Multi-resource wave sand dam knowledge base construction method and medium

Abstract

The invention discloses a multi-resource sand-forming dam knowledge base construction method and a medium. The method comprises the following steps: obtaining image data of an outcrop area of a sand-forming dam to construct outcrop geologic features; obtaining modern deposition data screening suitability parameters of the sand wave dam; different types of sand-formed wave dam research data are obtained, and the sedimentary microfacies-rock corresponding relation of the different types of sand-formed wave dams is obtained; obtaining macroscopic image data of the sand-formed-wave dam and extracting macroscopic constraints of the sand-formed-wave dam; and simulating a dynamic deposition process formed by the sand-formed-wave dam through a preset model according to the geologic features, the adaptability parameters, the sedimentary microfacies-rock corresponding relationships of the different types of sand-formed-wave dams and the macroscopic constraints of the sand-formed-wave dam, and obtaining parameters in the simulation process for association to obtain a sand-formed-wave dam knowledge base. According to the method, the integrated sand-formed-wave dam knowledge base is constructed based on the multi-resource data, so that the reservoir configuration analysis of the underground sand-formed-wave dam is finer.

Description

Technical Field

[0001] This invention relates to the field of geological technology, and in particular to a method and medium for constructing a knowledge base of multi-resource wave-formed sand dams. Background Technology

[0002] Wave-formed sandbars, as important reservoirs formed in transitional marine and shallow marine sedimentary environments, are often one of the main reservoir types for oil and gas development due to their moderate size and good reservoir performance. Accurately grasping the geological characteristics, configuration patterns, and parameter regularities of wave-formed sandbar reservoirs is a prerequisite for achieving detailed reservoir dissection, improving oil and gas recovery rates, and accurately predicting the distribution of remaining oil.

[0003] Currently, research on wave-formed sandbar reservoirs largely relies on single data sources or localized data, which has significant limitations: First, field outcrop studies often employ traditional measurement methods, which are inefficient and difficult to achieve three-dimensional characterization, failing to fully capture the spatial distribution characteristics of morphological interfaces and units; second, modern sedimentary studies are often limited to specific regions, and the parameters obtained lack universality and sufficient analogical correlation with subsurface reservoirs; third, literature reviews are mostly fragmented summaries, failing to form a systematic quantitative parameter system, making it difficult to directly serve modeling needs; fourth, the application of satellite data and sedimentary simulation technology is fragmented, failing to effectively integrate with field and modern sedimentary data, resulting in biased geological understanding and insufficient completeness and reliability of the knowledge base.

[0004] The aforementioned problems have resulted in deficiencies in the existing knowledge base of wave-formed sandbars, such as missing parameters, a single model, and fragmented information from multiple sources. These deficiencies prevent the provision of comprehensive and accurate technical support for the analysis of underground reservoir structures, thus hindering the level of precision in oil and gas field development. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address at least one defect of the related technologies mentioned in the background: the existing wave-formed sand dam knowledge base has defects such as inaccurate parameters, single model, and fragmented multi-source information, and to provide a method and medium for constructing a multi-resource wave-formed sand dam knowledge base.

[0006] The technical solution adopted by this invention to solve its technical problem is: to construct a multi-resource wave-forming sandbar knowledge base construction method, which includes: S1: Acquire image data of the Langcheng sandbar outcrop area and construct outcrop geological features based on the Langcheng sandbar outcrop image data; S2: Obtain modern sedimentary data of wave-formed sandbars and screen suitable parameters through modern sedimentary data of wave-formed sandbars; S3: Obtain survey data on different types of wave-formed sandbars, and obtain the corresponding relationship between sedimentary microfacies and lithosomes of different types of wave-formed sandbars through the survey data on different types of wave-formed sandbars; S4: Obtain macroscopic image data of wave-formed sandbars and extract macroscopic constraints of wave-formed sandbars from the macroscopic image data of wave-formed sandbars; S5: The dynamic sedimentary process of wave-forming sandbar formation is simulated by a preset model using geological features, adaptability parameters, the microfacies-lithological correspondence of different types of wave-forming sandbars, and macroscopic constraints of wave-forming sandbars. The parameters obtained in the simulation process are then correlated to obtain a wave-forming sandbar knowledge base.

