Well-seismic combined transparent oil reservoir construction method

Abstract

The invention discloses a well-seismic combined transparent oil reservoir construction method. The method comprises the steps that the thickness and distribution of hydrocarbon source rocks are predicted through a TOC-shale content-seismic attribute method; predicting the thickness distribution of the sand body by adopting phase control quasi-impedance configuration inversion, and predicting the distribution of effective reservoirs in the sand body by applying model constraint Bayesian porosity inversion; predicting time-space distribution of the mudstone cover layer; combining the fault interpretation result of the seismic section with the horizon interpretation data to generate a fault plane distribution diagram; integrating the seismic geological research of each reservoir forming condition to establish a three-dimensional geological model and setting reservoir forming simulation parameters; simulating and recovering fluid potential characteristics of a research area under the constraint of the three-dimensional geological model so as to intuitively and accurately display oil-gas migration in a geological historical period; and performing 3D reservoir forming simulation based on the three-dimensional geologic model. The formation process and spatial distribution characteristics of the oil and gas reservoir are visualized, so that the oil and gas exploration efficiency is improved, the exploration risk is reduced, and a scientific basis is provided for fine development of the oil and gas reservoir.

Description

Technical Field

[0001] The invention relates to a method for constructing a transparent oil reservoir by combining well and seismic exploration, and belongs to the technical field of oil and gas field geological exploration. Background Art

[0002] Oil and gas accumulation is a key research area in the field of petroleum and natural gas geology. It is a complex, dynamic geological historical process encompassing oil and gas generation, migration, and accumulation. Different types of oil and gas reservoirs experience significant differences in geological tectonic movements and accumulation conditions, resulting in significant differences in oil and gas resources, migration pathways, accumulation areas, and fluid distribution characteristics. Detecting and identifying oil and gas reservoirs, revealing the spatiotemporal relationships of oil and gas accumulation conditions, and clarifying the mechanisms of oil and gas migration and accumulation are fundamental to calibrating favorable oil and gas accumulation areas and increasing the drilling rate of high-quality oil and gas reservoirs. This is of great significance for guiding the next steps of oil and gas exploration and development. Traditional oil and gas reservoir identification methods rely on geological drilling and indirect observation data. In underexplored areas with few wells, complex structures, and greater exploration difficulties, these methods are often limited by the quantity of drilling data and the limitations of analysis techniques. This reduces the accuracy of oil and gas reservoir identification and makes it difficult to reveal the spatiotemporal distribution characteristics of underground oil and gas reservoirs. With the development of seismic technology and computer technology, three-dimensional geological modeling technology and visualization technology based on high-precision seismic data analysis and research have been derived. Combined with advanced methods or oil and gas accumulation theories, the accuracy, reliability and efficiency of exploration results can be improved.

[0003] 3D geological modeling, based on theories from multiple disciplines such as geology and geophysics, combines spatial and computer technologies to digitally represent the spatial morphology and properties of geological bodies. It offers powerful data integration, processing, and spatial analysis capabilities. It not only integrates multi-source geological data, leveraging seismic, geological, and well logging data to construct various sub-models within the geological model that resemble sedimentary structures, but also conveniently extracts and analyzes geologic spatial attributes, enabling complex spatial analysis and simulation. 3D geological modeling technology enables exploration personnel to better assess the potential and value of oil and gas resources, improves the accuracy and reliability of exploration results, and provides a scientific basis for rational development and utilization. Visualization technology, utilizing computer graphics and virtual reality, can create highly realistic geological models. These models vividly and intuitively display complex geological phenomena through graphics, images, or animations. These technologies enable observation and analysis of the distribution and connections of geological bodies from multiple perspectives, facilitating communication with stakeholders. This improves the transparency and understandability of exploration work and facilitates the identification of hidden geological features and patterns.

