A method and system for predicting sandstone and mudstone reservoirs in a few well area with multiple sedimentary facies belts

Abstract

The present application relates to the field of oil and gas exploration, and provides a few well area multi-depositional facies belt sand shale reservoir prediction method and system, the method comprises the following steps: preprocessing seismic exploration data of the study area, extracting seismic attribute volume combined with depositional facies belt lithology difference, screening the seismic attribute volume through lithology sensitivity analysis to obtain core attribute volume; taking the core attribute volume as a constraint condition, performing primary inversion on the pretreated seismic exploration data through inversion algorithm to obtain primary inversion data volume; constructing pseudo well according to the primary inversion data volume and depositional facies belt characteristics, modeling the pseudo well and well logging data of real drilled well jointly to obtain low frequency model; taking the low frequency model as a constraint condition to perform iterative inversion, predicting sand shale reservoir from the iterative inversion result to obtain full area sand shale reservoir prediction result. The present application improves the adaptability of multi-depositional facies belt.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method and system for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies. Background Technology

[0002] In the field of oil and gas exploration and development, sandstone and mudstone reservoir prediction is a core technical link in evaluating oil and gas reservoir distribution and selecting drilling targets. As exploration work progresses into areas with complex geological conditions, reservoir prediction in areas with few wells and multiple sedimentary facies has become a key focus and challenge in geophysical exploration. Due to the limited number of wells in these areas, it is difficult to obtain sufficient subsurface lithological information. Furthermore, the development of multiple sedimentary facies zones results in sandstone and mudstone exhibiting differentiated geophysical response characteristics in different facies zones. Traditional reservoir prediction methods based on a single sedimentary background are ill-suited to these complex geological conditions, necessitating the establishment of a targeted technical methodology system.

[0003] Existing sandstone and mudstone reservoir prediction technologies primarily rely on seismic inversion methods, combining well logging data with seismic data to predict reservoir distribution. However, in areas with few wells, existing technologies have significant shortcomings: due to the limited number and uneven distribution of drilled wells, the constructed low-frequency models often lack effective constraints on the lateral variation patterns of sedimentary facies zones. This leads to distortion of low-frequency information in the inversion results at facies zone boundaries, resulting in decreased accuracy in sandstone and mudstone reservoir predictions. Particularly in areas with dramatic changes in sedimentary facies zones, the predicted results deviate significantly from the actual geological conditions, failing to provide a reliable basis for oil and gas exploration deployment.

[0004] Therefore, how to fully utilize seismic attribute information under conditions of few wells, construct low-frequency models adapted to the characteristics of multiple sedimentary facies zones, and improve the accuracy of sandstone and mudstone reservoir prediction has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] This invention provides a method and system for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies, in order to overcome the shortcomings of existing technologies.

[0006] The first aspect of this invention provides a method for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies, comprising:

[0007] S1: The seismic exploration data of the study area is preprocessed, and the seismic attribute volume is extracted by combining the lithological differences of sedimentary facies zones. The seismic attribute volume is screened by lithological sensitivity analysis to obtain the core attribute volume. S2: Using the core attribute volume as a constraint, the preprocessed seismic exploration data is initially inverted using an inversion algorithm to obtain the initial inversion data volume; S3: Construct a pseudo-well based on the initial inversion data volume and sedimentary facies zone characteristics, and jointly model the pseudo-well with the logging data of the actual drilled well to obtain a low-frequency model; S4: Use the low-frequency model as a constraint to perform iterative inversion, and use the iterative inversion results to predict sandstone and mudstone reservoirs, thereby obtaining the prediction results of sandstone and mudstone reservoirs in the whole area.

[0008] According to the present invention, a method for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies zones, step S1 further includes: S11: Denoising, static correction, amplitude compensation and frequency correction are performed on the original seismic exploration data of the study area to obtain preprocessed seismic exploration data; S12: Based on the preprocessed seismic exploration data, extract seismic attribute volumes for lithological differences in different sedimentary facies zones; S13: Combining core and logging data from actual wells, the seismic attribute bodies are evaluated through lithological sensitivity analysis and correlation screening. The core attribute bodies with the highest correlation to the lithological characteristics of the sedimentary facies are selected by redundancy, identifiability, and correlation indices.

