A method and system for identifying low-speed mudstone based on attribute clustering

Abstract

The present invention discloses a method and system for identifying low-velocity mudstone based on attribute clustering. It includes: by analyzing the logging data and seismic data of exploration wells in the work area, dividing the low-velocity mudstone into bright spot type and non-bright spot type, and analyzing their characteristics. Establishing a three-layer medium model and conducting seismic forward modeling to obtain the seismic response characteristics of different models. Then, extracting and optimizing seismic attributes from the forward modeling results. Using the CLARA clustering algorithm and the PAM method to conduct clustering analysis on the attributes to identify the types of low-velocity mudstone and gas-bearing sandstone. The present invention improves the identification accuracy of low-velocity mudstone and gas-bearing sandstone, and provides technical support for exploration and well location deployment.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing and oil and gas exploration, and in particular to a low-velocity mudstone identification method and system based on attribute clustering. Background Art

[0002] With the continuous advancement of oil and gas exploration in recent years, bright spot recognition technology has become an important means of identifying gas-bearing reservoirs. However, while this technology has achieved remarkable success in shallow reservoirs, it faces numerous challenges in identifying medium- and deep-level reservoirs. In particular, bright spot recognition errors have repeatedly occurred in exploration practice, resulting in reduced exploration accuracy and hindering the effective identification and development of reservoirs.

[0003] In-depth analysis revealed that one of the main reasons for these misidentifications is the widespread development of low-velocity mudstones. Low-velocity mudstones are a unique formation that typically forms beneath background mudstones. They have even lower velocities and appear as "bright spots" on seismic profiles, similar to those of gas reservoirs. These "bright spots" can easily be confused with actual gas-bearing sandstones, creating so-called low-velocity mudstone "bright spot traps," which significantly interfere with the correct identification of gas reservoirs.

[0004] The presence of low-velocity mudstone not only renders traditional bright spot identification techniques ineffective in medium- to deep-layer reservoirs, but can also lead to erroneous reservoir predictions during exploration, resulting in unnecessary drilling risks and economic losses. Therefore, accurately distinguishing low-velocity mudstone from gas-bearing reservoirs has become a pressing technical challenge in the exploration field.

[0005] References

[0006] [1] Zhang Mingwei, Gao Ling, Tan Jiancai, Pan Guangchao, Li Wentuo. Application of low-speed mudstone identification technology in regional exploration of Yinggehai Basin [J]. China Mining, 2018, 27(11): 145-150;

[0007] [2] Zhang Weiwei, He Min, Zhu Ming, et al. Application of AVO technology to identify non-bright spot gas reservoirs in deepwater areas, Petroleum Geophysical Exploration, 2015, 50(1): 123-128;

[0008] [3] Pei Jianxiang, Pan Guangchao, Zhu Peiyuan, Liu Feng. Identification of low-velocity mudstone traps in the overpressure zone of Yinggehai Basin and its implications for exploration. Petroleum Geophysical Exploration, 2016, 51(2): 361-370.

[0009] [4] Li Fang, Deng Yong, Liu Shiyou, et al. Effect of undercompacted low-velocity mudstone on seismic reflection and AVO [J]. Geological Science and Technology Information, 2017, 36(5): 244-248. Summary of the Invention

[0010] In view of the deficiencies of the prior art, the present invention provides a method and system for identifying low-velocity mudstone based on attribute clustering. By comprehensively applying seismic data analysis, geological modeling, and machine learning techniques, through in-depth analysis of seismic response characteristics and attribute optimization, effective discrimination between low-velocity mudstone and gas-bearing sandstone is achieved. This intelligent prediction scheme can significantly improve the reservoir identification accuracy in areas with developed low-velocity mudstone, reduce the interference of bright spot traps, and thus provide more reliable technical support for exploration decision-making.

[0011] To achieve the above invention objectives, the technical solutions adopted by the present invention are as follows:

[0012] A method for identifying low-velocity mudstone based on attribute clustering, comprising the following steps:

[0013] Step 1: Research on low-velocity mudstone patterns:

[0014] Based on the logging data and seismic data of exploration wells in the work area, conduct research and analysis on the development characteristics of low-velocity mudstone.