[0007] In some embodiments, step S1 includes: S11: Acquire image data of the outcrop area of ​​the wave-formed sandbar and process the acquired image data; S12: Identify and delineate the processed wave-formed sandbar image data to obtain the configuration interface and configuration unit of the wave-formed sandbar outcrop; S13: Based on the configurational interface and configurational unit of wave-formed sandbar outcrops, establish a wave-formed sandbar outcrop model and construct a knowledge base including outcrop geological features.

[0008] In some embodiments, in step S12, the processed wave-formed sandbar image data is divided based on a preset configuration level division standard.

[0009] In some embodiments, step S2 includes: S21: Obtain modern sedimentary data of wave-formed sandbars and establish a prototype model of modern sedimentary wave-formed sandbars; S22: Obtain modern quantitative parameters of sedimentary units associated with wave-forming sandbars based on modern sedimentary prototype models of wave-forming sandbars; S23: Compare and analyze modern quantitative parameters with paleoenvironmental quantitative parameters of the target study area to screen out suitable parameters.

[0010] In some embodiments, step S3 includes: S31: Obtain survey data on different types of wave-formed sandbars, and obtain quantitative information on the morphology and sedimentary configuration characteristics of different types of wave-formed sandbars through the survey data on different types of wave-formed sandbars; S32: Based on the quantitative morphological information and sedimentary configuration characteristics of different types of wave-formed sandbars, the parameters of different types of wave-formed sandbars are supplemented and improved respectively; S33: Based on the internal interlayer parameters and structural unit parameters of different types of wave-formed sandbars, obtain the sedimentary microfacies-lithological correspondence of different types of wave-formed sandbars.

[0011] In some embodiments, in step S32, the parameters of different types of wave-formed sandbars include: the internal interlayer parameters of different types of wave-formed sandbars and the configuration unit parameters of different types of wave-formed sandbars.

[0012] In some embodiments, step S4 includes: S41: Acquire macroscopic image data of wave-formed sandbars, and extract macroscopic size parameters of wave-formed sandbars and sedimentary units related to wave-formed sandbars based on the macroscopic image data of wave-formed sandbars; S42: Macroscopic features of wave-formed sandbars and associated sedimentary units are extracted; S43: Cross-validate macroscopic dimensional parameters and macroscopic features with measured data to obtain macroscopic constraints.

[0013] In some embodiments, step S5, simulating the dynamic deposition process of wave-formed sandbar formation using a preset model, includes: simulating the dynamic deposition process of wave-formed sandbar formation using a preset model combined with a preset parallel algorithm; The preset model is a numerical model of sedimentation dynamics.

[0014] In some embodiments, step S5, associating parameters in the simulation process, includes: constructing a data association analysis model based on the parameters in the simulation process, and clarifying the relationship between parameters from different data sources.

[0015] The present invention also constructs a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the multi-resource wave sand dam knowledge base construction method as described in any of the above embodiments.