[0009] According to the present invention, a method for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies is provided. The seismic attribute body in step S12 includes amplitude attributes, frequency attributes, phase attributes and attenuation attributes.

[0010] According to the present invention, a method for predicting sandstone and mudstone reservoirs in multi-sedimentary facies zones in a few-well area includes the following step after S2: The initial inversion data volume is verified based on actual well lithology data and sedimentary facies distribution patterns. The inversion parameters are adjusted through parameter iteration correction so that the initial inversion data volume reflects the lithological differences between different sedimentary facies zones.

[0011] According to the present invention, a method for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies zones, step S3 further includes: S31: Based on the initial inversion data volume, combined with the development intensity, thickness variation and distribution pattern of sandstone and mudstone in different sedimentary facies zones, extraction points are set up with differentiated density along the distribution direction of sedimentary facies zones, and equivalent logging parameters are extracted from the extraction points to obtain the initial pseudo-well curve; S32: Perform outlier removal and smoothing correction on the initial pseudo-well curve, and calibrate the initial pseudo-well curve with the actual drilling logging curve as the benchmark to obtain the calibrated pseudo-well curve; S33: Combine the calibration pseudo-well curve with the actual drilling logging data, the core attribute body and sedimentary facies geological knowledge, and incorporate the constraints of sedimentary facies zone boundaries and lithological change trends, and construct a low-frequency model covering the study area through interpolation algorithms.

[0012] According to the present invention, a method for predicting sandstone and mudstone reservoirs in multiple sedimentary facies zones in a few-well area is provided. The equivalent logging parameters include P-wave impedance and P-wave / S-wave velocity ratio. The actual well logging curve and the initial pseudo-well curve are both P-wave / S-wave velocity ratio curves. The interpolation algorithm is an inverse distance weighted interpolation algorithm.

[0013] According to the present invention, a method for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies zones, step S4 further includes: S41: Using the low-frequency model as a constraint, combined with seismic exploration data and the initial inversion data volume, an iterative convergence threshold is set, and the inversion results are continuously optimized through multiple rounds of iterative inversion. During the iteration process, the constraint weights and iteration number parameters are dynamically adjusted, and the low-frequency model is used to control the low-frequency trend of the inversion results to obtain the inversion data volume of the entire region. S42: Based on the sedimentary facies classification criteria and the geophysical response characteristics of sandstone and mudstone reservoirs, the inversion data volume of the whole region is predicted to determine the boundaries between sandstone and mudstone reservoirs and non-reservoirs in multiple sedimentary facies zones, and the prediction results of sandstone and mudstone reservoirs in the whole region are obtained.

[0014] A second aspect of the present invention provides a system for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies, comprising: The screening module is used to preprocess the seismic exploration data of the study area, extract seismic attribute volumes by combining the lithological differences of sedimentary facies zones, and screen the seismic attribute volumes through lithological sensitivity analysis to obtain the core attribute volumes. Inversion module: Used as a constraint condition, the core attribute volume is used to perform the initial inversion on the preprocessed seismic exploration data through the inversion algorithm to obtain the initial inversion data volume; Modeling module: used to construct pseudo-wells based on the initial inversion data volume and sedimentary facies zone characteristics, and to jointly model the pseudo-wells with the logging data of actual drilled wells to obtain a low-frequency model; Prediction module: Used to perform iterative inversion using the low-frequency model as a constraint, and to predict sandstone and mudstone reservoirs based on the iterative inversion results, thereby obtaining the prediction results of sandstone and mudstone reservoirs in the whole area.

[0015] A third aspect of the present invention provides a device for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies, comprising: A memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause a few-well-area, multi-sedimentary-facies sandstone-mudstone reservoir prediction device to perform a few-well-area, multi-sedimentary-facies sandstone-mudstone reservoir prediction method as described in any of the preceding claims.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed by a processor, implement a method for predicting sandstone and mudstone reservoirs in multiple sedimentary facies zones in a few-well area as described in any of the preceding claims.