[0015] Classify low-velocity mudstone into bright spot type low-velocity mudstone and non-bright spot type low-velocity mudstone, and analyze its performance in aspects including logging curves, cross plots, seismic profiles, and AVO characteristics to obtain well analysis results.

[0016] Step 2: Model construction and seismic forward modeling:

[0017] Based on the well analysis results, establish a three-layer horizontal layered medium model.

[0018] Construct Model 1, which includes thick-layer low-velocity mudstone plus thin-layer gas-bearing sandstone, and the seismic forward modeling response is low-frequency medium amplitude.

[0019] Construct Model 2, which includes thin-layer low-velocity mudstone plus thin-layer gas-bearing sandstone, and the seismic forward modeling response is low-frequency high amplitude.

[0020] Construct Model 3 and Model 4, which are bright spot type low-velocity mudstone, and the seismic forward modeling response is low-frequency high amplitude.

[0021] Construct Model 5, which is a non-bright spot type low-velocity mudstone model, and the seismic forward modeling response is low-frequency low amplitude.

[0022] Step 3: Seismic attribute extraction and optimization:

[0023] Extract various seismic attributes, including amplitude type, instantaneous type, and spectral type.

[0024] Eliminate insensitive attributes and retain the optimized seismic attributes for subsequent analysis.

[0025] Perform standardization processing on the optimized seismic attributes and calculate the correlation coefficient.

[0026] According to the correlation coefficient values, redundant attributes are removed, and attributes with relatively low correlation are retained for clustering analysis.

[0027] Step 4: Attribute clustering analysis:

[0028] Use the CLARA clustering algorithm to perform attribute clustering analysis on the selected seismic attributes.

[0029] Adopt the PAM method to find K optimal central points within the selected sample set, and apply these central points to the entire database for attribute clustering.

[0030] Through multiple loops, calculate and optimize the total clustering cost, and finally determine the optimal set of representative objects.

[0031] Step 5: Classification of identification results:

[0032] Based on the attribute clustering results, divide the identification results into five categories: normally compacted mudstone, bright spot type low-velocity mudstone, non-bright spot type low-velocity mudstone, and two types of gas-bearing sandstones with different seismic responses.

[0033] Compare and analyze the attribute clustering results with the well data to confirm the identification accuracy of low-velocity mudstone and gas-bearing sandstone.

[0034] Finally, based on the clustering results, identify and distinguish the reservoir and low-velocity mudstone to guide exploration and well location deployment.

[0035] Furthermore, the sub-steps of Step 3 are as follows:

[0036] Step 3.1: Extract seismic attributes;

[0037] Collect forward modeling result data: Obtain the seismic data obtained through seismic forward modeling simulation.

[0038] Select attribute types, including: amplitude attributes, instantaneous attributes, and spectral attributes;

[0039] Amplitude attributes include: peak amplitude, root mean square amplitude, amplitude ratio.

[0040] Instantaneous attributes include: instantaneous phase, instantaneous frequency, instantaneous amplitude.

[0041] Spectral attributes include: power spectral density, frequency peak, frequency band width.

[0042] Calculate seismic attributes, calculate for each attribute. Extract the specific values of the selected amplitude attributes, instantaneous attributes, and spectral attributes from the forward modeling data.

[0043] Step 3.2: Attribute sensitivity analysis;

[0044] Screen sensitive attributes: Analyze the seismic response characteristics of low-velocity mudstone and gas-bearing sandstone to screen out sensitive attributes. Sensitive attributes can effectively distinguish different types of reservoirs.

[0045] Eliminate attributes that have no significant contribution to target recognition and retain attributes that can identify low-velocity mudstone and gas-bearing sandstone.

[0046] Retain preferred attributes: From the originally extracted attributes, retain the seismic attributes useful for distinguishing low-velocity mudstone and gas-bearing sandstone.