[0004] Basin simulation technology combines three-dimensional geological modeling and visualization. Drawing on the fundamental principles of structural geology, sedimentary stratigraphy, geofluids, oil and gas geology, organic geochemistry, and petroleum geochemistry, it simulates the formation and distribution characteristics of oil and gas reservoirs in sedimentary basins over time and space during their formation and evolution. This allows for the prediction and evaluation of oil and gas resources and their distribution patterns. This genetic approach, centered around basin simulation, incorporates the entire oil and gas system, examining the static geological elements and dynamic geological processes involved in oil and gas accumulation. By considering the source of oil and gas, it identifies the primary directions of oil and gas migration during different accumulation phases and defines the areas and distribution of oil and gas accumulations. This method provides a comprehensive overview of the entire process from oil and gas generation to accumulation and formation. Compared to statistical and analogical methods, it excels in its clear principles for evaluating oil and gas resources, comprehensive consideration of factors influencing resource availability, rapid and efficient resource calculation, and quantitative visualization of resource evaluation results. It has been hailed as the "most scientific evaluation method" and can accurately predict the scale and distribution of oil and gas resources, significantly improving the accuracy of resource predictions and serving as an effective theoretical approach for guiding the development of transparent reservoirs.

[0005] However, there is currently a lack of an effective method that can intuitively display the spatiotemporal configuration relationship of oil and gas accumulation conditions, as well as the oil and gas accumulation mechanism and enrichment rules. Summary of the Invention

[0006] The present invention provides a method for transparent reservoir construction combining well and seismic data to solve the problems of unclear spatiotemporal configuration relationship of underground oil and gas accumulation conditions and lack of intuitive and effective characterization means for oil and gas accumulation mechanism and enrichment law.

[0007] The present invention solves the above technical problems by providing a technical solution: a method for transparent reservoir construction by combining well and seismic data, comprising the following steps:

[0008] Step S10: predicting the thickness and distribution of source rocks using the TOC-shale content-seismic attribute method;

[0009] Step S20: using phase-controlled pseudo-impedance configuration inversion to predict the thickness distribution of the sand body, and using model-constrained Bayesian porosity inversion to predict the distribution of effective reservoirs in the sand body;

[0010] Step S30, predicting the spatiotemporal distribution of the mudstone caprock based on a phase-controlled pseudo-impedance configuration inversion method;

[0011] Step S40: completing the interpretation of faults of different properties on the three-dimensional seismic profile of the study area, and combining the fault interpretation results of the seismic profile with the horizon interpretation data to generate a fault plane distribution map to clarify the temporal and spatial differences of the fault system;

[0012] Step S50: combining the seismic geological research results of various reservoir formation conditions with the drilling and test analysis data to establish a three-dimensional geological model and set the numerical simulation parameters for oil and gas reservoir formation;

[0013] Step S60: Using the three-dimensional geological model as a constraint, simulate and restore the fluid potential characteristics of the study area to intuitively and accurately display the oil and gas migration during the geological history period and visualize the oil and gas migration and accumulation trends;

[0014] Step S70: Perform 3D reservoir simulation based on the 3D geological model, visualize the complex geological process with intuitive scientific image transformation, and determine the temporal and spatial distribution patterns of oil and gas in a transparent display.

[0015] A further technical solution is that the specific process of step S10 is: using a neural network algorithm, taking the natural gamma and acoustic wave parameters of the same source rock layer in the adjacent area as input items, statistically analyzing the relationship between each curve and the TOC value, using the mud content corresponding to the TOC value as the basis for distinguishing source rock from good source rock, and calibrating the mud content with seismic attributes, determining the attribute threshold value corresponding to the source rock and good source rock according to the attribute mud content prediction function, and determining the thickness and distribution characteristics of the source rock in the study area by the process of TOC-mud content-seismic attributes.

[0016] A further technical solution is that in step S10, the mud content prediction function is calibrated and calculated with the measured data of the drilled strata in the study area and its corresponding root mean square amplitude attributes, so as to clearly distinguish the root mean square amplitude values ​​corresponding to the threshold mud content of source rocks and good source rocks, and use this as a basis to extract the planar thickness distribution of source rocks and determine the main source layer of the target layer in the study area.

[0017] A further technical solution is that the mud content prediction function is:

[0018] R=αX+βY+γZ+R0

[0019] Where R is the percentage of mudstone; X is the root mean square value of amplitude; Y is the root mean square value of frequency; Z is the root mean square value of stable phase; α, β, and γ are the corresponding coefficients of each attribute; and R0 is the base value.