[0017] This invention preprocesses seismic exploration data from the study area and extracts seismic attribute volumes by combining sedimentary facies lithological differences. Then, through lithological sensitivity analysis, core attribute volumes are obtained. This effectively eliminates data interference sources and retains seismic information most strongly correlated with the lithological characteristics of different sedimentary facies zones, providing high-quality constraint data for subsequent inversion and significantly reducing prediction errors caused by poor seismic data quality. Secondly, this invention uses the core attribute volumes as constraints for initial inversion to obtain initial inversion data volumes, which can preliminarily establish the relationship between seismic data and lithological characteristics, laying a reliable data foundation for pseudo-well construction. Thirdly, this invention constructs pseudo-wells based on the initial inversion data volumes and sedimentary facies zone characteristics, and jointly models them with actual drilling logging data to obtain a low-frequency model. Extraction points are then deployed along the distribution direction of the sedimentary facies zone at differentiated densities, and isotopes are extracted. This invention provides effective logging parameters to supplement low-frequency information in areas with few wells, enabling the low-frequency model to fully reflect the development intensity, thickness variation, and distribution patterns of sandstone and mudstone in different sedimentary facies zones. It solves the problem of missing low-frequency information caused by the limited number of actual drilled wells in traditional methods. Especially in areas with dramatic sedimentary facies zone changes, the density of extraction points significantly enhances the constraint on the lateral variation patterns of facies zone boundaries. Furthermore, by using the low-frequency model as a constraint condition for iterative inversion and dynamically adjusting the constraint weights and iteration times through continuous optimization, this invention can gradually eliminate inversion bias caused by distortion of low-frequency information in areas with few wells. This effectively controls the low-frequency trend of the inversion results in the facies zone boundary region, ultimately achieving high-precision prediction of sandstone and mudstone reservoirs throughout the region. This provides a reliable geological basis for oil and gas exploration and development deployment, demonstrating significant practical value and promising application prospects. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is a schematic diagram of the process for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies, provided by the present invention. Figure 2 This is a cross-plot analysis diagram of the P-wave impedance and P-wave / S-wave velocity ratio of the target section of the well logging curve provided in an embodiment of the present invention. Figure 3This is a diagram showing the prediction results of sandstone and mudstone reservoirs in different sedimentary facies zones provided in the embodiments of the present invention; Figure 4 This invention provides a schematic diagram of the structure of a sandstone and mudstone reservoir prediction system with multiple sedimentary facies in a low-well area. Detailed Implementation

[0020] 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, embodiments of this invention, and should not be construed as limiting the 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. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0021] The embodiments of the present invention are described below with reference to the figures.

[0022] like Figure 1 As shown, the first aspect of the present invention provides a method for predicting sandstone and mudstone reservoirs in multi-sedimentary facies zones in areas with few wells, comprising: S1: The seismic exploration data of the study area is preprocessed, and the seismic attribute volume is extracted by combining the lithological differences of sedimentary facies zones. The seismic attribute volume is then screened by lithological sensitivity analysis to obtain the core attribute volume.

[0023] Step S1 further includes: S11: Denoising, static correction, amplitude compensation, and frequency correction are performed on the original seismic exploration data of the study area to obtain preprocessed seismic exploration data.

[0024] In step S11, the present invention first collects the original seismic exploration data of the study area, and performs preprocessing operations such as denoising, static correction, amplitude compensation, and frequency correction in sequence. Denoising removes random noise and regular interference; static correction eliminates time difference caused by changes in surface elevation and low velocity zone; amplitude compensation corrects the energy loss caused by spherical diffusion and absorption attenuation during the propagation of seismic waves; and frequency correction unifies the frequency response under different acquisition parameters. Finally, the output seismic exploration data has the consistency and stability to meet the requirements of subsequent analysis.

[0025] S12: Based on the preprocessed seismic exploration data, extract seismic attribute volumes for lithological differences in different sedimentary facies zones; the seismic attribute volumes include amplitude attributes, frequency attributes, phase attributes, and attenuation attributes.

[0026] Furthermore, in step S12, based on the preprocessed seismic exploration data, this invention extracts four types of seismic attribute volumes—amplitude, frequency, phase, and attenuation—based on the lithological differences of different sedimentary facies zones in the study area, such as braided river deltas, fan deltas, and semi-deep to deep lacustrine areas. Among these, amplitude attributes reflect the wave impedance differences at lithological interfaces; frequency attributes reflect the selective absorption of seismic wave frequencies by strata; phase attributes reflect the continuity changes in seismic wave waveforms; and attenuation attributes reflect the differential attenuation response of sandstone and mudstone to seismic energy. After extraction, multiple attribute volume datasets are ultimately output.