[0047] Step 3.3: Standardize the retained seismic attributes so that they have the same scale and mean.

[0048] Step 3.4: Calculate the correlation coefficients between all seismic attributes. The correlation coefficient is used to measure the linear relationship between attributes and ranges from -1 to 1.

[0049] Perform statistical analysis on the calculated correlation coefficients to generate a correlation coefficient matrix.

[0050] Identify attribute pairs with a correlation coefficient value higher than 0.8 and consider them to have a strong correlation, and consider removing redundant attributes.

[0051] Identify attribute pairs with a correlation coefficient value lower than 0.2 and consider their correlation to be very weak and not suitable for attribute clustering.

[0052] Step 3.5: According to the results of the correlation coefficient analysis, select attributes with a correlation coefficient between 0.25 and 0.75 for subsequent attribute clustering analysis.

[0053] Organize the screened seismic attributes into a data format suitable for clustering analysis, ensuring that the data set is complete and meets the requirements of clustering analysis.

[0054] Furthermore, the sub-steps of Step 4 are as follows:

[0055] Step 4.1: Select the seismic attributes optimized in Step 3 as the analysis object and organize the data to ensure that the format and structure meet the requirements of the CLARA clustering algorithm.

[0056] Step 4.2: Set the parameters of the CLARA clustering algorithm, including the sample size and the number of sampling times, and randomly extract a subsample set from the entire database. The sample size is determined by the parameter settings.

[0057] Step 4.3: Initialization: Use the PAM method to select K initial center points in the selected sample set; Assignment step: Assign each data point in the sample set to the nearest center point to form K clusters; Update: Calculate the total cost of the points within each cluster, find and update the center point of each cluster to reduce the total cost within the cluster; Iteration: Repeat the assignment and update steps until the center point no longer changes significantly or the upper limit of the number of iterations is reached.

[0058] Step 4.4: Expand the center points and apply the K center points obtained by the PAM method to the entire database. Each data point is assigned to the nearest center point to form the final K clusters.

[0059] Step 4.5: Calculate the final clustering cost, including the total distance of points within the cluster and the total distance of the center point; adjust the K value or other parameters and re-execute the CLARA algorithm to optimize the clustering results; use clustering evaluation indicators (such as silhouette coefficient, clustering effectiveness index, etc.) to evaluate the quality of the clustering results, compare the consistency of the clustering results with the actual data, and verify the effectiveness of the clustering.

[0060] Step 4.6: Output the results, record the final attribute clustering results, including the center point of each cluster and the data points within the cluster; generate a report, prepare an attribute clustering analysis report, and include a detailed description and analysis of the clustering results.

[0061] The present invention also discloses a low-velocity mudstone identification system based on attribute clustering, which can be used to implement the above-mentioned low-velocity mudstone identification method based on attribute clustering. The system includes:

[0062] Data acquisition module: responsible for collecting and organizing logging data and seismic data from exploration wells, including data acquisition instruments, data storage devices and data transmission interfaces.

[0063] Data preprocessing module: cleans, denoises and converts the collected data, including data cleaning algorithms, denoising filters and format conversion tools.

[0064] Low-velocity mudstone pattern research module: Analyzes the pattern of low-velocity mudstone based on exploration well data, including low-velocity mudstone classification analysis, feature extraction and pattern research tools. It divides low-velocity mudstone into bright spot type and non-bright spot type, and analyzes its performance in well logging curves, crossplots, seismic profiles and AVO characteristics.

[0065] Model building and seismic forward modeling module: Based on the well analysis results, a three-layer horizontal layered medium model is established and seismic forward simulation is performed. It includes model building tools, forward simulation software and model verification functions, and constructs multiple models (such as Model 1 to Model 5) to simulate different seismic response characteristics.

[0066] Seismic attribute extraction module: Extract various seismic attributes from the forward modeling results, including amplitude-based, instantaneous-based, and spectral-based attributes, and perform attribute extraction, screening, and analysis.