[0020] A further technical solution is that step S20 specifically includes the following steps:

[0021] Step S21: Based on seismic geomorphology methods and techniques, a high-frequency sequence stratigraphic framework is established using well-seismic time-frequency analysis technology, and based on attribute analysis, the sedimentary microfacies distribution of the target layer in each depositional period is characterized using multi-attribute fusion technology;

[0022] Step S22: Compare and analyze the sensitivity of different rock physical parameters to lithology and reservoirs, and select sensitive parameters and threshold values ​​of lithology and physical properties for quantitative characterization as an effective basis for identifying sand bodies and predicting reservoirs;

[0023] Step S23: using phase-controlled pseudo-impedance configuration inversion to predict sand body distribution;

[0024] Step S24: using the inversion result of step S23 as a model constraint, and then using the Bayesian formula to combine the likelihood function of the seismic convolution model with the Cauchy prior distribution of the reflection coefficient for inversion;

[0025] Step S25: Compare the target layer porosity obtained by inversion in step S24 with the measured porosity of the single well in the vertical direction, and extract the reservoir thickness plane distribution map obtained by inversion based on the lower limit value of the porosity of the effective reservoir.

[0026] A further technical solution is that in step 22, the natural gamma ray high-frequency component spectrum curve and the wave impedance low-frequency component spectrum curve, which respectively reflect the formation lithology change information and the geological background low-frequency information, are fused and reconstructed by using the pseudo-acoustic wave construction technology and the acoustic wave impedance to obtain the pseudo-acoustic wave impedance curve.

[0027] A further technical solution is that in step S23, the reconstructed pseudo-acoustic impedance curve and the well extrapolated waveform are used to jointly constrain the low-frequency information, and the high-frequency sequence framework and sedimentary phase are used to establish the high-frequency configuration, and the conventional variation function is used for random simulation to obtain the ultra-high frequency part; by combining the four parts of frequency information and performing inversion under the Bayesian framework, the inversion result of the full frequency band is obtained.

[0028] A further technical solution is that the specific process of step S30 is: using the drilling data of the study area in combination with seismic data, taking the cumulative thickness of mudstone as the standard for characterizing the sealing ability of the cap rock, applying the phase-controlled pseudo-impedance configuration inversion method, identifying the mudstone based on the pseudo-impedance value range of rock physics analysis, extracting the planar distribution map of the mudstone thickness of each layer under well constraints, and clarifying the differential development of the mudstone cap rock.

[0029] A further technical solution is that the reservoir simulation parameters in step S50 are specifically set to simulate the stratigraphic framework and interface, boundary conditions and hydrocarbon generation dynamics model, two-dimensional skeleton profile and migration algorithm, fault system and lithofacies.

[0030] Beneficial effects of the present invention: The present invention visualizes the formation process and spatial distribution characteristics of oil and gas reservoirs, thereby improving oil and gas exploration efficiency, reducing exploration risks, and providing a scientific basis for the refined development of oil and gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1It is a flowchart of the present invention;

[0032] Figure 2 This is the relationship between mud content and TOC of the Pinghu Formation in the KQT area of ​​the Xihu Sag;

[0033] Figure 3 This is a cross-section of the source rock properties of the Pinghu Formation in the Y structure of the Xihu Sag;

[0034] Figure 4 This is a plan view of the source rocks of the Pinghu Formation in the Y structure of the Xihu Sag;

[0035] Figure 5 A histogram showing the division of the H3-H6 sequence in the Yinyue area of ​​the Xihu Sag;

[0036] Figure 6 This is a depiction of the H3-H6 fusion sedimentary microfacies in the Yinyue area of ​​the Xihu Sag;

[0037] Figure 7 This is the intersection analysis diagram of GR and IMP before and after the pseudo-impedance of the Huagang Formation in the Y structure of Xihu Sag;

[0038] Figure 8 This is a comparison chart of the inversion results of the Huagang Formation in the Y structure of the Xihu Sag;

[0039] Figure 9 This is the H3-H6 sand body thickness distribution map of the Huagang Formation in the Y structure of Xihu Sag using phase-controlled pseudo-impedance inversion.