[0027] S13: Combining core and logging data from actual wells, the seismic attribute bodies are evaluated through lithological sensitivity analysis and correlation screening. The core attribute bodies with the highest correlation to the lithological characteristics of the sedimentary facies are selected by redundancy, identifiability, and correlation indices.

[0028] In step S13, the present invention compares the aforementioned multiple attribute datasets with core and logging data from actual wells to conduct lithological sensitivity analysis and correlation screening. Specifically, the present invention uses a redundancy index to remove attributes with high information overlap; a distinctiveness index to remove attributes with weak differences in response to sandstone and mudstone; and a correlation index to remove attributes with low statistical correlation to lithological characteristics. After stepwise screening using these three indicators, the attribute datasets with the highest correlation to the lithological characteristics of different sedimentary facies zones are retained as core attribute datasets. In a specific embodiment, the screening result is a chaotic attribute dataset, and the final output is a dedicated attribute dataset.

[0029] S2: Using the core attribute volume as a constraint, the preprocessed seismic exploration data is initially inverted using an inversion algorithm to obtain the initial inversion data volume.

[0030] Step S2 is followed by: The initial inversion data volume is verified based on actual well lithology data and sedimentary facies distribution patterns. The inversion parameters are adjusted through parameter iteration correction so that the initial inversion data volume reflects the lithological differences between different sedimentary facies zones.

[0031] In step S2, this invention uses the core attribute volume as a low-frequency trend constraint in the inversion process, selects a pre-stack seismic inversion algorithm suitable for areas with few wells, and sets key parameters such as the seismic wavelet extracted from the well-side seismic traces and the 3Hz low-frequency merging frequency. The preprocessed seismic exploration data is then subjected to an initial inversion, outputting an inversion data volume that initially reflects the distribution characteristics of sandstone and mudstone in different sedimentary facies zones. This data volume includes spatial distribution information of parameters such as P-wave impedance and P-wave / S-wave velocity ratio. Subsequently, this invention performs point-by-point comparison and verification with the lithological data and sedimentary facies distribution patterns of actual drilled wells. Problem areas such as blurred facies boundaries and insufficient sandstone-mudstone differentiation are identified. Inversion parameters such as the inversion wavelet, low-frequency merging frequency, and constraint weights are adjusted accordingly, and the inversion is re-executed and verified again. This iterative correction is repeated until the lithological differences between different sedimentary facies zones in the inversion data volume are accurately reflected. A verified initial inversion data volume is then output, providing a data foundation for subsequent pseudo-well construction.

[0032] S3: Construct a pseudo-well based on the initial inversion data volume and sedimentary facies characteristics, and jointly model the pseudo-well with the logging data of the actual drilled well to obtain a low-frequency model.

[0033] Step S3 further includes: S31: Based on the initial inversion data volume, and combined with the development intensity, thickness variation and distribution pattern of sandstone and mudstone in different sedimentary facies zones, extraction points are set up with differentiated density along the distribution direction of the sedimentary facies zone, and equivalent logging parameters are extracted from the extraction points to obtain the initial pseudo-well curve.

[0034] This invention, based on the initial inversion data volume output by S2, combines the development intensity, thickness variation, and distribution patterns of sandstone and mudstone in different sedimentary facies zones such as braided river deltas, fan deltas, and semi-deep to deep lacustrine areas, and deploys extraction points along the distribution direction of each sedimentary facies zone. The deployment density follows a differentiated principle: denser deployment in areas with drastic lateral changes in facies zones, and moderately sparse deployment in areas with gentle lateral changes. Subsequently, at each extraction point, the invention reads the wave impedance and P-wave velocity ratio values ​​from the initial inversion data volume, and extracts them layer by layer along the longitudinal direction to form a continuous equivalent well logging parameter curve, i.e., the initial pseudo-well curve, outputting a multi-well curve dataset covering different facies zones and adapted to the sandstone and mudstone development characteristics of each facies zone.

[0035] S32: The initial pseudo-well curve is subjected to outlier removal and smoothing correction. The initial pseudo-well curve is calibrated based on the actual drilling logging curve to obtain the calibrated pseudo-well curve. The equivalent logging parameters include P-wave impedance and P-wave / S-wave velocity ratio.