[0067] Attribute optimization module: Perform standardization processing and correlation coefficient statistics on the extracted seismic attributes, eliminate insensitive attributes, and retain the optimized attributes for subsequent attribute clustering analysis, including standardization tools, correlation coefficient calculation tools, and redundant attribute elimination functions.

[0068] Attribute clustering analysis module: Use the CLARA clustering algorithm and the PAM method to perform attribute clustering analysis on the optimized seismic attributes, including the CLARA clustering algorithm tool, the PAM method implementation module, and the clustering result optimization function.

[0069] Recognition result classification module: Classify and analyze the attribute clustering results, including recognition result classification tools, data comparison and analysis functions, and result verification modules, and identify and distinguish reservoirs and low-velocity mudstones based on the attribute clustering results.

[0070] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned method for identifying low-velocity mudstones based on attribute clustering is implemented.

[0071] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-mentioned method for identifying low-velocity mudstones based on attribute clustering is implemented.

[0072] Compared with the prior art, the advantages of the present invention are as follows:

[0073] 1. Improve recognition accuracy: By using the method based on attribute clustering, the present invention can effectively distinguish between bright-spot and non-bright-spot low-velocity mudstones, thereby improving the recognition accuracy of low-velocity mudstones and reducing recognition errors.

[0074] 2. Optimize attribute selection: Through the extraction and optimization of seismic attributes, the present invention eliminates insensitive or redundant attributes and retains the attributes most valuable for the recognition of low-velocity mudstones, thereby improving the effect and efficiency of clustering analysis.

[0075] 3. Enhance model adaptability: By performing forward modeling using different seismic models (such as gas-bearing sandstone and low-velocity mudstone models), the present invention can adapt to the recognition requirements of low-velocity mudstones under various geological conditions and has strong adaptability and generality.

[0076] 4. Reduce manual intervention: Through automated data processing, model construction, attribute extraction, and clustering analysis, the present invention significantly reduces the need for manual intervention and improves work efficiency and consistency.

[0077] 5. Improve exploration accuracy: This invention provides reliable technical support for exploration and development by accurately identifying reservoirs and low-velocity mudstones, helping to reduce exploration risks and improve exploration success rates.

[0078] 6. Support decision optimization: The identification results generated by the present invention can provide a scientific basis for oil and gas field development decisions, optimize well location deployment and reservoir development strategies, and improve resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a flow chart of a low-velocity mudstone identification method based on attribute clustering according to an embodiment of the present invention. DETAILED DESCRIPTION

[0080] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0081] like Figure 1 As shown, the present invention provides a low-velocity mudstone identification method based on attribute clustering, comprising the following steps:

[0082] Step 1: Conduct model research on low-velocity mudstone based on the exploration wells in the work area;

[0083] Zhang Mingwei et al. (2018) divided low-velocity mudstone into bright-spot type low-velocity mudstone and non-bright-spot type low-velocity mudstone. [1] , this embodiment will classify low-velocity mudstone on this basis. Well A develops bright-spot type low-velocity mudstone. On the crossplot and logging curves, it can be seen that the velocity at 4000m shows an obvious reversal. From 4000m down, low-velocity mudstone develops and appears as a bright spot on the seismic profile, affecting the identification of gas-bearing sandstone. On the AVO characteristics, it appears as Class I, which is confused with the AVO characteristics of gas-bearing sandstone. Therefore, the low-velocity mudstone cannot be fully identified using the AVO characteristics. Well B develops non-bright-spot type low-velocity mudstone. On the logging crossplot and logging curves, a combination pattern of low-velocity mudstone and gas-bearing sandstone can be obtained. Low-velocity mudstone develops directly on the sandstone layer in this well. On the pre-stack AVO angle gather and AVO characteristics, the sandstone layer (Class I-II) and low-velocity mudstone (Class IV) can be clearly divided. On the seismic profile, the sandstone layer appears as a bright spot. Well C develops non-bright spot low-velocity mudstone. A significant velocity reversal occurs at 3994 meters. A thin sandstone layer develops above 3987 meters, followed by a 50-meter-thick low-velocity mudstone layer below. The mudstone is underlying gas-bearing sandstone. This well exhibits low-frequency, weak-amplitude vibrations in both forward modeling and seismic profiles, and a Class IV AVO signature, distinguishing it from gas-bearing sandstone. Based on the analysis of these three wells, the bright spot low-velocity mudstone is a false "bright spot" appearing on seismic profiles; low-velocity mudstone developing above sandstone layers does not affect sandstone identification.

