A method and device for predicting deep concave well-free hydrocarbon source rocks

Abstract

This invention provides a method for predicting source rocks in deep, well-free areas, comprising: constructing a stratigraphic velocity model; obtaining a three-dimensional stratigraphic structure image based on the stratigraphic velocity model and seismic data from the deep, well-free area, and performing seismic interpretation to determine the potential distribution range of source rocks; constructing a paleogeographic model based on the sedimentary facies and paleogeographic conditions of the deep, well-free area; constructing a tectonic model based on tectonic feature data; constructing a three-dimensional geological model based on the paleogeographic model, the tectonic model, and the stratigraphic data of the deep, well-free area; determining the distribution range of source rocks in the three-dimensional geological model; and overlaying the potential distribution area of ​​source rocks obtained from seismic analysis with the distribution range of source rocks obtained from the three-dimensional geological model to determine areas favorable for source rock distribution. By comprehensively utilizing seismic imaging and geological modeling methods, the accuracy of predicting the distribution of source rocks in deep, well-free areas is improved.

Description

Technical Field

[0001] This invention relates to the field of petroleum and natural gas geological exploration, and in particular to a method and apparatus for predicting source rocks in deep, well-free areas. Background Technology

[0002] As oil exploration deepens, the focus has gradually shifted from conventional shallow oil and gas reservoirs to deeper and more complex geological structures. Among these, oil and gas exploration in deep depressions has become a crucial direction in the field of oil and gas geology. However, due to their great depth and high exploration costs, deep depressions are typically less explored, often consisting of areas with few or no wells. This presents a significant challenge to the accurate evaluation of source rocks. Traditional prediction methods rely primarily on well-controlled data, but this approach is impractical in well-free areas. Therefore, there is an urgent need for a method capable of predicting the distribution of source rocks in well-free areas of deep depressions. Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a method and apparatus for predicting source rocks in deep, well-free areas that overcomes or at least partially solves the above problems.

[0004] In a first aspect, embodiments of the present invention provide a method for predicting hydrocarbon source rocks in deep, well-free areas, including:

[0005] Based on the velocity data of the formation in the pre-acquired deep concave wellless area, a velocity model of the formation is constructed;

[0006] Based on the velocity model of the strata and the pre-acquired seismic data of the deep concave wellless area, a three-dimensional strata structure image is obtained;

[0007] Seismic interpretation of three-dimensional stratigraphic images is used to determine the distribution areas of source rocks in the three-dimensional stratigraphic images;

[0008] Based on the pre-acquired sedimentary facies and paleogeographic conditions of the deep, unwell-free area, a paleogeographic model is constructed; based on the pre-acquired tectonic feature data of the deep, unwell-free area, a tectonic model is constructed.

[0009] Based on the paleogeographic model, tectonic model, and pre-acquired stratigraphic data of the deep, well-free area, a three-dimensional geological model is constructed.

[0010] The distribution area of ​​hydrocarbon source rocks is determined in the three-dimensional geological model;

[0011] The distribution area of ​​source rocks is determined based on the distribution area of ​​source rocks in the three-dimensional stratigraphic structure image and the distribution area of ​​source rocks in the three-dimensional geological model.

[0012] In one embodiment, before obtaining the three-dimensional stratigraphic image, the method further processes the pre-acquired seismic data from the deep, well-free area as follows:

[0013] The seismic data of the deep concave wellless area acquired by at least one seismic trace are superimposed to obtain enhanced seismic data of the deep concave wellless area acquired by at least one seismic trace.

[0014] Migration is performed on enhanced seismic data of deep concave wellless areas acquired by at least one seismic trace to obtain corrected seismic data of deep concave wellless areas acquired by at least one seismic trace.

[0015] A depth migration operation is performed on the corrected seismic data of the deep concave wellless area acquired by at least one seismic trace to obtain the seismic data of the deep concave wellless area.

[0016] In one embodiment, constructing a paleogeographic model based on pre-acquired sedimentary facies and paleogeographic conditions of a deep, well-free area includes:

[0017] Based on the sedimentary facies, identify the sedimentary environment and its spatiotemporal distribution;

[0018] Based on the sedimentary environment and its spatiotemporal distribution, and the paleogeographic conditions, a paleogeographic model is constructed.