[0036] In step S32, the present invention sequentially performs outlier removal and smoothing correction on the multiple initial pseudo-well curves output in S31. Outlier removal identifies and removes abrupt changes in the curves that deviate from the normal formation response range. Smoothing correction applies a moving average to the removed curves to eliminate high-frequency spikes and output a continuous, smooth pseudo-well curve. Subsequently, the present invention uses the P-wave and S-wave velocity ratio logging curves of actual drilled wells in the study area as a benchmark, based on... Figure 2 Cross-intersection analysis shows that the P-wave and S-wave velocity ratio can effectively distinguish between sandstone and mudstone. Sandstone corresponds to a low P-wave and S-wave velocity ratio, while mudstone corresponds to a high P-wave and S-wave velocity ratio. The numerical range and trend of the pseudo-well curve are aligned and calibrated, and the final output is a calibrated pseudo-well curve that is consistent with the actual drilling data in terms of dimensions and trends.

[0037] Figure 2 In the graph, the horizontal axis represents the P-wave impedance, measured in g / cm³·m / s, ranging from 8500 to 14000. It reflects the product of rock density and P-wave velocity; a higher value indicates denser rock. The vertical axis represents the P-wave to S-wave velocity ratio, dimensionless, ranging from 1.5 to 2.25. It reflects the ratio of P-wave velocity to S-wave velocity; a higher value indicates a higher clay content in the rock. Each scatter point in the graph represents a depth sampling point on the logging curve of the target layer. The color of the scatter point corresponds to the clay content indicated by the color scale on the right, with colors ranging from red to blue indicating increasing clay content. The horizontal dashed line in the figure represents the threshold for a P-S ratio of 1.8: below the dashed line, the scattered points are concentrated in the low P-S ratio range (1.5 to 1.8), with red and orange as the main colors, and low mud content, indicating sandstone; above the dashed line, the scattered points are concentrated in the high P-S ratio range (1.8 to 2.25), with blue and green as the main colors, and high mud content, indicating mudstone. Figure 2 The results of the cross plot show that the P-wave and S-wave velocity ratio of 1.8 can effectively distinguish sandstone and mudstone. Therefore, in this embodiment of the invention, the P-wave and S-wave velocity ratio curve is selected as the benchmark for the actual drilling logging curve, and the P-wave and S-wave velocity ratio is extracted from the initial inversion data volume as the pseudo-well curve parameter.

[0038] S33: Combine the calibration pseudo-well curve with the actual drilling logging data, the core attribute body and sedimentary facies geological knowledge, and incorporate the constraints of sedimentary facies zone boundaries and lithological change trends, and construct a low-frequency model covering the study area through an interpolation algorithm; the interpolation algorithm is an inverse distance weighted interpolation algorithm.

[0039] Furthermore, in step S33, this invention uses the calibration pseudo-well curve, actual drilling logging data, the chaotic attribute volume selected in S1, and sedimentary facies geological knowledge as joint inputs, and employs an inverse distance weighted interpolation algorithm to construct a low-frequency model. Specifically, the inverse distance weighted interpolation process is as follows: using the known P-wave and S-wave velocity ratio values ​​of each actual drilling well and pseudo-well as known points, for each unknown point on the spatial grid of the study area, the spatial distance between it and all surrounding known points is calculated. Known points that are closer are assigned higher weights, and known points that are farther away are assigned lower weights. Then, the P-wave and S-wave velocity ratio values ​​of all known points are weighted and summed to obtain the low-frequency attribute value of the unknown point. In the above process, this invention incorporates sedimentary facies zone boundaries and lithological variation trends as geological constraints, so that the interpolation results reflect the lateral abrupt changes in lithology at the facies zone boundaries, and finally outputs a low-frequency model data volume covering the entire study area and coupled with sedimentary facies characteristics.

[0040] The inverse distance weighting method is a deterministic interpolation method. Its core principle is that the attribute value of an unknown point is obtained by a weighted average of the attribute values ​​of its surrounding known points. The weight of a known point relative to the unknown point is inversely proportional to the k-th power of the spatial distance between them; the closer the distance, the higher the weight. k is a core adjustment parameter (default value in Jason is 2; a larger k value indicates more significant local variations, while a smaller k value results in smoother interpolation). Specifically, the interpolation steps are as follows: first, determine the known logging data points around the unknown point that will participate in the calculation (the range can be limited by a search radius or a fixed number of points); then, calculate the spatial distance between the unknown point and each known point. Then, the weights of each known point are calculated according to the first formula below. ( (The number of known points involved in the calculation) is used to finally obtain the attribute values ​​of the unknown points through a weighted summation using the second formula below. .

