A modeling method and device of a three-dimensional depositional facies model and a medium

Abstract

This invention provides a modeling method, apparatus, and medium for a three-dimensional sedimentary facies model, relating to the field of three-dimensional geological modeling technology. The method includes: constructing a geological conceptual model and a probabilistic statistical model of the study area; and generating a three-dimensional sedimentary facies model of the study area based on the geological conceptual model and the probabilistic statistical model, combined with seismic stratigraphic interpretation. Thus, by combining the geological conceptual model and the probabilistic statistical model with seismic stratigraphic interpretation for three-dimensional modeling, the accuracy and realism of the three-dimensional sedimentary facies model are improved, providing a theoretical basis for the quantitative integration of geological analysis and a solid foundation for subsequent reservoir production and prediction.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional geological modeling technology, and more specifically, to a modeling method, apparatus, and medium for three-dimensional sedimentary facies models. Background Technology

[0002] Three-dimensional geological modeling refers to the process of constructing a computer model that describes the geometry of a geological body and the distribution of its various physicochemical parameters. It is an integrated process that combines geological, well-logging, and geophysical data with various interpretation results or conceptual models to generate a three-dimensional quantitative stochastic model. This model can realistically reflect the morphology, structure, properties, and spatial relationships of the geological body, providing crucial information for the design, construction, and management of geological engineering projects. The three-dimensional geological modeling process includes data integration, model framework establishment, lithofacies model establishment, and rock property model establishment. The accuracy of a three-dimensional geological model is affected by many factors, primarily including the accuracy of the original data, the geological complexity, and the selection of interpolation and modeling methods.

[0003] The current process of 3D geological modeling is relatively complete. Based on certain well logging data and seismic data, a 3D geological model can be constructed using appropriate computer algorithms. However, the geological model constructed in this way is considered to lack realism because it is based on the frequency concept of traditional probability theory, and the accuracy of the model is insufficient to support the needs of actual production.

[0004] To address the problems of existing technologies, this invention provides a modeling method, apparatus, and medium for three-dimensional sedimentary phase models. Summary of the Invention

[0005] To address the problems of existing technologies, this invention provides a method, apparatus, and medium for modeling three-dimensional sedimentary phase models. The method includes:

[0006] Construct geological conceptual models and probabilistic statistical models for the study area;

[0007] Based on the geological conceptual model and the probabilistic statistical model, and combined with the seismic stratigraphic interpretation of the study area, a three-dimensional sedimentary facies model of the study area is generated.

[0008] According to an embodiment of the present invention, the geological concept model is constructed through the following steps:

[0009] Based on the seismic horizon interpretation, a framework for the study area is established;

[0010] Based on the aforementioned framework, and taking into account the interpretation of well logging hard data, sedimentary environment, and geological outcrops in the study area, the geological conceptual model is constructed.

[0011] According to an embodiment of the present invention, the probability and statistical model is constructed through the following steps:

[0012] Based on the aforementioned grid, the frequency of lithofacies types in all well logs of the study area is statistically analyzed, and the proportion of each lithofacies type in each well log is calculated to generate a lithofacies distribution map of the study area.

[0013] Based on the lithofacies distribution map and the Kriging algorithm, the probabilistic statistical model is constructed.

[0014] According to an embodiment of the present invention, the three-dimensional sedimentary phase model is generated through the following steps:

[0015] Based on the geological conceptual model and the probabilistic statistical model, a lithofacies probability model of the study area is generated;

[0016] Based on the geological conceptual model, the lithofacies distribution map, and the seismic stratigraphic interpretation, a lithofacies probability curve for the study area is generated.

[0017] The three-dimensional sedimentary facies model is generated based on the lithofacies probability model and the lithofacies probability curve.

[0018] According to an embodiment of the present invention, the lithofacies probability model is generated through the following steps:

[0019] Reference lines are drawn in the geological conceptual model to generate a geological zoning map of the study area.

[0020] The geological zoning map and the probabilistic statistical model are coupled to generate the lithofacies probabilistic model.

[0021] According to an embodiment of the present invention, the lithofacies probability curve is generated through the following steps:

[0022] Based on the geological zoning map and the lithofacies distribution map, a zoning model of the study area is generated;

[0023] Based on the partitioning model, a vertical scale curve for the study area is generated;

[0024] Based on the vertical scale curve, the seismic stratigraphic interpretation, and the lithofacies distribution map, the lithofacies probability curve is generated.