[0019] In one embodiment, constructing a structural model based on pre-acquired structural feature data of a deep, well-free region includes:

[0020] Based on the structural feature data, the fault strike, fault displacement, and their impact on the sedimentary environment are determined;

[0021] Based on the fault strike, fault displacement and its impact on the sedimentary environment, the tectonic evolution history of the deep depression without wells is determined, and the impact of major tectonic events on the formation of source rocks is analyzed.

[0022] Based on the geological history and the influence of major tectonic events on the formation of source rocks, a tectonic model is constructed.

[0023] In one embodiment, determining the distribution area of ​​source rocks in the three-dimensional geological model includes:

[0024] Based on the aforementioned three-dimensional geological model, a lithology model is constructed;

[0025] Based on the lithological model and the geochemical data of the deep depression without wells, the hydrocarbon generation process of the source rocks is simulated to obtain the thickness and distribution of the hydrocarbon generation potential zone and the hydrocarbon generation maturity of the source rocks.

[0026] Based on the thickness and distribution of hydrocarbon generation potential zones and the hydrocarbon generation maturity of source rocks, the distribution area of ​​source rocks in the three-dimensional geological model is determined.

[0027] In one embodiment, determining the source rock distribution area based on the source rock distribution area of ​​the three-dimensional stratigraphic structure image and the source rock distribution area of ​​the three-dimensional geological model includes: taking the area where the source rock distribution area of ​​the three-dimensional stratigraphic structure image and the source rock distribution area of ​​the three-dimensional geological model are superimposed as the source rock distribution area.

[0028] Secondly, embodiments of the present invention provide a device for predicting hydrocarbon source rocks in deep, well-free areas, comprising:

[0029] The velocity model construction module is used to construct a velocity model of the formation based on the velocity data of the formation in the pre-acquired deep well-free area.

[0030] The three-dimensional imaging module is used to obtain three-dimensional images of the stratigraphic structure based on the velocity model of the strata and pre-acquired seismic data of the deep concave wellless area;

[0031] The seismic interpretation module is used to perform seismic interpretation on three-dimensional stratigraphic images and determine the distribution area of ​​source rocks in the three-dimensional stratigraphic images;

[0032] The paleogeographic tectonic model building module is used to build paleogeographic models based on pre-acquired sedimentary facies and paleogeographic conditions of deep-seated unwelled areas; and to build tectonic models based on pre-acquired tectonic feature data of deep-seated unwelled areas.

[0033] The three-dimensional geological model construction module is used to construct a three-dimensional geological model based on the paleogeographic model, tectonic model and pre-acquired stratigraphic data of the deep concave well-free area;

[0034] A module for determining the distribution area of ​​source rocks in a three-dimensional geological model is used to determine the distribution area of ​​source rocks in the three-dimensional geological model.

[0035] The hydrocarbon source rock distribution module is used to determine the hydrocarbon source rock distribution area based on the hydrocarbon source rock distribution area in the three-dimensional stratigraphic structure image and the hydrocarbon source rock distribution area in the three-dimensional geological model.

[0036] Thirdly, embodiments of the present invention provide a computing device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the program executed by the processor implements a method for predicting hydrocarbon source rocks in deep, well-free areas.

[0037] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for predicting hydrocarbon source rocks in deep, well-free areas.

[0038] Fifthly, embodiments of the present invention provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements a method for predicting hydrocarbon source rocks in deep, well-free areas.

[0039] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0040] This invention provides a method for predicting source rocks in deep, well-free areas, comprising: constructing a velocity model of the formation, and obtaining a three-dimensional stratigraphic structure image based on the stratigraphic velocity model and seismic data of the deep, well-free area, and performing seismic interpretation to determine the distribution area of ​​source rocks in the three-dimensional stratigraphic structure image;

[0041] Based on the sedimentary facies and paleogeographic conditions of the deep, well-free area, a paleogeographic model is constructed; based on the tectonic feature data, a tectonic model is constructed; based on the paleogeographic model, the tectonic model, and the stratigraphic data of the deep, well-free area, a three-dimensional geological model is constructed; and the distribution area of ​​source rocks is determined in the three-dimensional geological model.

[0042] The distribution areas of source rocks are determined based on the distribution areas of source rocks in the three-dimensional stratigraphic structure image and the three-dimensional geological model.

[0043] The method for predicting source rocks in deep, well-free areas provided in this invention comprehensively utilizes three-dimensional seismic data obtained from seismic imaging and geological models fused in geological modeling. This method can predict the distribution of source rocks in deep, well-free areas, overcoming the prediction error problem caused by the lack of well control data in the past and improving the prediction accuracy.