[0041]

[0042]

[0043] in, For the index of the known data points participating in the interpolation calculation, The distance decay exponent, The total number of known data points involved in the calculation. For the first Interpolation weights of known data points to unknown points For the first The three-dimensional spatial distance between known data points (actual drilling or dummy well locations) and the currently unknown grid points to be interpolated. The interpolation result for the currently unknown grid points. Let be the known attribute value at the i-th known data point.

[0044] S4: Use the low-frequency model as a constraint to perform iterative inversion, and use the iterative inversion results to predict sandstone and mudstone reservoirs, thereby obtaining the prediction results of sandstone and mudstone reservoirs in the whole area.

[0045] Step S4 further includes: S41: Using the low-frequency model as a constraint, combined with seismic exploration data and the initial inversion data volume, an iterative convergence threshold is set, and the inversion results are continuously optimized through multiple rounds of iterative inversion. During the iteration process, the constraint weights and iteration number parameters are dynamically adjusted, and the low-frequency model is used to control the low-frequency trend of the inversion results to obtain the inversion data volume of the entire region.

[0046] In step S41, the low-frequency model constructed in S3 is used as the core constraint in the seismic inversion process. Simultaneously, the original seismic exploration data and the initial inversion data volume are input, and an iterative convergence threshold is set. In one specific embodiment, the convergence threshold is when the consistency between the inversion result and the actual drilling data reaches 80% or more. Subsequently, the invention initiates multiple rounds of iterative inversion. In each iteration, the consistency between the current inversion result and the actual drilling data is read, and the constraint weights and iteration number parameters are dynamically adjusted. The low-frequency model continuously controls the low-frequency trend of the inversion results, correcting deviations round by round until the consistency meets the convergence threshold. A high-precision full-area inversion data volume is output, containing a detailed distribution of parameters such as the P-wave and S-wave velocity ratios at various spatial points throughout the area.

[0047] S42: Based on the sedimentary facies classification criteria and the geophysical response characteristics of sandstone and mudstone reservoirs, the inversion data volume of the whole region is predicted to determine the boundaries between sandstone and mudstone reservoirs and non-reservoirs in multiple sedimentary facies zones, and the prediction results of sandstone and mudstone reservoirs in the whole region are obtained.

[0048] In step S42, based on the inversion data volume of the entire region and combined with the sedimentary facies classification standard of the study area, the present invention divides the entire region into facies zones such as braided river delta, fan delta, and semi-deep to deep lacustrine. Then, based on the geophysical response characteristics of sandstone and mudstone reservoirs in each facies zone, specifically using a P-wave / S-wave velocity ratio of 1.8 as the boundary, values ​​below 1.8 are classified as sandstone, and values ​​above 1.8 are classified as mudstone. After point-by-point discrimination of the P-wave / S-wave velocity ratio values ​​at each spatial grid point in the inversion data volume of the entire region, the spatial boundaries between sandstone and mudstone reservoirs and non-reservoirs within each sedimentary facies zone, as well as favorable reservoir areas, are determined, and the prediction results of sandstone and mudstone reservoirs for the entire region are output.

[0049] like Figure 3 The image shows the predicted results of sandstone and mudstone reservoirs in different sedimentary facies zones, and the spatial distribution of the inverted data volume in the whole area is displayed in the form of a three-dimensional seismic profile. Figure 3In the diagram, the vertical axis represents the time axis, with units in seconds (s). Values ​​range from 2.8 to 3.7, representing the two-way travel time of seismic waves; larger values ​​correspond to greater burial depths. The horizontal and depth axes together form a three-dimensional spatial display, showcasing reservoir prediction results at different locations and depths within the study area. Figure 3 The color scale, from blue to red, reflects the change in the P-wave and S-wave velocity ratio from high to low. The blue area corresponds to a high P-wave and S-wave velocity ratio, which is identified as mudstone or non-reservoir; the red and yellow areas correspond to a low P-wave and S-wave velocity ratio, which is identified as sandstone reservoir. Figure 3 The spatial locations of three sedimentary facies zones are marked: the left zone, labeled "fan delta," is located in the shallow layer near well A1, corresponding to a time interval of approximately 2.8 to 3.1 seconds, and exhibits a strong sandstone response; the middle zone, labeled "braided river delta," is located in the middle section of the study area and extends laterally over a wide area; the lower right zone, labeled "medium-deep lake," is located in a deeper time interval and is dominated by a blue mudstone response. The two red diagonal lines in the figure indicate the fault locations. Well A1, marked in the upper left corner, is the actual drilling location used to verify the inversion results.

