Complex structure area low-frequency model establishment method and device based on multi-parameter constraint, electronic equipment and medium

Abstract

The invention discloses a method and a device for establishing a low-frequency model of a complex construction area based on multi-parameter constraint, electronic equipment and a medium. The method comprises the following steps: obtaining a longitudinal wave velocity curve, a transverse wave velocity curve and a density curve; obtaining an interval velocity body after well correction, and determining seismic attribute features representing the reservoir; establishing a low-frequency model in an ultralow-frequency band range based on the seismic horizon velocity body after well correction, and establishing a low-frequency end low-frequency model based on seismic attribute features; combining the low-frequency model in the ultralow-frequency band range with the low-frequency end low-frequency model to obtain an initial low-frequency model; and fusing the special abnormal geologic body information into the initial low-frequency model to obtain a low-frequency model of the special geologic body characteristics. According to the method, the low-frequency model is established in a complex high and steep structure area under the condition of special abnormal geologic body development, so that the reliability of inversion reservoir prediction is improved, the uncertainty of a prediction result is reduced, and a data foundation is laid for analysis of a subsequent reservoir prediction result.

Description

Technical Field

[0001] This invention relates to the field of reservoir geophysics research, and more specifically, to a method, apparatus, electronic device, and medium for establishing a low-frequency model of a complex tectonic region based on multi-parameter constraints. Background Technology

[0002] Seismic impedance inversion technology is an important tool in oil and gas exploration and development, used to finely characterize the development and planar distribution of target reservoirs based on well logging, seismic, and geological data. However, due to limitations in seismic acquisition conditions, seismic data is often band-limited and lacks low-frequency components. Low-frequency components contain basic information about geological structures; their absence affects the control of the overall reservoir distribution characteristics and trends in the study area. Currently acquired conventional seismic data often lacks low-frequency information, which affects the accuracy of low- and high-frequency components in the inversion results, thus reducing the accuracy of impedance inversion. Therefore, it is usually necessary to construct an inversion low-frequency model to compensate for the missing low-frequency components and improve the accuracy of the inversion.

[0003] Currently, the main methods for constructing low-frequency models include constant low-frequency models, inter-well interpolation low-frequency models, compaction trend-constrained low-frequency models, seismic impedance inversion low-frequency models, and multi-information fusion artificial intelligence low-frequency models. Among these, constant low-frequency models are primarily suitable for study areas with minimal structural undulations, stable stratigraphic distribution, and relatively stable lithology, offering the advantage of rapid inversion effect analysis and evaluation. Inter-well interpolation low-frequency models are suitable for study areas with relatively abundant logging data, reasonable well spacing, and good control over the entire study area. Compaction trend low-frequency models are suitable for study areas with a clear compaction trend and a relatively reasonable distribution of drilling numbers and well locations. Seismic impedance inversion low-frequency models are based on frequency selection envelope information, using optimized low-frequency seismic records within a specific frequency band to construct a seismic wavefield envelope. Then, based on this envelope information, an inversion objective function is constructed to obtain the subsurface medium velocity, resulting in a subsurface medium impedance low-frequency model suitable for impedance inversion. Multi-information fusion artificial intelligence low-frequency models primarily obtain the target low-frequency model through optimized attribute combinations and nonlinear models.

[0004] Existing technologies can all construct low-frequency models required for seismic impedance inversion. However, in study areas with complex, steep tectonic structures and special anomalous geological bodies, there is still a lack of practical and effective targeted technologies. Existing technologies mainly suffer from the following three problems: ① Low-frequency models constrained by sequence stratigraphic frameworks and inter-well interpolation can be well applied to study areas with gentle structures, stable stratigraphic distribution, and reasonable well numbers and spacing, such as gently sloping delta plains, channel sand bodies, or unconventional shale reservoirs in tectonically stable areas. However, they cannot be applied to areas with large tectonic elevation differences and where most drilled wells are located in structurally high areas, especially in complex fault-block basins with complex structures, well-developed faults, and poor reservoir continuity. Conventionally constructed low-frequency models will exhibit bullseye effects due to inter-well interpolation, making accurate low-frequency model construction even more difficult. ② Low-frequency models constrained by compaction trends can be used in structurally complex areas. Low-frequency models can be established within the domain, but this is only applicable to the target stratigraphic segment in the study area where there is a clear compaction trend. If the stratigraphy is frequently tectonically active, severely damaged, or highly modified in the later stages, the compaction trend is not obvious and it is difficult to obtain an accurate low-frequency model. ③ Low-frequency models obtained by seismic impedance inversion and multi-information fusion artificial intelligence low-frequency models can further integrate seismic and geological information into the model, which can improve the model accuracy. This includes making full use of information such as sequence stratigraphy, tectonics, sedimentation, and seismic attributes. However, sometimes seismic waveform information cannot fully and accurately reflect geological information such as lithology and fluids, especially when there are special anomalous geological bodies, which can lead to multiple interpretations in the prediction results and reduce the accuracy of low-frequency information.

[0005] Therefore, it is necessary to develop a method, device, electronic equipment, and medium for establishing low-frequency models of complex structural regions based on multi-parameter constraints.

[0006] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] This invention proposes a method, apparatus, electronic device, and medium for establishing low-frequency models in complex structural regions based on multi-parameter constraints. It can realize the establishment of low-frequency models in complex and steep structural regions with special anomalous geological bodies, thereby improving the reliability of reservoir inversion prediction and reducing the uncertainty of prediction results, laying a data foundation for the subsequent analysis of reservoir prediction results.