[0016] By implementing this invention, the following beneficial effects are achieved: This invention constructs geological features from wave-formed sandbar image data, screens adaptability parameters for modern sedimentary deposits of wave-formed sandbars, obtains the microfacies-lithological correspondences of different types of wave-formed sandbars from survey data, extracts macroscopic constraints of wave-formed sandbars from macroscopic image data, and then simulates the dynamic sedimentary process of wave-formed sandbar formation using a pre-set model. The parameters obtained during the simulation are then correlated to obtain a wave-formed sandbar knowledge base. This application constructs an integrated wave-formed sandbar knowledge base based on multi-resource data, enabling more refined analysis of subsurface wave-formed sandbar reservoir configurations. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 A flowchart of one embodiment of the multi-resource wave-forming sandbar knowledge base construction method of the present invention is shown; Figure 2 This illustrates a flowchart of step S1 in an embodiment of the multi-resource wave-forming sandbar knowledge base construction method of the present invention; Figure 3 This diagram illustrates an embodiment of the multi-resource wave-formed sand dam knowledge base construction method of the present invention, showing an outcrop digital profile and a structural anatomy and parameter measurement of the reservoir. Figure 4This illustrates a flowchart of step S2 in an embodiment of the multi-resource wave-forming sandbar knowledge base construction method of the present invention; Figure 5 A schematic diagram of a portion of modern sedimentary profiles is shown in one embodiment of the multi-resource wave-formed sandbar knowledge base construction method of the present invention; Figure 6 This diagram illustrates the trench configuration profile and parameter measurement of one embodiment of the multi-resource wave-formed sand dam knowledge base construction method of the present invention; Figure 7 This illustrates a flowchart of step S3 in an embodiment of the multi-resource wave-forming sandbar knowledge base construction method of the present invention; Figure 8 This paper shows the particle size probability feature diagrams of partial samples from different structural units in an embodiment of the multi-resource wave-formed sand dam knowledge base construction method of the present invention; Figure 9 This illustrates a flowchart of step S4 in an embodiment of the multi-resource wave-forming sandbar knowledge base construction method of the present invention; Figure 10 This diagram illustrates a satellite image of a wave-forming sandbar and measurements of some geological knowledge base parameters, representing an embodiment of the multi-resource wave-forming sandbar knowledge base construction method of the present invention. Figure 11 This diagram illustrates a geological knowledge base of wave-controlled delta front sediments and wave-formed sandbars derived from literature research, representing an embodiment of the multi-resource wave-formed sandbar knowledge base construction method of the present invention. Detailed Implementation

[0018] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] It should be noted that the flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0020] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0021] like Figure 1 As shown, this invention constructs a method for building a multi-resource wave-forming sandbar knowledge base, which includes: S1: Acquire image data of the Langcheng sandbar outcrop area and construct outcrop geological features based on the Langcheng sandbar outcrop image data; Image data of the Langcheng Sandbar outcrop can be acquired by using an industrial-grade drone equipped with a high-definition camera to conduct a continuous, all-around scan of the French-speaking area of ​​the Langcheng Sandbar outcrop, obtaining high-resolution image data of the Langcheng Sandbar outcrop area.

[0022] Outcrop geological features refer to all macroscopic and microscopic attributes that can be directly observed in geological bodies (rock strata, structures, ore bodies, etc.) that are naturally or artificially exposed on the earth's surface. These include: rock appearance, color, composition, grain size, cement, porosity, fossils, nodules, weathering crust, etc.; internal structure, bedding type (parallel, cross-bedding, sand ripples, massive, etc.), layer thickness and rhythm, erosion surfaces, soft sediment deformation, bioturbation, etc.; geometric shape and scale, how far a single layer extends, thickness variations, lateral pinch-outs, lenses, ridge and trough morphology, dip angle and strike, etc.; contact and interface relationships, whether the relationship with the underlying strata is abrupt or gradual, whether there are silt layers, scour surfaces, biological burrows, hard bottoms, calcareous crusts, etc.; and traces of later alteration, fault / joint occurrence, striations, calcite veins, pyrite mineralization, weathering halos, infiltrated mud crust, modern plant root cavities, etc.

[0023] like Figure 2 As shown, in some embodiments, step S1 includes: S11: Acquire image data of the outcrop area of ​​the wave-formed sandbar, and process the acquired image data; the processing method is stitching, correction and three-dimensional modeling. S12: Identify and delineate the processed wave-formed sandbar image data to obtain the configuration interface and configuration unit of the wave-formed sandbar outcrop; like Figure 3 As shown, the structural interface of the Langcheng sand dam outcrop is mainly composed of thin layers of silty mud between accretion bodies. These interfaces exhibit horizontal accretionary contact in the vertical direction. The lithology is mostly dark yellow to yellowish-brown silt, with a thickness of 1–5 cm, containing muddy components, and the distribution is stable. They can be regarded as the separating surfaces of different accretion bodies within a single dam.

