial location i. Indicates spatial location (i.e., grid points in the prediction area); T s,i P represents the predicted sand body thickness at the i-th spatial location; 2.5% (T s,i P97.5%(Ts,i) refers to the value at the 2.5% position in ascending order among all predicted values ​​of point i in all N Monte Carlo sampling predictions (i.e., the lower bound); P97.5%(Ts,i) refers to the value at the 97.5% position in ascending order among all predicted values ​​of point i in all N Monte Carlo sampling predictions (i.e., the upper bound).

[0051] It should be noted that confidence maps can be further generated based on confidence intervals at each spatial location: It should be noted that the confidence interval width for each spatial location can be calculated first, as follows: width(CI 95% (i))=P 2.5% (T s,i -P 2.5% (T s,i ) Next, iterate through all spatial locations i within the entire prediction region to find the maximum value of the confidence interval width, denoted as max width; then calculate the confidence value C(i) for each location i; finally, fill the calculated C(i) values ​​for each spatial location i into a raster map identical to the prediction region according to its spatial coordinates, thus obtaining the confidence plane map.

[0052] In practical implementation, multiple sets of sand body thickness distribution maps and corresponding confidence level maps can be systematically integrated and automatically analyzed to ultimately generate a technical document report for geological exploration. Specifically, firstly, the "best estimated" sand body thickness distribution map (such as the median of multiple results), fault activity response map, and three-dimensional sand body distribution model are automatically synthesized and presented; then, the confidence interval map and confidence level map are integrated to intuitively reveal the possible range of variation of the predicted thickness and the spatial distribution of its reliability, thereby achieving a quantitative evaluation of the "geological potential" and "predicted risk" of the exploration target.

[0053] In this embodiment, multiple rounds of perturbation of the input variables are generated using Monte Carlo and bootstrap sampling methods, and the confidence interval and probability distribution of the sand body thickness are calculated based on these multiple rounds of prediction results. This provides uncertainty analysis for sand body distribution prediction, achieving an improvement from "single prediction" to "probabilistic prediction".

[0054] The sand body distribution prediction device provided by the present invention will be described below. The sand body distribution prediction device based on quantitative characterization of fault activity in the rift basin described below can be referred to in correspondence with the sand body distribution prediction method based on quantitative characterization of fault activity in the rift basin described above.

[0055] like Figure 3 As shown, the sand body distribution prediction device includes: The acquisition module 10 is used to acquire three-dimensional seismic data and well logging data of the target area, and to perform structural interpretation on the three-dimensional seismic data to obtain structural interpretation data. Calculation module 20 is used to calculate the thickness difference grid between the hanging wall and the footwall of the target fault within the target segment based on the constructed interpretation data. The calculation module 20 is used to calculate the fault activity index of the target fault during the deposition period of the target segment based on the structural interpretation data. The determination module 30 is used to determine the allowable space and supply ratio of the target interval based on the structural interpretation data and the well logging data. The generation module 40 is used to generate a sand body thickness distribution map of the target segment based on the thickness difference grid, the fault activity index, and the allowable space and supply ratio.

[0056] Optionally, the acquisition module 10 is further configured to: acquire initial well logging data of the target area; The initial logging data is subjected to depth correction, layer alignment, and curve standardization to obtain standardized logging data; the standardized logging data is then lithologically calibrated using core data and thin section analysis data to obtain calibrated logging data; the calibrated logging data is then standardized to obtain logging data.

[0057] Optionally, the calculation module 20 is further configured to: extract discrete sedimentary thickness data on both sides of the fault within the target segment from the structural interpretation data; calculate the discretely distributed thickness difference between the hanging wall and the footwall of the fault based on the discrete sedimentary thickness data; perform spatial interpolation on the discretely distributed thickness difference using an anisotropic kriging interpolation algorithm with the strike of the target fault as the main range direction to generate a continuous thickness difference initial grid; and apply a structural consistency spatial smoothing constraint to the continuous thickness difference initial grid to generate a thickness difference grid.

[0058] Optionally, the calculation module 20 is further configured to: obtain the vertical subsidence rate of the target fault during the deposition period of the target segment using structural inversion technology and stratigraphic reconstruction technology based on the structural interpretation data, and obtain the stratigraphic thickness of the target segment; identify different cutting strata of the target fault from the structural interpretation data, and determine the number of times the target fault has been active based on the number of cutting strata; extract the late displacement amplitude of the target fault during late tectonic activity from the structural interpretation data; and calculate the fault activity index based on the vertical subsidence rate, the stratigraphic thickness, the number of times the fault has been active, and the late displacement amplitude.