[0040] Figure 10 This is the reservoir thickness profile of the Huagang Formation in the Y structure of the Xihu Sag using porosity inversion;

[0041] Figure 11 This is the H3-H6 reservoir thickness distribution map of the Huagang Formation model constrained Bayesian inversion in the Y structure of the Xihu Sag;

[0042] Figure 12 This is the planar distribution map of the H1-H6 caprocks of the Huagang Formation in the Y structure of the Xihu Sag;

[0043] Figure 13 This is the plane distribution map of the faults in the H3-H6 segment of the Huagang Formation in the Y structure of the Xihu Sag;

[0044] Figure 14 This is a three-dimensional model of the top surface structure of the Huagang Formation H3-H7 in the Y structure of the Xihu Sag;

[0045] Figure 15 This is the fluid potential diagram of H3-H6 of Huagang Formation in Y structure of Xihu Sag;

[0046] Figure 16 This is the current flow potential map of H3-H6 of the Huagang Formation in the Y structure of the Xihu Sag;

[0047] Figure 17This is a map of oil and gas accumulation in the H3-H6 section of the Huagang Formation in the Y structure of the Xihu Sag at different periods. DETAILED DESCRIPTION

[0048] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0049] like Figure 1 As shown, the present invention provides a method for transparent reservoir construction by combining well and seismic analysis, comprising the following steps:

[0050] Step A: predicting the thickness and distribution of source rocks using the TOC-shale content-seismic attribute method;

[0051] Using a neural network algorithm, the natural gamma and acoustic parameters of the Pinghu Formation in the KQT area adjacent to the Y structure in the study area were used as input items. The relationship between each curve and the TOC value was statistically analyzed. It was found that there was a good correlation between TOC and mud content. The two were distributed in an exponential curve. The mud content percentages of 50% and 66% corresponding to TOC values ​​of 1.5% and 4.0% can be used as the basis for distinguishing between source rocks and good source rocks (Table 1, Figure 1 ), and shale content was calibrated with seismic attributes. The attribute threshold values ​​corresponding to source rocks and good source rocks were determined based on the attribute shale content prediction function. The distribution characteristics of the source rocks in the Pinghu Formation of the Y structure were clarified through the process of TOC-shale content-seismic attributes, and then its planar thickness distribution and the main source layer interval were determined.

[0052] Table 1 Evaluation criteria for organic matter abundance in Paleogene coal-bearing source rocks in the Xihu Sag

[0053]

[0054] The prediction function of the root mean square amplitude attribute for the mud content in the rock layer is shown in the following formula:

[0055] R=0.01X+5

[0056] Where: R is the percentage of mudstone; X is the root mean square amplitude attribute value.

[0057] According to the mud content prediction function of the root mean square amplitude attribute, the root mean square amplitude values ​​of the rock layers in the study area are 4500 and 6100 when the mud content R=50% and R=66%, respectively. Values ​​greater than the former can be regarded as source rocks, and this is used as the threshold value for extracting the plane distribution of the thickness of the Pinghu Formation source rocks. Values ​​greater than the latter can be regarded as source rocks. By comparing the approximate proportions of source rocks and good source rocks in each layer on the Pinghu Formation attribute profile, the main source rock layers of the Pinghu Formation can be determined.

[0058] like Figure 3 As shown in the figure, the root mean square amplitude attribute profiles of different inline survey lines in the north and south of the study area show that the proportion of attribute values ​​greater than 4500 in the upper and middle sections of the Pinghu Formation is higher than that in the lower section of the Pinghu Formation, and the areas with attribute values ​​greater than 6100 are mainly located in the middle and upper sections of the Pinghu Formation. The proportion of source rocks and good source rocks with high TOC in the middle and upper sections is significantly higher, which is the main source layer in the study area. In addition, the proportion of source rocks in each section of the Pinghu Formation shows a characteristic of high in the west and low in the east in the study area as a whole. Figure 3-7 As shown in the figure, the source rocks in the middle and upper sections of the Pinghu Formation, the main source layer, are about 400 to 650 m thick, and the source rocks are mainly distributed in the western and central parts of the study area, showing an overall characteristic of being thick in the west and thin in the east; the source rocks in the lower section of the Pinghu Formation are about 300 to 550 m thick, and its source rocks are mainly distributed in the western part of the study area.