[0024] The structural units of wave-formed sandbar outcrops mainly include wave-formed rippled sandstone facies (Sw), parallel-bedding sandstone facies (Sh), and massive-bedding sandstone facies (Sm). Wave-formed rippled sandstone facies (Sw): Ribbed bedding formed by wave action, commonly found in the main body of wave-formed sandbars, reflecting the continuous modification of sediments by waves; Parallel-bedding sandstone facies (Sh): Represents sedimentation under high-flow conditions, usually appearing at the top or leading edge of the sandbar, indicating a strong hydrodynamic environment; Massive-bedding sandstone facies (Sm): May reflect rapid deposition or environments with strong post-depositional bioturbation, locally appearing within the sandbar. These structural units often combine vertically to form accretionary bodies, and multiple accretionary bodies are separated by thin layers of silty mudstone, forming higher-level structural units.

[0025] In some embodiments, in step S12, the processed wave-formed sandbar image data is divided based on a preset configuration hierarchy classification standard. The preset configuration hierarchy classification standard includes configuration units at various levels such as conforming sandbars, single sandbars, and sandbar interlayers.

[0026] S13: Based on the configurational interface and configurational unit of wave-formed sandbar outcrops, establish a wave-formed sandbar outcrop model and construct a knowledge base including outcrop geological features.

[0027] In this embodiment, the wave-forming sandbar outcrop model is a 3D numerical outcrop model. By analyzing the sedimentary dynamic conditions, formation mechanism, sedimentary facies types and characteristics (such as lithological assemblage, grain size distribution, bedding structure, etc.) of the 3D numerical outcrop model, and the spatial configuration relationship of different bitumen configuration units, a basic model of wave-forming sandbar reservoir configuration is summarized, and a knowledge base module containing outcrop geological characteristics is initially constructed.

[0028] S2: Obtain modern sedimentary data of wave-formed sandbars and screen suitable parameters through modern sedimentary data of wave-formed sandbars; The modern sedimentary data of wave-formed sandbars were obtained by selecting representative modern sedimentary observation points of wave-formed sandbars within the range (covering different climate zones, hydrodynamic conditions, and sediment supply backgrounds), and conducting systematic research through field measurements, profile observations, and sample collection.

[0029] like Figure 4 As shown, in some embodiments, step S2 includes: S21: Obtain modern sedimentary data of wave-formed sandbars and establish a prototype model of modern sedimentary wave-formed sandbars; such as Figure 5 and Figure 6As shown, the modern sedimentary prototype model of wave-formed sandbars is a "living template" that can be directly compared with outcrops, established through large-scale physical simulation (flume experiment). Its core is to divide the modern coastal zone into four functional zones according to wave energy zones, and use measurable hydrodynamic-sedimentary response relationships to visualize the entire process of sandbar development from "embryo" to "maturity" in one go, and provide quantitative geometric parameters for direct application in subsurface reservoir configuration modeling.

[0030] S22: Obtain modern quantitative parameters of sedimentary units related to wave-forming sandbars based on modern sedimentary prototype models of wave-forming sandbars; focus on obtaining quantitative parameters of sedimentary units related to wave-forming sandbars, such as deltaic underwater distributary islands, estuary bars, wave-forming sandbars, and sheet sands. Quantitative parameters include, but are not limited to: width, length, planar morphology, curvature, angle with the coastline, and angle with the dominant wave direction.

[0031] S23: Compare and analyze modern quantitative parameters with paleoenvironmental quantitative parameters of the target study area to screen out suitable parameters. Paleoenvironmental parameters include paleoclimate, paleohydrodynamics, and paleopropagation.

[0032] In this embodiment, by comparing and analyzing the obtained modern sedimentary quantitative parameters with the paleoenvironment, i.e., sedimentary background quantitative parameters of the study area, suitable parameters are selected and added to the wave-forming sandbar knowledge base to improve the correlation between the knowledge base and the study area.

[0033] S3: Obtain survey data on different types of wave-formed sandbars, and obtain the corresponding relationship between sedimentary microfacies and lithosomes of different types of wave-formed sandbars through the survey data on different types of wave-formed sandbars; The survey data on different types of wave-formed sandbars includes a comprehensive survey of relevant literature on wave-formed sandbars both domestically and internationally, covering academic papers, research reports, and patent documents from multiple fields such as geological exploration, sedimentology, and reservoir modeling.

