Carbonate reservoir prediction method and system based on seismic sedimentology

Abstract

The application discloses a carbonate reservoir prediction method and system based on seismic sedimentology, and comprises the following steps: collecting data of a research area; identifying and dividing sequence interfaces of a target layer by analyzing seismic data and well logging curves; evaluating seismic amplitude data of the target layer and determining an effective signal range; performing frequency division processing on the seismic data in the effective signal range; performing high-precision sequence interface seismic tracking interpretation; analyzing distribution rules of reservoirs and distribution conditions of special lithology in a sequence framework; establishing a seismic forward geology model; performing seismic forward to confirm seismic response characteristics of the reservoirs and the special lithology; selecting appropriate seismic reservoir prediction methods or technologies for reservoirs at different sequence positions to perform reservoir prediction work; and evaluating reservoir prediction results and removing systematic errors leading to abnormal areas. The application has the advantages of improving the precision and accuracy of reservoir prediction, improving the efficiency and economic benefits of data processing and exploration and development.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration and development technology, and in particular to a method and system for predicting carbonate reservoirs based on seismic sedimentology in complex lithological contexts. Background Technology

[0002] Reservoir prediction is one of the most critical aspects of the oil and gas industry today. With the continuous advancement of global oil and gas exploration and development, the target of reservoir prediction is gradually shifting from thick reservoirs with simple lithology to thin reservoirs with complex lithology, leading to increasingly higher requirements for prediction accuracy. Traditional reservoir prediction methods relying on geological data such as outcrops and wells are insufficient to meet the demands for high-precision reservoir prediction. Because geophysical data has high lateral resolution, it is an effective means of predicting the spatial distribution of reservoirs. Therefore, seismic prediction is currently the primary reservoir prediction method in the industry.

[0003] Seismic reservoir prediction can be divided into qualitative and quantitative prediction. Qualitative reservoir analysis primarily focuses on seismic attribute analysis. Seismic attributes refer to the geometric, kinematic, dynamic, and statistical characteristics of seismic waves derived from pre-stack or post-stack seismic data through mathematical transformations. Attribute analysis extracts information about reservoirs, structures, and physical properties hidden within the seismic data, achieving the goal of describing reservoir characteristic parameters. Quantitative prediction mainly refers to seismic inversion, which involves inferring the internal structure, morphology, and material composition of a target body within the Earth using known geophysical and geological data, and quantitatively calculating various related rock physical parameters to achieve the purpose of reservoir prediction.

[0004] Due to the complexity of geological conditions and the limitations of the technology itself, various seismic attribute analyses have certain applicable conditions, and their prediction results are often ambiguous. Seismic inversion technology, firstly, is unsuitable for large-scale use in the early stages of exploration and development due to its enormous computational load and cost, and secondly, the problem of ambiguity persists when predicting reservoirs in complex lithological settings. Therefore, there is currently no truly effective technical means for reservoir prediction in complex lithological settings. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by providing a method and system for predicting carbonate reservoirs based on seismic sedimentology.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for predicting carbonate reservoirs based on seismic sedimentology includes the following steps:

[0008] S1. Collect regional geological survey data, seismic data, well logging curves, drilling information, and stratigraphic data for the study area;

[0009] S2. By analyzing seismic data and well logging curves, identify and delineate the single-well sequence boundaries of the target layer, and perform sequence comparison.

[0010] S3. Evaluate the seismic amplitude data of the target layer and perform spectral analysis to determine the effective signal range;

[0011] S4. Within the effective signal range, the seismic data is processed by frequency division to obtain seismic data in multiple frequency bands and to produce frequency-divided single-well synthetic records. The frequency-divided data volume with the highest correlation coefficient is selected, and the sequence interface tracing and interpretation scheme is clarified.

[0012] S5. Perform high-precision sequence boundary seismic tracking interpretation to determine the range of sequence plane distribution;

[0013] S6. Analyze the distribution pattern of reservoirs within the well-seismic sequence framework and the distribution of special lithologies;

[0014] S7. Establish seismic forward modeling geological models for sequence stratigraphy, internal reservoirs, and special lithologies;

[0015] S8. Select an appropriate forward modeling method to perform seismic forward modeling to confirm the seismic response characteristics of reservoirs and special lithologies;

[0016] S9. Based on S5 to S8, select appropriate seismic reservoir prediction methods or technologies for reservoirs at different sequence locations and carry out reservoir prediction work.