[0044] The method for predicting source rocks in deep, well-free areas provided in this invention is applicable to areas with large reservoir depths, high exploration costs, and low exploration levels. By combining geological models with seismic imaging, it provides reliable predictions of source rock distribution even without direct well control data, thus expanding the application scope of oil and gas exploration and evaluation technologies.

[0045] The method for predicting source rocks in deep, well-free areas provided in this invention reduces exploration risks and improves exploration efficiency by predicting source rocks in these areas. It also reduces exploration costs and comprehensively improves exploration effectiveness.

[0046] 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 written description, claims, and drawings.

[0047] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0048] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0049] Figure 1 A method for predicting hydrocarbon source rocks in deep, well-free areas is provided in this embodiment of the invention;

[0050] Figure 2 A method for predicting the distribution of hydrocarbon source rocks in well-free areas of deep depressions by combining seismic imaging and geological modeling, provided in this embodiment of the invention;

[0051] Figure 3 Maximum amplitude attribute map of source rock development intervals in a three-dimensional stratigraphic image provided in this embodiment of the invention;

[0052] Figure 4 This is a diagram showing the lithological assemblage and basic geochemical characteristics of the source rock development intervals provided in this embodiment of the invention.

[0053] Figure 5 Favorable facies zones for source rock development in the paleogeographic model provided in this embodiment of the invention;

[0054] Figure 6 This is a planar distribution diagram of the source rock provided in an embodiment of the present invention;

[0055] Figure 7 This is a structural block diagram of a prediction device for source rocks in deep, well-free areas, provided as an embodiment of the present invention. Detailed Implementation

[0056] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0057] Before implementing the prediction method for deep concave well-free areas provided in this embodiment of the invention, data acquisition and data processing steps are required:

[0058] Data collection:

[0059] A seismic source and a geophone are deployed in the target deep concave wellless area. The seismic waves provided by the seismic source propagate in the underground medium and are reflected back to the geophone by different geological interfaces. The geophone records the seismic wave reflection data.

[0060] Seismic wave reflection data includes: reflected wave arrival time, reflected wave amplitude data, and reflected wave frequency data.

[0061] Data processing:

[0062] Filtering: Removes high-frequency and / or low-frequency noise from seismic wave reflection data, retaining the signal in the desired frequency band.

[0063] Noise reduction: Reduce noise signals in seismic wave reflection data caused by external interference, such as electromagnetic interference or environmental noise.

[0064] Normalization: Adjusting the signal amplitude of seismic wave reflection data to eliminate inconsistencies caused by changes in source intensity or surface conditions.

[0065] The purpose of processing the aforementioned seismic wave reflection data is to improve the quality of seismic data and reduce the impact of multiple interpretations on the deep concave wellless area hydrocarbon source rock prediction method provided in this embodiment of the invention.

[0066] This invention provides a method for predicting hydrocarbon source rocks in deep, well-free areas, the flowchart of which is shown below. Figure 1 As shown, it includes:

[0067] S11. Construct a velocity model of the formation based on the velocity data of the formation in the pre-acquired deep concave wellless area;

[0068] S12. Based on the velocity model of the strata and the pre-acquired seismic data of the deep concave wellless area, a three-dimensional strata structure image is obtained;

[0069] S13. Perform seismic interpretation on the three-dimensional stratigraphic structure image to determine the distribution area of ​​source rocks in the three-dimensional stratigraphic structure image;

[0070] S14. Based on the pre-acquired sedimentary facies and paleogeographic conditions of the deep depression unwell-free area, construct a paleogeographic model; based on the pre-acquired tectonic feature data of the deep depression unwell-free area, construct a tectonic model.

[0071] S15. Construct a three-dimensional geological model based on the paleogeographic model, tectonic model, and pre-acquired stratigraphic data of the deep concave wellless area;

[0072] S16. Determine the distribution area of ​​hydrocarbon source rocks in the three-dimensional geological model;

[0073] S17. Determine the source rock distribution area based on the source rock distribution area in the three-dimensional stratigraphic structure image and the source rock distribution area in the three-dimensional geological model.

[0074] The method for predicting source rocks in deep, well-free areas provided in this invention comprehensively utilizes three-dimensional seismic data obtained from seismic imaging and geological models fused in geological modeling. This method can predict the distribution of source rocks in deep, well-free areas, overcoming the prediction error problem caused by the lack of well control data in the past and improving the prediction accuracy.