[0034] like Figure 7 As shown, in some embodiments, step S3 includes: S31: Obtain survey data on different types of wave-formed sandbars, and obtain quantitative information on the morphology and sedimentary configuration characteristics of different types of wave-formed sandbars through the survey data on different types of wave-formed sandbars; The survey data on different types of wave-formed sandbars will be categorized, sorted, and compared comprehensively, such as... Figure 8 As shown, the quantitative information obtained includes aspect ratio, thickness variation pattern, etc.; the sedimentary configuration features obtained are the sum of the smallest building units and their spatial combination patterns that can be identified, tracked and compared within the sedimentary body, including: configuration units, geometric scale, stacking pattern, interface properties and heterogeneous order, etc.

[0035] S32: Based on the quantitative morphological information and sedimentary configuration characteristics of different types of wave-formed sandbars, the parameters of different types of wave-formed sandbars are supplemented and improved respectively; In some embodiments, in step S32, the parameters of different types of wave-formed sandbars include: the internal interlayer parameters of different types of wave-formed sandbars and the configuration unit parameters of different types of wave-formed sandbars.

[0036] The parameters of the internal interlayer include, but are not limited to, interlayer style, interlayer type (such as muddy interlayer, silty interlayer), interlayer thickness and variation range, interlayer planar shape, interlayer area ratio, interlayer width-to-thickness ratio and length-to-thickness ratio, etc.; the parameters of the structural unit include, but are not limited to, the core parameters such as the shape, thickness, area, width-to-thickness ratio, and length-to-thickness ratio of the structural unit.

[0037] S33: Based on the internal interlayer parameters and structural unit parameters of different types of wave-formed sandbars, obtain the sedimentary microfacies-lithological correspondence of different types of wave-formed sandbars.

[0038] The sedimentary microfacies-lithofacies relationship refers to a stable and repeatable combination of macroscopic and microscopic characteristics (lithofacies) of rocks and the "microenvironment" (sedimentary microfacies) in a sedimentological sense within the same hydrodynamic-geomorphic unit. In this embodiment, the sedimentary microfacies-lithofacies correspondences of different types of wave-formed sandbars are summarized to form a systematic literature parameter database, which is then incorporated into the wave-formed sandbar knowledge base to enrich the data reference of the wave-formed sandbar knowledge base.

[0039] S4: Acquire macroscopic image data of wave-forming sandbars and extract macroscopic constraints of wave-forming sandbars from this data. Macroscopic image data of wave-forming sandbars can be obtained through high-resolution satellite imagery (such as Google Maps). Utilizing the wide coverage of satellite maps compensates for the spatial scale limitations of field measurements and modern sedimentary observations.

[0040] like Figure 9 As shown, in some embodiments, step S4 includes: like Figure 10 As shown, S41: Acquire macroscopic image data of wave-formed sandbars, and extract macroscopic size parameters of wave-formed sandbars and related sedimentary units based on the macroscopic image data; such as Figure 11 As shown, macroscopic dimensional parameters include the width, length, planar morphology, curvature, angle with the coastline, and angle with the wave direction of wave-formed sandbars and their associated sedimentary units (deltaic underwater distributary channels, estuary bars, and sheet sands) of different regions and scales.

[0041] S42: Macroscopic features of wave-formed sandbars and associated sedimentary units are extracted; S43: Cross-validate macroscopic dimensional parameters and macroscopic features with measured data to obtain macroscopic constraints. The measured data can be trench test data. By cross-validating the trench test data with macroscopic dimensional parameters, the accuracy of the macroscopic dimensional parameters extracted from satellite maps is ensured, and the obtained macroscopic constraints are incorporated into the wave-forming sand dam knowledge base.

[0042] S5: The dynamic sedimentary process of wave-forming sandbar formation is simulated by a preset model using geological features, adaptability parameters, the microfacies-lithological correspondence of different types of wave-forming sandbars, and macroscopic constraints of wave-forming sandbars. The parameters obtained in the simulation process are then correlated to obtain a wave-forming sandbar knowledge base.

[0043] In some embodiments, step S5, simulating the dynamic deposition process of wave-formed sandbar formation using a preset model, includes: simulating the dynamic deposition process of wave-formed sandbar formation using a preset model combined with a preset parallel algorithm; wherein, the preset model is a numerical model of depositional dynamics.