[0017] S91. Select sequence stratigraphic units that are related to the distribution of reservoirs and special lithologies, and use the characteristics of these stratigraphic units as a priori conditions for subsequent reservoir prediction.

[0018] S92. Based on the results of S92, select qualitative or quantitative reservoir prediction methods to predict reservoir development zones.

[0019] S10. Evaluation and analysis of reservoir prediction results: The data of regional geological understanding and drilling test results related to reservoir development are superimposed and evaluated to eliminate abnormal areas caused by systematic errors, so as to realize reservoir prediction in complex lithological backgrounds.

[0020] Furthermore, the identification markers for single-well sequence interface identification and sequence correlation in S2 include, but are not limited to, markers of lithology, electrical properties, paleontology, and geochemical indicators.

[0021] Furthermore, the effective signal range parameters in S3 include: main frequency and bandwidth.

[0022] Furthermore, in S4, frequency division techniques based on fast matching pursuit, wavelet transform, and S-transform are used for testing, and the processing method with the best profile effect and layer sequence characterization capability is selected to perform frequency division processing on the original amplitude data volume.

[0023] Furthermore, in S5, for sequence interfaces with clear reflection features, clear interfaces, and strong traceability, seed point technology is used to assist in interpretation and tracing to reduce the interpretation workload; for sequence interfaces with relatively blurry reflection interfaces and poor traceability, manual interpretation is the main method, supplemented by auxiliary tracing and solution techniques.

[0024] Furthermore, in S6, within the well-seismic sequence framework, the impedance characteristics of special lithologies should be given special attention, especially lithologies that are easily confused with reservoir sections; for these special lithologies, a comprehensive analysis should be conducted in conjunction with downhole geological information and seismic data to ensure accurate identification of reservoir sections and avoid confusion.

[0025] Furthermore, in S7, based on the analysis of sequence stratigraphy, reservoir, and distribution of special lithological bodies, the thickness, sonic transit time, and density of the theoretical model unit are obtained; the parameters are verified and corrected using seismic data and downhole geological data to ensure that the obtained theoretical model unit parameters are accurate and reliable.

[0026] Furthermore, in S8, the theoretical wavelet in the seismic forward modeling should be selected to have the same dominant frequency and bandwidth as the actual seismic data; by picking up and implementing the well-side wavelet, it is ensured that the selected theoretical wavelet conforms to the characteristics of the actual seismic data; the seismic forward modeling excitation method is selected by ray tracing to achieve reasonable seismic wave excitation and propagation.

[0027] Furthermore, in S9, according to sedimentological principles, reservoir development and the depositional distribution of special lithologies are controlled by related sequence stratigraphy. Therefore, it is necessary to conduct correlation analysis between sequence stratigraphy, reservoirs, and special lithologies. Through the correlation analysis between sequence stratigraphy, reservoirs, and special lithologies, the development probability of reservoirs and characteristic lithologies in a certain area is indicated, serving as a priori conditions for reservoir prediction, thereby improving the accuracy of reservoir prediction. When conducting the analysis, it is important to note that the analysis scope of isochronous stratigraphic thickness should not only include the sequence stratigraphy but also consider the systems tracts that constitute the sequence stratigraphy to ensure the comprehensiveness and accuracy of the analysis.

[0028] Furthermore, in S10, the reservoir prediction results are corrected based on the actual drilling conditions to ensure that the prediction results are consistent with the actual situation; the prediction results are verified and confirmed by combining the data evaluation results in S3, regional geological understanding, and drilling results data, and abnormal areas that may be caused by abnormal data are eliminated; by eliminating abnormal areas, the accuracy and reliability of the prediction results are improved, ensuring the accuracy and effectiveness of subsequent work.

[0029] This invention also discloses a carbonate reservoir prediction system based on seismic sedimentology. This system can be used to implement the aforementioned carbonate reservoir prediction method based on seismic sedimentology, specifically including:

[0030] Basic data collection module: used to collect and input regional geological survey data, seismic data, well logging curves, drilling information and stratigraphic data of the study area.

[0031] Sequence boundary identification and segmentation module: Identifies and segments the sequence boundary of the target layer by analyzing seismic data and well logging curves.

[0032] Target Layer Amplitude Data Evaluation and Spectrum Analysis Module: Evaluates the seismic amplitude data of the target layer and performs spectrum analysis to determine the effective signal range.

