Block formation pressure prediction system and method based on three-dimensional seismic information

Abstract

The invention relates to the technical field of oil-gas exploration and development, in particular to a block formation pressure prediction system and method based on three-dimensional seismic information. Comprising a data acquisition module, a depth domain conversion module, a three-dimensional grid chemical engineering area modeling module, a dynamic lithology quantity version generation module, a thinning survey line compaction modeling module, a three-dimensional pressure field calculation module and a dynamic regulation and control module. The global space coverage is realized by constructing a three-dimensional grid model with a well position mark, and the limitation of two-dimensional data is eliminated; pure mudstone node data is processed by adopting a thinning measuring line strategy controlled by structural features, so that the calculation load is remarkably reduced; a reliable data source is screened based on a dynamic lithology probability threshold, and the pressure prediction precision is improved in combination with a sandstone region lithology correction coefficient; the prediction result is verified in a closed-loop mode through the real-time drilling DC index, and a control instruction is triggered. According to the method, the technical bottlenecks of insufficient space coverage, low calculation efficiency and large prediction deviation of the complex geological structure area are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and particularly to a block formation pressure prediction system and method based on three-dimensional seismic information. Background Art

[0002] Three-dimensional seismic exploration arranges a sensor array with a regular grid on the surface of the exploration area, and artificially excites seismic waves to propagate downward; when the waves encounter underground interfaces with different densities or lithologies, part of the energy is reflected back to the surface and recorded by multi-channel receivers; after integrating high-density spatial sampling and time-delay information, processing and inversion techniques are used to reconstruct the three-dimensional geometric shape and physical property distribution of geological bodies; this three-dimensional data volume eliminates the geometric limitations of two-dimensional profiles, significantly improving the analytical ability of the spatial evolution of complex reservoir structures or fault systems, and is widely used in oil and gas reservoir identification, mineral resource assessment, and regional geological model construction.

[0003] Existing formation pressure prediction methods rely on well logging data or two-dimensional seismic data. The former only provides one-dimensional vertical information at discrete well point positions, and the latter can only generate local two-dimensional profiles, resulting in a serious shortage of three-dimensional space sampling density; when facing complex geological structures (such as fault cutting, lateral lithological mutation, or lens body development), lacking continuous spatial data support, it is difficult to establish a three-dimensional pressure field model that conforms to geological laws. At the same time, limited by data processing capabilities, existing technologies usually adopt simplified global compaction curves or static lithology threshold values, which cannot accurately characterize the velocity and pressure response relationship of heterogeneous formations. For example, in a sandstone-shale interbedded block, if the sandstone data points are not effectively removed and the compaction trend line is directly fitted, the slope of the normal pressure trend line will be distorted; if only sparse well point data is relied on for pressure interpolation, the boundary of the overpressure seal box between wells cannot be identified, resulting in risks of well kick or well leakage during the drilling process due to pressure prediction deviation. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a block formation pressure prediction system and method based on three-dimensional seismic information, which solves the problems of limited three-dimensional space coverage, low calculation efficiency, and insufficient prediction accuracy of existing formation pressure prediction methods.

[0005] To solve the above technical problems, the specific technical solutions of the present invention are as follows: In a first aspect, the present invention provides a block formation pressure prediction system based on three-dimensional seismic information, including: A data acquisition module, which acquires three-dimensional seismic wave impedance or velocity data volume, wellbore acoustic travel time, and formation stratification data, performs data abnormal trace rejection and unit unification operations, and outputs a standardized seismic data volume and depth-aligned well data; The depth domain conversion module receives the standardized seismic data volume and wellbore acoustic travel time output by the data acquisition module, performs time-depth conversion to generate the acoustic velocity volume in the depth domain, and establishes a wave impedance and acoustic travel time conversion function; The 3D grid chemical industrial zone modeling module constructs a 3D Cartesian grid model with well position marks based on the acoustic velocity volume in the depth domain generated by the depth domain conversion module and the depth-aligned well data output by the data acquisition module; The dynamic lithology template generation module is based on the 3D grid model constructed by the 3D grid chemical industrial zone modeling module, extracts the acoustic velocity of the near-wellbore trace from the model, analyzes the probability correlation between the acoustic velocity and lithology, generates a dynamic lithology boundary threshold, and screens pure shale grid nodes; The sparse survey line compaction modeling module selects sparse survey lines controlled by structural features based on the acoustic velocity volume in the depth domain generated by the depth domain conversion module and the pure shale node marks screened by the dynamic lithology template generation module, fits a single-channel compaction curve, and interpolates to generate a compaction parameter field for the entire industrial zone; The 3D pressure field calculation module calculates the pore pressure value and performs lithology correction based on the compaction parameter field generated by the sparse survey line compaction modeling module, the acoustic velocity volume in the depth domain generated by the depth domain conversion module, and the dynamic lithology boundary threshold generated by the dynamic lithology template generation module, and outputs a 3D pressure data volume and fluid potential distribution; The dynamic regulation module is based on the 3D pressure data volume output by the 3D pressure field calculation module, and combines the real-time drilling DC index to match the drilling trajectory pressure value with the measured data, and triggers a mud density regulation instruction.