[0044] Based on the numerical model of sedimentary dynamics, a fully coupled analysis model of "river-wave (strong)-wind field-tidal (weak)-flow field" is constructed to simulate the dynamic sedimentary process of wave-formed sandbar formation. The preset parallel algorithm can be the MPI parallel algorithm, which improves computational efficiency and enables full three-dimensional high-precision sedimentary simulation (vertical accuracy ≤0.1m), ensuring that the simulation results accurately reflect the fine structural characteristics of wave-formed sandbars. By adjusting key parameters such as provenance intensity, hydrodynamic conditions, and changes in the sedimentary base level, a series of three-dimensional geological models covering the sedimentary characteristics of the target study area are constructed to obtain various quantitative geological parameters of wave-formed sandbars under different geological conditions, optimizing the applicability and dynamic variation patterns of parameters in the knowledge base.

[0045] In some embodiments, step S5, which associates parameters in the simulation process, further includes: establishing a standardized processing flow for multi-source data, and performing unified format conversion, accuracy verification, and redundancy removal on various types of data from field outcrops, modern sediments, literature surveys, satellite maps, and sedimentary simulations.

[0046] In some embodiments, step S5, associating parameters in the simulation process, includes: constructing a data association analysis model based on the parameters in the simulation process, and clarifying the relationship between parameters from different data sources.

[0047] In this embodiment, a data correlation analysis model is constructed to clarify the intrinsic connections and complementarities between parameters from different data sources. For example, sedimentary simulation parameters are used to verify the configuration patterns observed in field outcrops, and macroscopic parameters extracted from satellite maps are calibrated using modern sedimentary parameters. Based on the correlation analysis results, an integrated reservoir geology knowledge base is formed, encompassing multi-dimensional information such as the sedimentary background, genetic mechanism, sedimentary facies characteristics, configuration unit parameters, interlayer characteristics, and spatial distribution patterns of wave-formed sandbars. This enables the systematic integration and efficient retrieval of geological information on wave-formed sandbars of different scales and types.

[0048] For example, this embodiment provides a method for constructing a multi-resource wave-formed sandbar knowledge base according to the present invention. A specific oilfield in a basin of my country is selected as the target study area. This area has Paleogene wave-formed sandbar reservoirs, which are the main development strata of the oilfield. However, due to the complex reservoir configuration and insufficient understanding of reservoir structure and interlayers, a precise geological knowledge base is urgently needed to support refined development. The study area has accumulated some field outcrop data, modern sedimentary observation data, and literature review results, providing a foundation for the fusion of multi-source data. The method includes the following steps: Step 1: Field Outcrop Data Acquisition and Processing: A coastal wave-formed sandbar outcrop area was selected. A drone equipped with a high-definition camera was used to continuously scan the outcrop area at a flight altitude of 100m and an overlap of 80%, acquiring more than 2,000 high-definition images. Image stitching and 3D modeling were performed using processing software to generate a 3D numerical outcrop model with a resolution of 0.05m. The model identified three levels of structural units: composite sandbars, single sandbars, and mudstone interlayers. The thickness of a single sandbar was measured to be 2-5m, its width 50-150m, and its length 200-800m. The structural pattern of "multi-stage superposition and wave-adjacent extension" was summarized.

[0049] Step 2: Modern Sedimentary Observation and Parameter Acquisition: A modern wave-forming sandbar sedimentary area in a certain sea area was selected as the observation point. Two profiles and five observation points were set up for field measurements, and 60 rock samples were collected for grain size analysis. The length, width, tortuosity, angle with the coastline, and angle with the dominant wave direction of the wave-forming sandbar were obtained by total station measurement. At the same time, parameters such as the width of the deltaic underwater distributary channel and the length of the estuary bar were measured. By comparing with the paleosedimentary background of the study area, six suitable parameters were selected.

[0050] Step 3: Literature Review and Parameter Extraction: Using existing databases, retrieve relevant literature on wave-formed sandbars for a specific time period and focus on analyzing the measured data from core literature. Extract parameters such as the thickness, width-to-thickness ratio, and length-to-thickness ratio of the mudstone interlayers within the wave-formed sandbar; and the width-to-thickness ratio and length-to-thickness ratio of the structural units. Summarize the corresponding relationships between sedimentary microfacies and lithomorphic phases and add them to the knowledge base.