[0059] Step B: using phase-controlled pseudo-impedance configuration inversion to predict the thickness distribution of the sand body, and using model-constrained Bayesian porosity inversion to predict the distribution of effective reservoirs in the sand body;

[0060] Step B1, using seismic geomorphology methods to depict sedimentary microfacies distribution;

[0061] Based on seismic geomorphological methods and techniques, a high-frequency sequence stratigraphic framework was established using well-seismic time-frequency analysis technology. Based on attribute analysis, multi-attribute fusion technology was used to characterize the sedimentary microfacies distribution of the target layer in each depositional period, providing constraints for reservoir seismic inversion.

[0062] Step B2, reservoir rock physical analysis;

[0063] Against the backdrop of severe acoustic impedance overlap in sandstone and mudstone, the GR parameter, which is highly effective in identifying lithology, was selected. Using pseudo-acoustic wave construction technology and acoustic impedance fusion, the high-frequency component spectrum curves of the natural gamma ray (GR) and the low-frequency component spectrum curves of the acoustic impedance (IMP), which reflect information on formation lithologic changes and low-frequency geological background information, respectively, were fused and reconstructed based on the concept of "low-frequency compensation and high-frequency recovery." The resulting pseudo-acoustic impedance curves significantly enhance the ability to distinguish sandstone and mudstone compared to traditional acoustic impedance curves, and the reconstructed value range remains essentially unchanged, with the threshold value for sandstone and mudstone remaining at 110,000 m / s·g / cm. 3 .

[0064] Based on the cross-analysis of the physical properties and logging parameters of the proven gas, dry and water layers, it was found that the porosity parameter can clearly distinguish between reservoirs and non-reservoir layers. The gas layers and dry layers show obvious high porosity and low porosity and permeability characteristics, respectively. The physical property standard for effective reservoirs is determined to be a porosity ≥ 7.0%.

[0065] Step B3, predicting sand body distribution by phase-controlled pseudo-impedance configuration inversion;

[0066] The implementation of phase-controlled configuration pseudo-impedance inversion uses the reconstructed pseudo-impedance curve and the extrapolated well waveform as constraints to obtain low-frequency information. Spectral inversion results provide seismic mid-frequency information, and high-frequency configurations are established using the high-frequency sequence framework and sedimentary facies. The ultra-high frequency portion is obtained through stochastic simulation using conventional variograms. By combining the four frequency components and performing inversion within a Bayesian framework, full-band inversion results are obtained, resulting in a more objective and rational distribution of sand bodies, consistent with the strong heterogeneity of the target layer.

[0067] The pseudo-impedance threshold of sandstone (11000m / s·g / cm 3 ) is used as the basis for extracting the inverted sand thickness. The sand thickness obtained by the phase-controlled configuration pseudo-impedance inversion is compared with the actual sand thickness of the well drilled. After the reliability and effectiveness of the inversion are verified by predicting the error range and average error of the sand thickness, the plane distribution map of the sand body thickness obtained by the inversion is extracted using the sand-mudstone threshold value.

[0068] Step B4: predicting reservoir distribution using model-constrained Bayesian porosity inversion;

[0069] The Huagang Formation reservoir properties in the study area will be predicted using a model-constrained Bayesian porosity inversion method. This method uses pseudo-impedance inversion results as a model constraint to supplement the inverted low- and medium-frequency components. The Bayesian formula then combines the likelihood function of the seismic convolution model with the Cauchy prior distribution of the reflection coefficient for inversion. This method simplifies the sparse pulse inversion process while also improving the accuracy of random inversion. The porosity of the target layer derived from the model-constrained Bayesian porosity inversion is compared vertically with the measured porosity of individual wells. The inverted results are similar to the measured porosity of the target layer in each individual well, and the morphological distribution is relatively natural. Based on this, a planar distribution map of the reservoir thickness obtained from the inversion is extracted, using the 7% lower limit of the effective reservoir porosity as the reference.

[0070] Table 2 The lower limit of effective reservoir thickness of Huagang Formation in Y structure of Xihu Sag

[0071]

[0072] Step C, predicting the spatiotemporal distribution of the mudstone caprock based on the phase-controlled pseudo-impedance configuration inversion method;

[0073] Based on the well logging data of Huagang Formation in the Y structure of the study area and seismic data, the cumulative thickness of mudstone is used as the standard to characterize the sealing ability of the caprock. The phase-controlled pseudo-impedance configuration inversion method is applied. According to the pseudo-impedance range of mudstone (5000~11000m / s·g / cm 3 ) for identification, and the planar distribution map of mudstone thickness of each layer in the study area was extracted with well constraints to clarify the differential development of the mudstone caprock of the Huagang Formation.