[0008] In a first aspect, embodiments of this disclosure provide a method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints, including:

[0009] Obtain the P-wave velocity curve, S-wave velocity curve, and density curve;

[0010] Obtain well-corrected layer velocity volumes to determine seismic attribute characteristics of the reservoir;

[0011] A low-frequency model within the ultra-low frequency band is established based on the well-corrected seismic layer velocity volume, and a low-frequency model at the low-frequency end is established based on the seismic attribute characteristics.

[0012] The low-frequency model within the ultra-low frequency band is combined with the low-frequency model at the low frequency end to obtain an initial low-frequency model.

[0013] Information on special geological anomalies is incorporated into the initial low-frequency model to obtain a low-frequency model of the characteristics of the special geological bodies.

[0014] As a specific implementation of this disclosure, obtaining the P-wave velocity curve, the S-wave velocity curve, and the density curve includes:

[0015] Correction processing is performed on the initial P-wave velocity curve, S-wave velocity curve, and density curve;

[0016] Missing information is filled in for the corrected P-wave velocity curve, S-wave velocity curve, and density curve to obtain the P-wave velocity curve, S-wave velocity curve, and density curve.

[0017] As a specific implementation of this disclosure, the missing information completion for the corrected P-wave velocity curve, S-wave velocity curve, and density curve includes:

[0018] Rock physics modeling is performed based on complete well logging data, supplementing information on P-wave velocity, S-wave velocity, and density curves.

[0019] As a specific implementation of this disclosure, obtaining the well-corrected layer velocity volume includes:

[0020] Obtain the seismic migration velocity volume and convert it into a layer velocity volume;

[0021] The layer velocity volume is corrected by using the P-wave velocity curve, the S-wave velocity curve, and the density curve to match the seismic layer velocity data with the drilled velocities in all regions, thus obtaining the well-corrected layer velocity volume.

[0022] As a specific implementation of this disclosure, determining the seismic attribute characteristics of a reservoir includes:

[0023] Based on post-stack seismic data, attribute analysis was conducted to identify sensitive attributes for sandstone and mudstone strata and to clarify the seismic attribute characteristics that characterize the reservoirs.

[0024] As a specific implementation of this disclosure, establishing a low-frequency end low-frequency model based on the seismic attribute characteristics includes:

[0025] Based on the seismic attribute characteristics, drilled wells are identified, and a facies-controlled sequence stratigraphic framework is established for the drilled wells in the same facies zone using the seismic interpretation of the stratigraphic position. On this basis, the low-frequency end low-frequency model is established.

[0026] As a specific implementation of this disclosure, the low-frequency model that incorporates information about special anomalous geological bodies into the initial low-frequency model to obtain the characteristics of the special geological bodies includes:

[0027] Identify the information of the special anomalous geological bodies based on the seismic attribute characteristics;

[0028] The information of the special geological anomalies is integrated into the initial low-frequency model, and then post-stack inversion is performed to obtain the identification and characterization results of the special geological bodies. The model is continuously iterated and optimized to obtain a low-frequency model of the characteristics of the special geological bodies.

[0029] Secondly, embodiments of this disclosure also provide an apparatus for establishing a low-frequency model of a complex structural region based on multi-parameter constraints, comprising:

[0030] The acquisition module obtains the P-wave velocity curve, S-wave velocity curve, and density curve.

[0031] The characterization module acquires the well-corrected layer velocity volume and determines the seismic attribute characteristics of the reservoir.

[0032] The modeling module establishes a low-frequency model within the ultra-low frequency band based on the well-corrected seismic layer velocity volume, and establishes a low-frequency model at the low-frequency end based on the seismic attribute characteristics.

[0033] The module combines the low-frequency model within the ultra-low frequency band with the low-frequency model at the low frequency end to obtain an initial low-frequency model.

[0034] The integration module incorporates information about special geological anomalies into the initial low-frequency model to obtain a low-frequency model of the characteristics of the special geological bodies.

[0035] As a specific implementation of this disclosure, obtaining the P-wave velocity curve, the S-wave velocity curve, and the density curve includes:

[0036] Correction processing is performed on the initial P-wave velocity curve, S-wave velocity curve, and density curve;

[0037] Missing information is filled in for the corrected P-wave velocity curve, S-wave velocity curve, and density curve to obtain the P-wave velocity curve, S-wave velocity curve, and density curve.

[0038] As a specific implementation of this disclosure, the missing information completion for the corrected P-wave velocity curve, S-wave velocity curve, and density curve includes:

[0039] Rock physics modeling is performed based on complete well logging data, supplementing information on P-wave velocity, S-wave velocity, and density curves.

[0040] As a specific implementation of this disclosure, obtaining the well-corrected layer velocity volume includes:

[0041] Obtain the seismic migration velocity volume and convert it into a layer velocity volume;

[0042] The layer velocity volume is corrected by using the P-wave velocity curve, the S-wave velocity curve, and the density curve to match the seismic layer velocity data with the drilled velocities in all regions, thus obtaining the well-corrected layer velocity volume.

[0043] As a specific implementation of this disclosure, determining the seismic attribute characteristics of a reservoir includes:

[0044] Based on post-stack seismic data, attribute analysis was conducted to identify sensitive attributes for sandstone and mudstone strata and to clarify the seismic attribute characteristics that characterize the reservoirs.