[0006] Furthermore, for the block formation pressure prediction system based on 3D seismic information of the present invention, the data acquisition module is configured as follows: Parse the header information of the seismic SGY file, and perform amplitude statistics on the abnormal seismic traces in the seismic wave impedance or velocity data volume; Mark the abnormal traces with amplitude values exceeding the range of ±3 times the standard deviation as NULL values to obtain a purified seismic data volume; Perform unit conversion on the wellbore acoustic travel time data, unify the travel time units from different sources to μs / ft; synchronously align the formation stratification data with the wellbore depth reference system, correct it to the industrial zone depth reference plane, and control the depth error within 0.1% to generate depth-aligned well data; Integrate the purified seismic data volume, the acoustic travel time data with unified units, and the depth-aligned well data, and output the standardized seismic data volume and depth-aligned well data.

[0007] Furthermore, for the block formation pressure prediction system based on 3D seismic information of the present invention, the data processing module is configured as follows: Receive the standardized seismic data volume output by the data acquisition module as the input time-domain data; apply formation dip correction to the time-domain data and generate a depth-domain acoustic velocity volume using the layer velocity integration method. Extract the seismic amplitude sequence of the well-side trace in the depth-domain acoustic velocity volume and convolve it with the acoustic travel-time curve output by the data acquisition module to generate a synthetic seismogram. Perform time-shift scanning and comparison between the synthetic seismogram and the actual well-side trace seismic amplitude envelope to construct a wave impedance and acoustic travel-time conversion function.

[0008] Further, for the block formation pressure prediction system based on three-dimensional seismic information according to the present invention, the three-dimensional grid chemical industrial area modeling module is configured as follows: Based on the depth-domain acoustic velocity volume generated by the depth-domain conversion module, define a grid coordinate system: the X-axis corresponds to the main seismic line direction, the Y-axis corresponds to the cross-line direction, and the Z-axis is the depth axis. Map the depth-domain acoustic velocity volume to grid nodes and simultaneously call the depth-aligned well data output by the data acquisition module. According to the well trajectory coordinates in the depth-aligned well data, calculate the intersection coordinates of the well trajectory and the grid cells to generate well position markers. Combined with seismic structure interpretation data, add fault property labels to the grid cells in the fault zone and prohibit cross-fault data interpolation.

[0009] Further, for the block formation pressure prediction system based on three-dimensional seismic information according to the present invention, the dynamic lithology template generation module is configured as follows: Extract the acoustic velocity values and corresponding lithology labels of the grid nodes penetrated by the well from the three-dimensional grid model with well position markers constructed by the three-dimensional grid chemical industrial area modeling module. Divide the data segments at vertical depth intervals and apply the Bayesian classification algorithm to calculate the velocity and lithology probability distributions within each depth segment. Construct a dynamic boundary curve with a lithology probability > 70% as the demarcation point. Based on the dynamic boundary curve, traverse all the grid nodes in the industrial area and screen the grid nodes with velocity values lower than the demarcation threshold corresponding to the current depth, and mark them as pure shale nodes.

[0010] Further, for the block formation pressure prediction system based on three-dimensional seismic information according to the present invention, the decimated survey line compaction modeling module is configured as follows: Combined with the fault property labels in the three-dimensional grid chemical industrial area modeling module and the seismic structure interpretation data, extract 1 representative survey line every 50 lines in the main seismic line direction to cover the structural high points, low points and fault transition zones in the industrial area. Based on the pure shale node markers screened by the dynamic lithology template generation module, extract the acoustic travel time data corresponding to the pure shale grid nodes along the thinned survey line; Fit the logarithmic linear compaction equation of the acoustic travel time of pure shale and depth; Adopt the Kriging interpolation algorithm constrained by the structural strike variogram to extend the single-channel compaction curve parameters to the entire work area and generate a three-dimensional compaction parameter field.

[0011] Furthermore, for the block formation pressure prediction system based on three-dimensional seismic information of the present invention, the three-dimensional pressure field calculation module is configured to: Call the three-dimensional compaction parameter field generated by the thinned survey line compaction modeling module and calculate the theoretical acoustic travel time corresponding to each grid unit; Obtain the depth-domain acoustic velocity volume generated by the depth-domain conversion module and extract the actual acoustic travel time values therefrom; Convert and generate pore pressure values according to the deviation degree of the actual acoustic travel time from the theoretical acoustic travel time; For the grid nodes in the sandstone development area, based on the lithology probability distribution data output by the dynamic lithology template generation module, use the correction coefficient associated with lithology probability to reduce the pressure anomaly amplitude; Output a three-dimensional pressure data volume with lithology correction marks and fluid potential distribution.

[0012] Furthermore, for the block formation pressure prediction system based on three-dimensional seismic information of the present invention, the dynamic regulation module is configured to: Based on the three-dimensional grid model constructed by the three-dimensional grid work area modeling module, locate the grid units through which the drilling trajectory passes; Extract the predicted pore pressure values from the three-dimensional pressure data volume output by the three-dimensional pressure field calculation module; Receive the real-time drilling DC index data stream and convert it into an equivalent formation pressure curve; Compare the predicted pore pressure values with the real-time data of the equivalent formation pressure curve. When the pressure coefficient > 1.2 and the relative deviation > 10%, trigger the mud density increment calculation instruction.

[0013] Furthermore, the block formation pressure prediction system based on three-dimensional seismic information of the present invention further includes a structural boundary processing sub-module, which is integrated in the three-dimensional grid work area modeling module The structural boundary processing sub-module is configured to: when detecting the hanging wall area of a thrust fault, trigger the triangulation method to densify the local grid based on the fault attribute label; Identify the grid units without data at the work area boundary, fill in NULL values and prohibit parameter interpolation; Call the depth-domain acoustic velocity volume data. When the acoustic velocity of the salt layer > 4500 m / s, automatically block the input of the time difference data of this layer.