[0025] According to an embodiment of the present invention, the lithofacies probability curve is generated through the following steps:

[0026] Statistical analysis of the lithofacies distribution map yielded a histogram of the study area;

[0027] The histogram is corrected based on the geological zoning map to obtain the target histogram of the study area;

[0028] Based on the seismic horizon interpretation, a seismic data interpretation profile for the study area is generated;

[0029] Based on the target histogram and the seismic data interpretation profile, the vertical scale curve is adjusted to obtain the lithofacies probability curve.

[0030] According to an embodiment of the present invention, the three-dimensional sedimentary phase model is generated through the following steps:

[0031] Based on the lithofacies probability model, the lithofacies probability curve, and the probability field formula, a three-dimensional probability cube of the study area is generated.

[0032] The three-dimensional probabilistic cube is used to generate the three-dimensional sedimentary facies model using the indicator kriging algorithm and the sequential indicator simulation method.

[0033] According to another aspect of the invention, a storage medium is also provided, comprising a series of instructions for performing the steps of the method as described in any of the preceding claims.

[0034] According to another aspect of the present invention, a modeling apparatus for a three-dimensional sedimentary phase model is also provided, which performs the method as described in any of the preceding claims, the apparatus comprising:

[0035] The building module is used to construct geological conceptual models and probabilistic statistical models of the study area;

[0036] The generation module is used to generate a three-dimensional sedimentary facies model of the study area based on the geological concept model and the probabilistic statistical model, combined with the seismic stratigraphic interpretation of the study area.

[0037] This invention provides a modeling method, apparatus, and medium for three-dimensional sedimentary phase models, which has the following advantages compared with existing technologies:

[0038] This invention first constructs a geological conceptual model, improving its accuracy and applicability. Next, it constructs a probabilistic statistical model, enhancing its objectivity and accuracy. Then, based on seismic stratigraphic interpretation, it improves the precision and resolution of the seismic stratigraphic interpretation data, enhancing its accuracy and flexibility, making it applicable to complex geological environments. Thus, by combining the geological conceptual model and the probabilistic statistical model with seismic stratigraphic interpretation for three-dimensional modeling, the accuracy and realism of the three-dimensional sedimentary facies model are improved. This provides a theoretical foundation for the quantitative integration of geological analysis and a solid basis for subsequent reservoir production and prediction.

[0039] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0041] Figure 1 A flowchart illustrating a modeling method for a three-dimensional sedimentary phase model according to an embodiment of the present invention is shown;

[0042] Figure 2 A flowchart illustrating a modeling method for a three-dimensional sedimentary phase model according to yet another embodiment of the present invention is shown;

[0043] Figure 3 A schematic diagram of a lattice frame according to an embodiment of the present invention is shown;

[0044] Figure 4 A schematic diagram of a lithofacies distribution map according to an embodiment of the present invention is shown;

[0045] Figure 5 A schematic diagram of a probability and statistics model according to an embodiment of the present invention is shown;

[0046] Figure 6 A schematic diagram of a geological zoning map according to an embodiment of the present invention is shown;

[0047] Figure 7 A schematic diagram of a lithofacies probability model according to an embodiment of the present invention is shown;

[0048] Figure 8 A schematic diagram of a partitioning model according to an embodiment of the present invention is shown;

[0049] Figure 9 A schematic diagram of a vertical scaling curve according to an embodiment of the present invention is shown;

[0050] Figure 10 A schematic diagram of a histogram according to an embodiment of the present invention is shown;

[0051] Figure 11 A schematic diagram of a seismic data interpretation profile according to an embodiment of the present invention is shown;

[0052] Figure 12 A schematic diagram of a lithofacies probability curve according to an embodiment of the present invention is shown;

[0053] Figure 13 A schematic diagram of a three-dimensional probability cube according to an embodiment of the present invention is shown;

[0054] Figure 14 A schematic diagram of a three-dimensional sedimentary phase model according to an embodiment of the present invention is shown;

[0055] Figure 15 A block diagram of a modeling apparatus for a three-dimensional sedimentary phase model according to an embodiment of the present invention is shown.

[0056] In the accompanying drawings, the same parts use the same reference numerals. Also, the drawings are not drawn to scale.

