Marine clastic rock reservoir modeling method based on geological cause information

Abstract

The invention discloses a marine clastic rock reservoir modeling method based on geological cause information, which relates to the technical field of geological modeling, and comprises the steps of collecting and preprocessing modeling data, obtaining preprocessed data, performing geological background research on a target area, performing profile analysis and interface identification on the preprocessed data, and obtaining a marine clastic rock reservoir model. Extracting and converting inclination angle information from the preprocessed data, identifying and quantifying interlayer features, generating a probability distribution constraint body, performing sequential indication modeling, and verifying, optimizing and applying the model; according to the method, multi-source data is integrated to deeply research the sea entering and sea exiting rule, an interface is accurately identified, a sedimentary facies belt is divided, a foundation is laid for reservoir analysis, dip angle information is innovatively utilized, the dip angle information is converted into a probability value and fused into sequential indication modeling, interlayer distribution is accurately simulated in combination with reservoir characteristics and a variation function, and the reservoir analysis result is obtained. And the characterization capability of the model on a complex reservoir structure is obviously improved.

Description

Technical Field

[0001] The invention relates to the technical field of geological modeling, and in particular to a marine clastic rock reservoir modeling method based on geological genesis information. Background Art

[0002] Clastic reservoirs are mainly composed of clastic sedimentary rocks such as sandstone, conglomerate, and gravel, and are one of the important reservoir types in oil and gas fields. This type of reservoir is widely distributed around the world, including Daqing, Shengli, Dagang and other oil fields in my country. The storage space of clastic reservoirs is mainly composed of pores between clastic particles, including primary pores and secondary pores. Its reservoir properties are affected by many factors such as mineral composition, particle size, sorting, cement type and content. The geological characteristics of clastic reservoirs are complex and diverse, but they usually have two basic properties: porosity and permeability. They are an important target for oil and gas exploration and development.

[0003] Marine clastic reservoirs refer to reservoirs formed in the marine environment, which are formed by the transportation, deposition and consolidation of weathered and eroded rock debris. This type of reservoir is rich in sandy and silty sediments, often accompanied by biological debris and chemical sediments. Marine clastic reservoirs are widely distributed in major sedimentary basins around the world, and their reservoir properties are controlled by many factors such as sedimentary environment, ancient water flow direction, biological activity and diagenesis. This type of reservoir not only has well-developed pores, but also has high permeability, and is an important storage place for oil and gas resources. In oil and gas exploration and development, in-depth research on marine clastic reservoirs is of great significance for accurately evaluating the potential of oil and gas resources and formulating development plans.

[0004] Traditional methods determine and reflect marine clastic reservoirs, mainly through geological exploration, core analysis, logging data interpretation and other means. Geological exploration reveals the distribution range and morphology of reservoirs, core analysis directly observes rock composition, structure, porosity and other characteristics, and logging data provides continuous underground physical property data. These methods are used in combination to preliminarily determine the existence, thickness, lithology and changes of reservoirs. However, traditional methods mostly rely on empirical judgment and limited drilling data, and the description of the internal complexity of the reservoir is relatively limited. It is difficult to fully reflect the heterogeneity and dynamic changes of marine clastic reservoirs, and it does not fully consider the impact of geological genesis information on marine clastic reservoirs. Therefore, a more scientific and systematic modeling method is needed to improve the accuracy and reliability of the description of marine clastic reservoirs. Summary of the invention

[0005] The purpose of the present invention is to provide a method for modeling a marine clastic reservoir based on geological genesis information to solve the above-mentioned deficiencies in the prior art.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for modeling a marine clastic reservoir based on geological genesis information, comprising the following steps:

[0007] S1. Collect and preprocess modeling data to obtain preprocessed data;

[0008] S2. Conduct geological background research on the target area;

[0009] S3, performing profile analysis and interface identification on the preprocessed data;

[0010] S4, extracting and converting inclination information from the preprocessed data;

[0011] S5. Identify and quantify interlayer characteristics;

[0012] S6, generating a probability distribution constraint body;

[0013] S7, conduct sequential instruction modeling;

[0014] S8. Verify, optimize and apply the model.