[0014] In a second aspect, the method for predicting formation pressure in a block based on three-dimensional seismic information provided by the present invention is applied to the system for predicting formation pressure in a block based on three-dimensional seismic information, and includes: Step 1: Obtain three-dimensional seismic wave impedance or velocity data volume, wellbore acoustic travel time, and formation stratification data, perform data abnormal trace elimination and unit unification operations, and output a standardized seismic data volume and depth-aligned well data; Step 2: Receive the standardized seismic data volume and wellbore acoustic travel time output by the data acquisition module, perform time-depth conversion to generate a depth-domain acoustic velocity volume, and establish a conversion function between wave impedance and acoustic travel time; Step 3: Based on the depth-domain acoustic velocity volume generated by the depth-domain conversion module and the depth-aligned well data output by the data acquisition module, construct a three-dimensional Cartesian grid model with well position marks; Step 4: Based on the three-dimensional grid model constructed by the three-dimensional grid chemical industrial zone modeling module, extract the acoustic velocity of the trace beside the well from the model, analyze the probability correlation between the acoustic velocity and lithology, generate a dynamic lithology boundary threshold, and screen pure shale grid nodes; Step 5: Based on the depth-domain acoustic velocity volume generated by the depth-domain conversion module and the pure shale node marks screened by the dynamic lithology template generation module, select a sparse survey line controlled by structural features, fit a single-trace compaction curve, and interpolate to generate a compaction parameter field for the entire industrial zone; Step 6: Based on the compaction parameter field generated by the sparse survey line compaction modeling module, the depth-domain acoustic velocity volume generated by the depth-domain conversion module, and the dynamic lithology boundary threshold generated by the dynamic lithology template generation module, calculate the pore pressure value and perform lithology correction, and output a three-dimensional pressure data volume and fluid potential distribution; Step 7: Based on the three-dimensional pressure data volume output by the three-dimensional pressure field calculation module, and in combination with the real-time drilling DC index, match the drilling trajectory pressure value with the measured data, and trigger a mud density regulation instruction.

[0015] Advantages of the present invention; By constructing a three-dimensional grid geological model to integrate the whole-region seismic and well data, the present invention eliminates the spatial limitation of the two-dimensional profile; adopts a sparse survey line strategy controlled by structural features to process pure shale node data, reducing the calculation amount; screens reliable data sources based on the dynamic lithology probability threshold, and combines the lithology correction coefficient in the sandstone development area to eliminate false overpressure misjudgment; verifies the pressure prediction result through a closed-loop of real-time drilling data, systematically solving the three major problems of spatial coverage blind area, low calculation efficiency, and insufficient accuracy. In the application in the thin interbed block of sandstone and shale, the recognition accuracy of the three-dimensional pressure field is improved, the error of depicting the boundary of the overpressure seal box in the complex reverse fault area is reduced, the real-time regulation response speed reaches the second level, and the well kick accident rate is significantly reduced. Description of the drawings

[0016] To more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other accompanying drawings can also be obtained based on the accompanying drawings without creative efforts.

[0017] Figure 1 It is a flowchart of the method for predicting the formation pressure of a block based on three-dimensional seismic information provided by an embodiment of the present invention. Specific implementation manners

[0018] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments and the corresponding accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention will be described in detail below with reference to the accompanying drawings. To better understand the objectives of the present invention, the present invention will be further described in detail below.

[0019] The present invention provides a system for predicting the formation pressure of a block based on three-dimensional seismic information and the specific implementation process. The time-domain wave impedance data volume collected by three-dimensional seismic exploration is stored in the SGY format, covering the range of inline 50 - 200 and crossline 100 - 400 in the work area. At the same time, the acoustic travel time curves (original unit: μs / m) of three wells and the formation stratification data of the Ng formation are obtained. The geographic coordinate system uses the WGS84 projection, and the depth datum is set as the mean sea level.

[0020] Data standardization stage: Analyze the header information of the seismic SGY file, detect that the amplitude value of the 120th trace of Inline73 exceeds the range of ±3 times the standard deviation of the average amplitude of the work area, mark it as a NULL value and interpolate for compensation. Uniformly convert the acoustic travel time unit of the three wells to μs / ft to eliminate the systematic errors of different logging series. Correct the wellbore depth based on the elevation difference between the rotary table elevation and the sea level (+5.2 m) to generate depth-aligned well data. Integrate and purify the seismic data volume, standardize the acoustic travel time, and the stratified data after depth correction, and output the standardized data stream.

[0021] Depth domain conversion and grid modeling: The time-domain data is converted using the layer velocity integration method with dip correction. In the specific implementation, seismic interpretation horizons are introduced as control surfaces in the footwall area of the F1 fault, and the layer velocity field is constructed section by section. The seismic amplitude sequence of the well side trace of Inline100 is convolved with the corrected acoustic travel time curve to generate a synthetic record, and a linear conversion function of wave impedance - acoustic travel time (correlation coefficient 0.85) is established through time shift scanning. After generating the acoustic velocity volume in the depth domain, a Cartesian grid model with a horizontal grid spacing of 25m × 25m and a vertical stratification accuracy of 10m is constructed. The well positions are mapped to the grid nodes according to the well trajectory coordinates, and fault property labels are added to the grids on both sides of the F1 fault to block cross-fault interpolation.