[0051] Step 4: Satellite Map Parameter Extraction and Verification: Acquire high-resolution satellite imagery (2m resolution) of a modern sedimentary area surrounding the study area. Measure the macroscopic parameters of five wave-formed sandbars, including sandbar length and width, and compare and analyze them with field measurement data. Incorporate the verified parameters into a knowledge base as macroscopic configuration constraint indicators.

[0052] Step 5: Sedimentation Simulation and Parameter Optimization: A coupled sedimentary dynamics model of the study area was constructed using a 3D numerical simulation software. The sediment supply rate, wave intensity, and tidal amplitude were set, and a three-dimensional simulation was performed using the MPI parallel algorithm. The simulation yielded the range of wave-formed sandbar thickness variations, and the size differences were analyzed by comparing the simulation with measured data from field outcrops. Simultaneously, the interlayer distribution patterns under different hydrodynamic conditions were simulated, and the optimal range of interlayer thickness parameters suitable for the study area was obtained.

[0053] Step 6: Construction and Application of an Integrated Knowledge Base: A comprehensive geological knowledge base for wave-formed sandbars in the study area was established, comprising six modules including sedimentary background, configuration parameters, and interlayer characteristics. More than 20 quantitative parameters and five configuration patterns were entered. The application of the knowledge base to the reservoir configuration analysis results in the study area showed a high degree of agreement with actual drilling data, verifying the practicality and reliability of the knowledge base.

[0054] This invention aims to construct a complete technical process of "multi-source data acquisition - specialized analysis and extraction - information fusion and integration - formation of an integrated knowledge base", providing core technical support for the detailed analysis of underground wave-formed sandbar reservoir configuration, the formulation of oil and gas field development plans, and the prediction of remaining oil distribution.

[0055] Compared to existing technologies, the following significant advancements have been achieved: 1. Comprehensive data coverage and high knowledge base integrity: It integrates multi-source data such as field, modern, literature, satellite, and simulation data, breaks through the limitations of a single data source, realizes the all-round capture of geological information of Langcheng sandbar, and solves the problems of missing parameters and one-sided information in traditional knowledge bases.

[0056] 2. Precise and strong parameter system: Through UAV modeling, modern sedimentary measurements, and high-precision simulation, a large number of quantitative parameters are obtained to establish a systematic parameter system, providing precise quantitative basis for detailed analysis of underground reservoir structure and improving the accuracy of remaining oil distribution prediction.

[0057] 3. Deep integration of multi-source information, high reliability: Construct a multi-source data association and verification mechanism, and eliminate data bias through cross-validation to ensure the consistency and reliability of knowledge base information, thereby reducing the geological risks of underground reservoir prediction.

[0058] 4. Integrated technical process, convenient and efficient application: It forms a complete technical process from data collection, analysis and extraction to fusion and database construction, realizes efficient integration and rapid access to geological information, provides efficient technical support for the formulation of oil and gas field development plans, and significantly improves the efficiency of development decision-making.

[0059] In some embodiments of the present invention, a computer-readable storage medium is also constructed, on which a computer program is stored, which, when executed by a processor, implements the multi-resource wave sand dam knowledge base construction method as described in any of the above embodiments.

[0060] It is understood that the above embodiments only illustrate some implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above embodiments or technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. That is, the embodiments described "in some embodiments" can be freely combined with any of the preceding and following embodiments. Therefore, all equivalent transformations and modifications made within the scope of the claims of the present invention should be covered by the claims of the present invention.