[0015] Furthermore, the S1 includes:

[0016] S11, collecting multi-source data of the target area, wherein the multi-source data includes well logging data, core samples and laboratory analysis results;

[0017] S12. Perform accuracy detection and preprocessing on the collected data, wherein the preprocessing includes data cleaning, denoising, interpolation, correction and standardization.

[0018] Furthermore, the geological background research in S2 is a study on sedimentary environment changes.

[0019] Further, the S3 includes:

[0020] S31. Perform profile processing and detailed analysis on the pre-processed data to identify transgressive and regressive interfaces, unconformities and potential interlayer interfaces;

[0021] S32. Analyze the sedimentary characteristics and lithological changes on both sides of the transgressive and regressive interface, determine the sedimentary facies and sedimentary environment, and provide a basis for reservoir characteristics analysis.

[0022] Further, the S4 includes:

[0023] S41, extracting formation dip information using the preprocessed data, where the formation dip value ranges from 0 to 10 degrees;

[0024] S42. The formation dip values ​​are converted into probability values ​​ranging from 0 to 1 by normalization method so as to be used as an indicator of spatial variability in sequential indicator modeling.

[0025] Furthermore, the normalization method comprises the following steps:

[0026] Suppose the formation dip angle θ is in the range of 0≤θ≤10, and suppose a function f(θ) is in the range of 0≤f(θ)≤1;

[0027] The normalized mathematical expression is:

[0028]

[0029] Among them, θ min is the minimum value of the formation dip, θ max is the maximum value of the formation dip, θ min = 0 and θ max =10;

[0030] θ min =0 and θ max =10 Substitute into the normalization formula and we get:

[0031]

[0032] f(θ) is a linear function that linearly maps the formation dip angle θ to the interval from 0 to 1. For a given θ value within the range of 0-10, its normalized value can be obtained by calculating f(θ).

[0033] Further, the S5 includes:

[0034] S51. In the profile analysis, attention should be paid to the identification of interlayers, and their lithology, thickness, distribution range and relationship with the main reservoir should be analyzed;

[0035] S52. Conduct in-depth research on the formation mechanism of interlayers and evaluate their impact on oil and gas migration and storage.

[0036] Further, the S6 includes:

[0037] S61, taking the dip information of the clastic reservoir and the interlayer characteristics as input parameters;

[0038] S62, defining a variogram of each input parameter to describe its spatial variability;

[0039] S63. Combining all input parameters and variograms, a probability distribution constraint body is generated to characterize the interlayer distribution characteristics inside the clastic reservoir.

[0040] Furthermore, the S7 adopts a sequential indicator modeling method to access the grid nodes according to a set random path and calculate the probability that each node belongs to a different category.

[0041] Further, the S8 includes:

[0042] S81. Validate the model using a single well sparse sampling validation method;

[0043] S82. Adjust modeling parameters according to the validation results and optimize the model to improve its prediction accuracy and practicality;

[0044] S83. Apply the optimized model to oil and gas exploration and development for reservoir evaluation, oil and gas resource estimation, development plan design and practical work.

[0045] Compared with the prior art, the present invention provides a marine clastic reservoir modeling method based on geological genesis information:

[0046] First, the traditional methods often focus on the analysis of a single data source or local geological characteristics, and the understanding and application of the important geological process of transgression and regression in regional geological history are relatively limited, resulting in the neglect of sedimentary phase changes and reservoir characteristic changes caused by transgression and regression in reservoir prediction and interface identification. The present invention integrates multi-source data, widely collects multi-source data such as well logging and core data, and conducts in-depth research on sedimentary environmental changes to fully grasp the impact of transgression and regression processes on reservoir formation and evolution. At the same time, the present invention finely identifies interfaces, pays special attention to the identification of key interfaces such as transgression and regression interfaces and unconformity surfaces in profile analysis, and combines the sedimentary characteristics and lithology changes on both sides of the interface to more accurately divide sedimentary facies and sedimentary environments, providing a solid foundation for reservoir characteristic analysis.