[0075] The method for predicting source rocks in deep, well-free areas provided in this invention is applicable to areas with large reservoir depths, high exploration costs, and low exploration levels. By combining geological models with seismic imaging, it provides reliable predictions of source rock distribution even without direct well control data, thus expanding the application scope of oil and gas exploration and evaluation technologies.

[0076] The method for predicting source rocks in deep, well-free areas provided in this invention reduces exploration risks and improves exploration efficiency by predicting source rocks in these areas. It also reduces exploration costs and comprehensively improves exploration effectiveness.

[0077] In step S11, for example, first-arrival wave analysis and layer velocity analysis methods can be used to calculate the velocity distribution of the subsurface medium and obtain the velocity data of the strata in the deep concave wellless area obtained in step S11. The velocity model in step S11 is an important basis for subsequent seismic imaging and depth migration.

[0078] The first arrival wave is the first seismic wave to reach the geophone, containing rich information about subsurface geology. First arrival analysis methods are a series of techniques that analyze various characteristics of the first arrival wave, such as arrival time, waveform, and amplitude, to infer information about the velocity structure of the subsurface medium, the depth of stratigraphic interfaces, and geological structures. This method is a crucial step in seismic exploration data processing, providing a foundation for subsequent, more complex seismic data interpretation.

[0079] Layer velocity refers to the speed at which seismic waves propagate within a specific geological stratum. Layer velocity analysis is a technique used to determine the velocity values ​​of different subsurface strata, and it is of paramount importance in seismic exploration. Accurate layer velocity information helps in understanding subsurface geological structures, including lithological variations and porosity distribution, and plays a crucial role in seismic imaging and reservoir prediction.

[0080] Before the aforementioned step S12, the pre-acquired seismic data of the deep concave wellless area can be processed as follows:

[0081] Stacking operation: Seismic data from at least one seismic trace in a deep, well-free area are stacked to obtain enhanced seismic data for the same area. For example, horizontal stacking, weighted stacking, covariance stacking, or pre-stack angular domain stacking methods can be used, as well as any existing method for stacking seismic data. This embodiment of the invention does not limit the specific methods used. The stacking operation integrates seismic data recorded by multiple seismic traces, enhancing seismic wave reflection data.

[0082] In seismic exploration data processing, overlay is an important method. It involves adding and combining multiple seismic records from the same subsurface reflection point (common reflection point, CRP). These seismic records are typically acquired at different excitation and reception locations, but all correspond to the same subsurface reflection location.

[0083] Migration operation: An enhanced seismic data set from at least one seismic trace in a deep, well-free area is migrated to obtain corrected seismic data for the same area. Methods such as post-stack migration, pre-stack migration, time migration, or depth migration can be used, as well as any existing seismic data migration method. This embodiment of the invention does not limit the specific methods used. The migration operation corrects for non-vertical components in the reflected wave path, improving the accuracy of subsequent seismic imaging.

[0084] Seismic migration is a key technique in seismic data processing used to reposition seismic reflected waves to their actual underground reflection locations. In seismic exploration, due to the complexity of seismic wave propagation paths, the location of the received reflected wave in the time-space record does not correspond to the actual location of its reflection point underground. Migration processing aims to correct this positional discrepancy.

[0085] Depth migration operation: A depth migration operation is performed on the corrected seismic data of the deep concave wellless area acquired by at least one seismic trace to obtain the seismic data of the deep concave wellless area. For example, the finite difference method or the wave equation inverse time method can be used, or any existing method for depth migration of seismic data can be used. This embodiment of the invention does not limit this method. The depth migration operation can convert the seismic reflection time into the actual depth, providing accurate depth data for subsequent seismic imaging.

[0086] Depth migration is a seismic data processing technique that accurately relocates seismic reflected waves to their actual reflection locations in the depth domain. This is because during seismic exploration, the propagation path of seismic waves is affected by complex underground media (such as different lithologies, velocity variations, and structural morphology), leading to spatial discrepancies between the recorded reflection location and the actual reflection location. Depth migration aims to correct this discrepancy.