[0059] Optionally, the generation module 40 is further configured to: construct a learnable mapping relationship between the thickness difference grid, the fault activity index, and the allowable space and supply ratio as input variables to form a baseline prediction model; train the baseline prediction model using a weighted least squares algorithm to obtain parameter estimates of the baseline prediction model; generate initial prediction results based on the parameter estimates, apply Laplace regularization constraints to the initial prediction results, and generate a sand body thickness distribution map by minimizing the spatial second derivative of the prediction results.

[0060] Optionally, the generation module 40 is further configured to: obtain multiple new input variables by perturbing the input variables multiple times based on Monte Carlo and bootstrap sampling methods; generate multiple sets of sand body thickness distribution maps based on the multiple new input variables; calculate the confidence interval of sand body thickness at each spatial location based on the multiple sets of sand body thickness distribution maps, and generate a confidence level plane map corresponding to each set of sand body thickness distribution maps; and generate a comprehensive prediction report based on the multiple sets of sand body thickness distribution maps and the corresponding confidence level plane maps.

[0061] The sand body distribution prediction device for rift basins based on quantitative characterization of fault activity provided in this application employs the sand body distribution prediction method for rift basins based on quantitative characterization of fault activity in the above embodiments, and can solve the technical problem of low accuracy in existing sand body distribution predictions. Compared with the prior art, the beneficial effects of the sand body distribution prediction device for rift basins based on quantitative characterization of fault activity provided in this application are the same as those of the sand body distribution prediction method for rift basins based on quantitative characterization of fault activity provided in the above embodiments, and other technical features in the sand body distribution prediction device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0062] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other through the communications bus 840. The processor 410 can call logical instructions in the memory 430 to execute a method for predicting sand body distribution in rift basins based on quantitative characterization of fault activity. This method includes: acquiring three-dimensional seismic data and well logging data of the target area, and performing structural interpretation on the three-dimensional seismic data to obtain structural interpretation data; calculating a thickness difference grid between the hanging wall and footwall of the target fault within the target interval based on the structural interpretation data; calculating the fault activity index of the target fault during the depositional period of the target interval based on the structural interpretation data; determining the allowable space and supply ratio of the target interval based on the structural interpretation data and the well logging data; and generating a sand body thickness distribution map of the target interval based on the thickness difference grid, the fault activity index, and the allowable space and supply ratio.

[0063] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0064] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity provided by the above methods. The method includes: acquiring three-dimensional seismic data and well logging data of a target area, and performing structural interpretation on the three-dimensional seismic data to obtain structural interpretation data; calculating the thickness difference grid between the hanging wall and footwall of a target fault within the target segment based on the structural interpretation data; calculating the fault activity index of the target fault during the deposition period of the target segment based on the structural interpretation data; determining the accommodation space and supply ratio of the target segment based on the structural interpretation data and the well logging data; and generating a sand body thickness distribution map of the target segment based on the thickness difference grid, the fault activity index, and the accommodation space and supply ratio.

[0065] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a method for predicting sand body distribution in rift basins based on quantitative characterization of fault activity, as provided by the methods described above. This method includes: acquiring three-dimensional seismic data and well logging data of a target area, and performing structural interpretation on the three-dimensional seismic data to obtain structural interpretation data; calculating a thickness difference grid between the hanging wall and footwall of a target fault within a target segment based on the structural interpretation data; calculating a fault activity index of the target fault during the depositional period of the target segment based on the structural interpretation data; determining the allowable space and supply ratio of the target segment based on the structural interpretation data and the well logging data; and generating a sand body thickness distribution map of the target segment based on the thickness difference grid, the fault activity index, and the allowable space and supply ratio.

[0066] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0067] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity, characterized in that, include: Acquire three-dimensional seismic data and well logging data of the target area, and perform structural interpretation on the three-dimensional seismic data to obtain structural interpretation data; Based on the constructed interpretation data, the thickness difference grid between the hanging wall and the footwall of the target fault is calculated within the target segment; Based on the structural interpretation data, the fault activity index of the target fault during the deposition period of the target segment is calculated; Based on the structural interpretation data and the well logging data, the allowable space and supply ratio of the target interval are determined; Based on the thickness difference grid, the fault activity index, and the allowable space and supply ratio, a sand body thickness distribution map of the target layer is generated.