[0047] 2. Combination of dip data processing and sequential indicator modeling. Traditional methods do not fully utilize formation dip information as an important indicator of spatial variability when processing it, resulting in deficiencies in the model when characterizing complex structures such as interlayers inside the reservoir. The present invention accurately utilizes dip information, extracts formation dip information through logging data, and converts it into probability values. This step not only retains the physical meaning of dip data, but also facilitates it as a direct constraint on spatial variability in the modeling process, while enhancing the sequential indicator modeling capability. In the sequential indicator modeling process, dip information is used as one of the key input parameters, combined with other reservoir characteristics and variograms, to more accurately simulate the distribution characteristics of interlayers inside the reservoir. This multi-parameter comprehensive modeling method significantly improves the model's ability to characterize complex reservoir structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0049] Figure 1This is an overall flow chart provided for Embodiment 1 of the present invention;

[0050] Figure 2 A first flow chart provided for Embodiment 1 of the present invention;

[0051] Figure 3 A second flow chart provided for the first embodiment of the present invention;

[0052] Figure 4 A schematic diagram of the top surface structure of a small layer provided in Example 1 of the present invention;

[0053] Figure 5 A schematic diagram of the distribution of the advance-retreat interface in a small layer of a work area provided in Example 1 of the present invention;

[0054] Figure 6 A first flow chart provided for Embodiment 2 of the present invention;

[0055] Figure 7 A second flow chart provided for Embodiment 2 of the present invention;

[0056] Figure 8 A schematic diagram of a partition identification plate provided in the second embodiment of the present invention;

[0057] Fig. 9 A schematic diagram of the interlayer tendency characteristics provided in the second embodiment of the present invention;

[0058] Fig.10 A schematic diagram showing the relationship between the maximum thickness and the maximum extension length of the shoreline provided in the second embodiment of the present invention;

[0059] Fig.11 A first flow chart provided for Embodiment 3 of the present invention;

[0060] Fig.12 A schematic diagram of a variation function provided in Embodiment 3 of the present invention;

[0061] Fig.13 A schematic diagram of an azimuth constraint body provided in Embodiment 3 of the present invention;

[0062] Fig.14 A schematic diagram of the relationship between the interlayer and the oil-water distribution provided in the third embodiment of the present invention;

[0063] Fig.15 A second flow chart provided for Embodiment 3 of the present invention;

[0064] Fig.16 This is a schematic diagram of model accuracy verification provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0065] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0066] Embodiment 1:

[0067] See also Figure 1-Figure 5 A method for modeling a marine clastic reservoir based on geological genesis information comprises the following steps:

[0068] S1. Collect and preprocess modeling data to obtain preprocessed data;

[0069] S2. Conduct geological background research on the target area;

[0070] S3, performing profile analysis and interface identification on the preprocessed data;

[0071] S4, extracting and converting inclination information from the preprocessed data;

[0072] S5. Identify and quantify interlayer characteristics;

[0073] S6, generating a probability distribution constraint body;

[0074] S7, conduct sequential instruction modeling;

[0075] S8. Verify, optimize and apply the model.

[0076] S1 includes:

[0077] S11. Collect multi-source data of the target area, including well logging data, core samples and laboratory analysis results;

[0078] Specific implementation method: Well logging is a technology that measures the physical properties of rocks in boreholes and obtains logging data to infer formation characteristics. Logging data includes the measurement values ​​of multiple physical quantities such as resistivity, acoustic wave time difference, natural gamma, etc., which can reflect information such as rock lithology; cores are rock samples taken directly from underground and can provide the most direct formation information. Laboratory analysis includes rock thin section identification, mineral composition analysis, etc., which can deeply understand the microstructure and physical properties of rocks. The acquisition of multi-source data enriches the data sources for modeling and reflects the marine clastic reservoir from more dimensions.