[0033] Data frequency division processing module: Performs frequency division processing on seismic data within the effective signal range, acquires seismic data of multiple frequency bands, and produces frequency-divided single-well composite records.

[0034] Sequence boundary tracking and interpretation module: Performs high-precision seismic tracking and interpretation of sequence boundaries to determine the range of sequence plane distribution.

[0035] Reservoir and Special Lithology Distribution Pattern Analysis Module: Analyzes the distribution pattern of reservoirs within the sequence framework and the distribution of special lithologies.

[0036] Establish a seismic forward modeling geological model module: Establish seismic forward modeling geological models for sequence stratigraphy, internal reservoirs, and special lithologies.

[0037] Seismic forward modeling module: Select an appropriate forward modeling method to perform seismic forward modeling to confirm the seismic response characteristics of reservoirs and special lithologies.

[0038] Reservoir prediction module: Select appropriate seismic reservoir prediction methods or technologies for reservoirs at different sequence locations to perform reservoir prediction work.

[0039] Reservoir prediction results evaluation and analysis module: Evaluates reservoir prediction results and eliminates systematic errors that cause abnormal areas.

[0040] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for predicting carbonate reservoirs based on seismic sedimentology.

[0041] Compared with the prior art, the advantages of the present invention are as follows:

[0042] 1. More accurate prediction: By analyzing seismic data and well logging curves, and combining the principles of seismic sedimentology, reservoir distribution can be identified and predicted more accurately.

[0043] 2. Multi-module integrated analysis: including sequence boundary identification and division, spectrum analysis, reservoir distribution pattern analysis, etc., can comprehensively analyze geological information from different aspects, and improve the accuracy and comprehensiveness of prediction.

[0044] 3. Establishment of seismic forward modeling geological model: By establishing a seismic forward modeling geological model, the seismic response characteristics of reservoirs and special lithologies can be better simulated, which helps to accurately predict the location and properties of reservoirs.

[0045] 4. Improve the reliability of reservoir prediction: By combining information from seismic data, well logging information, geological principles and other aspects, reservoir prediction becomes more based on evidence and more reliable.

[0046] 5. Optimize drilling decisions: Accurate reservoir prediction helps optimize drilling plans, reduce drilling risks, and improve the efficiency and economic benefits of exploration and development.

[0047] 6. Reservoir prediction based on post-stack migration data requires less computation and is faster than pre-stack reservoir prediction methods, thus improving data processing efficiency.

[0048] 7. By adopting relevant theories and methods of seismic sedimentology, the reservoir was separated from the complex lithology through sequence stratigraphy, which effectively solved the problem of the difficulty in distinguishing between reservoirs and special lithologies, thereby improving the accuracy and precision of reservoir prediction. Attached Figure Description

[0049] Figure 1 This is a flowchart of the method of the present invention;

[0050] Figure 2 This is a target stratigraphic spectrum analysis diagram of the original post-stack data in an embodiment of the present invention;

[0051] Figure 3 This is the objective layer-by-layer interface identification and division marker in the embodiments of the present invention;

[0052] Figure 4 This is a frequency-division seismic data synthesis record and sequence boundary calibration diagram according to an embodiment of the present invention;

[0053] Figure 5 This is a histogram of impedance distribution between complex lithology (siliceous rock) and reservoir (limestone) in an embodiment of the present invention;

[0054] Figure 6 This is a well-connected comparative profile of sequence stratigraphy, reservoirs, and complex lithology (siliceous rocks) according to an embodiment of the present invention;

[0055] Figure 7 These are forward modeling geological models and seismic forward modeling gather maps according to embodiments of the present invention;

[0056] Figure 8This is a correlation analysis diagram of sequence system tract thickness (isochronous stratigraphic unit) and complex lithology and reservoir selected in an embodiment of the present invention.

[0057] Figure 9 This is a planar distribution map of the thickness (isochronous stratigraphic unit) of the sequence system tract selected in an embodiment of the present invention.

[0058] Figure 10 This is a planar distribution map of reservoirs in complex lithology (siliceous rock) development areas predicted based on sequence stratigraphy constraints in an embodiment of the present invention.

[0059] Figure 11 This is a planar distribution map of reservoirs in underdeveloped areas with complex lithology (siliceous rocks) predicted based on sequence stratigraphy constraints, according to an embodiment of the present invention.

[0060] Figure 12 This is the final reservoir prediction plan view completed in the embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and examples.