[0057] The meanings of the reference numerals in the attached figures are as follows: Figure 4 The reddish-brown areas are mudstone, and the yellow areas are sandstone. Figure 9 , Figure 12 From left to right, the areas are: the entire area, area A, area B, area C, and area D. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0059] In existing technologies, hard logging data is often lacking in the 3D modeling process, so probabilistic methods are generally used to simulate the spatial distribution of geological bodies. However, frequency methods are often too random, while Bayesian methods are too subjective, and both methods have limited accuracy and applicability.

[0060] In view of the above-mentioned deficiencies of the prior art, the present invention provides a modeling method, apparatus and medium for three-dimensional sedimentary phase models. Figure 1 A flowchart illustrating a modeling method for a three-dimensional sedimentary phase model according to an embodiment of the present invention is shown. Figure 2 A flowchart illustrating a modeling method for a three-dimensional sedimentary phase model according to yet another embodiment of the present invention is shown, the method comprising:

[0061] S101, Construct a geological conceptual model and a probabilistic statistical model for the study area;

[0062] S102, based on geological conceptual models and probabilistic statistical models, combined with seismic stratigraphic interpretation of the study area, generates a three-dimensional sedimentary facies model of the study area.

[0063] For example, geological conceptual models can be constructed based on tendency theory. Tendency theory attributes probability to the physical conditions, configurations, and arrangements of phenomena; this theory represents the objective relational attributes of phenomena and allows the concept of probability to be conveyed as assumptions or conjectures based on physical conditions of occurrence, according to tendency theory. This improves the accuracy and applicability of geological conceptual models.

[0064] For example, probabilistic statistical models can be constructed based on geostatistical concepts. Geostatistics refers to the spatial description of geological phenomena in Earth science applications, ranging from using variability diagrams to characterize rock continuity to three-dimensional probabilistic representations of rock facies and physical properties. This improves the objectivity and accuracy of probabilistic statistical models.

[0065] Seismic horizon interpretation is a method for global geological modeling of 3D seismic data based on the relationships between seismic cells. This method automatically detects the set of links between seismic points using relevant images obtained from seismic trace pairs. The quality of the links is estimated using a cost function that measures the consistency of the model based on underlying seismic signals. The method matches the optimal model by locally moving certain links until a global minimum is reached. Thus, seismic horizon interpretation improves the accuracy and resolution of the seismic horizon interpretation data, enhancing its accuracy and flexibility, and making it applicable to complex geological environments.

[0066] Specifically, a three-dimensional sedimentary facies model can be generated using geological modeling software based on a geological conceptual model, a probabilistic statistical model, and seismic stratigraphic interpretation. This invention can be implemented using geological modeling software.

[0067] This invention first constructs a geological conceptual model, improving its accuracy and applicability. Next, it constructs a probabilistic statistical model, enhancing its objectivity and accuracy. Then, based on seismic stratigraphic interpretation, it improves the precision and resolution of the seismic stratigraphic interpretation data, enhancing its accuracy and flexibility, making it applicable to complex geological environments. Thus, by combining the geological conceptual model and the probabilistic statistical model with seismic stratigraphic interpretation for three-dimensional modeling, the accuracy and realism of the three-dimensional sedimentary facies model are improved. This provides a theoretical foundation for the quantitative integration of geological analysis and a solid basis for subsequent reservoir production and prediction.

[0068] In one possible embodiment, a geological conceptual model is constructed through the following steps:

[0069] Based on seismic horizon interpretation, a framework for the study area was established;

[0070] Based on the framework, a geological conceptual model is constructed based on the interpretation of well logging hard data, sedimentary environment, and geological outcrops in the study area.

[0071] Among these, based on seismic horizon interpretation, the boundaries and stratigraphic interfaces of the study area can be established, thereby creating the framework of the study area, such as... Figure 3 As shown, the Silurian two-phase reservoir with 12 wells in Gejia is about 33 km long in the east-west direction, about 30 km long in the north-south direction, and about 700 m thick.

[0072] Subsequently, based on the framework, a geological conceptual model can be constructed based on the interpretation of well logging hard data, sedimentary environment, and geological outcrops in the study area.

[0073] In this way, based on seismic stratigraphic interpretation, well logging hard data, sedimentary environment, and geological outcrops, a geological conceptual model was constructed, which improved the accuracy and applicability of the geological conceptual model.

[0074] In one possible implementation, a probabilistic statistical model is constructed through the following steps:

[0075] Based on the grid, the frequency of lithofacies types in all well logs in the study area was statistically analyzed, and the proportion of lithofacies types in each well log was calculated to generate a lithofacies distribution map of the study area.