[0084] Step 2: Model construction and seismic forward modeling;

[0085] Based on comprehensive well analysis, a three-layer horizontal layered medium model was established and seismic forward simulation was carried out. Both Model 1 and Model 2 are gas-bearing sandstones. Among them, Model 1 is a thick low-velocity mudstone plus a thin gas-bearing sandstone layer, and the forward response is low-frequency medium amplitude; Model 2 is a thin low-velocity mudstone plus a thin gas-bearing sandstone layer, and the forward response is low-frequency high amplitude. Model 3 and Model 4 are bright spot type low-velocity mudstones, and their forward responses are both low-frequency high amplitude. Model 5 is a non-bright spot type low-velocity mudstone model, and the forward response is low-frequency low amplitude.

[0086] Step 3: Seismic attribute extraction and optimization;

[0087] Multiple seismic attributes were extracted according to the forward results, including amplitude type, instantaneous type, frequency spectrum type, etc. Insensitive seismic attributes were excluded, and the following 13 seismic attributes were used for subsequent attribute clustering analysis. The forward results show that both low-velocity mudstones and gas-bearing sandstones have abnormal amplitudes in seismic responses, but they can be distinguished in seismic attributes. Then, the 13 optimized seismic attributes were standardized and the correlation coefficients were statistically analyzed. The higher the correlation coefficient value in the statistical results, the better the correlation between the two attributes. According to the statistical results, it is considered that when the correlation coefficient is greater than 0.8, it indicates that one of the two attributes is a redundant attribute and can be removed; when the correlation coefficient is less than 0.2, it indicates that the two attributes have basically no correlation and are not suitable for carrying out attribute clustering. According to the statistical results, the correlation coefficients among the four seismic attributes of instantaneous true amplitude * instantaneous phase cosine, amplitude kurtosis, maximum peak amplitude, and average instantaneous phase are all between 0.25 and 0.75, and can be used for attribute clustering analysis.

[0088] Step 4: Attribute clustering and result analysis;

[0089] Since the development of seismic attribute technology to date, it has become quite mature. With the widespread application of neural networks in the field of oil and gas exploration, the technology of using unsupervised neural networks for seismic attribute clustering has emerged as the times require. This method uses the clustering method in large applications (Clustering Large Applications, CLARA). After optimizing seismic attributes, it uses attribute clustering to identify reservoirs and low-velocity mudstones. The CLARA clustering algorithm does not consider the entire data set, but selects a small part of the data as a sample, uses the partitioning around medoid (PAM) method for each sample set, and takes the best clustering as the output. The steps of the CLARA algorithm are as follows: ① Repeat steps ② to ④ in a loop with the step size being the number of sampling times; ② Randomly extract a sample of N objects from the entire database, and call the PAM method to find the K optimal central points of the sample from the sample; ③ Apply these K central points to the entire database. For each non-representative object X, determine which representative object selected from the sample it is closest to; ④ Calculate the total cost of the clustering obtained in the previous step. If this value is less than the current minimum value, replace the current minimum value with this value, and retain the K representative objects obtained in this sampling as the set of the best representative objects obtained so far; ⑤ Return to step ① to start the next loop. After the algorithm ends, the set of the best representative objects is obtained.