[0022] Lithology identification and compaction modeling: The acoustic velocity value (3120 m / s) and lithology label (sandstone proportion 85%) of the grid penetrated by Well W1 are extracted from the 3D grid model. The data segments are divided at a vertical interval of 500m, and the Bayesian algorithm is applied to calculate the velocity-lithology probability distribution in the depth range of 3000 - 3500m. The sand-shale boundary threshold is determined to be 3150 m / s (mudstone probability 72%). The grid nodes with velocity values lower than this threshold are screened and marked as pure mudstone nodes. Combining with the fault property labels, four survey lines of Inline50 / 100 / 150 / 200 covering the highs and lows of the structure are extracted. The acoustic travel time of the pure mudstone nodes is extracted along Inline100, and a logarithmic linear compaction equation (slope k = 0.28, R² = 0.91) is fitted. Kriging interpolation constrained by the structural trend variogram is used to generate a 3D compaction parameter field with a slope of 0.22 - 0.25 in the footwall and 0.28 - 0.32 in the hanging wall of the fault.

[0023] Pressure calculation and real-time regulation: The theoretical acoustic travel time is calculated based on the compaction parameter field, and compared with the actual travel time values extracted from the depth domain velocity volume. In the sandstone development area (probability > 80%), a lithology correction coefficient is applied (reducing the pressure anomaly amplitude by 15%) to output a 3D pressure data volume with a correction flag. When a directional well drills to 3250m (Ng formation), the predicted pore pressure coefficient 1.28 is extracted from the grid model, and the real-time DC index is simultaneously converted into an equivalent pressure coefficient 1.32. When it is monitored that the pressure coefficient > 1.2 and the relative deviation (3%) continues to expand to the 10% threshold, a command is triggered to increase the mud density from 1.20 g / cm³ to 1.25 g / cm³. For the salt rock high-velocity layer (velocity > 4500 m / s), its interference to the compaction model is automatically shielded.

[0024] Effect verification: During the implementation in a certain work area with thrust faults developed, the 3D pressure field model identified an overpressure body with sealing property in the footwall of the F1 fault, and the error from the actual drilling pressure test was < 5%. The thinning survey line strategy increased the calculation efficiency by 8 times, and the pure mudstone screening mechanism reduced the slope error of the compaction trend line from 0.32 to 0.28, and the drilling well kick accident rate decreased.

[0025] In the application of 3D seismic exploration data, the data acquisition module first parses the header information of SGY format files to identify the spatial distribution range and sampling parameters of seismic wave impedance or velocity data volumes. When performing amplitude statistical analysis on each seismic trace in the data volume, the standard deviation threshold method is used to detect outliers: when the amplitude value of a certain seismic trace exceeds the range of the average amplitude of the work area ± 3 times the standard deviation, it is marked as a NULL value and weighted interpolation compensation of adjacent traces is performed. The borehole acoustic travel time data is sourced from the logging curves of multiple wells and needs to be uniformly converted to the standard measurement unit μs / ft to eliminate the systematic deviation of different logging series. The formation stratification data and the borehole depth reference system are aligned to the work area depth reference plane through linear offset correction, and the correction amount is calculated based on the elevation difference between the kelly bushing elevation of the wellhead and the reference plane, with the depth error controlled within 0.1%. Finally, the seismic volume, standardized acoustic travel time, and depth-corrected well data are integrated and purified to form a standardized output with a unified spatial reference.

[0026] After receiving the standardized seismic data volume, the depth domain conversion module uses it as the input source data in the time domain. The time-depth conversion is performed using the layer velocity integration method. In areas with developed faults, seismic interpretation horizons are introduced as velocity modeling control surfaces, and the layer velocity field is constructed in segments to solve the problem of formation inversion distortion. The seismic amplitude sequence of the trace adjacent to the well is extracted from the depth domain acoustic velocity volume generated by the conversion, and is convolved with the standardized acoustic travel time curve to generate a synthetic seismic record. By comparing the synthetic record with the actual seismic trace amplitude envelope morphology through time shift scanning technology, the time migration amount and amplitude scale factor are dynamically adjusted to establish a quantitative conversion relationship between wave impedance and acoustic travel time. This process outputs a depth domain velocity volume that eliminates time propagation distortion and provides a physical basis for attribute conversion.

[0027] Based on the spatial distribution characteristics of the depth domain acoustic velocity volume, a Cartesian grid coordinate system is defined: the X-axis is parallel to the main seismic survey line direction, the Y-axis is parallel to the cross survey line direction, and the Z-axis extends along the depth direction. A horizontal grid spacing of 25m × 25m and a vertical layering accuracy of 10m are set, and the velocity attribute values are mapped to the grid nodes. The spatial coordinates of the well trajectory in the depth-aligned well data are called, and the intersections of the trajectory and the grid cells are calculated using cubic spline interpolation to generate well position marking attributes. Combining the seismic structure interpretation results, fault attribute labels are added to the grid cells in the area covered by the fault polygon, and an interpolation isolation zone is set to block cross-fault data transfer. This grid model integrates seismic attributes, well position distribution, and structural boundary information to construct a unified spatial data carrier for the entire work area.

[0028] The dynamic lithology template generation module extracts the acoustic velocity values and corresponding lithology labels of the well-penetrated grid nodes from the 3D grid model with well position marks. The lithology labels are derived from the borehole logging interpretation data and have been associated with the corresponding nodes during the grid mapping stage. The data is segmented at intervals of 500 meters in the vertical depth direction. In each depth segment, the Bayesian classification algorithm is applied: the occurrence frequencies of mudstone and sandstone in different velocity intervals are statistically analyzed, and the conditional probability distribution of lithology under a given velocity condition is calculated. Using a lithology probability greater than 70% as the demarcation threshold, a dynamic demarcation curve varying with depth is constructed. All grid nodes in the entire work area are traversed, and the acoustic velocity value of the current node is compared with the demarcation threshold at the corresponding depth. Nodes with velocity values lower than the threshold are screened and marked as pure mudstone nodes. This dynamic threshold mechanism adapts to the variation law of formation velocity with depth and avoids misjudgment of lithology caused by a fixed threshold value.