[0079] S12. Perform accuracy detection and preprocessing on the collected data. The preprocessing includes data cleaning, denoising, interpolation, correction and standardization.

[0080] Specific implementation method: The multi-source data obtained have different sources, acquisition methods, acquisition environments, sampling equipment status levels, and there are also differences in the professional qualities of the sampling personnel. Before importing the data, it is necessary to make an accuracy judgment, correct and remove duplicate, erroneous or inconsistent data records to ensure data quality; the logging data may contain noise signals that interfere with the interpretation and modeling of the data, and denoising processing is required to reduce or eliminate these noises and improve the signal-to-noise ratio of the data; since data sampling may be uneven or missing, interpolation technology is used to fill data gaps or increase data density. This is a prior art and will not be described in detail here; finally, the processed data is converted into a unified format and measurement standard to facilitate subsequent data processing and modeling analysis.

[0081] The geological background research in S2 is the study of sedimentary environment changes.

[0082] Specific implementation method: Analyzing the changes and laws of sedimentary environments in different geological periods, including the source of sediments, transportation methods, sedimentation rates, etc., is of great significance for identifying sedimentary facies belts and determining sedimentary environments;

[0083] The research on sedimentary environmental changes mainly revolves around calcareous interlayers. The formation of calcareous interlayers is deeply affected by the leaching process of atmospheric fresh water. When atmospheric fresh water rich in carbon dioxide seeps into the ground, it turns into weakly acidic water, which has a dissolving effect on rocks, especially releasing minerals such as calcium ions. As these ions migrate underground and reach appropriate chemical and physical conditions, they will re-precipitate to form carbonates, gradually constructing calcareous interlayers. This complex geological process is also jointly regulated by multiple factors such as climatic conditions, geological structures and subsequent diagenesis.

[0084] Through S1 and S2, a comprehensive understanding of the geological background of the target area can be established, providing a solid foundation for subsequent marine clastic reservoir modeling.

[0085] S3 includes:

[0086] S31. Perform profile processing and detailed analysis on the pre-processed data to identify transgressive and regressive interfaces, unconformities and potential interlayer interfaces;

[0087] Specific implementation method: The continuity and morphology of the stratigraphic section are constructed using the lithology change information of the logging data, providing more detailed stratigraphic lithology and physical property information;

[0088] Based on the thickness changes, lithology changes and continuity of sedimentary sequences in the profile, the interfaces formed during transgression and regression are identified. These interfaces are usually accompanied by significant changes in sediment grain size, lithology, fossils and other characteristics. During transgression, as seawater advances toward land, the sedimentary environment gradually changes from continental to marine. The grain size of the sediment usually changes from coarse at the top to fine at the bottom. If the grain size becomes finer upward, it usually indicates transgression; conversely, if the grain size becomes coarser upward, it indicates regression.

[0089] Unconformity is the interface where the stratigraphic record is interrupted or missing, usually representing a major tectonic event or sedimentary discontinuity in geological history. In the cross-section, unconformity often appears as angular unconformity or parallel unconformity between strata, as well as missing or repeated strata.

[0090] Interlayers are non-permeable or low-permeability rock layers in the reservoir, which have an important impact on oil and gas migration and storage. By analyzing the mutation points of physical quantities such as resistivity and acoustic wave time difference in the logging curve, potential interlayer interfaces can be identified.

[0091] S32. Analyze the sedimentary characteristics and lithological changes on both sides of the transgressive and regressive interface, determine the sedimentary facies and sedimentary environment, and provide a basis for reservoir characteristics analysis.