[0090] According to the wellhead analysis and attribute analysis results, the attribute clustering results are divided into 5 categories, namely normally compacted mudstone, bright spot type low-velocity mudstone, non-bright spot type low-velocity mudstone, and two types of gas-bearing sandstones with different seismic responses. Attribute clustering analysis was carried out at the bottom of the H layer. According to the well data, it can be seen that the three wells are all mudstones at the bottom of the H layer. Well B is a normally compacted mudstone, Well A is a bright spot type low-velocity mudstone, and Well C is a non-bright spot type low-velocity mudstone. In the clustering results, the point of Well B shows as a normally compacted mudstone; Well A shows as a bright spot type low-velocity mudstone; but Well C shows as a non-bright spot type low-velocity mudstone in the clustering results. The clustering results are consistent with the understanding at the wellhead.

[0091] Currently, the existing methods use AVO types and AVO anomalies to distinguish low-velocity mudstones and sandstone reservoirs [2-4] , this technology has relatively high requirements for pre-stack seismic data and requires high-quality pre-stack seismic data to achieve the extraction and analysis of AVO. Compared with AVO analysis technology, using post-stack seismic for attribute clustering can avoid the influence of pre-stack seismic data on AVO.

[0092] In another embodiment of the present invention, a low-velocity mudstone identification system based on attribute clustering is provided. This system can be used to implement the above-mentioned low-velocity mudstone identification method based on attribute clustering. Specifically, it includes:

[0093] Data acquisition module: Responsible for collecting and organizing well logging data and seismic data of exploration wells, including data acquisition instruments, data storage devices, and data transmission interfaces.

[0094] Data preprocessing module: Cleans, denoises, and converts the format of the collected data, including data cleaning algorithms, denoising filters, and format conversion tools.

[0095] Low-velocity mudstone pattern research module: Based on the analysis of exploration well data, studies the patterns of low-velocity mudstone, including low-velocity mudstone classification analysis, feature extraction, and pattern research tools. Classifies low-velocity mudstone into bright spot type and non-bright spot type, and analyzes its performance in well logging curves, cross plots, seismic profiles, and AVO characteristics.

[0096] Model construction and seismic forward modeling module: According to the well analysis results, establishes a three-layer horizontal layered medium model and conducts seismic forward modeling simulation, including model construction tools, forward modeling software, and model verification functions. Constructs multiple models (such as Model 1 to Model 5) to simulate different seismic response characteristics.

[0097] Seismic attribute extraction module: Extracts various seismic attributes from the forward modeling results, including amplitude type, instantaneous type, and spectral type, and conducts attribute extraction, screening, and analysis.

[0098] Attribute optimization module: Conducts standardization processing and correlation coefficient statistics on the extracted seismic attributes, eliminates insensitive attributes, and retains the optimized attributes for subsequent attribute clustering analysis, including standardization tools, correlation coefficient calculation tools, and redundant attribute elimination functions.

[0099] Attribute clustering analysis module: Uses the CLARA clustering algorithm and the PAM method to conduct attribute clustering analysis on the optimized seismic attributes, including the CLARA clustering algorithm tool, the PAM method implementation module, and the clustering result optimization function.

[0100] Identification result classification module: Classifies and analyzes the attribute clustering results, including identification result classification tools, data comparison and analysis functions, and result verification modules. Identifies and differentiates reservoirs and low-velocity mudstone based on the attribute clustering results.

[0101] In another embodiment of the present invention, a terminal device is provided. The terminal device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function. The processor described in the embodiments of the present invention can be used for the operations of the low-speed mudstone identification method based on attribute clustering in the above embodiments.

[0102] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is the memory device in the terminal device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And in this storage space, one or more instructions suitable for being loaded and executed by the processor are also stored. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0103] One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the low-speed mudstone identification method based on attribute clustering in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0104] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0105] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0106] These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0108] Those of ordinary skill in the art will realize that the embodiments described herein are for helping readers understand the implementation methods of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.