[0029] The sparsely sampled survey line compaction modeling module first identifies key areas such as structural highs, lows, and fault transition zones in the work area based on the fault attribute labels in the 3D grid model and seismic structural interpretation data. One representative survey line is extracted every 50 lines along the main survey line direction to achieve full coverage of structural features. Based on the pure mudstone node marks output by the dynamic lithology template module, the acoustic time difference data of the corresponding grids is extracted along the sparsely sampled survey lines. The least squares method is used to fit the logarithmic linear relationship equation between the acoustic time difference of pure mudstone and depth to obtain the single-channel compaction trend line parameters. The spatial correlation of geological bodies is quantified using the structural trend variogram, and the Kriging interpolation algorithm is constrained to extrapolate the single-channel parameters to the entire work area to generate a 3D compaction parameter field. This strategy improves the calculation efficiency by data downsampling controlled by the structure while maintaining the continuity of geological laws.

[0030] The 3D pressure field calculation module calls the 3D compaction parameter field generated by the sparsely sampled survey line compaction modeling module to calculate the theoretical acoustic time difference of each grid cell under normal compaction conditions. The actual acoustic time difference values are extracted from the depth-domain acoustic velocity volume output by the depth-domain conversion module. According to the deviation degree between the actual time difference and the theoretical time difference, the pore pressure value is converted and generated using the compaction equilibrium principle. For the grid nodes in the sandstone-developed area, the lithology probability distribution data output by the dynamic lithology template module is referenced: when the sandstone probability exceeds the threshold, a pressure anomaly amplitude correction is applied based on the probability value ratio (e.g., 85% sandstone probability corresponds to a 15% amplitude reduction) to eliminate the false overpressure deviation caused by high-velocity sandstone. Finally, a 3D pressure data volume with lithology correction marks and a fluid potential distribution map are output, where the correction marks record whether the pressure value has been corrected for lithology. This process realizes the dynamic precision control of pressure calculation in heterogeneous formations.

[0031] The dynamic regulation module locates the grid cells penetrated by the drilling trajectory based on the three-dimensional grid model, and matches the corresponding grid positions in real time according to the spatial coordinates of the wellbore trajectory. It extracts the predicted pore pressure values of the target grid from the three-dimensional pressure data volume and generates a pressure profile curve that varies with depth. It synchronously receives the DC index data stream transmitted from the drilling site and generates an equivalent formation pressure curve through the Eaton model conversion. By comparing the real-time data points of the predicted pore pressure curve and the DC index conversion curve, when it is monitored that the pressure coefficient exceeds 1.2 and the relative deviation between the two continuously exceeds 10%, it triggers the instruction for calculating the mud density increment. This instruction dynamically adjusts the mud density value according to the overpressure amplitude, forming a closed-loop control chain from pressure prediction to engineering regulation. This module breaks through the limitations of traditional post-event regulation and realizes early warning of drilling risks.

[0032] The structural boundary processing sub-module runs integrated within the three-dimensional grid chemical industrial zone modeling module. During the grid construction stage, it detects the hanging wall area of the thrust fault in real time: when the fault attribute label indicates that the dip angle > 30°, it automatically triggers the triangulation algorithm to densify the local grid to a 10m spacing to accurately represent the fault morphology. After identifying the grid cells with no data coverage at the industrial zone boundary, it fills in NULL values and sets interpolation shielding marks to block the boundary distortion caused by parameter extrapolation. It calls the depth-domain acoustic velocity volume data for horizon scanning. When the average acoustic velocity of a certain horizon continuously > 4500m / s and is laterally continuously distributed, it is determined as a salt rock layer and its acoustic time difference input is automatically shielded. This mechanism optimizes the grid structure and data processing rules for special structures to ensure the reliability of pressure field modeling in complex geological areas.

[0033] The grid model optimized by the structural boundary processing sub-module provides an accurate spatial positioning basis for dynamic regulation. The grid densification in the thrust fault area reduces the drilling trajectory positioning error to the 5-meter level, improving the pressure extraction accuracy; the filling of NULL values at the boundary avoids the interference of false pressure values in the data-free area on the regulation decision-making; the salt rock layer shielding mechanism eliminates the interference of high-speed anomalies on pressure conversion and ensures the reliability of the DC index comparison benchmark. The drilling pressure data provided in real time by the dynamic regulation module verifies the rationality of the structural boundary processing in reverse.

[0034] The following embodiments are based on typical application scenarios such as sandstone-shale interbedded blocks, thrust fault zones, and salt rock development areas, and detail the specific implementation process. In each embodiment, the coverage area of the three-dimensional seismic data ≥ 200km², the seismic trace interval is 25m × 25m, and the vertical sampling interval is 2ms.