[0076] A probabilistic statistical model is constructed based on lithofacies distribution maps and the Kriging algorithm.

[0077] For example, the lithofacies type can be mudstone and sandstone. Geological modeling software can choose the Kriging algorithm, or it can be based on other algorithms.

[0078] Based on the grid framework, the frequency of lithofacies types in all well logs of the study area can be statistically analyzed, and the proportion of each lithofacies type in each well log can be calculated to generate a lithofacies distribution map of the study area, such as... Figure 4 As shown, next, based on the lithofacies distribution map and the Kriging algorithm, a probabilistic statistical model can be generated, such as... Figure 5 The figure shows a probabilistic statistical model for sandstone facies.

[0079] In this way, based on the lithofacies distribution map and the Kriging algorithm, a probabilistic statistical model can be generated, which improves the objectivity and accuracy of the probabilistic statistical model.

[0080] In one possible embodiment, a three-dimensional sedimentary phase model is generated through the following steps:

[0081] Based on geological conceptual models and probabilistic statistical models, a lithofacies probability model of the study area is generated.

[0082] Based on geological conceptual models, lithofacies distribution maps, and seismic stratigraphic interpretation, lithofacies probability curves for the study area are generated.

[0083] A three-dimensional sedimentary facies model is generated based on the lithofacies probability model and lithofacies probability curve.

[0084] Among these, a two-dimensional lithofacies probability model can be generated based on geological conceptual models and probabilistic statistical models, such as... Figure 7 The image shows a sandstone facies probability model. Next, based on the geological conceptual model, lithofacies distribution map, and seismic stratigraphic interpretation, a one-dimensional lithofacies probability curve can be generated, as shown below. Figure 12 As shown, next, a three-dimensional sedimentary facies model of sandstone can be generated based on the sandstone facies probability model and lithofacies probability curve, such as... Figure 14 As shown.

[0085] Similarly, three-dimensional sedimentary facies models of mudstone can also be generated.

[0086] In this way, a three-dimensional sedimentary facies model can be generated based on the lithofacies probability model and lithofacies probability curve, which improves the accuracy and realism of the three-dimensional sedimentary facies model, provides a theoretical basis for the quantitative integration of geological analysis, and provides a solid basis for subsequent reservoir production and prediction.

[0087] In one possible embodiment, a lithofacies probability model is generated through the following steps:

[0088] Draw reference lines in the geological conceptual model to generate a geological zoning map of the study area;

[0089] By coupling geological zoning maps with probabilistic statistical models, lithofacies probabilistic models are generated.

[0090] Among these methods, drawing reference lines in the geological conceptual model can generate a geological zoning map of the study area, such as... Figure 6 As shown; subsequently, the geological zoning map and the probabilistic statistical model can be coupled to generate a lithofacies probabilistic model, as shown. Figure 7 The figure shows the probability model of sandstone facies.

[0091] In this way, based on geological zoning maps and probabilistic statistical models, lithofacies probabilistic models can be generated, providing a basis for the establishment of three-dimensional sedimentary facies models.

[0092] In one possible embodiment, the lithofacies probability curve is generated through the following steps:

[0093] Based on geological zoning maps and lithofacies distribution maps, a zoning model of the study area is generated;

[0094] Based on the partitioning model, the vertical scale curve of the study area is generated;

[0095] Based on the vertical scale curve, seismic horizon interpretation, and lithofacies distribution map, a lithofacies probability curve is generated.

[0096] By coupling geological zoning maps and lithofacies distribution maps, zoning models can be generated, such as... Figure 8 As shown; subsequently, inputting the zoning model into geological modeling software can generate vertical scale curves, such as... Figure 9As shown; next, based on the vertical scale curve, seismic horizon interpretation, and lithofacies distribution map, a lithofacies probability curve can be generated, as shown below. Figure 12 As shown.

[0097] Thus, based on the geological conceptual model, lithofacies distribution map, and seismic stratigraphic interpretation, lithofacies probability curves for the study area are generated, providing a basis for the establishment of a three-dimensional sedimentary facies model.

[0098] In one possible embodiment, the lithofacies probability curve is generated through the following steps:

[0099] Statistical analysis of the lithofacies distribution map yielded a histogram of the study area;

[0100] The target histogram of the study area is obtained by correcting the histogram based on the geological zoning map.