[0087] In step S13 above, the seismic interpretation of the three-dimensional stratigraphic image can specifically include the following steps:

[0088] Based on geological background knowledge, the reflection characteristics on seismic profiles are analyzed, including: strong reflection layers, phase changes, and reflection waveforms. Strong reflection layers correspond to changes in interfaces and can reflect lithological boundaries or the distribution of source rocks. Phase changes reflect changes in the internal properties of geological bodies. Reflection waveforms indicate the continuity or fracture of geological interfaces.

[0089] Step S13 is used to reveal the spatial distribution of underground geological bodies through three-dimensional seismic imaging and analysis of seismic profiles, and to infer the distribution area of ​​source rocks in the three-dimensional stratigraphic image; the inference of the distribution area of ​​source rocks in the three-dimensional stratigraphic image can be, for example, according to... Figure 3 The maximum amplitude attribute map of the source rock development interval in the three-dimensional stratigraphic image is used to infer the structure. Figure 3 This is a planar maximum attribute map, which displays the distribution of maximum attribute values ​​on a horizontal plane within a specific hydrocarbon source rock development zone, much like a map. Figure 1 This method can reveal the relative advantages and disadvantages of this attribute in different regions and is often used to analyze the differences in source rocks in different tectonic locations within a region.

[0090] In steps S13 and S14, the method may, for example, also acquire seismic data of the source rock development intervals required in steps S14-S17, including: geological data, geophysical data, geochemical data, and well data; the lithological assemblage and basic geochemical characteristics data of the source rock development intervals may, for example, be as follows: Figure 4 As shown;

[0091] Geological data includes: stratigraphic thickness data, lithological data, sedimentary facies data, and geological background data of deep, well-free areas.

[0092] Geophysical data includes: earthquake data, gravity data, and magnetic data.

[0093] Geochemical data include: organic matter content data and thermal evolution index data.

[0094] Well data includes drilling and logging data, which provide accurate formation correlation and physical property parameters, providing basic data for subsequent analysis and ensuring the scientific validity and completeness of the model.

[0095] In step S14 above, a paleogeographic model is constructed, for example, in the following manner:

[0096] Based on sedimentary facies, sedimentary environments and their spatiotemporal distribution can be identified; identifying sedimentary environments and their spatiotemporal distribution can help assess the provenance, deposition rate, and material accumulation pattern of sediments.

[0097] Based on the sedimentary environment, its spatial and temporal distribution, and paleogeographic conditions, paleogeographic models are constructed. These models can clarify the formation conditions of potential source rocks, such as anoxic environments and the likelihood of sediment organic matter enrichment. Facies maps of favorable source rock development from paleogeographic models can be, for example, shown in... Figure 5 As shown.

[0098] Step S14 involves analyzing sedimentary facies and paleogeographic conditions to determine the formation conditions and possible distribution range of potential source rocks.

[0099] In step S14 above, the construction model is built, for example, in the following manner:

[0100] Based on structural feature data, the fault strike, fault displacement and their impact on the sedimentary environment are determined in order to clarify how tectonic activity affects the burial, preservation and later maturity of source rocks.

[0101] Based on the fault strike, fault displacement and its impact on the sedimentary environment, the tectonic evolution history of the deep depression without wells is determined, and the impact of major tectonic events on the formation of source rocks is analyzed.

[0102] Based on the geological history and the influence of major tectonic events on the formation of source rocks, a tectonic model was constructed, laying the foundation for subsequent 3D modeling.

[0103] Constructing a structural model can clarify the impact of tectonics on the deposition and preservation of source rocks.

[0104] In step S15 above, the three-dimensional geological model can be constructed in the following manner, for example:

[0105] Based on sedimentary facies, tectonic, and stratigraphic data, specialized geological modeling software is used to construct three-dimensional models that display the spatial distribution of geological units, sequence relationships, and key geological boundaries. Examples of specialized geological modeling software include Petrel and KingdomSuite.

[0106] After the three-dimensional geological model is constructed, drilling and logging data can be used to optimize its accuracy. Specifically, this includes modifying the geological unit boundaries, thickness, and lithology of the three-dimensional geological model based on drilling and logging data.

[0107] In step S16 above, determining the distribution area of ​​source rocks in the three-dimensional geological model may include, for example, the following steps:

[0108] Construct a lithology model based on the three-dimensional geological model;

[0109] Based on the lithological model and pre-acquired geochemical data of the deep depression without wells, the hydrocarbon generation process of the source rocks is simulated to obtain the thickness and distribution of the hydrocarbon generation potential zone and the hydrocarbon generation maturity of the source rocks.