[0035] Example 1: Sandstone-shale interbedded block: In a certain onshore work area, thin interbeds of sandstone and shale are developed in the Ng Formation. The 3D seismic data volume includes Inline 200 - 500 lines. The data acquisition module detects the amplitude anomaly (>3σ) of the 80th trace of Inline 305, marks NULL values and interpolates for compensation. The original acoustic travel time data of Well W1 is in the unit of μs / m, which is uniformly converted to μs / ft; corrected to the sea level datum according to the kelly bushing elevation +7.5m, and the depth error is 0.08%. The depth domain conversion module uses the dip-corrected layer velocity integration method to establish an impedance-acoustic travel time conversion function (correlation coefficient 0.82) at the well-side trace. The 3D grid modeling defines a 10m vertical stratification. When mapping the trajectory of Well W1 to the grid nodes, it is found that the deviation between the deviated section of the well and the grid is >5m, triggering local grid refinement. The dynamic lithology template generation module calculates the demarcation velocity of sandstone and shale as 3150m / s (mudstone probability 75%) in the depth range of 3000 - 3500m, and screens out the pure mudstone nodes accounting for 68% of the work area. The thinned survey lines are extracted at a rate of 1 per 50 lines (a total of 6 lines), and the slope k of the compaction curve is fitted to be 0.29. The pressure calculation module applies an 18% correction factor to the sandstone area (probability >85%) and outputs a pressure coefficient of 1.25. When drilling to 3250m, the DC index shows a pressure coefficient of 1.32, and the deviation >10% triggers the mud density to be increased from 1.18g / cm³ to 1.23g / cm³ to avoid the risk of well kick.

[0036] Example 2: Thrust fault zone: In a certain work area, the F2 thrust fault is developed, and the strata in the hanging wall of the fault are overturned. After the structural boundary processing sub-module detects the hanging wall area, the grid is refined from 25m to 10m using the triangulation method. The 3D grid modeling module adds fault attribute tags to block cross-fault interpolation. The dynamic lithology template generation module corrects the demarcation velocity to 3400m / s (mudstone probability 80%) in the strongly compacted area of the hanging wall (depth 4000m). The thinned survey lines are densely extracted along the fault strike for 12 survey lines, and the Kriging interpolation generates a parameter field with a compaction slope of 0.24 for the hanging wall and 0.31 for the footwall. The 3D pressure field calculation shows a closed overpressure body in the hanging wall (pressure coefficient 1.45), and the error is <5% verified by actual drilling. The dynamic regulation module matches the pressure curve in real time when crossing the fault, and adjusts the mud density to 1.40g / cm³ 20m in advance when drilling into the high-pressure layer in the hanging wall.

[0037] Example 3 Salt rock high-speed layer interference area: In the salt dome development work area, the structural boundary processing sub-module identifies salt rock layers with a velocity > 4800 m / s and automatically masks their acoustic time difference data. The data acquisition module eliminates 23 abnormal seismic traces on the flanks of the salt dome. The depth domain conversion module uses salt rock layer-constrained layer velocity modeling to reduce the depth error of the T4 reflection layer. The dynamic lithology template generation module excludes salt rock interference when constructing the demarcation curve in the subsalt formation (4500 - 5000 m), and sets the demarcation velocity to 3800 m / s. The sparsely sampled survey lines avoid the salt dome boundary and extract 8 representative survey lines, and the fitting R² of the compaction equation is > 0.9. The pressure calculation module outputs the subsalt overpressure body distribution map to guide well location optimization to avoid high-pressure areas, and the drilling accident rate decreases.

[0038] Example 4 Efficiency optimization in a large work area: In a work area with an area of 300 km², the sparsely sampled survey line compaction modeling module only processes 5% of the seismic trace data (the existing method requires 100%). 30 survey lines are extracted according to the structural units, and the Kriging interpolation time is reduced from 72 hours to 9 hours. The 3D grid modeling module fills NULL values in the data-free areas at the boundaries to avoid extrapolation errors. The dynamic regulation module collects DC index data streams every 10 seconds and matches the grid pressure values in real time. During the horizontal section drilling of Well P1, when it is monitored that the pressure deviation increases from 5% to 12%, the system triggers the mud density regulation instruction within 2 seconds to control the bottom hole pressure fluctuation within the range of ±0.02 g / cm³.

[0039] The present invention systematically solves the three major problems of limited three-dimensional space coverage, low calculation efficiency, and insufficient prediction accuracy through the following technical paths: Construct a 3D grid chemical work area modeling module, fuse the depth domain acoustic velocity volume and depth-aligned well data into a Cartesian grid model with well location marks to achieve continuous spatial coverage of the entire work area. The grid coordinate system (the X / Y axis corresponds to the main seismic survey line / transverse survey line, and the Z axis is the depth axis) eliminates the limitations of the two-dimensional profile. The structural boundary processing sub-module locally densifies the grid using the triangulation method in the hanging wall of the thrust fault to block cross-fault interpolation and solve the data blind area problem in the fault sealing area. The salt rock shielding mechanism automatically filters out interference data of layers with a velocity > 4500 m / s to ensure the spatial integrity of the complex structural area.

[0040] The sparsely sampled survey line compaction modeling module extracts 5% - 10% representative survey lines (such as extracting 1 survey line for every 50 main survey lines) according to the structural characteristics (high points / low points / fault zones), and only processes the acoustic time difference data of pure shale nodes. Through Kriging interpolation constrained by the structural trend variogram, the single-channel compaction curve parameters are extended to the entire work area to avoid the consumption of traditional global computing resources. The dynamic lithology template generation module divides the data by depth segments and applies the Bayesian classification algorithm to process the velocity-lithology relationship of each segment in parallel, reducing the ineffective calculation amount by more than 75%.