[0045] As a specific implementation of this disclosure, establishing a low-frequency end low-frequency model based on the seismic attribute characteristics includes:

[0046] Based on the seismic attribute characteristics, drilled wells are identified, and a facies-controlled sequence stratigraphic framework is established for the drilled wells in the same facies zone using the seismic interpretation of the stratigraphic position. On this basis, the low-frequency end low-frequency model is established.

[0047] As a specific implementation of this disclosure, the low-frequency model that incorporates information about special anomalous geological bodies into the initial low-frequency model to obtain the characteristics of the special geological bodies includes:

[0048] Identify the information of the special anomalous geological bodies based on the seismic attribute characteristics;

[0049] The information of the special geological anomalies is integrated into the initial low-frequency model, and then post-stack inversion is performed to obtain the identification and characterization results of the special geological bodies. The model is continuously iterated and optimized to obtain a low-frequency model of the characteristics of the special geological bodies.

[0050] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0051] Memory, which stores executable instructions;

[0052] A processor that executes the executable instructions in the memory to implement the method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints.

[0053] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints.

[0054] Its beneficial effects are as follows:

[0055] 1. While sequence stratigraphic framework-constrained low-frequency models and inter-well interpolation low-frequency models are generally applicable to study areas with gentle structures, stable stratigraphic distribution, and reasonable well numbers and spacing, such as gently sloping delta plains with channel sand bodies or unconventional shale reservoirs in structurally stable areas, they are not suitable for regions with large structural elevation differences and where most drilled wells are located in structurally high areas. This is especially true in complex fault-block basins with complex structures, well-developed faults, and poor reservoir continuity. Conventionally constructed low-frequency models often exhibit bullseye effects due to inter-well interpolation, making accurate low-frequency model establishment even more difficult. This invention, based on multi-parameter constraints, constructs a low-frequency model that better reflects the overall structural sedimentary features and unique geological characteristics of the study area. Seismic information plays a greater controlling role, avoiding uncertainties caused by factors such as the number of wells, well spacing, and logging anomalies. The proposed fusion-iteration method better characterizes structural features, sedimentary trends, and anomalous changes in unique geological bodies, making it suitable for low-frequency model construction in fault-block development study areas with complex structural backgrounds.

[0056] 2. While low-frequency models constrained by compaction trends can be established in structurally complex areas, this approach is only applicable to study areas with a clear compaction trend within the target stratigraphic segment. If subsequent tectonic activity is frequent, formation damage is severe, or the degree of alteration is high, the compaction trend may be unclear, making it difficult to obtain an accurate low-frequency model. The multi-parameter constrained low-frequency model construction method described in this invention is well-suited for various types of study areas with complex structural features. It eliminates the possibility of unreasonable low-frequency model results due to unreasonable compaction trends. Furthermore, the low-frequency model establishment process integrates multiple factors, including seismic, geological, and well logging data, maximizing the representation of inter-well differences and ensuring accurate and reliable results.

[0057] 3. By using seismic impedance inversion low-frequency models and multi-information fusion artificial intelligence low-frequency models, seismic and geological information can be further integrated into the model, which can improve the model accuracy. This includes making full use of information such as sequence stratigraphy, tectonics, sedimentation, and seismic attributes. However, sometimes seismic waveform information cannot fully and accurately reflect geological information such as lithology and fluids, especially when there are special anomalous geological bodies. This can lead to multiple interpretations in the prediction results, which in turn reduces the accuracy of low-frequency information. This invention employs a multi-attribute analysis method under dominant frequency constraints to identify special geological bodies. This method effectively eliminates the influence of other lithologies and comprehensively considers the analysis of multiple prediction results, including well logging curve characteristics, seismic attributes, and wave impedance inversion, to determine the development and distribution characteristics of special geological bodies. Then, the information of special anomalous geological bodies identified by sensitive seismic attributes is integrated into an initial low-frequency model. Based on this initial low-frequency model, post-stack inversion is performed to obtain the identification and characterization results of special geological bodies. The obtained special geological bodies are then integrated into the low-frequency model, and the inversion results are continuously iterated and optimized to improve the accuracy of the low-frequency model. This results in a final low-frequency model that conforms to geological laws and can better reflect tectonic depositional trends and the characteristics of special geological bodies. This eliminates the uncertainty in the identification and characterization results of special geological bodies caused by relying solely on human experience, demonstrating a certain degree of innovation and advancement.

[0058] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0059] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0060] Figure 1 A flowchart illustrating the steps of a method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints is shown.

[0061] Figure 2 A flowchart illustrating the steps of a method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints according to an embodiment of the present invention is shown.

[0062] Figure 3 A schematic diagram of the characteristics of well logging curves for intrusive diabase according to an embodiment of the present invention is shown.

[0063] Figure 4 A schematic diagram of rock physics modeling and shear wave prediction according to an embodiment of the present invention is shown.

[0064] Figure 5 A block diagram of a device for establishing a low-frequency model of a complex structural region based on multi-parameter constraints according to an embodiment of the present invention is shown.

[0065] Explanation of reference numerals in the attached figures:

[0066] 201. Acquisition Module; 202. Representation Module; 203. Modeling Module; 204. Combination Module; 205. Integration Module. Detailed Implementation

[0067] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0068] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0069] Example 1

[0070] Figure 1 A flowchart illustrating the steps of a method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints is shown.