[0074] Step D: Comprehensive interpretation of seismic profiles to identify fault development characteristics;

[0075] Combined with regional geological and tectonic movement data, the interpretation of faults of different properties was completed on the three-dimensional seismic survey line profile in the study area, and the fault interpretation results of the seismic profile were combined with the stratigraphic interpretation data in the Shuanghu software for mapping, so as to more intuitively show the plane distribution characteristics of the faults H3-H6 of the Huagang Formation of the Y structure and clarify the temporal and spatial differences of the fault system.

[0076] Step E: combining the seismic geological research results of various reservoir formation conditions with the drilling and test analysis data to establish a three-dimensional geological model and set the numerical simulation parameters for oil and gas reservoir formation;

[0077] The specific parameters to be simulated include stratigraphic framework and interface, boundary conditions and hydrocarbon generation dynamics model, two-dimensional profile skeleton and migration algorithm, fault system and lithofacies; the stratigraphic framework and interface, and fault system are set based on the results of drilling measurements and comprehensive seismic structural interpretation.

[0078] The braided river delta sedimentary facies of the Huagang Formation in the study area can determine that the paleowater depth in the boundary condition is between 0 and 10 m. The paleoheat flow (HF) of the thermal history model is believed to be distributed in the range of 50 to 75 mW / m based on previous research results. 2 , now about 53mW / m 2 Paleotemperatures were obtained using a globally unified sedimentary water surface temperature-time template, combined with the present-day geographic location of the East China Sea Basin (31°N), through comprehensive software prediction. Because the Pinghu Formation source rocks have an organic matter content (TOC%) of 9%, a hydrogen index (HI) of 200 mgHC / gTOC, and a Type III kerogen, the hydrocarbon generation kinetic model was selected based on Burnham (1989)_TⅢ.

[0079] The Huagang Formation in the Y structure belongs to a low-porosity and low-permeability reservoir, and the correlation between porosity and permeability is poor. The algorithm is used to select the reverse permeation method suitable for low-porosity and low-permeability reservoirs.

[0080] The Huagang Formation in the study area has a large set of sandstones vertically developed, with interbedded siltstone, mudstone, and silty mudstone. The sandstone reservoir is characterized by "rich in quartz, low in feldspar, and low in lithic debris," and is mainly composed of feldspar lithic sandstone and lithic feldspar sandstone. The porosity data for the simulation study mainly comes from the model-constrained Bayesian porosity seismic inversion results, and the permeability comes from actual drilling data. The lithofacies setting is completed by integrating lithologic and physical property data.

[0081] Based on the three-dimensional geological model and the setting of simulation parameters, it is possible to simulate the development and evolution of sedimentary basins, simulate the changes in fluid dynamics and chemical dynamics within the basin, and reproduce the oil and gas migration and accumulation trends and accumulation processes in the geological history.

[0082] Step F, simulating and restoring the fluid potential characteristics of the study area in a three-dimensional geological model to intuitively and accurately display the oil and gas migration during the geological history and visualize the oil and gas migration and accumulation trends;

[0083] Early exploration indicated that the Huagang Formation in the Y structure is mainly a gas reservoir, and the source rock products are mainly gaseous hydrocarbons. The study of fluid potential will be based on the widely accepted Hubbert gas potential formula:

[0084]

[0085] Where: is the fluid potential; z is the elevation; p is the fluid pressure; ρ is the density of gaseous hydrocarbons; and V is the flow velocity.

[0086] The formula indicates that there are five factors that control the fluid potential in the formation, namely elevation, fluid density, formation pressure, gravitational acceleration (constant) and flow rate. The fluid density of the present invention is provided by actual drilling measurements, the gravitational acceleration is determined based on the latitude of Yinyue (31°), the elevation data is constrained by the top surface structural contour maps of each layer H3-H7 of the Huagang Formation provided when setting the model, and the formation pressure is obtained by calibrating and recovering the actual drilling pressure based on seismic data. The fluid viscosity and density, porosity and permeability, and temperature and pressure that affect the flow rate will be obtained from actual drilling measurements, seismic inversion data, and simulated temperature and pressure changes.