2. The method for predicting sand body distribution in rift basins based on quantitative characterization of fault activity according to claim 1, characterized in that, Before the steps of acquiring the three-dimensional seismic data and well logging data of the target area, the method further includes: Acquire initial well logging data for the target area; The initial logging data is subjected to depth correction, same-layer alignment, and curve standardization to obtain standardized logging data; The standardized logging data were lithologically calibrated using core data and thin section analysis data to obtain calibrated logging data. The calibration logging data is standardized to obtain logging data.

3. The method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity according to claim 1, characterized in that, The calculation of the thickness difference grid between the hanging wall and footwall of the target fault within the target segment, based on the constructed interpretation data, includes: Extract discrete sedimentary thickness data on both sides of the fault within the target segment from the structural interpretation data; Based on the discrete deposition thickness data, the discrete thickness difference between the hanging wall and the footwall of the fault is calculated; Using the strike of the target fault as the main range direction, an anisotropic kriging interpolation algorithm is used to spatially interpolate the discrete thickness differences to generate an initial grid of continuous thickness differences. A construction consistency spatial smoothing constraint is applied to the initial grid of continuous thickness difference to generate a thickness difference grid.

4. The method for predicting sand body distribution in rift basins based on quantitative characterization of fault activity according to claim 1, characterized in that, The calculation of the fault activity index of the target fault during the depositional period of the target segment, based on the structural interpretation data, includes: Based on the structural interpretation data, the vertical subsidence rate of the target fault during the deposition of the target segment is obtained through structural inversion technology and stratigraphic restoration technology, and the stratigraphic thickness of the target segment is obtained. Different cutting layers of the target fault are identified from the structural interpretation data, and the number of times the target fault is active is determined based on the number of cutting layers. Extract the late displacement amplitude of the target fault during late tectonic activity from the structural interpretation data; The fault activity index is calculated based on the vertical subsidence rate, the formation thickness, the number of activity events, and the late displacement amplitude.

5. The method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity according to claim 1, characterized in that, The process of generating a sand body thickness distribution map of the target layer based on the thickness difference grid, the fault activity index, and the allowable space to supply ratio includes: Using the thickness difference grid, the fault activity index, and the allowable space and supply ratio as input variables, a learnable mapping relationship between the thickness of the sand body and the sand body thickness is constructed as a baseline prediction model. The baseline prediction model is trained using a weighted least squares algorithm to obtain the parameter estimates of the baseline prediction model. An initial prediction result is generated based on the parameter estimates, and a Laplace regularization constraint is applied to the initial prediction result. By minimizing the spatial second derivative of the prediction result, a sand body thickness distribution map is generated.

6. The method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity according to claim 5, characterized in that, After the steps of generating an initial prediction result based on the parameter estimates, applying a Laplace regularization constraint to the initial prediction result, and generating a sand body thickness distribution map by minimizing the spatial second derivative of the prediction result, the method further includes: Based on Monte Carlo and bootstrap sampling methods, multiple new input variables are obtained by perturbing the input variables multiple times. Based on the aforementioned new input variables, multiple sets of sand body thickness distribution maps are generated; Based on the multiple sets of sand body thickness distribution maps, calculate the confidence interval of sand body thickness at each spatial location, and generate a confidence plane map corresponding to each set of sand body thickness distribution maps; A comprehensive prediction report is generated based on the multiple sets of sand body thickness distribution maps and the corresponding confidence level plane maps.

7. A device for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity, characterized in that, include: The acquisition module is used to acquire three-dimensional seismic data and well logging data of the target area, and to perform structural interpretation on the three-dimensional seismic data to obtain structural interpretation data; The calculation module is used to calculate the thickness difference grid between the hanging wall and the footwall of the target fault within the target segment based on the constructed interpretation data. The calculation module is used to calculate the fault activity index of the target fault during the deposition period of the target segment based on the structural interpretation data. The determination module is used to determine the allowable space and supply ratio of the target interval based on the structural interpretation data and the well logging data. The generation module is used to generate a sand body thickness distribution map of the target layer based on the thickness difference grid, the fault activity index, and the allowable space and supply ratio.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for predicting the distribution of sand bodies in rift basins based on quantitative characterization of fault activity as described in any one of claims 1 to 6.

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