Transmission method and system applied to comprehensive logging monitoring video

Abstract

The invention relates to the technical field of oil and gas field development and monitoring, in particular to a transmission method and system applied to a comprehensive logging monitoring video. The method comprises the following steps: acquiring oil-gas exploration well sensing data, and performing underground image feature extraction according to the oil-gas exploration well sensing data so as to obtain underground image data; performing exploration well anomaly detection according to the underground image data so as to obtain exploration well anomaly data; performing well wall stability analysis according to the abnormal data of the exploration well to obtain well wall stability data; performing drilling fluid parameter optimization according to the well wall stability data so as to obtain drilling fluid optimization parameter data; acquiring oil-gas exploration well rock stratum data; and sound wave sensing feature extraction is carried out according to the oil-gas exploration well sensing data so as to obtain sound wave sensing data. The video transmission stability is improved based on the oil and gas field development and monitoring technology.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development and monitoring, and in particular to a transmission method and system for integrated well logging monitoring video. Background Art

[0002] Traditional logging monitoring video transmission systems have a variety of technical problems, such as transmission delays, data loss, signal interference, and bandwidth limitations. These problems affect the clarity and stability of monitoring videos, making it impossible for operators to obtain on-site conditions in a timely and accurate manner, which in turn affects the scientific nature and safety of drilling decisions. Since the complex underground structure in the well site environment will hinder the video signal, it will cause problems such as video freezes, frame drops, and even interruptions during transmission, making it difficult to ensure the continuity and stability of video transmission. Existing technologies usually lack efficient response measures when dealing with the dynamic changes and multiple interferences in complex logging environments, especially in remote data transmission and multi-point information synchronization. In the logging environment, signal transmission is not only affected by complex underground structures and equipment interference, but also involves extreme conditions such as high temperature and high pressure, which places high demands on the reliability and robustness of the transmission method. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for transmitting integrated logging monitoring video to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a transmission method for comprehensive well logging monitoring video is provided, comprising the following steps:

[0005] Step S1: Acquire sensor data of an oil and gas exploration well, and perform downhole image feature extraction based on the sensor data of the oil and gas exploration well to obtain downhole image data; perform exploration well anomaly detection based on the downhole image data to obtain exploration well anomaly data;

[0006] Step S2: performing wellbore stability analysis based on the abnormal data of the exploration well to obtain wellbore stability data; optimizing drilling fluid parameters based on the wellbore stability data to obtain drilling fluid optimization parameter data;

[0007] Step S3: Acquire rock formation data of the oil and gas exploration well; perform acoustic sensing feature extraction based on the oil and gas exploration well sensor data to obtain acoustic sensing data; perform formation production capacity analysis on the rock formation data of the oil and gas exploration well based on the acoustic sensing data to obtain formation production capacity data;

[0008] Step S4: performing formation production efficiency evaluation on the formation production capacity data based on the drilling fluid optimization parameter data, thereby obtaining formation production efficiency data; performing video acquisition on the formation production efficiency data based on the oil and gas exploration well sensor data, thereby obtaining formation production video acquisition data;

[0009] Step S5: Encode and compress the formation production video acquisition data to obtain formation production video encoding and compression data; perform 5G network transmission on the formation production video encoding and compression data to obtain formation production video transmission data, and upload it to the comprehensive logging monitoring platform to execute the formation production video transmission task.