[0062] like Figure 1 As shown, a method for predicting carbonate reservoirs based on seismic sedimentology includes the following steps:

[0063] S1. Basic Data Collection: Collect data for the study area, including: regional geological survey data, post-stack migration seismic bodies, conventional logging curves, drilling and logging data, well deviation data, core data, stratigraphic data, seismic interpretation horizons, single-well test productivity, etc.

[0064] S2. Using data including conventional logging, imaging logging, core data, thin section data, seismic data, and geochemical indicators, the sequence boundaries of single wells in the embodiments are identified and divided, and sequence comparisons are carried out.

[0065] S3. Evaluation and spectrum analysis of the original post-stack amplitude data of the target layer, clarifying the effective signal range of the target layer data. The main frequency of the target layer data is 36Hz, and the effective bandwidth is 9-65Hz.

[0066] S4. Within the effective signal range of the original amplitude data volume, the data volume is frequency-divided to obtain seismic data volumes of multiple frequency bands. In this example, using a wavelet transform-based frequency division technique with a starting frequency of 15Hz, an ending frequency of 45Hz, and a step size of 5Hz, seven narrow frequency bands with dominant frequencies of 15Hz, 20Hz, 25Hz, 30Hz, 35Hz, 40Hz, and 45Hz are obtained. After creating frequency-divided single-well synthetic records, the correlation coefficient between the narrow frequency band data with the dominant frequency of 20Hz and the synthetic trace is 0.79, which is the highest among the seven frequency-divided data. This data is preferred as the main research data for sequence identification and characterization. After calibration, it was found that the three sequence interfaces SB1, SB2, and SB3 from bottom to top in the area all show wave crest phases and strong lateral traceability and comparability.

[0067] S5. Conduct high-precision seismic tracking and interpretation of sequence interfaces. In this example, SB1 and SB3 have stable phases and strong lateral traceability and comparability. This interface is interpreted using seed point tracking technology, with manual intervention for quality control. The lateral traceability of SB2 is weaker, so manual interpretation is the primary method. The planar distribution of the SQ1 and SQ2 sequences is obtained through fine-tuned variable-speed mapping and other work.

[0068] Analysis of reservoir distribution and special lithological patterns within the well-seismic sequence framework (S6) reveals that in the example, areas with thicker stratigraphic layers in the SQ1 highstand zone (HST) show better reservoir development, while areas with thinner stratigraphic layers show poorer reservoir development, and siliceous rock deposits are thicker. According to sedimentological principles, carbonate hill-shoal facies deposition exhibits significant geomorphic inheritance. Therefore, areas with thicker early hill-shoal deposits (i.e., thicker stratigraphic layers) have a significant constructive effect on subsequent hill-shoal formations, representing geomorphic highlands. Conversely, areas with thinner sediments, representing relatively lower energy levels, are more prone to the deposition of silica-rich fluids, resulting in siliceous rocks with impedance characteristics and reservoir mixtures as seen in the example. Therefore, the stratigraphic thickness in the SQ1 highstand zone of the example can qualitatively constrain the siliceous rock development area and reservoir development area.

[0069] S7. Based on the actual drilling conditions, extract key parameters such as the thickness, velocity and density of theoretical sequence stratigraphic units, reservoirs and special lithologies, and establish a seismic forward modeling geological model.

[0070] S8. Based on actual seismic data, select the optimal forward modeling method, conduct seismic forward modeling, and determine the seismic response characteristics of reservoirs and special lithologies. In the example, it can be found that when the top reservoir of the SQ2 sequence is developed in the study area, it is characterized by a weakening of the top amplitude. The reservoirs in the middle of the SQ2 sequence associated with siliceous rocks are characterized by weak peak amplitude. However, the peak reflection in the siliceous rock development area is stronger than that in the reservoir development area.

[0071] S9. Based on S5 to S8, select the best seismic reservoir prediction methods or technologies for reservoirs at different sequence locations and carry out reservoir prediction work.

[0072] S91. Select sequence stratigraphic units related to reservoir and special lithology distribution, and use the characteristics of the stratigraphic unit as a priori conditions for subsequent reservoir prediction. In the example, in particular, complex lithology (siliceous rock) is mixed with reservoirs in the middle of the SQ2 sequence. When the strata in the upper domain of the SQ1 sequence are thin, their peak reflection is a siliceous rock response, while in the area where the upper domain of the SQ1 sequence is thick, its peak reflection is a reservoir response.