[0071] like Figure 1 As shown, the method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints includes: Step 101, obtaining the P-wave velocity curve, S-wave velocity curve, and density curve; Step 102, obtaining the well-corrected layer velocity volume and determining the seismic attribute characteristics of the reservoir; Step 103, establishing a low-frequency model within the ultra-low frequency band based on the well-corrected seismic layer velocity volume, and establishing a low-frequency model at the low-frequency end based on the seismic attribute characteristics; Step 104, combining the low-frequency model within the ultra-low frequency band with the low-frequency model at the low-frequency end to obtain an initial low-frequency model; Step 105, incorporating information on special anomalous geological bodies into the initial low-frequency model to obtain a low-frequency model with characteristics of special geological bodies.

[0072] In one example, obtaining the P-wave velocity profile, S-wave velocity profile, and density profile includes:

[0073] Correction processing is performed on the initial P-wave velocity curve, S-wave velocity curve, and density curve;

[0074] Missing information is filled in for the corrected P-wave velocity curve, S-wave velocity curve, and density curve to obtain the P-wave velocity curve, S-wave velocity curve, and density curve.

[0075] In one example, missing information completion for the corrected P-wave velocity curve, S-wave velocity curve, and density curve includes:

[0076] Rock physics modeling is performed based on complete well logging data, supplementing information on P-wave velocity, S-wave velocity, and density curves.

[0077] In one example, obtaining the well-corrected layer velocity volume includes:

[0078] Obtain the seismic migration velocity volume and convert it into a layer velocity volume;

[0079] By correcting the layer velocity volume using P-wave velocity curves, S-wave velocity curves, and density curves, the seismic layer velocity data is matched with the drilled velocities in all regions, resulting in a well-corrected layer velocity volume.

[0080] In one example, determining the seismic properties characterizing a reservoir includes:

[0081] Based on post-stack seismic data, attribute analysis was conducted to identify sensitive attributes for sandstone and mudstone strata and to clarify the seismic attribute characteristics that characterize the reservoirs.

[0082] In one example, building a low-frequency model at the low-frequency end based on seismic attribute characteristics includes:

[0083] Based on the seismic attribute characteristics, drilled wells are identified, and a facies-controlled sequence stratigraphic framework is established for drilled wells in the same facies zone using the seismic interpretation of the stratigraphic position. On this basis, a low-frequency end low-frequency model is established.

[0084] In one example, incorporating information about specific geological anomalies into an initial low-frequency model, the resulting low-frequency model of the specific geological features includes:

[0085] Identify information on special geological anomalies based on earthquake attribute characteristics;

[0086] Information on special geological anomalies is incorporated into an initial low-frequency model, followed by post-stack inversion to obtain the identification and characterization results of special geological bodies. Through continuous iterative optimization, a low-frequency model of the characteristics of special geological bodies is obtained.

[0087] Specifically, seismic velocity parameters can effectively reflect changes in tectonic undulations and geological sedimentary patterns. After correcting for seismic velocities from drilled wells, they can be used to constrain ultra-low frequency (0–3 Hz) information, enabling accurate control of large-scale stratigraphic characteristics. Based on sensitive seismic attribute analysis, drilled wells are selected, and facies-controlled sequence stratigraphic frameworks are established using seismically interpreted stratigraphic positions from wells in the same facies zone. On this basis, low-frequency models are established at the low-frequency end, achieving low-frequency (3–10 Hz) information compensation. Simultaneously, multiple parameters from seismic, geological, and well logging factors are considered, thereby improving the accuracy of low-frequency information. By integrating information on special anomalous geological bodies identified by sensitive seismic attributes into the low-frequency model, post-stack inversion is performed based on the initial low-frequency model to obtain the identification and characterization results of special geological bodies. The obtained special geological bodies are integrated into the low-frequency model, and the inversion results are continuously iterated and optimized to improve the accuracy of the low-frequency model. This enables accurate low-frequency model establishment in complex, steep tectonic areas with special geological bodies.

[0088] Figure 2 A flowchart illustrating the steps of a method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints according to an embodiment of the present invention is shown.

[0089] like Figure 2 As shown, the system organizes the logging curve data of drilled wells, records the P-wave velocity, S-wave velocity, and density curve information, and performs quality control analysis on the logging curves. The focus is on analyzing whether the value range of the logging curves is reasonable and whether there are any abnormal curve segments. Simultaneously, the logging curves are corrected to obtain high-quality P-wave velocity, S-wave velocity, and density curves. It is important to note that logging curve anomalies can occur in cases of wellbore collapse, logging instrument errors, and the presence of special geological bodies. A comparative analysis of whether all three curves show anomalies simultaneously is necessary to determine the reasonableness of the logging curves. For example, wellbore collapse will cause a decrease in the density curve value range; coal seam development will simultaneously decrease velocity and density; igneous rocks will cause a simultaneous increase in velocity and density. A comprehensive judgment and analysis are required. For sections with special geological bodies, it is best to perform secondary calibration and quality control using logging information and core thin section observations at the corresponding depths to clarify the development of special and abnormal geological bodies.

[0090] For wells lacking information such as shear wave velocity, rock physical modeling is conducted based on existing complete well logging data to supplement and improve P-wave velocity, S-wave velocity, and density curve information. It should be noted that the rock physical model for the same layer should have a certain applicability; that is, the rock physical model should be applicable to different well areas to obtain more reliable prediction results.

[0091] Seismic migration velocity volumes are collected and converted into layer velocity volumes. The seismic layer velocity volumes are then corrected using well logging data (P-wave velocity curves, S-wave velocity curves, and density curves) to match the seismic layer velocity data with the well velocities in all areas, resulting in well-corrected seismic layer velocity volumes. It should be noted that the well data used for correction must be authentic and reliable, and a cross-analysis of well velocities and seismic layer velocities must be performed during quality control analysis to clarify their trends and relative errors.