[0050] like Figure 4 As shown, this invention provides a prediction system for sandstone and mudstone reservoirs in multi-sedimentary facies zones with few wells, comprising: Screening module 100: Used to preprocess the seismic exploration data of the study area, extract seismic attribute volumes by combining the lithological differences of sedimentary facies zones, and screen the seismic attribute volumes through lithological sensitivity analysis to obtain core attribute volumes; Inversion module 200: Used to perform initial inversion on the preprocessed seismic exploration data using the core attribute volume as a constraint condition and an inversion algorithm to obtain the initial inversion data volume; Modeling module 300: used to construct a pseudo-well based on the initial inversion data volume and sedimentary facies zone characteristics, and to jointly model the pseudo-well with the logging data of actual drilled wells to obtain a low-frequency model; Prediction module 400: Used to perform iterative inversion using the low-frequency model as a constraint, and to predict sandstone and mudstone reservoirs based on the iterative inversion results, thereby obtaining the prediction results of sandstone and mudstone reservoirs in the whole area.

[0051] 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.

[0052] This invention also provides a device for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies, comprising: A memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause a few-well-area, multi-sedimentary-facies sandstone-mudstone reservoir prediction device to perform a few-well-area, multi-sedimentary-facies sandstone-mudstone reservoir prediction method as described in any of the preceding claims.

[0053] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement a method for predicting sandstone and mudstone reservoirs in multiple sedimentary facies zones in a few well areas as described in any of the preceding claims.

[0054] This invention discloses a method for predicting sandstone and mudstone reservoirs in areas with few wells, overcoming the bottleneck of data scarcity in these areas and improving the adaptability to multiple sedimentary facies zones. Compared with existing technologies that rely on low-frequency information from actual drilled wells, are prone to low-frequency omissions in areas with few wells, and suffer from the limitation of inversion being disconnected from facies zones, this invention uses seismic attribute volume optimization constraints and pseudo-well construction to supplement the data. It can construct a low-frequency model of the entire area coupled with sedimentary facies characteristics without the need for a large number of actual drilled wells, making up for data shortages and optimizing the inversion process. This significantly improves the adaptability to the development law of sandstone and mudstone in multiple sedimentary facies zones, broadens the scope of application, reduces exploration risks, and provides a scientific basis for well location deployment and reserve assessment.

[0055] 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 sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies, characterized in that, include: S1: The seismic exploration data of the study area is preprocessed, and the seismic attribute volume is extracted by combining the lithological differences of sedimentary facies zones. The seismic attribute volume is screened by lithological sensitivity analysis to obtain the core attribute volume. S2: Using the core attribute volume as a constraint, the preprocessed seismic exploration data is initially inverted using an inversion algorithm to obtain the initial inversion data volume; S3: Construct a pseudo-well based on the initial inversion data volume and sedimentary facies zone characteristics, and jointly model the pseudo-well with the logging data of the actual drilled well to obtain a low-frequency model; S4: Use the low-frequency model as a constraint to perform iterative inversion, and use the iterative inversion results to predict sandstone and mudstone reservoirs, thereby obtaining the prediction results of sandstone and mudstone reservoirs in the whole area.

2. The method for predicting sandstone and mudstone reservoirs in multi-sedimentary facies zones in a few-well area according to claim 1, characterized in that, Step S1 further includes: S11: Denoising, static correction, amplitude compensation and frequency correction are performed on the original seismic exploration data of the study area to obtain preprocessed seismic exploration data; S12: Based on the preprocessed seismic exploration data, extract seismic attribute volumes for lithological differences in different sedimentary facies zones; S13: Combining core and logging data from actual wells, the seismic attribute bodies are evaluated through lithological sensitivity analysis and correlation screening. The core attribute bodies with the highest correlation to the lithological characteristics of the sedimentary facies are selected by redundancy, identifiability, and correlation indices.