[0092] Specific implementation method: Analyze the lithology changes on both sides of the transgressive and regressive interface, including rock type, color, grain size and other characteristics, to reflect the changes in the sedimentary environment. By observing the sedimentary structural characteristics in the profile, such as bedding, cross-bedding, ripples, etc., the water flow conditions and sedimentary environment when the sediments were deposited are reflected. In addition, according to the sedimentary sequence of the stratigraphic profile, the superposition relationship and sedimentary process of the sediments are analyzed to infer the evolution history of the sedimentary environment.

[0093] Sedimentary facies belts are combinations of rock formations with similar sedimentary characteristics and biological communities, reflecting the sedimentary environment and sedimentary processes of a specific period. Based on the analysis results of sedimentary characteristics and biological markers, stratigraphic sections are divided into different sedimentary facies belts. Combining the division results of sedimentary facies belts with the information from geological background research, the sedimentary environment type and evolution history of the target area are determined, providing important geological basis for subsequent reservoir characteristics analysis.

[0094] Embodiment 2:

[0095] See also Figure 6-Figure 10 This embodiment provides a technical solution based on the first embodiment:

[0096] S4 includes:

[0097] S41, extracting formation dip information using the preprocessed data, where the formation dip value ranges from 0 to 10 degrees;

[0098] Specific implementation method: The formation dip is the angle between the formation interface and the horizontal plane, which reflects the inclination of the formation;

[0099] For oil and gas exploration and development, reservoirs with moderate formation inclination often have good development and utilization value. Such formation inclination will neither cause difficulty in oil and gas migration due to being too gentle, nor increase the difficulty of drilling and mining due to excessive inclination. Therefore, the formation inclination is set in the range of 0-10 degrees in the present invention.

[0100] S42. The formation dip values ​​are converted into probability values ​​ranging from 0 to 1 by normalization method so as to be used as an indicator of spatial variability in sequential indicator modeling.

[0101] The normalization method includes the following steps:

[0102] Suppose the formation dip angle θ is in the range of 0≤θ≤10, and suppose a function f(θ) is in the range of 0≤f(θ)≤1;

[0103] The normalized mathematical expression is:

[0104]

[0105] Among them, θ min is the minimum value of the formation dip, θ max is the maximum value of the formation dip, θ min = 0 and θ max =10;

[0106] θ min =0 and θ max =10 Substitute into the normalization formula and we get:

[0107]

[0108] f(θ) is a linear function that linearly maps the formation dip angle θ to the interval from 0 to 1. For a given θ value within the range of 0-10, its normalized value can be obtained by calculating f(θ).

[0109] Specific implementation method: In sequential indicator modeling, input parameters need to be dimensionless and are usually converted into probability values ​​or similarity metrics so as to be uniformly processed in the modeling process. As a physical quantity, the original value range of formation dip is not suitable for direct modeling, so it needs to be converted into probability values;

[0110] In sequential indicator modeling, formation dip information, as an important indicator of spatial variability, is used to constrain model generation. By converting formation dip into probability values, it can be more conveniently used as an input parameter together with other reservoir characteristics such as rock type. Its spatial variability can be described by a variogram and comprehensively considered in the modeling process. The generated model will be able to more accurately reflect the actual structure of the formation and reservoir characteristics.

[0111] S5 includes:

[0112] S51. In the profile analysis, attention should be paid to the identification of interlayers, and their lithology, thickness, distribution range and relationship with the main reservoir should be analyzed;

[0113] S52. Conduct in-depth research on the formation mechanism of interlayers and evaluate their impact on oil and gas migration and storage.

[0114] Specific implementation method: The interlayer is identified by using the identification plate and the machine learning method at the same time, and the two methods are interactively verified to obtain the final single well interlayer data;

[0115] In geological profiles, interlayers often appear as layers with obvious lithological differences from the main reservoir. The existence of interlayers and their contact relationship with the main reservoir can be intuitively identified through natural profiles or columnar profiles.