[0110] Based on the thickness and distribution of hydrocarbon generation potential zones and the hydrocarbon generation maturity of source rocks, the distribution area of ​​source rocks in the three-dimensional geological model is determined.

[0111] In step S17 above, the source rock distribution area is determined, for example, by using the following method: the area where the source rock distribution area of ​​the three-dimensional stratigraphic structure image is superimposed with the source rock distribution area of ​​the three-dimensional geological model is taken as the source rock distribution area. The determined source rock distribution area can be, for example, as follows: Figure 6As shown.

[0112] Based on the same inventive concept, this invention also provides a method for predicting the distribution of hydrocarbon source rocks in well-free areas of deep depressions by combining seismic imaging and geological modeling, the flowchart of which is shown below. Figure 2 As shown, in Figure 2 The first step is to confirm the basic geological conditions of the study area. Based on these conditions, seismic data acquisition and processing, and geological modeling are carried out. Seismic data acquisition and processing consists of three modules: 3D seismic data acquisition, seismic data processing, and source rock reflection characteristics. The seismic data processed by these three modules is used for source rock seismic imaging. Geological modeling requires the construction of a tectonic geological model, a sedimentary environment model, and an organic matter enrichment model. Based on these models, a source rock development geological model is constructed. Based on the source rock seismic imaging and the source rock development geological model, the distribution of source rocks in the deep depression area without wells is predicted.

[0113] Based on the same inventive concept, this invention also provides a device for predicting source rocks in deep, well-free areas, the structural block diagram of which is shown below. Figure 7 As shown, it includes:

[0114] The velocity model construction module 71 is used to construct a velocity model of the formation based on the velocity data of the formation in the pre-acquired deep concave wellless area.

[0115] The three-dimensional imaging module 72 is used to obtain a three-dimensional image of the stratigraphic structure based on the velocity model of the strata and the pre-acquired seismic data of the deep concave wellless area;

[0116] Seismic interpretation module 73 is used to perform seismic interpretation on three-dimensional stratigraphic images and determine the distribution area of ​​source rocks in the three-dimensional stratigraphic images;

[0117] The paleogeographic tectonic model construction module 74 is used to construct a paleogeographic model based on the pre-acquired sedimentary facies and paleogeographic conditions of the deep-pitched, well-free area; and to construct a tectonic model based on the pre-acquired tectonic feature data of the deep-pitched, well-free area.

[0118] The 3D geological model construction module 75 is used to construct a 3D geological model based on the paleogeographic model, the tectonic model and the stratigraphic data of the pre-acquired deep concave well-free area;

[0119] The 3D geological model hydrocarbon source rock distribution area determination module 76 is used to determine the hydrocarbon source rock distribution area in the 3D geological model;

[0120] The source rock distribution module 77 is used to determine the source rock distribution area based on the source rock distribution area in the three-dimensional stratigraphic structure image and the source rock distribution area in the three-dimensional geological model.

[0121] Based on the same inventive concept, this embodiment of the invention also provides a computing device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the program executed by the processor implements a method for predicting hydrocarbon source rocks in deep, well-free areas.

[0122] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for predicting source rocks in deep, well-free areas.

[0123] Based on the same inventive concept, embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements a method for predicting hydrocarbon source rocks in deep, well-free areas.

[0124] Since the principle behind these devices is similar to the aforementioned method for predicting source rocks in deep, well-free areas, the implementation of these devices can be found in the implementation of the aforementioned methods, and the repetitions will not be repeated.

[0125] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0126] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0127] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0128] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0129] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for predicting source rocks in deep, well-free areas, characterized in that, include: Based on the velocity data of the formation in the pre-acquired deep concave wellless area, a velocity model of the formation is constructed; Based on the velocity model of the strata and the pre-acquired seismic data of the deep concave wellless area, a three-dimensional strata structure image is obtained; Seismic interpretation of three-dimensional stratigraphic images is used to determine the distribution areas of source rocks in the three-dimensional stratigraphic images; Based on the pre-acquired sedimentary facies and paleogeographic conditions of the deep, unwell-free area, a paleogeographic model is constructed; based on the pre-acquired tectonic feature data of the deep, unwell-free area, a tectonic model is constructed. Based on the paleogeographic model, tectonic model, and pre-acquired stratigraphic data of the deep, well-free area, a three-dimensional geological model is constructed. The distribution area of ​​hydrocarbon source rocks is determined in the three-dimensional geological model; The distribution area of ​​source rocks is determined based on the distribution area of ​​source rocks in the three-dimensional stratigraphic structure image and the distribution area of ​​source rocks in the three-dimensional geological model.