[0092] Based on post-stack seismic data, attribute analysis was conducted, and sensitive attributes such as root mean square amplitude were selected for sandstone and mudstone strata to clearly characterize the seismic attribute features of the reservoir.

[0093] Based on well-corrected seismic layer velocity information constraints, a low-frequency model (0–3 Hz) is established within the ultra-low frequency band to control the structural and sedimentary trends of large strata. Based on sensitive seismic attribute analysis, drilled wells are selected, and facies-controlled sequence stratigraphic frameworks are established using seismically interpreted stratigraphic positions from drilled wells in the same facies zone. On this basis, a low-frequency model (3–10 Hz) is established at the low-frequency end, fully utilizing seismic attributes and geological facies zone constraints to make the inter-well patterns more consistent with structural sedimentary understanding. The low-frequency information is then fused to form an initial low-frequency model (0–10 Hz).

[0094] For special geological anomalies, a multi-attribute analysis method under dominant frequency constraints is used to identify them. This effectively eliminates the influence of other lithologies (such as high-velocity sandstone), qualitatively analyzes the distribution characteristics of the anomalous geological bodies, and combines well data to analyze the development of special geological features in the study area. If special geological bodies are found, the low-frequency model needs further optimization. By integrating the information of special geological anomalies identified by sensitive seismic attributes into the initial low-frequency model, post-stack inversion is performed based on the initial low-frequency model to obtain the identification and characterization results of special geological bodies. The obtained special geological bodies are then integrated into the low-frequency model, and the inversion results are continuously iterated and optimized to improve the accuracy of the low-frequency model. Finally, a low-frequency model that conforms to geological laws and can better reflect tectonic and sedimentary trends and the characteristics of special geological bodies is obtained.

[0095] Example 2

[0096] The present invention also provides a device for establishing a low-frequency model of a complex structural region based on multi-parameter constraints, comprising:

[0097] The acquisition module obtains the P-wave velocity curve, S-wave velocity curve, and density curve.

[0098] The characterization module acquires the well-corrected layer velocity volume and determines the seismic attribute characteristics of the reservoir.

[0099] The modeling module establishes a low-frequency model within the ultra-low frequency band based on the well-corrected seismic layer velocity volume, and establishes a low-frequency model at the low-frequency end based on seismic attribute characteristics.

[0100] By combining the module, the low-frequency model in the ultra-low frequency band range is combined with the low-frequency model at the low frequency end to obtain the initial low-frequency model;

[0101] The integration module incorporates information about special geological anomalies into the initial low-frequency model to obtain a low-frequency model of the characteristics of the special geological bodies.

[0102] In one example, obtaining the P-wave velocity profile, S-wave velocity profile, and density profile includes:

[0103] Correction processing is performed on the initial P-wave velocity curve, S-wave velocity curve, and density curve;

[0104] Missing information is filled in for the corrected P-wave velocity curve, S-wave velocity curve, and density curve to obtain the P-wave velocity curve, S-wave velocity curve, and density curve.

[0105] In one example, missing information completion for the corrected P-wave velocity curve, S-wave velocity curve, and density curve includes:

[0106] Rock physics modeling is performed based on complete well logging data, supplementing information on P-wave velocity, S-wave velocity, and density curves.

[0107] In one example, obtaining the well-corrected layer velocity volume includes:

[0108] Obtain the seismic migration velocity volume and convert it into a layer velocity volume;

[0109] By correcting the layer velocity volume using P-wave velocity curves, S-wave velocity curves, and density curves, the seismic layer velocity data is matched with the drilled velocities in all regions, resulting in a well-corrected layer velocity volume.

[0110] In one example, determining the seismic properties characterizing a reservoir includes:

[0111] Based on post-stack seismic data, attribute analysis was conducted to identify sensitive attributes for sandstone and mudstone strata and to clarify the seismic attribute characteristics that characterize the reservoirs.

[0112] In one example, building a low-frequency model at the low-frequency end based on seismic attribute characteristics includes:

[0113] Based on the seismic attribute characteristics, drilled wells are identified, and a facies-controlled sequence stratigraphic framework is established for drilled wells in the same facies zone using the seismic interpretation of the stratigraphic position. On this basis, a low-frequency end low-frequency model is established.

[0114] In one example, incorporating information about specific geological anomalies into an initial low-frequency model, the resulting low-frequency model of the specific geological features includes:

[0115] Identify information on special geological anomalies based on earthquake attribute characteristics;

[0116] Information on special geological anomalies is incorporated into an initial low-frequency model, followed by post-stack inversion to obtain the identification and characterization results of special geological bodies. Through continuous iterative optimization, a low-frequency model of the characteristics of special geological bodies is obtained.

[0117] Specifically, the system organizes the logging data from drilled wells, recording P-wave velocity, S-wave velocity, and density curves. Quality control analysis is performed on the logging curves, focusing on the reasonableness of the value range and the presence of abnormal curve segments. Simultaneously, correction analysis is conducted on the logging curves to obtain high-quality P-wave velocity, S-wave velocity, and density curves. It is important to note that logging curve anomalies can occur in cases of wellbore collapse, logging instrument errors, and the presence of special geological bodies. A comparative analysis of whether all three curves show anomalies simultaneously is necessary to determine the reasonableness of the logging curves. For example, wellbore collapse can cause a decrease in the density curve value range; coal seam development can simultaneously decrease velocity and density; and igneous rocks can cause a simultaneous increase in velocity and density. A comprehensive analysis is required. For sections with special geological bodies, secondary calibration and quality control should ideally be performed using logging information and core thin section observations at the corresponding depths to clarify the development of these special and abnormal geological bodies.