[0087] The fluid potential technology was used to restore the fluid potential of the Huagang Formation in the geological history period (12Ma) and the present study area, and to identify the inherited large-scale oil and gas convergence area that is consistent with the long-term stable low-pressure area of ​​the Y structure.

[0088] Step G: Visualize the reservoir formation process and make the temporal and spatial distribution of oil and gas transparent.

[0089] By using reservoir simulation technology to integrate geological, geophysical and fluid simulation data, the spatial relationship of various reservoir-forming geological elements is transparently displayed under a unified time and space benchmark. The reverse permeability method suitable for the low-porosity and low-permeability reservoirs of the Huagang Formation in the Y structure is used to perform 3D reservoir simulation. The complex geological process is visualized through intuitive scientific image transformation, revealing the dynamic evolution process of oil and gas from generation, migration to accumulation in a time series, clarifying the key time nodes and main controlling factors of the Huagang Formation reservoir, and determining the temporal and spatial distribution patterns of oil and gas in a transparent display.

[0090] Experimental effect analysis

[0091] From the implementation effect of the method, Figure 3 and Figure 4 As shown in the figure, based on the TOC-shale content-seismic attribute method, the problem of unclear distribution of main source layers and source rocks in the study area caused by the absence of deep Pinghu Formation source rocks in the drilling process can be solved. Figure 8 and Figure 10 As shown, the sandbody distribution morphology obtained from seismic inversion is more objective, with smaller errors and better consistency between reservoirs and those encountered by well logging, effectively improving reservoir prediction accuracy. The favorable oil and gas convergence areas of fluid potential simulation are consistent with the stable, low-pressure areas of the Y structure and the locations of the drilled oil and gas reservoirs, visualizing oil and gas migration and accumulation trends while maintaining accuracy. Figure 15-17 The 3D reservoir simulation shown here visualizes the complex, dynamic evolution of oil and gas from generation and migration to accumulation through intuitive, scientific image transformations. This helps clarify the key time points and controlling factors of the Huagang Formation reservoir and clearly demonstrates the temporal and spatial distribution patterns of oil and gas. This demonstrates the advancement and practicality of this transparent reservoir construction method, which combines well and seismic data, utilizes seismic information, techniques, and methods to study reservoir formation conditions and establish a 3D geological model, and uses numerical reservoir simulation software to visualize and make transparent the oil and gas reservoir formation process and temporal and spatial distribution patterns.

[0092] The above description does not limit the present invention in any form. Although the present invention has been disclosed through the above embodiments, it is not intended to limit the present invention. Any technician familiar with the profession can use the technical content disclosed above to make some changes or modifications to equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are within the scope of the technical solution of the present invention.

Claims

1. A method for transparent reservoir construction by combining well and seismic data, characterized in that: The following steps are involved: Step S10: predicting the thickness and distribution of source rocks using the TOC-shale content-seismic attribute method; Step S20: using phase-controlled pseudo-impedance configuration inversion to predict the thickness distribution of the sand body, and using model-constrained Bayesian porosity inversion to predict the distribution of effective reservoirs in the sand body; Step S30, predicting the spatiotemporal distribution of the mudstone caprock based on a phase-controlled pseudo-impedance configuration inversion method; Step S40: completing the interpretation of faults of different properties on the three-dimensional seismic profile of the study area, and combining the fault interpretation results of the seismic profile with the horizon interpretation data to generate a fault plane distribution map to clarify the temporal and spatial differences of the fault system; Step S50: combining the seismic geological research results of various reservoir formation conditions with the drilling and test analysis data to establish a three-dimensional geological model and set the numerical simulation parameters for oil and gas reservoir formation; Step S60: Using the three-dimensional geological model as a constraint, simulate and restore the fluid potential characteristics of the study area to intuitively and accurately display the oil and gas migration during the geological history period and visualize the oil and gas migration and accumulation trends; Step S70: Perform 3D reservoir simulation based on the 3D geological model, drilling data, and test analysis data, visualize the complex geological process with intuitive scientific image transformation, and determine the temporal and spatial distribution patterns of oil and gas in a transparent display.