2. The method as described in claim 1, characterized in that, Before obtaining the three-dimensional stratigraphic image, the method further processes the pre-acquired seismic data from the deep, well-free area as follows: The seismic data of the deep concave wellless area acquired by at least one seismic trace are superimposed to obtain enhanced seismic data of the deep concave wellless area acquired by at least one seismic trace. Migration is performed on enhanced seismic data of deep concave wellless areas acquired by at least one seismic trace to obtain corrected seismic data of deep concave wellless areas acquired by at least one seismic trace. A depth migration operation is performed on the corrected seismic data of the deep concave wellless area acquired by at least one seismic trace to obtain the seismic data of the deep concave wellless area.

3. The method as described in claim 1, characterized in that, The process of constructing a paleogeographic model based on pre-obtained sedimentary facies and paleogeographic conditions of deep, well-free areas includes: Based on the sedimentary facies, identify the sedimentary environment and its spatiotemporal distribution; Based on the sedimentary environment and its spatiotemporal distribution, and the paleogeographic conditions, a paleogeographic model is constructed.

4. The method as described in claim 1, characterized in that, The step of constructing a structural model based on pre-acquired structural feature data of deep, well-free areas includes: Based on the structural feature data, the fault strike, fault displacement, and their impact on the sedimentary environment are determined; Based on the fault strike, fault displacement and its impact on the sedimentary environment, the tectonic evolution history of the deep depression without wells is determined, and the impact of major tectonic events on the formation of source rocks is analyzed. Based on the tectonic evolution history and the influence of major tectonic events on the formation of source rocks, a tectonic model is constructed.

5. The method as described in claim 1, characterized in that, Determining the distribution area of ​​source rocks in the three-dimensional geological model includes: Based on the aforementioned three-dimensional geological model, a lithology model is constructed; Based on the lithological model and the geochemical data of the deep depression without wells, the hydrocarbon generation process of the source rocks is simulated to obtain the thickness and distribution of the hydrocarbon generation potential zone and the hydrocarbon generation maturity of the source rocks. Based on the thickness and distribution of hydrocarbon generation potential zones and the hydrocarbon generation maturity of source rocks, the distribution area of ​​source rocks in the three-dimensional geological model is determined.

6. The method as described in claim 1, characterized in that, The step of determining the source rock distribution area based on the source rock distribution area in the three-dimensional stratigraphic structure image and the source rock distribution area in the three-dimensional geological model includes: taking the area where the source rock distribution area in the three-dimensional stratigraphic structure image and the source rock distribution area in the three-dimensional geological model are superimposed as the source rock distribution area.

7. A device for predicting hydrocarbon source rocks in deep, well-free areas, characterized in that, include: The velocity model construction module is used to construct a velocity model of the formation based on the velocity data of the formation in the pre-acquired deep well-free area. The three-dimensional imaging module is used to obtain three-dimensional images of the stratigraphic structure based on the velocity model of the strata and pre-acquired seismic data of the deep concave wellless area; The seismic interpretation module is used to perform seismic interpretation on three-dimensional stratigraphic images and determine the distribution area of ​​source rocks in the three-dimensional stratigraphic images; The paleogeographic tectonic model building module is used to build paleogeographic models based on pre-acquired sedimentary facies and paleogeographic conditions of deep-seated unwelled areas; and to build tectonic models based on pre-acquired tectonic feature data of deep-seated unwelled areas. The three-dimensional geological model construction module is used to construct a three-dimensional geological model based on the paleogeographic model, tectonic model and pre-acquired stratigraphic data of the deep concave well-free area; A module for determining the distribution area of ​​source rocks in a three-dimensional geological model is used to determine the distribution area of ​​source rocks in the three-dimensional geological model. The hydrocarbon source rock distribution module is used to determine the hydrocarbon source rock distribution area based on the hydrocarbon source rock distribution area in the three-dimensional stratigraphic structure image and the hydrocarbon source rock distribution area in the three-dimensional geological model.

8. A computing device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the program executed by the processor implements the method for predicting source rocks in deep, well-free areas as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for predicting source rocks in deep, well-free areas as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method for predicting source rocks in deep, well-free areas as described in any one of claims 1-6.

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