[0118] For wells lacking information such as shear wave velocity, rock physical modeling is conducted based on existing complete well logging data to supplement and improve P-wave velocity, S-wave velocity, and density curve information. It should be noted that the rock physical model for the same layer should have a certain applicability; that is, the rock physical model should be applicable to different well areas to obtain more reliable prediction results.

[0119] Seismic migration velocity volumes are collected and converted into layer velocity volumes. The seismic layer velocity volumes are then corrected using well logging data (P-wave velocity curves, S-wave velocity curves, and density curves) to match the seismic layer velocity data with the well velocities in all areas, resulting in well-corrected seismic layer velocity volumes. It should be noted that the well data used for correction must be authentic and reliable, and a cross-analysis of well velocities and seismic layer velocities must be performed during quality control analysis to clarify their trends and relative errors.

[0120] Based on post-stack seismic data, attribute analysis was conducted, and sensitive attributes such as root mean square amplitude were selected for sandstone and mudstone strata to clearly characterize the seismic attribute features of the reservoir.

[0121] Based on well-corrected seismic layer velocity information constraints, a low-frequency model (0–3 Hz) is established within the ultra-low frequency band to control the structural and sedimentary trends of large strata. Based on sensitive seismic attribute analysis, drilled wells are selected, and facies-controlled sequence stratigraphic frameworks are established using seismically interpreted stratigraphic positions from drilled wells in the same facies zone. On this basis, a low-frequency model (3–10 Hz) is established at the low-frequency end, fully utilizing seismic attributes and geological facies zone constraints to make the inter-well patterns more consistent with structural sedimentary understanding. The low-frequency information is then fused to form an initial low-frequency model (0–10 Hz).

[0122] For special geological anomalies, a multi-attribute analysis method under dominant frequency constraints is used to identify them. This effectively eliminates the influence of other lithologies (such as high-velocity sandstone), qualitatively analyzes the distribution characteristics of the anomalous geological bodies, and combines well data to analyze the development of special geological features in the study area. If special geological bodies are found, the low-frequency model needs further optimization. By integrating the information of special geological anomalies identified by sensitive seismic attributes into the initial low-frequency model, post-stack inversion is performed based on the initial low-frequency model to obtain the identification and characterization results of special geological bodies. The obtained special geological bodies are then integrated into the low-frequency model, and the inversion results are continuously iterated and optimized to improve the accuracy of the low-frequency model. Finally, a low-frequency model that conforms to geological laws and can better reflect tectonic and sedimentary trends and the characteristics of special geological bodies is obtained.

[0123] Example 3

[0124] This method was applied to a study area in the Gaoyou Depression of the Subei Basin. This area features significant structural variations, well-developed fault blocks, and drilled wells revealing a unique geological body (intrusive diabase). However, existing techniques struggle to accurately establish low-frequency models, resulting in inversion results that fail to accurately characterize reservoir distribution and patterns. This invention utilizes a method that, based on seismic velocity, well logging data, seismic attribute analysis, sedimentary facies characteristics, and constraints imposed by the unique geological body, iteratively optimizes the initial low-frequency model by integrating multiple parameters. This yields a final low-frequency model that better reflects the tectonic-depositional trends and the characteristics of the unique geological body. This model not only effectively controls the overall tectonic-depositional trends of the study area but also distinguishes the intrusive diabase from other high-energy anomalies and integrates them into the low-frequency model, making the prediction results more accurate and reliable. Furthermore, drilled well information can be used to calibrate and quality control the final inversion results, cross-validating them to eliminate uncertainties caused by factors such as the number of wells, well spacing, and abnormal logging curves, thus improving the reliability of the prediction results.

[0125] Figure 3 A schematic diagram of the characteristics of well logging curves for intrusive diabase according to an embodiment of the present invention is shown.

[0126] Figure 4A schematic diagram of rock physics modeling and shear wave prediction according to an embodiment of the present invention is shown.