2. The method for transparent reservoir construction by combining well and seismic data according to claim 1, characterized in that: The specific process of step S10 is as follows: using a neural network algorithm, taking the natural gamma and acoustic wave parameters of the same source rock layer in the adjacent area as input items, statistically analyzing the relationship between each curve and the TOC value, using the mud content corresponding to the TOC value as the basis for distinguishing source rocks from good source rocks, calibrating the mud content with seismic attributes, determining the attribute threshold values ​​corresponding to source rocks and good source rocks based on the attribute mud content prediction function, and determining the thickness and distribution characteristics of the source rocks in the study area through the process of TOC-mud content-seismic attributes.

3. The method for transparent reservoir construction by combining well and seismic data according to claim 2, characterized in that: In step S10, the mud content prediction function is calibrated and calculated with the measured data of the drilled strata in the study area and their corresponding root mean square amplitude attributes, so as to clearly distinguish the root mean square amplitude values ​​corresponding to the threshold mud content of source rocks and good source rocks, and use this as a basis to extract the planar thickness distribution of source rocks and determine the main source layer of the target layer in the study area.

4. The method for transparent reservoir construction by combining well and seismic data according to claim 3, characterized in that: The mud content prediction function is: R=αX+βY+γZ+R0 Where R is the percentage of mudstone; X is the root mean square value of amplitude; Y is the root mean square value of frequency; Z is the root mean square value of stable phase; α, β, and γ are the corresponding coefficients of each attribute; and R0 is the base value.

5. The method for transparent reservoir construction by combining well and seismic data according to claim 1, characterized in that: The step S20 specifically includes the following steps: Step S21: Based on seismic geomorphology methods and techniques, a high-frequency sequence stratigraphic framework is established using well-seismic time-frequency analysis technology, and based on attribute analysis, the sedimentary microfacies distribution of the target layer in each depositional period is characterized using multi-attribute fusion technology; Step S22: Compare and analyze the sensitivity of different rock physical parameters to lithology and reservoirs, and select sensitive parameters and threshold values ​​of lithology and physical properties for quantitative characterization as an effective basis for identifying sand bodies and predicting reservoirs; Step S23: using phase-controlled pseudo-impedance configuration inversion to predict sand body distribution; Step S24: using the inversion result of step S23 as a model constraint, and then using the Bayesian formula to combine the likelihood function of the seismic convolution model with the Cauchy prior distribution of the reflection coefficient for inversion; Step S25: Compare the target layer porosity obtained by inversion in step S24 with the measured porosity of the single well in the vertical direction, and extract the reservoir thickness plane distribution map obtained by inversion based on the lower limit value of the porosity of the effective reservoir.

6. The method for transparent reservoir construction by combining well and seismic data according to claim 5, characterized in that: In step 22, the pseudo-acoustic wave construction technology is combined with the acoustic impedance to fuse the natural gamma ray high-frequency component spectrum curve and the wave impedance low-frequency component spectrum curve, which respectively reflect the formation lithology change information and the geological background low-frequency information, to obtain the pseudo-acoustic impedance curve.

7. The method for transparent reservoir construction by combining well and seismic data according to claim 6, characterized in that: In step S23, the reconstructed pseudo-acoustic impedance curve and the well extrapolated waveform are used to jointly constrain the low-frequency information, and the high-frequency structure is established using the high-frequency sequence framework and sedimentary facies. The conventional variation function is used for random simulation to obtain the ultra-high frequency part; the inversion result of the full frequency band is obtained by combining the four parts of frequency information and performing inversion under the Bayesian framework.

8. The method for transparent reservoir construction by combining well and seismic data according to claim 1, characterized in that: The specific process of step S30 is as follows: using the drilling data of the study area in combination with seismic data, taking the cumulative thickness of mudstone as the standard for characterizing the sealing ability of the cap rock, applying the phase-controlled pseudo-impedance configuration inversion method, identifying the mudstone based on the pseudo-impedance value range of rock physics analysis, extracting the planar distribution map of the mudstone thickness of each layer under well constraints, and clarifying the differential development of the mudstone cap rock.

9. The method for transparent reservoir construction by combining well and seismic data according to claim 1, characterized in that: The reservoir simulation parameters set in step S50 specifically include the simulated stratigraphic framework and interface, boundary conditions and hydrocarbon generation dynamics model, two-dimensional skeleton profile and migration algorithm, fault system and lithofacies.

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