[0041] The dynamic lithology template generation module constructs a threshold curve with a lithology probability > 70% as the dynamic demarcation point to replace the fixed lithology threshold. The three-dimensional pressure field calculation module screens pure shale nodes based on this threshold to establish a compaction model, eliminating the interference of sandstone pore effects; a correction coefficient associated with the lithology probability is applied to the sandstone development area (for example, when the sandstone probability > 85%, the correction amount is +18%) to solve the misjudgment of false overpressure in high-velocity sandstone. The real-time regulation module compares the predicted value of the three-dimensional pressure data volume with the measured DC index curve, and triggers mud density regulation when the pressure coefficient > 1.2 and the deviation > 10%, forming a closed-loop verification mechanism.

[0042] The above technical path systematically overcomes the defects of existing methods through a logical chain of spatial global modeling, data intelligent thinning, dynamic parameter correction, and real-time feedback verification, achieving coordinated optimization of spatial coverage, efficiency, and accuracy.

Claims

1. A block formation pressure prediction system based on three-dimensional seismic information, characterized in that Including: A data acquisition module that acquires 3D seismic wave impedance or velocity data volume, borehole acoustic travel time, and formation stratification data, performs data abnormal trace rejection and unit unification operations, and outputs a standardized seismic data volume and depth-aligned well data; A depth domain conversion module that receives the standardized seismic data volume and borehole acoustic travel time output by the data acquisition module, performs time-depth conversion to generate a depth domain acoustic velocity volume, and establishes a wave impedance and acoustic travel time conversion function; A 3D grid chemical industrial zone modeling module that constructs a 3D Cartesian grid model with well position markers based on the depth domain acoustic velocity volume generated by the depth domain conversion module and the depth-aligned well data output by the data acquisition module; A dynamic lithology template generation module that, based on the 3D grid model constructed by the 3D grid chemical industrial zone modeling module, extracts the acoustic velocity of the well-side trace from the model, analyzes the probability correlation between the acoustic velocity and lithology, generates a dynamic lithology boundary threshold, and screens pure shale grid nodes; A sparsified survey line compaction modeling module that, based on the depth domain acoustic velocity volume generated by the depth domain conversion module and the pure shale node markers screened by the dynamic lithology template generation module, selects sparsified survey lines controlled by structural features, fits a single-trace compaction curve, and interpolates to generate a compaction parameter field for the entire industrial zone; A 3D pressure field calculation module that calculates pore pressure values and performs lithology correction based on the compaction parameter field generated by the sparsified survey line compaction modeling module, the depth domain acoustic velocity volume generated by the depth domain conversion module, and the dynamic lithology boundary threshold generated by the dynamic lithology template generation module, and outputs a 3D pressure data volume and fluid potential distribution; A dynamic regulation module that, based on the 3D pressure data volume output by the 3D pressure field calculation module and in combination with the real-time drilling DC index, matches the drilling trajectory pressure value with the measured data and triggers a mud density regulation instruction.

2. The formation pressure prediction system for a block based on three-dimensional seismic information according to claim 1, wherein The data acquisition module is configured to: Parse the seismic SGY file header information and perform amplitude statistics on the abnormal seismic traces in the seismic wave impedance or velocity data volume; Mark the abnormal traces with amplitude values exceeding the range of ±3 times the standard deviation as NULL values to obtain a purified seismic data volume; Perform unit conversion on the borehole acoustic travel time data, unify the travel time units from different sources to μs / ft; synchronously align the formation stratification data with the borehole depth reference system, correct it to the industrial zone depth reference plane, and control the depth error within 0.1% to generate depth-aligned well data; Integrate the purified seismic data volume, the acoustic travel time data with unified units, and the depth-aligned well data, and output a standardized seismic data volume and depth-aligned well data.

3. The formation pressure prediction system for a block based on three-dimensional seismic information according to claim 2, wherein, The data processing module is configured to: Receive the standardized seismic data volume output by the data acquisition module as input time domain data; apply formation dip correction to the time domain data and generate a depth domain acoustic velocity volume using the layer velocity integration method; Extract the seismic amplitude sequence of the well-side trace in the depth domain acoustic velocity volume and convolve it with the acoustic travel time curve output by the data acquisition module to generate a synthetic seismogram; Perform time shift scanning and comparison on the synthetic seismogram and the actual well-side trace seismic amplitude envelope to construct a wave impedance and acoustic travel time conversion function.

4. The formation pressure prediction system for a block based on three-dimensional seismic information according to claim 3, wherein The three-dimensional grid chemical industrial zone modeling module is configured to: Based on the depth-domain acoustic wave velocity volume generated by the depth-domain conversion module, define a grid coordinate system: the X-axis corresponds to the direction of the main seismic survey line, the Y-axis corresponds to the cross survey line direction, and the Z-axis is the depth axis; Map the depth-domain acoustic wave velocity volume to grid nodes, and at the same time call the depth-aligned well data output by the data acquisition module; According to the well trajectory coordinates in the depth-aligned well data, calculate the intersection coordinates of the well trajectory and the grid cells, and generate well position marks; Combined with seismic structure interpretation data, add fault property labels to the grid cells in the fault zone, and prohibit cross-fault data interpolation.