[0073] S92. Based on the results obtained in S91, select qualitative or quantitative reservoir prediction methods to predict reservoir development zones. In this example, with the SQ1-HST thickness as a constraint, the amplitude attribute of the bottom peak of the SQ2-HST is used to characterize the reservoir in the middle of the SQ2 sequence. In areas where the SQ1-HST thickness is thinner, the area with a weaker peak amplitude is a more developed reservoir region; in areas where the SQ1-HST thickness is thicker, the area with a stronger peak amplitude is a more developed reservoir region. Based on this, the two sets of reservoir predicted thickness maps are merged into a single map.

[0074] S10. Evaluation and analysis of reservoir prediction results: This involves overlaying and evaluating relevant data such as regional geological understanding and drilling test results related to reservoir development, removing outliers caused by data limitations, and finally generating a map. In this example, the reservoir prediction results generally show a high degree of agreement with regional drilling conditions and previous results.

[0075] like Figure 2 As shown, clearly defining the effective signal range of the original post-stack seismic data is a fundamental condition for determining the frequency division range.

[0076] like Figure 3 As shown, the identification and division marks of the hierarchical interface are clearly defined, and a horizontal comparative analysis of the hierarchical sequence is carried out.

[0077] like Figure 4 As shown, by combining frequency division synthesis records, the sequence interface is accurately calibrated, and the planar distribution range and distribution pattern of each sequence and each system domain are determined.

[0078] like Figure 5 As shown in the histogram of impedance distribution of complex lithology (siliceous rock) and reservoir (limestone), it is clear that subsequent sequence stratigraphy analysis and lithological distribution should focus on the distribution relationship between siliceous rock and reservoir.

[0079] like Figure 6 As shown, based on the well profile, the distribution patterns of various geological units such as sequence stratigraphy, reservoirs, and complex lithology (siliceous rocks) are clarified, as well as key parameters such as the thickness and velocity of the forward geological model.

[0080] like Figure 7As shown, forward modeling provides a theoretical basis for sequence stratigraphy identification, tracking, and eliminating the influence of complex lithology (siliceous rocks).

[0081] like Figure 8 As shown, based on the correlation analysis between sequence stratigraphy and complex lithology and reservoirs, sequence stratigraphic units constrained by reservoir prediction are selected.

[0082] like Figure 9 As shown, the sequence stratigraphic unit planar distribution map constrained by reservoir prediction is used to delineate the dominant distribution areas of complex lithology (siliceous rocks).

[0083] like Figure 10 As shown, based on sequence stratigraphy constraints, the reservoir development in complex lithology (siliceous rock) development zones is predicted.

[0084] like Figure 11 As shown, based on sequence stratigraphy constraints, the reservoir development in underdeveloped areas with complex lithology (siliceous rocks) is predicted.

[0085] like Figure 12 As shown, the reservoir prediction plan map is the final result of outlier removal, fusion, correction, and comprehensive evaluation of the reservoir prediction plan maps in different zones.

[0086] In another embodiment of the present invention, a seismic sedimentology-based carbonate reservoir prediction system is provided. This system can be used to implement the above-described seismic sedimentology-based carbonate reservoir prediction method, specifically including:

[0087] Basic data collection module: used to collect and input regional geological survey data, seismic data, well logging curves, drilling information and stratigraphic data of the study area.

[0088] Sequence boundary identification and segmentation module: Identifies and segments the sequence boundary of the target layer by analyzing seismic data and well logging curves.

[0089] Target Layer Amplitude Data Evaluation and Spectrum Analysis Module: Evaluates the seismic amplitude data of the target layer and performs spectrum analysis to determine the effective signal range.

[0090] Data frequency division processing module: Performs frequency division processing on seismic data within the effective signal range, acquires seismic data of multiple frequency bands, and produces frequency-divided single-well composite records.

[0091] Sequence boundary tracking and interpretation module: Performs high-precision seismic tracking and interpretation of sequence boundaries to determine the range of sequence plane distribution.

[0092] Reservoir and Special Lithology Distribution Pattern Analysis Module: Analyzes the distribution pattern of reservoirs within the sequence framework and the distribution of special lithologies.

[0093] Establish a seismic forward modeling geological model module: Establish seismic forward modeling geological models for sequence stratigraphy, internal reservoirs, and special lithologies.