3. The method for predicting sandstone and mudstone reservoirs in multi-sedimentary facies zones in a few-well area according to claim 2, characterized in that, The seismic attribute body in step S12 includes amplitude attributes, frequency attributes, phase attributes, and attenuation attributes.

4. The method for predicting sandstone and mudstone reservoirs in multi-sedimentary facies zones in a few-well area according to claim 1, characterized in that, Step S2 is followed by: The initial inversion data volume is verified based on actual well lithology data and sedimentary facies distribution patterns. The inversion parameters are adjusted through parameter iteration correction so that the initial inversion data volume reflects the lithological differences between different sedimentary facies zones.

5. The method for predicting sandstone and mudstone reservoirs in multi-sedimentary facies zones in a few-well area according to claim 1, characterized in that, Step S3 further includes: S31: Based on the initial inversion data volume, combined with the development intensity, thickness variation and distribution pattern of sandstone and mudstone in different sedimentary facies zones, extraction points are set up with differentiated density along the distribution direction of sedimentary facies zones, and equivalent logging parameters are extracted from the extraction points to obtain the initial pseudo-well curve; S32: Perform outlier removal and smoothing correction on the initial pseudo-well curve, and calibrate the initial pseudo-well curve with the actual drilling logging curve as the benchmark to obtain the calibrated pseudo-well curve; S33: Combine the calibration pseudo-well curve with the actual drilling logging data, the core attribute body and sedimentary facies geological knowledge, and incorporate the constraints of sedimentary facies zone boundaries and lithological change trends, and construct a low-frequency model covering the study area through interpolation algorithms.

6. The method for predicting sandstone and mudstone reservoirs in multi-sedimentary facies zones in a few-well area according to claim 5, characterized in that, The equivalent logging parameters include P-wave impedance and P-wave / S-wave velocity ratio. The actual well logging curve and the initial pseudo-well logging curve are both P-wave / S-wave velocity ratio curves. The interpolation algorithm is an inverse distance weighted interpolation algorithm.

7. The method for predicting sandstone and mudstone reservoirs in multi-sedimentary facies zones in a few-well area according to claim 1, characterized in that, Step S4 further includes: S41: Using the low-frequency model as a constraint, combined with seismic exploration data and the initial inversion data volume, an iterative convergence threshold is set, and the inversion results are continuously optimized through multiple rounds of iterative inversion. During the iteration process, the constraint weights and iteration number parameters are dynamically adjusted, and the low-frequency model is used to control the low-frequency trend of the inversion results to obtain the inversion data volume of the entire region. S42: Based on the sedimentary facies classification criteria and the geophysical response characteristics of sandstone and mudstone reservoirs, the inversion data volume of the whole region is predicted to determine the boundaries between sandstone and mudstone reservoirs and non-reservoirs in multiple sedimentary facies zones, and the prediction results of sandstone and mudstone reservoirs in the whole region are obtained.

8. A prediction system for sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies, characterized in that, include: The screening module is used to preprocess the seismic exploration data of the study area, extract seismic attribute volumes by combining the lithological differences of sedimentary facies zones, and screen the seismic attribute volumes through lithological sensitivity analysis to obtain the core attribute volumes. Inversion module: Used as a constraint condition, the core attribute volume is used to perform the initial inversion on the preprocessed seismic exploration data through the inversion algorithm to obtain the initial inversion data volume; Modeling module: used to construct pseudo-wells based on the initial inversion data volume and sedimentary facies zone characteristics, and to jointly model the pseudo-wells with the logging data of actual drilled wells to obtain a low-frequency model; Prediction module: Used to perform iterative inversion using the low-frequency model as a constraint, and to predict sandstone and mudstone reservoirs based on the iterative inversion results, thereby obtaining the prediction results of sandstone and mudstone reservoirs in the whole area.

9. A device for predicting sandstone and mudstone reservoirs in areas with few wells and multiple sedimentary facies, characterized in that, include: A memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause a few-well-area, multi-sedimentary-facies sandstone-mudstone reservoir prediction device to perform a few-well-area, multi-sedimentary-facies sandstone-mudstone reservoir prediction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a processor, implement a method for predicting sandstone and mudstone reservoirs in multiple sedimentary facies zones in a few-well area as described in any one of claims 1 to 7.

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