5. The formation pressure prediction system for a block based on three-dimensional seismic information according to claim 4, characterized in that The dynamic lithology template generation module is configured to: Extract the acoustic wave velocity values and corresponding lithology labels of the grid nodes penetrated by the wells from the three-dimensional grid model with well position marks constructed by the three-dimensional grid chemical industrial zone modeling module; Divide the data segments at vertical depth intervals, and apply the Bayesian classification algorithm to calculate the velocity and lithology probability distributions within each depth segment; Construct a dynamic boundary curve with a lithology probability > 70% as the demarcation point; Based on the dynamic boundary curve, traverse the grid nodes in the entire industrial zone, and screen the grid nodes with velocity values lower than the boundary threshold corresponding to the current depth, and mark them as pure shale nodes.

6. The formation pressure prediction system for a block based on three-dimensional seismic information according to claim 5, characterized in that The sparse survey line compaction modeling module is configured to: Combined with the fault property labels in the three-dimensional grid chemical industrial zone modeling module and seismic structure interpretation data, extract 1 representative survey line every 50 in the direction of the main survey line to cover the structural high points, low points and fault transition zones in the industrial zone; Based on the pure shale node marks screened by the dynamic lithology template generation module, extract the acoustic time difference data corresponding to the pure shale grid nodes along the sparse survey line; Fit the logarithmic linear compaction equation of the acoustic time difference of pure shale and depth; Use the Kriging interpolation algorithm constrained by the structural trend variogram to extend the single-channel compaction curve parameters to the entire industrial zone to generate a three-dimensional compaction parameter field.

7. The formation pressure prediction system for a block based on three-dimensional seismic information according to claim 6, wherein The three-dimensional pressure field calculation module is configured to: Call the three-dimensional compaction parameter field generated by the sparse survey line compaction modeling module to calculate the theoretical acoustic time difference corresponding to each grid cell; Obtain the depth-domain acoustic wave velocity volume generated by the depth-domain conversion module, and extract the actual acoustic time difference values therefrom; Convert and generate pore pressure values according to the deviation degree of the actual acoustic time difference from the theoretical acoustic time difference; For the grid nodes in the sandstone development area, based on the lithology probability distribution data output by the dynamic lithology template generation module, use the correction coefficient associated with the lithology probability to reduce the pressure anomaly amplitude; Output a three-dimensional pressure data volume with lithology correction marks and fluid potential distribution.

8. The formation pressure prediction system for a block based on three-dimensional seismic information according to claim 7, characterized in that The dynamic regulation module is configured to: Based on the three-dimensional grid model constructed by the three-dimensional grid chemical industrial zone modeling module, locate the grid cells penetrated by the drilling trajectory; Extract the predicted pore pressure values from the three-dimensional pressure data volume output by the three-dimensional pressure field calculation module; Receive the real-time drilling DC index data stream and convert it into an equivalent formation pressure curve; Compare the real-time data of the predicted pore pressure value and the equivalent formation pressure curve. When the pressure coefficient > 1.2 and the relative deviation > 10%, trigger the mud density increment calculation instruction.

9. The formation pressure prediction system for a block based on three-dimensional seismic information according to claim 8, characterized in that, It also includes a structural boundary processing sub-module, which is integrated into the three-dimensional grid chemical industrial zone modeling module. The structural boundary processing sub-module is configured to: when the hanging wall area of the thrust fault is detected, trigger the triangulation method to encrypt the local grid based on the fault attribute label; Identify the grid cells without data at the industrial zone boundary, fill in NULL values and prohibit parameter interpolation; Call the acoustic wave velocity volume data in the depth domain. When the acoustic wave velocity of the salt layer is > 4500 m / s, automatically block the input of the interval travel time data of this layer.

10. A method for predicting formation pressure in a block based on three-dimensional seismic information, which is applied to the system for predicting formation pressure in a block based on three-dimensional seismic information according to any one of claims 1 to 9, and is characterized in that, It includes: Step 1: Obtain the three-dimensional seismic wave impedance or velocity data volume, wellbore acoustic wave interval travel time, and formation stratification data, perform data abnormal trace elimination and unit unification operations, and output the standardized seismic data volume and depth-aligned well data; Step 2: Receive the standardized seismic data volume and wellbore acoustic wave interval travel time output by the data acquisition module, perform time-depth conversion to generate the acoustic wave velocity volume in the depth domain, and establish the conversion function between wave impedance and acoustic wave interval travel time; Step 3: Based on the acoustic wave velocity volume in the depth domain generated by the depth domain conversion module and the depth-aligned well data output by the data acquisition module, construct a three-dimensional Cartesian grid model with well position marks; Step 4: Based on the three-dimensional grid model constructed by the three-dimensional grid chemical industrial zone modeling module, extract the acoustic wave velocity of the trace beside the well from this model, analyze the probability correlation between the acoustic wave velocity and lithology, generate the dynamic lithology boundary threshold, and screen the pure shale grid nodes; Step 5: Based on the acoustic wave velocity volume in the depth domain generated by the depth domain conversion module and the pure shale node marks screened by the dynamic lithology template generation module, select the sparsely sampled survey lines controlled by structural features, fit the single-trace compaction curve, and interpolate to generate the compaction parameter field of the entire industrial zone; Step 6: Based on the compaction parameter field generated by the sparsely sampled survey line compaction modeling module, the acoustic wave velocity volume in the depth domain generated by the depth domain conversion module, and the dynamic lithology boundary threshold generated by the dynamic lithology template generation module, calculate the pore pressure value and perform lithology correction, and output the three-dimensional pressure data volume and fluid potential distribution; Step 7: Based on the three-dimensional pressure data volume output by the three-dimensional pressure field calculation module, and combined with the real-time drilling DC index, match the pressure value of the drilling trajectory with the measured data, and trigger the mud density regulation instruction.

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