[0094] Seismic forward modeling module: Select an appropriate forward modeling method to perform seismic forward modeling to confirm the seismic response characteristics of reservoirs and special lithologies.

[0095] Reservoir prediction module: Select appropriate seismic reservoir prediction methods or technologies for reservoirs at different sequence locations to perform reservoir prediction work.

[0096] Reservoir prediction results evaluation and analysis module: Evaluates reservoir prediction results and eliminates systematic errors that cause abnormal areas.

[0097] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0098] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the carbonate reservoir prediction method based on seismic sedimentology in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps:

[0099] S1. Collect regional geological survey data, seismic data, well logging curves, drilling information, and stratigraphic data for the study area.

[0100] S2. By analyzing seismic data and well logging curves, identify and delineate the single-well sequence boundaries of the target layer, and perform sequence comparison.

[0101] S3. Evaluate the seismic amplitude data of the target layer and perform spectral analysis to determine the effective signal range;

[0102] S4. Within the effective signal range, the seismic data is processed by frequency division to obtain seismic data in multiple frequency bands and to produce frequency-divided single-well synthetic records. The frequency-divided data volume with the highest correlation coefficient is selected, and the sequence interface tracing and interpretation scheme is clarified.

[0103] S5. Perform high-precision sequence boundary seismic tracking interpretation to determine the range of sequence plane distribution;

[0104] S6. Analyze the distribution pattern of reservoirs within the well-seismic sequence framework and the distribution of special lithologies;

[0105] S7. Establish seismic forward modeling geological models for sequence stratigraphy, internal reservoirs, and special lithologies;

[0106] S8. Select an appropriate forward modeling method to perform seismic forward modeling to confirm the seismic response characteristics of reservoirs and special lithologies;

[0107] S9. Based on S5 to S8, select appropriate seismic reservoir prediction methods or technologies for reservoirs at different sequence locations and carry out reservoir prediction work.

[0108] S91. Select sequence stratigraphic units that are related to the distribution of reservoirs and special lithologies, and use the characteristics of these stratigraphic units as a priori conditions for subsequent reservoir prediction.

[0109] S92. Based on the results of S92, select qualitative or quantitative reservoir prediction methods to predict reservoir development zones.

[0110] S10. Evaluation and analysis of reservoir prediction results: The data of regional geological understanding and drilling test results related to reservoir development are superimposed and evaluated to eliminate abnormal areas caused by systematic errors, so as to realize reservoir prediction in complex lithological backgrounds.

[0111] 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, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] 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.

[0113] 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 1 The function specified in one or more boxes.

[0114] 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.

[0115] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the implementation methods of the present invention, and should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of the present invention.

Claims

1. A method for predicting carbonate reservoirs based on seismic sedimentology, characterized in that, Includes the following steps: S1. Collect regional geological survey data, seismic data, well logging curves, drilling information, and stratigraphic data for the study area; S2. By analyzing seismic data and well logging curves, identify and delineate the single-well sequence boundaries of the target layer, and perform sequence comparison. S3. Evaluate the seismic amplitude data of the target layer and perform spectral analysis to determine the effective signal range; S4. Within the effective signal range, the seismic data is processed by frequency division to obtain seismic data in multiple frequency bands and to produce frequency-divided single-well synthetic records. The frequency-divided data volume with the highest correlation coefficient is selected, and the sequence interface tracing and interpretation scheme is clarified. S5. Perform high-precision sequence boundary seismic tracking interpretation to determine the range of sequence plane distribution; S6. Analyze the distribution pattern of reservoirs within the well-seismic sequence framework and the distribution of special lithologies; S7. Establish seismic forward modeling geological models for sequence stratigraphy, internal reservoirs, and special lithologies; S8. Select an appropriate forward modeling method to perform seismic forward modeling to confirm the seismic response characteristics of reservoirs and special lithologies; S9. Based on S5 to S8, select appropriate seismic reservoir prediction methods or technologies for reservoirs at different sequence locations and carry out reservoir prediction work. S91. Select sequence stratigraphic units that are related to the distribution of reservoirs and special lithologies, and use the characteristics of these stratigraphic units as a priori conditions for subsequent reservoir prediction. S92. Based on the results of S91, select qualitative or quantitative reservoir prediction methods to predict reservoir development zones. S10. Evaluation and analysis of reservoir prediction results: The data of regional geological understanding and drilling test results related to reservoir development are superimposed and evaluated to eliminate abnormal areas caused by systematic errors, so as to realize reservoir prediction in complex lithological backgrounds.