[0127] First, the system organizes the drilling logging data, recording P-wave velocity, S-wave velocity, and density curve information. Quality control analysis is then performed on the logging curves, focusing on whether the value ranges are reasonable and whether abnormal curve segments exist. Simultaneously, correction analysis is conducted on the logging curves to obtain high-quality P-wave velocity, S-wave velocity, and density curves. Figure 3 As shown. From Figure 3 As can be seen, anomalies are observed in the GR, AC, DEN, and resistivity curves within the black box area, exhibiting significantly low GR, low AC, high density, and high resistivity characteristics. This aligns with the logging facies characteristics of special geological anomalies in intrusive diabase, confirming the encounter of an intrusive diabase body during drilling. For study areas with such special geological anomalies, the significant differences between these anomalies and the surrounding rocks affect P-wave velocity, S-wave velocity, and density parameters. Therefore, their unique characteristics must be considered when establishing the low-frequency model. This is achieved by integrating the information of special geological anomalies identified by sensitive seismic attributes into the initial low-frequency model. Post-stack inversion is then performed based on the initial low-frequency model to obtain the identification and characterization results of the special geological bodies. The obtained special geological bodies are then integrated into the low-frequency model, and the inversion results are continuously iterated and optimized to improve the accuracy of the low-frequency model. The final low-frequency model, conforming to geological laws and effectively reflecting tectonic-depositional trends and the characteristics of special geological bodies, is thus obtained. The results show that the sonic transit time of the intrusive diabase ranges from 52 to 68 μS / m, that of the sandstone from 57 to 88 μS / m, and that of the mudstone from 80 to 106 μS / m. The density of the intrusive diabase ranges from 2.55 to 2.67 g / cm³. 3 The density of sandstone ranges from 2.3 to 2.6 g / cm³. 3 The density of mudstone ranges from 2.45 to 2.63 g / cm³. 3 The resistivity distribution ranges from 40 to 165 Ω·m for intrusive diabase, 3.5 to 15.8 Ω·m for sandstone, and 3.3 to 13.2 Ω·m for mudstone. Comparative analysis reveals that the sonic transit time, density, and resistivity curves of the intrusive diabase development zone all exhibit abnormal characteristics in well logging, suggesting the encounter of an intrusive diabase body. Fine-grained well-seismic calibration, combined with post-stack seismic data, clarifies the location of the intrusive diabase development zone on the seismic profile. For wells lacking information such as shear wave velocity, rock physics modeling is conducted based on existing complete well logging data to supplement and improve P-wave velocity, S-wave velocity, and density curve information, such as... Figure 4 As shown. From Figure 4It can be seen that in some research areas, due to varying degrees of completeness of logging data, some key logging curves, such as P-wave velocity, S-wave velocity, and density, are missing, directly affecting the construction of low-frequency models. To address this data gap issue, rock physical modeling can be used to supplement the data. It should be noted that the rock physical model for the same layer must have a certain applicability; that is, the rock physical model should be applicable to different well areas to ensure more reliable prediction results. As shown in the figure, the three curves on the left represent the input data required for rock physical modeling, while the three curves on the right (blue) represent actual curve data and (red) represent predicted curve data. Comparison shows that the predicted VP and DEN curves agree well with the measured results, thus confirming the reliability of the predicted VS curve data and achieving the goal of supplementing logging data.

[0128] Seismic migration velocity volumes were collected and converted into layer velocity volumes. These volumes were then corrected using well logging data from drilled wells to match the data with the velocities of all drilled wells in the region, resulting in well-corrected seismic layer velocity volumes. Based on the well-corrected seismic layer velocity information, a low-frequency model (0–3 Hz) was established within the ultra-low frequency band to control the structural and sedimentary trends of large strata. Based on sensitive seismic attribute analysis, drilled wells were selected, and facies-controlled sequence stratigraphic frameworks were established using seismically interpreted stratigraphic positions from wells in the same facies zone. On this basis, a low-frequency model (3–10 Hz) was established at the low-frequency end, fully utilizing seismic attributes and geological facies constraints to make the inter-well patterns more consistent with structural sedimentary understanding. The low-frequency information was then fused to form an initial low-frequency model (0–10 Hz). For specific geological anomalies, [further details are needed]. Under the constraint of dominant frequency, multi-attribute analysis method is used to identify special geological bodies, qualitatively analyze the distribution characteristics of anomalous geological bodies, and analyze the special geological development of the study area in combination with the information of drilled wells. By integrating the information of special anomalous geological bodies identified by sensitive seismic attributes into the initial low-frequency model, post-stack inversion is carried out based on the initial low-frequency model to obtain the identification and characterization results of special geological bodies. The obtained special geological bodies are integrated into the low-frequency model to continuously iterate and optimize the inversion results to improve the accuracy of the low-frequency model, and obtain the final low-frequency model that conforms to geological laws and can better reflect the tectonic deposition trend and the characteristics of special geological bodies.

[0129] Example 4

[0130] Figure 5 A block diagram of a device for establishing a low-frequency model of a complex structural region based on multi-parameter constraints according to an embodiment of the present invention is shown.

[0131] like Figure 5 As shown, the device for establishing a low-frequency model of a complex structural region based on multi-parameter constraints includes:

[0132] Module 201 acquires the P-wave velocity curve, S-wave velocity curve, and density curve;

[0133] Characterization module 202 acquires the well-corrected layer velocity volume and determines the seismic attribute characteristics of the reservoir;

[0134] Modeling module 203 establishes a low-frequency model within the ultra-low frequency band based on the well-corrected seismic layer velocity volume, and establishes a low-frequency model at the low-frequency end based on seismic attribute characteristics;

[0135] By combining module 204, the low-frequency model in the ultra-low frequency band range is combined with the low-frequency model at the low frequency end to obtain the initial low-frequency model;

[0136] Module 205 integrates information on special geological anomalies into the initial low-frequency model to obtain a low-frequency model of the characteristics of the special geological bodies.

[0137] In one example, obtaining the P-wave velocity profile, S-wave velocity profile, and density profile includes:

[0138] Correction processing is performed on the initial P-wave velocity curve, S-wave velocity curve, and density curve;

[0139] Missing information is filled in for the corrected P-wave velocity curve, S-wave velocity curve, and density curve to obtain the P-wave velocity curve, S-wave velocity curve, and density curve.

[0140] In one example, missing information completion for the corrected P-wave velocity curve, S-wave velocity curve, and density curve includes:

[0141] Rock physics modeling is performed based on complete well logging data, supplementing information on P-wave velocity, S-wave velocity, and density curves.

[0142] In one example, obtaining the well-corrected layer velocity volume includes:

[0143] Obtain the seismic migration velocity volume and convert it into a layer velocity volume;

[0144] By correcting the layer velocity volume using P-wave velocity curves, S-wave velocity curves, and density curves, the seismic layer velocity data is matched with the drilled velocities in all regions, resulting in a well-corrected layer velocity volume.