[0116] The thickness of interlayers can be measured by using profiles or logging data. The thickness of interlayers with different genesis and depositional environments may vary greatly.

[0117] Through regional geological surveys and geological modeling techniques, the distribution range of interlayers in plane and section is determined to help understand the macroscopic control of interlayers on oil and gas storage and migration;

[0118] Analyze the contact relationship between the interlayer and the main reservoir, including the characteristics of the upper and lower contact surfaces, the occurrence relationship, etc., to help understand the microscopic impact mechanism of the interlayer on oil and gas migration and storage;

[0119] Through detailed profile analysis, lithology identification, characteristic analysis and genetic mechanism research conducted by S5, we have gained an in-depth understanding of the properties of interlayers and their impact on oil and gas migration and storage. This information is of great significance for reservoir evaluation, well site deployment and production plan design in oil and gas exploration and development.

[0120] Embodiment three:

[0121] See also Figure 11-Figure 16 This embodiment provides a technical solution based on the second embodiment:

[0122] S6 includes:

[0123] S61, taking the dip information of the clastic reservoir and the interlayer characteristics as input parameters;

[0124] Specific implementation method: Use the sequential indicator modeling model of petre l software, which is a software designed for the fields of geology, petroleum and geophysics. It takes the three-dimensional geological model as the center and provides an integrated platform for exploration and development. Modeling is completed by inputting parameters. The dip information of the clastic reservoir refers to the dip angle and direction of the reservoir layer or the main structural surface, reflecting the spatial distribution of the reservoir and the direction of oil and gas migration. The interlayer characteristics include the lithology, thickness, distribution range and relationship of the interlayer with the main reservoir, reflecting the storage and migration of oil and gas.

[0125] S62, defining a variogram of each input parameter to describe its spatial variability;

[0126] Specific implementation method: The variogram is a basic tool in geostatistics for describing the spatial structure and randomness of regionalized variables. It reflects the spatial variation characteristics of variables by quantifying the similarity or difference of variables in space. According to the actual distribution of data, a spherical model can be selected to complete it and determine the parameters of the variogram at the same time;

[0127] The spherical model includes the following steps:

[0128]

[0129] γ(h) is the variogram value, which represents the variability between two points with a distance of h;

[0130] C 0 is the nugget value, which indicates variability over very short distances;

[0131] C is the height of the arch, which represents the part of the total variability caused by the spatial structure;

[0132] C 0 +C is the sill value;

[0133] a is the range, which represents the distance at which variables begin to lose correlation in space.

[0134] S63. Combining all input parameters and variograms, a probability distribution constraint body is generated to characterize the interlayer distribution characteristics inside the clastic reservoir.

[0135] Specific implementation method: The calculated probability value of each grid node is comprehensively processed to generate a complete probability distribution map. According to the probability distribution map, a probability distribution constraint body characterizing the distribution characteristics of interlayers inside the clastic reservoir is constructed. This constraint body not only reflects the spatial distribution characteristics of the interlayers, but also reflects its relationship with the main reservoir and its spatial variability.

[0136] S7 uses a sequential indicator modeling method to visit grid nodes according to a set random path and calculate the probability that each node belongs to a different category.

[0137] Specific implementation method: Sequential indicator modeling is a modeling method based on random simulation. It generates a reservoir model by point-by-point or grid-by-grid simulation, and sets one or more random paths in the modeling area to traverse all grid nodes. Finally, according to the defined variogram and input parameters, the probability of each grid node belonging to different geological categories such as main reservoir, interlayer, etc. is calculated.

[0138] S6 and S7 successfully generated a probability distribution constraint body characterizing the distribution characteristics of interlayers inside clastic reservoirs by integrating input parameters such as the main characteristics of clastic reservoirs, dip information, and interlayer characteristics, and using geostatistical methods and the sequential indicator modeling technology of petre l software. This constraint body provides important geological basis and reference for subsequent oil and gas exploration and development.