[0101] Based on seismic horizon interpretation, a seismic data interpretation profile for the study area is generated.

[0102] Based on the target histogram and the seismic data interpretation profile, the vertical scale curve is adjusted to obtain the lithofacies probability curve.

[0103] Among these methods, statistical analysis of the lithofacies distribution map can yield a histogram of the study area, such as... Figure 10 As shown in Table 1, due to the error interference caused by sampling bias, the proportion of lithofacies types in the histogram can be corrected by using a zonal clustering method based on the geological zoning map. Based on the corrected data in Table 1, the target histogram can be drawn.

[0104] Table 1. Comparison of the proportions of lithofacies types

[0105] lithofacies type Raw data After correction mudstone 45.7 47.9 sandstone 54.3 52.1

[0106] Subsequently, based on seismic horizon interpretation and cost function minimization algorithms, the seismic data interpretation profile can be obtained as follows: Figure 11 As shown; subsequently, the vertical scale curve can be adjusted based on the target histogram and seismic data interpretation profile to obtain the lithofacies probability curve, as shown. Figure 12 As shown.

[0107] In this way, based on the vertical scale curve, seismic horizon interpretation, and lithofacies distribution map, lithofacies probability curves are generated, providing a basis for the generation of three-dimensional sedimentary facies models.

[0108] In one possible embodiment, a three-dimensional sedimentary phase model is generated through the following steps:

[0109] Based on the lithofacies probability model, lithofacies probability curve, and probability field formula, a three-dimensional probability cube of the study area is generated.

[0110] A three-dimensional sedimentary facies model is generated by using the indicator kriging algorithm and sequential indicator simulation method to generate a three-dimensional probabilistic cube.

[0111] Among these features, the normalization factor of the probability field formula can be adjusted to generate a three-dimensional probability cube that most closely approximates the geological sedimentary environment of the study area, such as... Figure 13 As shown; subsequently, in geological modeling software, a three-dimensional sedimentary facies model is constructed from the three-dimensional probabilistic cube using the indicator kriging algorithm and sequential indicator simulation method, as shown. Figure 14 As shown.

[0112] In this way, based on the lithofacies probability model and lithofacies probability curve, a three-dimensional sedimentary facies model can be generated, which improves the accuracy and realism of the three-dimensional sedimentary facies model, provides a theoretical basis for the quantitative integration of geological analysis, and provides a solid basis for subsequent reservoir production and prediction.

[0113] The modeling method for a three-dimensional sedimentary facies model provided by this invention can also be used in conjunction with a computer-readable storage medium. The storage medium stores a computer program, which is executed to run the modeling method for the three-dimensional sedimentary facies model. The computer program can execute computer instructions, which include computer program code. The computer program code can be in the form of source code, object code, executable files, or some intermediate form.

[0114] Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0115] It should be noted that the contents of computer-readable storage media may be appropriately added to or subtracted from the contents according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media may not include electrical carrier signals and telecommunication signals.

[0116] According to another aspect of the present invention, a modeling apparatus for a three-dimensional sedimentary facies model is also provided, which performs a modeling method for a three-dimensional sedimentary facies model. Figure 15 A block diagram of a modeling apparatus for a three-dimensional sedimentary phase model according to an embodiment of the present invention is shown. The apparatus includes:

[0117] Module 510 is used to construct geological conceptual models and probabilistic statistical models of the study area;

[0118] Module 520 is used to generate a three-dimensional sedimentary facies model of the study area based on the geological conceptual model and probabilistic statistical model, combined with the seismic stratigraphic interpretation of the study area.

[0119] In summary, this invention provides a modeling method, apparatus, and medium for three-dimensional sedimentary phase models, which have the following advantages compared with existing technologies:

[0120] This invention first constructs a geological conceptual model, improving its accuracy and applicability. Next, it constructs a probabilistic statistical model, enhancing its objectivity and accuracy. Then, based on seismic stratigraphic interpretation, it improves the precision and resolution of the seismic stratigraphic interpretation data, enhancing its accuracy and flexibility, making it applicable to complex geological environments. Thus, by combining the geological conceptual model and the probabilistic statistical model with seismic stratigraphic interpretation for three-dimensional modeling, the accuracy and realism of the three-dimensional sedimentary facies model are improved. This provides a theoretical foundation for the quantitative integration of geological analysis and a solid basis for subsequent reservoir production and prediction.