Claims

1. A low-velocity mudstone identification method based on attribute clustering, characterized in that: The following steps are involved: Step 1: Low-velocity mudstone model study: Based on the logging data and seismic data of the exploration wells in the work area, the development characteristics of low-velocity mudstone are studied and analyzed; The low-velocity mudstone is divided into bright-spot type low-velocity mudstone and non-bright-spot type low-velocity mudstone, and its performance in well logging curves, crossplots, seismic sections and AVO characteristics is analyzed to obtain well analysis results; Step 2: Model construction and earthquake forward modeling: Based on the well analysis results, a three-layer horizontal layered medium model was established; Model 1 was constructed, which included thick low-velocity mudstone and thin gas sandstone, and the seismic forward modeling response was low-frequency and medium-amplitude; Model 2 was constructed, which included a thin layer of low-velocity mudstone and a thin layer of gas-bearing sandstone. The seismic forward modeling response was low frequency and high amplitude. Models 3 and 4 were constructed, which are bright-spot type low-velocity mudstones, and the seismic forward modeling response is low frequency and high amplitude; Model 5 is constructed, which is a non-bright spot type low-velocity mudstone model, and the seismic forward modeling response is low frequency and low amplitude; Step 3: Seismic attribute extraction and optimization: Extract multiple seismic attributes, including amplitude, instantaneous, and spectrum; Eliminate insensitive attributes and retain preferred seismic attributes for subsequent analysis; The selected seismic attributes are standardized and the correlation coefficients are calculated; According to the correlation coefficient value, redundant attributes are eliminated and attributes with lower correlation are retained for cluster analysis; Step 4: Attribute cluster analysis: CLARA clustering algorithm is used to perform attribute clustering analysis on the selected seismic attributes; The PAM method is used to find the K best center points in the selected sample set, and these center points are applied to the entire database for attribute clustering; Through multiple cycles, the total clustering cost is calculated and optimized, and the best set of representative objects is finally determined; Step 5: Classification of recognition results: According to the attribute clustering results, the identification results are divided into five categories: normal compacted mudstone, bright spot type low-velocity mudstone, non-bright spot type low-velocity mudstone and two types of gas sandstone with different seismic responses; The attribute clustering results were compared with the well data to confirm the accuracy of identifying low-velocity mudstone and gas sandstone; Finally, based on the clustering results, reservoirs and low-velocity mudstones are identified and differentiated to guide exploration and well deployment.

2. The low-velocity mudstone identification method based on attribute clustering according to claim 1 is characterized in that: The sub-steps of step three are as follows: Step 3.1: Extract earthquake attributes; Collect forward modeling result data: obtain seismic data obtained through earthquake forward modeling; Select the attribute type, including amplitude attributes, instantaneous attributes and spectrum attributes; Amplitude attributes include: peak amplitude, root mean square amplitude, and amplitude ratio; Instantaneous attributes include: instantaneous phase, instantaneous frequency, instantaneous amplitude; Spectrum attributes include: power spectrum density, frequency peak, and bandwidth; Calculate seismic attributes and calculate each attribute; extract the specific values of the selected amplitude, instantaneous and spectrum attributes from the forward modeling data; Step 3.2: Attribute sensitivity analysis; Screening sensitive attributes: Based on the analysis of the seismic response characteristics of low-velocity mudstone and gas sandstone, sensitive attributes are screened out; sensitive attributes can effectively distinguish different types of reservoirs; Eliminate attributes that have no significant contribution to target identification, leaving only attributes that have identification effects on low-velocity mudstone and gas sandstone; Retain preferred attributes: From the originally extracted attributes, retain the seismic attributes that are useful for distinguishing low-velocity mudstone and gas sandstone; Step 3.3: Normalize the retained earthquake attributes to have the same scale and mean; Step 3.4: Calculate the correlation coefficient between all seismic attributes. The correlation coefficient is used to measure the linear relationship between attributes and ranges from -1 to 1. Perform statistical analysis on the calculated correlation coefficients to generate a correlation coefficient matrix; Identify attribute pairs with correlation coefficient values higher than 0.8, consider them to have strong correlation, and consider removing redundant attributes; Attribute pairs with correlation coefficient values lower than 0.2 were identified, and the correlation between them was considered weak and unsuitable for attribute clustering; Step 3.5: Based on the correlation coefficient analysis results, select attributes with correlation coefficients between 0.25 and 0.75 for subsequent attribute cluster analysis; The screened earthquake attributes are organized into a data format suitable for cluster analysis to ensure that the data set is complete and meets the requirements of cluster analysis.