2. The method for predicting carbonate reservoirs based on seismic sedimentology according to claim 1, characterized in that: The identification markers for single-well sequence interface identification and sequence correlation in S2 include, but are not limited to, markers based on lithology, electrical properties, paleontology, and geochemical indicators.

3. The method for predicting carbonate reservoirs based on seismic sedimentology according to claim 1, characterized in that: The effective signal range parameters in S3 include: main frequency and bandwidth.

4. The method for predicting carbonate reservoirs based on seismic sedimentology according to claim 1, characterized in that: In S4, frequency division techniques based on fast matching pursuit, wavelet transform, and S-transform are used for testing. The processing method with the best profile effect and layer sequence characterization capability is selected to perform frequency division processing on the original amplitude data volume.

5. The method for predicting carbonate reservoirs based on seismic sedimentology according to claim 1, characterized in that: In step S5, for sequence interfaces with clear reflection features, clear interfaces, and strong traceability, seed point technology is used to assist in interpretation and tracing; for sequence interfaces with relatively blurry reflection interfaces and poor traceability, manual interpretation is the primary method, supplemented by auxiliary tracing and solution techniques.

6. The method for predicting carbonate reservoirs based on seismic sedimentology according to claim 1, characterized in that: In S6, within the well-seismic sequence framework, the impedance characteristics of special lithologies should be given special attention, especially lithologies that are easily confused with reservoir sections. For these special lithologies, a comprehensive analysis should be conducted in conjunction with downhole geological information and seismic data to ensure accurate identification of reservoir sections and avoid confusion.

7. The method for predicting carbonate reservoirs based on seismic sedimentology according to claim 1, characterized in that: In S7, based on the analysis of sequence stratigraphy, reservoir, and distribution of special lithological bodies, the thickness, sonic transit time, and density of the theoretical model unit are obtained; and the model is verified and corrected using seismic data and downhole geological data.

8. The method for predicting carbonate reservoirs based on seismic sedimentology according to claim 1, characterized in that: In S8, the theoretical wavelet in seismic forward modeling should be selected to have the same dominant frequency and bandwidth as the actual seismic data; this is achieved by picking up wellside wavelets; and the ray tracing method is selected as the excitation mode for seismic forward modeling.

9. The method for predicting carbonate reservoirs based on seismic sedimentology according to claim 1, characterized in that: In S10, the reservoir prediction results are corrected based on the actual drilling conditions to ensure that the prediction results are consistent with the actual situation; the prediction results are verified and confirmed by combining the data evaluation results in S3, regional geological understanding and drilling results data, and abnormal areas that may be caused by abnormal data are eliminated.

10. A carbonate reservoir prediction system based on seismic sedimentology, characterized in that: This system can be used to implement the seismic sedimentology-based carbonate reservoir prediction method according to any one of claims 1 to 9, specifically including: Basic data collection module: used to collect and input regional geological survey data, seismic data, well logging curves, drilling information and stratigraphic data for the study area; Sequence boundary identification and segmentation module: Identifies and segments the sequence boundary of the target layer by analyzing seismic data and well logging curves; Target Layer Amplitude Data Evaluation and Spectrum Analysis Module: Evaluates the seismic amplitude data of the target layer and performs spectrum analysis to determine the effective signal range; Data frequency division processing module: performs frequency division processing on seismic data within the effective signal range, acquires seismic data of multiple frequency bands, and produces frequency-divided single-well synthetic records; Sequence boundary tracking and interpretation module: performs high-precision seismic tracking and interpretation of sequence boundaries to determine the range of sequence plane distribution; Reservoir and Special Lithology Distribution Pattern Analysis Module: Analyzes the distribution pattern of reservoirs within the sequence framework and the distribution of special lithologies; Establish a seismic forward modeling geological model module: Establish seismic forward modeling geological models for sequence stratigraphy, internal reservoirs, and special lithologies; Seismic forward modeling module: Select an appropriate forward modeling method to perform seismic forward modeling to confirm the seismic response characteristics of reservoirs and special lithologies; Reservoir prediction module: Select appropriate seismic reservoir prediction methods or technologies for reservoirs at different sequence locations to perform reservoir prediction work; Reservoir prediction results evaluation and analysis module: Evaluates reservoir prediction results and eliminates systematic errors that cause abnormal areas.

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