[0121] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0122] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0123] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0124] Certain terms are used throughout this application to refer to specific system components. As those skilled in the art will recognize, the same components may often be referred to by different names, and therefore this application is not intended to distinguish those components that differ only in name and not in function. In this application, the terms “comprise,” “include,” and “have” are used in an open-ended manner and should therefore be interpreted as meaning “including, but not limited to…”. Furthermore, the terms “substantially,” “materially,” or “approximately” as used herein refer to industry-accepted tolerances for the corresponding terms. The term “coupling,” as may be used herein, includes direct coupling and indirect coupling via additional components, elements, circuits, or modules, wherein, for indirect coupling, the intermediate component, element, circuit, or module does not alter the information of the signal but may adjust its current level, voltage level, and / or power level. Inferred coupling (e.g., one element is inferredly coupled to another element) includes direct and indirect coupling between two elements in the same manner as “coupling.”

[0125] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0126] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

[0127] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method of modeling a three-dimensional depositional facies model, the method comprising: The method includes: Construct geological conceptual models and probabilistic statistical models for the study area; Based on the geological conceptual model and the probabilistic statistical model, and combined with the seismic stratigraphic interpretation of the study area, a three-dimensional sedimentary facies model of the study area is generated.

2. The method of claim 1, wherein, The geological concept model is constructed through the following steps: Based on the seismic horizon interpretation, a framework for the study area is established; Based on the aforementioned framework, and taking into account the interpretation of well logging hard data, sedimentary environment, and geological outcrops in the study area, the geological conceptual model is constructed.

3. The method of claim 2, wherein, The probability and statistical model is constructed using the following steps: Based on the aforementioned grid, the frequency of lithofacies types in all well logs of the study area is statistically analyzed, and the proportion of each lithofacies type in each well log is calculated to generate a lithofacies distribution map of the study area. Based on the lithofacies distribution map and the Kriging algorithm, the probabilistic statistical model is constructed.

4. The method of claim 3, wherein, The three-dimensional sedimentary facies model is generated through the following steps: Based on the geological conceptual model and the probabilistic statistical model, a lithofacies probability model of the study area is generated; Based on the geological conceptual model, the lithofacies distribution map, and the seismic stratigraphic interpretation, lithofacies probability curves for the study area are generated. The three-dimensional sedimentary facies model is generated based on the lithofacies probability model and the lithofacies probability curve.

5. The method of claim 4, wherein, The lithofacies probability model is generated through the following steps: Reference lines are drawn in the geological conceptual model to generate a geological zoning map of the study area. The geological zoning map and the probabilistic statistical model are coupled to generate the lithofacies probabilistic model.

6. The method of claim 5, wherein, The lithofacies probability curve is generated through the following steps: Based on the geological zoning map and the lithofacies distribution map, a zoning model of the study area is generated; Based on the partitioning model, a vertical scale curve for the study area is generated; Based on the vertical scale curve, the seismic stratigraphic interpretation, and the lithofacies distribution map, the lithofacies probability curve is generated.

7. The method of claim 6, wherein, The lithofacies probability curve is generated through the following steps: Statistical analysis of the lithofacies distribution map yielded a histogram of the study area; The histogram is corrected based on the geological zoning map to obtain the target histogram of the study area; Based on the seismic horizon interpretation, a seismic data interpretation profile for the study area is generated; Based on the target histogram and the seismic data interpretation profile, the vertical scale curve is adjusted to obtain the lithofacies probability curve.

8. The method of any one of claims 4-7, wherein, The three-dimensional sedimentary facies model is generated through the following steps: Based on the lithofacies probability model, the lithofacies probability curve, and the probability field formula, a three-dimensional probability cube of the study area is generated. The three-dimensional probabilistic cube is used to generate the three-dimensional sedimentary facies model using the indicator kriging algorithm and the sequential indicator simulation method.

9. A storage medium, characterized by It includes a series of instructions for performing the method steps as described in any one of claims 1-8.

10. A modeling device of a three-dimensional depositional facies model, characterized by, The apparatus for performing the method as described in any one of claims 1-8 comprises: The building module is used to construct geological conceptual models and probabilistic statistical models of the study area; The generation module is used to generate a three-dimensional sedimentary facies model of the study area based on the geological concept model and the probabilistic statistical model, combined with the seismic stratigraphic interpretation of the study area.

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