[0139] S8 includes:

[0140] S81. Validate the model using a single well sparse sampling validation method;

[0141] Specific implementation method: Single well thinning verification is a geostatistical method used to evaluate the accuracy and reliability of the probability distribution constraint body that characterizes the interlayer distribution characteristics of the clastic reservoir. This method removes the data of some wells from the original data set, that is, the thinning process, and then uses the remaining data to rebuild the model and predict the geological characteristics of the removed wells. Finally, the predicted results are compared with the actual data for verification;

[0142] Collect and analyze production data, compare the production curve predicted by the model with the actual production curve, evaluate the predictive ability of the model, and adjust parameters as needed. The production data reflects the actual development status of the oil and gas reservoir. By comparing these data with the model prediction results, the accuracy of the model in predicting the dynamics of the oil and gas reservoir can be verified.

[0143] S82. Adjust modeling parameters according to the validation results and optimize the model to improve its prediction accuracy and practicality;

[0144] Specific implementation method: According to the verification results, relevant parameters in the model such as variogram parameters, geological interface positions, and lithology distribution are adjusted, and the model is re-run for verification.

[0145] S83. Apply the optimized model to oil and gas exploration and development for reservoir evaluation, oil and gas resource estimation, development plan design and practical work.

[0146] Specific implementation method: Use the optimized model to comprehensively evaluate the lithology, physical properties, oil and gas content of the reservoir to provide a decision-making basis for exploration and development. At the same time, based on the geological information and oil and gas distribution laws in the model, the oil and gas resources are estimated. In addition, according to the oil and gas distribution and development dynamics predicted by the model, a reasonable development plan is designed, including well location deployment, mining methods, production systems, etc., to achieve efficient development of oil and gas reservoirs;

[0147] The S8 part ensures the accuracy and practicality of the geological model through multi-scale verification, parameter adjustment and optimization, and practical application, providing strong support for oil and gas exploration and development.

[0148] Working principle: The present invention constructs a high-precision marine clastic reservoir model by integrating geological genesis information. First, multi-source data of the target area is collected and pre-processed, including well logging, core and laboratory analysis results, to ensure the accuracy and consistency of the data.

[0149] Subsequently, the geological background of the target area is studied in depth, including the study of sedimentary environment changes, to provide macro-background support for subsequent reservoir modeling.

[0150] Based on data preprocessing, profile analysis and interface identification are carried out to identify the transgressive and regressive interfaces, unconformity surfaces and potential interlayer interfaces, and analyze the sedimentary characteristics and lithological changes on both sides of the interface to determine the sedimentary facies belts and sedimentary environments. At the same time, the stratigraphic dip information is extracted and converted into probability values ​​to reflect the spatial variability of the reservoir.

[0151] Identify and quantify the characteristics of interlayers, including their lithology, thickness, distribution range and relationship with the main reservoir, and evaluate their impact on oil and gas migration and storage.

[0152] After obtaining sufficient reservoir information, the sequential indicator modeling method of petrel software is used, and the reservoir dip information and interlayer characteristics are used as input parameters to define the variogram of each parameter to describe its spatial variability. The grid nodes are accessed through the set random path, and the probability of each node belonging to different categories is calculated, and finally a probability distribution constraint body is generated to characterize the distribution characteristics of interlayers inside the clastic reservoir.

[0153] Finally, the model is verified by inter-well comparison to ensure the accuracy and reliability of the model. The modeling parameters are adjusted according to the verification results, and the model is optimized to improve its prediction accuracy and practicality. Finally, the optimized model is applied to oil and gas exploration and development for reservoir evaluation, oil and gas resource estimation, and development plan design, providing scientific basis and technical support for actual production.