Claims

1. A method for constructing a multi-resource wave-forming sandbar knowledge base, characterized in that, The method includes: S1: Acquire image data of the Langcheng sandbar outcrop area and construct outcrop geological features based on the Langcheng sandbar outcrop image data; S2: Obtain modern sedimentary data of wave-formed sandbars and screen suitable parameters through modern sedimentary data of wave-formed sandbars; S3: Obtain survey data on different types of wave-formed sandbars, and obtain the corresponding relationship between sedimentary microfacies and lithosomes of different types of wave-formed sandbars through the survey data on different types of wave-formed sandbars; S4: Obtain macroscopic image data of wave-formed sandbars and extract macroscopic constraints of wave-formed sandbars from the macroscopic image data of wave-formed sandbars; S5: The dynamic sedimentary process of wave-forming sandbar formation is simulated by a preset model using geological features, adaptability parameters, the microfacies-lithological correspondence of different types of wave-forming sandbars, and macroscopic constraints of wave-forming sandbars. The parameters obtained in the simulation process are then correlated to obtain a wave-forming sandbar knowledge base.

2. The method for constructing a multi-resource wave-forming sandbar knowledge base according to claim 1, characterized in that, Step S1 includes: S11: Acquire image data of the outcrop area of ​​the wave-formed sandbar and process the acquired image data; S12: Identify and delineate the processed wave-formed sandbar image data to obtain the configuration interface and configuration unit of the wave-formed sandbar outcrop; S13: Based on the configurational interface and configurational unit of wave-formed sandbar outcrops, establish a wave-formed sandbar outcrop model and construct a knowledge base including outcrop geological features.

3. The method for constructing a multi-resource wave-forming sandbar knowledge base according to claim 2, characterized in that, In step S12, the wave-formed sandbar image data is divided based on a preset configuration level classification standard.

4. The method for constructing a multi-resource wave-forming sandbar knowledge base according to claim 2, characterized in that, Step S2 includes: S21: Obtain modern sedimentary data of wave-formed sandbars and establish a prototype model of modern sedimentary wave-formed sandbars; S22: Obtain modern quantitative parameters of sedimentary units associated with wave-forming sandbars based on modern sedimentary prototype models of wave-forming sandbars; S23: Compare and analyze modern quantitative parameters with paleoenvironmental quantitative parameters of the target study area to screen out suitable parameters.

5. The method for constructing a multi-resource wave-forming sandbar knowledge base according to claim 4, characterized in that, Step S3 includes: S31: Obtain survey data on different types of wave-formed sandbars, and obtain quantitative information on the morphology and sedimentary configuration characteristics of different types of wave-formed sandbars through the survey data on different types of wave-formed sandbars; S32: Based on the quantitative morphological information and sedimentary configuration characteristics of different types of wave-formed sandbars, the parameters of different types of wave-formed sandbars are supplemented and improved respectively; S33: Based on the internal interlayer parameters and structural unit parameters of different types of wave-formed sandbars, obtain the sedimentary microfacies-lithological correspondence of different types of wave-formed sandbars.

6. The method for constructing a multi-resource wave-forming sandbar knowledge base according to claim 5, characterized in that, In step S32, the parameters of different types of wave-formed sandbars include: the internal interlayer parameters of different types of wave-formed sandbars and the configuration unit parameters of different types of wave-formed sandbars.

7. The method for constructing a multi-resource wave-forming sandbar knowledge base according to claim 5, characterized in that, Step S4 includes: S41: Acquire macroscopic image data of wave-formed sandbars, and extract macroscopic size parameters of wave-formed sandbars and sedimentary units related to wave-formed sandbars based on the macroscopic image data of wave-formed sandbars; S42: Macroscopic features of wave-formed sandbars and associated sedimentary units are extracted; S43: Cross-validate macroscopic dimensional parameters and macroscopic features with measured data to obtain macroscopic constraints.

8. The method for constructing a multi-resource wave-forming sandbar knowledge base according to claim 7, characterized in that, In step S5, the dynamic deposition process of wave-formed sandbar formation is simulated by a preset model, including: simulating the dynamic deposition process of wave-formed sandbar formation by a preset model combined with a preset parallel algorithm. The preset model is a numerical model of sedimentation dynamics.

9. The method for constructing a multi-resource wave-forming sandbar knowledge base according to claim 7, characterized in that, In step S5, the parameters in the simulation process are associated, including: constructing a data association analysis model based on the parameters in the simulation process, and clarifying the relationship between parameters from different data sources.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for constructing a multi-resource wave sand dam knowledge base as described in any one of claims 1-9.

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