3. The low-velocity mudstone identification method based on attribute clustering according to claim 1 is characterized in that: The sub-steps of step 4 are as follows: Step 4.1: Select the earthquake attributes selected in step 3 as the analysis object and organize the data to ensure that the format and structure meet the requirements of the CLARA clustering algorithm; Step 4.2: Set the parameters of the CLARA clustering algorithm, including the sample size and number of samplings, and randomly select a subsample from the entire database. The sample size is determined by the parameter settings. Step 4.3: Initialization: In the selected sample set, use the PAM method to select K initial center points; Assignment step: Assign each data point in the sample set to the nearest center point to form K clusters; Update: Calculate the total cost of the points within each cluster, find and update the center point of each cluster to reduce the total cost within the cluster; Iterate, repeat the allocation and update steps until the center point no longer changes significantly or the upper limit of the number of iterations is reached; Step 4.4: Expand the center points and apply the K center points obtained by the PAM method to the entire database, assigning each data point to the nearest center point to form the final K clusters; Step 4.5: Calculate the final clustering cost, including the total distance of the points within the cluster and the total distance of the center point; adjust the K value or other parameters and re-execute the CLARA algorithm to optimize the clustering results; Use clustering evaluation indicators to evaluate the quality of clustering results, compare the consistency of clustering results with actual data, and verify the effectiveness of clustering; Step 4.6: Output the results, record the final attribute clustering results, including the center point of each cluster and the data points within the cluster; Generate reports and prepare attribute cluster analysis reports, including detailed descriptions and analysis of clustering results.

4. A low-velocity mudstone identification system based on attribute clustering, characterized by: The system can be used to implement the low-velocity mudstone identification method based on attribute clustering according to any one of claims 1 to 3, and the system comprises: Data acquisition module: responsible for collecting and organizing logging data and seismic data from exploration wells, including data acquisition instruments, data storage devices and data transmission interfaces; Data preprocessing module: cleans, denoises and converts the collected data, including data cleaning algorithms, denoising filters and format conversion tools; Low-velocity mudstone pattern research module: Analyzes low-velocity mudstone patterns based on exploration well data, including low-velocity mudstone classification analysis, feature extraction, and pattern research tools. It divides low-velocity mudstone into bright-spot and non-bright-spot types, and analyzes their performance in well logging curves, crossplots, seismic sections, and AVO characteristics. Model building and seismic forward modeling module: Based on well analysis results, a three-layer horizontal layered medium model is established and seismic forward modeling is performed. This module includes model building tools, forward modeling software, and model verification functions. Models 1 to 5 are constructed to simulate different seismic response characteristics. Seismic attribute extraction module: extracts multiple seismic attributes from forward modeling results, including amplitude, instantaneous, and spectrum attributes, and performs attribute extraction, screening, and analysis; Attribute optimization module: This module performs standardization and correlation coefficient statistics on the extracted seismic attributes, eliminates insensitive attributes, and retains the preferred attributes for subsequent attribute clustering analysis. It includes standardization tools, correlation coefficient calculation tools, and redundant attribute elimination functions. Attribute clustering analysis module: Uses the CLARA clustering algorithm and PAM method to perform attribute clustering analysis on selected seismic attributes, including the CLARA clustering algorithm tool, the PAM method implementation module, and the clustering result optimization function; Identification result classification module: classifies and analyzes the attribute clustering results, including identification result classification tools, data comparison and analysis functions, and result verification modules. It identifies and distinguishes reservoirs and low-velocity mudstones based on the attribute clustering results.

5. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the low-speed mudstone identification method based on attribute clustering according to one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the method for identifying low-speed mudstone based on attribute clustering as claimed in any one of claims 1 to 3 is implemented.

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