[0154] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for modeling marine clastic reservoirs based on geological genesis information, characterized in that: The steps include: S1. Collect and preprocess modeling data to obtain preprocessed data; S2. Conduct geological background research on the target area; S3, performing profile analysis and interface identification on the preprocessed data; S4, extracting and converting inclination information from the preprocessed data; S5. Identify and quantify intercalation characteristics; S6, generating a probability distribution constraint body; S7, conduct sequential instruction modeling; S8. Verify, optimize and apply the model.

2. A method for modeling a marine clastic reservoir based on geological genesis information according to claim 1, characterized in that: The S1 includes: S11, collecting multi-source data of the target area, wherein the multi-source data includes well logging data, core samples and laboratory analysis results; S12. Perform accuracy detection and preprocessing on the collected data, wherein the preprocessing includes data cleaning, denoising, interpolation, correction and standardization.

3. A method for modeling marine clastic reservoirs based on geological genesis information according to claim 2, characterized in that: The geological background research in S2 is a study on the changes in sedimentary environment.

4. A method for modeling a marine clastic reservoir based on geological genesis information according to claim 3, characterized in that: The S3 includes: S31. Perform profile processing and detailed analysis on the pre-processed data to identify transgressive and regressive interfaces, unconformities and potential interlayer interfaces; S32. Analyze the sedimentary characteristics and lithological changes on both sides of the transgressive and regressive interface, determine the sedimentary facies and sedimentary environment, and provide a basis for reservoir characteristics analysis.

5. A method for modeling marine clastic reservoirs based on geological genesis information according to claim 4, characterized in that: The S4 includes: S41, extracting formation dip information using the preprocessed data, where the formation dip value ranges from 0 to 10 degrees; S42. The formation dip values ​​are converted into probability values ​​ranging from 0 to 1 by normalization method so as to be used as an indicator of spatial variability in sequential indicator modeling.

6. A method for modeling marine clastic reservoirs based on geological genesis information according to claim 5, characterized in that: The normalization method comprises the following steps: Suppose the formation dip angle θ is in the range of 0≤θ≤10, and suppose a function f(θ) is in the range of 0≤f(θ)≤1; The normalized mathematical expression is: Among them, θ min is the minimum value of the formation dip, θ max is the maximum value of the formation dip, θ min = 0 and θ max =10; θ min =0 and θ max =10 Substitute into the normalization formula and we get: f(θ) is a linear function that linearly maps the formation dip angle θ to the interval from 0 to 1. For a given θ value within the range of 0-10, its normalized value can be obtained by calculating f(θ).

7. A method for modeling marine clastic reservoirs based on geological genesis information according to claim 6, characterized in that: The S5 includes: S51. In the profile analysis, attention should be paid to the identification of interlayers, and their lithology, thickness, distribution range and relationship with the main reservoir should be analyzed; S52. Conduct in-depth research on the formation mechanism of interlayers and evaluate their impact on oil and gas migration and storage.

8. A method for modeling marine clastic reservoirs based on geological genesis information according to claim 7, characterized in that: The S6 includes: S61, taking the dip information of the clastic reservoir and the interlayer characteristics as input parameters; S62, defining a variogram of each input parameter to describe its spatial variability; S63. Combining all input parameters and variograms, a probability distribution constraint body is generated to characterize the interlayer distribution characteristics inside the clastic reservoir.

9. A method for modeling marine clastic reservoirs based on geological genesis information according to claim 8, characterized in that: The S7 uses a sequential indicator modeling method to access grid nodes according to a set random path and calculate the probability that each node belongs to a different category.

10. A method for modeling marine clastic reservoirs based on geological genesis information according to claim 9, characterized in that: The S8 includes: S81. Validate the model using a single well sparse sampling validation method; S82. Adjust modeling parameters according to the validation results and optimize the model to improve its prediction accuracy and practicality; S83. Apply the optimized model to oil and gas exploration and development for reservoir evaluation, oil and gas resource estimation, development plan design and practical work.

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