[0145] In one example, determining the seismic properties characterizing a reservoir includes:

[0146] Based on post-stack seismic data, attribute analysis was conducted to identify sensitive attributes for sandstone and mudstone strata and to clarify the seismic attribute characteristics that characterize the reservoirs.

[0147] In one example, building a low-frequency model at the low-frequency end based on seismic attribute characteristics includes:

[0148] Based on the seismic attribute characteristics, drilled wells are identified, and a facies-controlled sequence stratigraphic framework is established for drilled wells in the same facies zone using the seismic interpretation of the stratigraphic position. On this basis, a low-frequency end low-frequency model is established.

[0149] In one example, incorporating information about specific geological anomalies into an initial low-frequency model, the resulting low-frequency model of the specific geological features includes:

[0150] Identify information on special geological anomalies based on earthquake attribute characteristics;

[0151] Information on special geological anomalies is incorporated into an initial low-frequency model, followed by post-stack inversion to obtain the identification and characterization results of special geological bodies. Through continuous iterative optimization, a low-frequency model of the characteristics of special geological bodies is obtained.

[0152] Example 5

[0153] This disclosure provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the aforementioned method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints.

[0154] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0155] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0156] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0157] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0158] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0159] Example 6

[0160] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints.

[0161] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0162] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0163] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0164] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints, characterized in that, include: Obtain the P-wave velocity curve, S-wave velocity curve, and density curve; Obtain well-corrected layer velocity volumes to determine seismic attribute characteristics of the reservoir; A low-frequency model within the ultra-low frequency band is established based on the well-corrected seismic layer velocity volume, and a low-frequency model at the low-frequency end is established based on the seismic attribute characteristics. The low-frequency model within the ultra-low frequency band is combined with the low-frequency model at the low frequency end to obtain an initial low-frequency model. Information on special geological anomalies is incorporated into the initial low-frequency model to obtain a low-frequency model of the characteristics of the special geological bodies.

2. The method for establishing a low-frequency model of complex structural regions based on multi-parameter constraints according to claim 1, wherein, Obtaining P-wave velocity curves, S-wave velocity curves, and density curves includes: Correction processing is performed on the initial P-wave velocity curve, S-wave velocity curve, and density curve; Missing information is filled in for the corrected P-wave velocity curve, S-wave velocity curve, and density curve to obtain the P-wave velocity curve, S-wave velocity curve, and density curve.

3. The method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints according to claim 2, wherein, The missing information for the corrected P-wave velocity curve, S-wave velocity curve, and density curve is filled in, including: Rock physics modeling is performed based on complete well logging data, supplementing information on P-wave velocity, S-wave velocity, and density curves.

4. The method for establishing a low-frequency model of complex structural regions based on multi-parameter constraints according to claim 1, wherein, The well-corrected layer velocity volume includes: Obtain the seismic migration velocity volume and convert it into a layer velocity volume; The layer velocity volume is corrected by using the P-wave velocity curve, the S-wave velocity curve, and the density curve to match the seismic layer velocity data with the drilled velocities in all regions, thus obtaining the well-corrected layer velocity volume.

5. The method for establishing a low-frequency model of complex structural regions based on multi-parameter constraints according to claim 1, wherein, Determining the seismic properties characterizing reservoirs includes: Based on post-stack seismic data, attribute analysis was conducted to identify sensitive attributes for sandstone and mudstone strata and to clarify the seismic attribute characteristics that characterize the reservoirs.

6. The method for establishing a low-frequency model of complex structural regions based on multi-parameter constraints according to claim 1, wherein, The establishment of a low-frequency end low-frequency model based on the aforementioned seismic attribute characteristics includes: Based on the seismic attribute characteristics, drilled wells are identified, and a facies-controlled sequence stratigraphic framework is established for the drilled wells in the same facies zone using the seismic interpretation of the stratigraphic position. On this basis, the low-frequency end low-frequency model is established.

7. The method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints according to claim 1, wherein, By incorporating information about special geological anomalies into the initial low-frequency model, a low-frequency model of the characteristics of these special geological bodies is obtained, including: Identify the information of the special anomalous geological bodies based on the seismic attribute characteristics; The information of the special geological anomalies is integrated into the initial low-frequency model, and then post-stack inversion is performed to obtain the identification and characterization results of the special geological bodies. The model is continuously iterated and optimized to obtain a low-frequency model of the characteristics of the special geological bodies.

8. A device for establishing a low-frequency model of a complex structural region based on multi-parameter constraints, characterized in that, include: The acquisition module obtains the P-wave velocity curve, S-wave velocity curve, and density curve. The characterization module acquires the well-corrected layer velocity volume and determines the seismic attribute characteristics of the reservoir. The modeling module establishes a low-frequency model within the ultra-low frequency band based on the well-corrected seismic layer velocity volume, and establishes a low-frequency model at the low-frequency end based on the seismic attribute characteristics. The module combines the low-frequency model within the ultra-low frequency band with the low-frequency model at the low frequency end to obtain an initial low-frequency model. The integration module incorporates information about special geological anomalies into the initial low-frequency model to obtain a low-frequency model of the characteristics of the special geological bodies.

9. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the method for establishing a low-frequency model of a complex structural region based on multi-parameter constraints as described in any one of claims 1-7.

10. 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 establishing a low-frequency model of a complex structural region based on multi-parameter constraints as described in any one of claims 1-7.

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