[0010] By acquiring sensor data from oil and gas exploration wells and extracting downhole image features, the present invention enables real-time monitoring and identification of downhole anomalies, effectively improving the accuracy and timeliness of anomaly detection and reducing the probability of potential risks. By using downhole image data to detect anomalies in exploration wells, the ability to determine wellbore stability is further enhanced, ensuring the safety of the drilling process and the integrity of the wellbore. By combining the data obtained from anomaly detection with wellbore stability analysis and optimizing drilling fluid parameters, the use of drilling fluid is made more scientific and reasonable, thereby improving the stability and reliability of drilling operations. The application of acoustic sensor feature extraction, combined with rock formation data, enables accurate analysis of formation production capacity, thereby assisting in the development of efficient production strategies. Formation production efficiency is assessed based on drilling fluid optimization parameters and production capacity data, enabling more comprehensive optimization of production plans and promoting efficient resource utilization. Combining video acquisition with sensor data enables multi-angle visual recording of formation production efficiency, enhancing the intuitiveness and reliability of field data. The coding and compression technology used for formation production video ensures effective data quality during transmission. Transmission via the 5G network significantly improves the speed and stability of video data transmission, meeting the requirements for real-time and efficient transmission. This integrated approach ultimately enables the upload of comprehensive production data from oil and gas exploration to the monitoring platform, establishing a closed-loop information management system and providing scientific support for remote decision-making and production optimization.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: acquiring sensor data of the oil and gas exploration well, and performing downhole image feature extraction based on the sensor data of the oil and gas exploration well, thereby obtaining downhole image data;

[0013] Step S12: performing water accumulation anomaly detection on the downhole image data, thereby obtaining water accumulation anomaly data of the exploration well;

[0014] Step S13: performing fracture anomaly detection on the downhole image data to obtain fracture anomaly data of the exploration well;

[0015] Step S14: integrating the abnormal features of the exploration wells based on the abnormal water accumulation data and the abnormal fracture data of the exploration wells, thereby obtaining abnormal data of the exploration wells.

[0016] By acquiring sensor data from oil and gas exploration wells and extracting downhole image features, the present invention can achieve efficient data collection and processing in complex downhole environments, providing a reliable data basis for subsequent anomaly detection. By performing water accumulation anomaly detection on downhole image data, it is possible to quickly identify and locate downhole water accumulation problems, ensure the stability of the well wall and construction safety, and prevent the risk of production interruptions in advance. Performing crack anomaly detection helps to identify the existence and distribution of cracks in the well wall, prevent the instability of the well wall structure and the resulting safety hazards. Combining water accumulation and crack anomaly data for anomaly feature integration can comprehensively analyze the comprehensive anomaly features of the downhole environment, provide a more complete downhole condition assessment, and support decision makers in accurately predicting and targeted interventions in downhole risks. This data integration method improves the coverage and accuracy of anomaly detection, helps reduce the risk of missed detection, and improves the overall safety and production efficiency of oil and gas exploration.

[0017] Optionally, step S12 is specifically as follows:

[0018] Step S121: performing brightness enhancement on the downhole image data, thereby obtaining brightness enhanced downhole image data;

[0019] Step S122: identifying high-brightness pixel areas based on the brightness-enhanced downhole image data, thereby obtaining high-brightness pixel area data;

[0020] Step S123: performing edge detection based on the brightness enhanced downhole image data to obtain downhole image edge data;

[0021] Step S124: performing irregular region statistics on the downhole image edge data, thereby obtaining the downhole image edge irregular region data;

[0022] Step S125: performing a water accumulation area intersection operation based on the high-brightness pixel area data and the irregular edge area data of the downhole image, thereby obtaining water accumulation area data;

[0023] Step S126: performing a multi-point laser beam projection simulation on the waterlogged area data to obtain multi-point laser beam projection data;

[0024] Step S127: performing three-dimensional space coordinate conversion according to the multi-point laser beam projection data, thereby obtaining three-dimensional space coordinate data;

[0025] Step S128: Obtaining data on the maximum safe depth of accumulated water;

[0026] Step S129: reconstruct the three-dimensional depth distribution map of the waterlogged area based on the three-dimensional spatial coordinate data to obtain the three-dimensional depth distribution map data of the waterlogged area, and compare the three-dimensional depth distribution map data of the waterlogged area with the waterlogging anomaly of the exploration well based on the maximum safe depth data of the waterlogged area to obtain the waterlogging anomaly data of the exploration well.

[0027] The present invention helps improve the visibility of images by enhancing the brightness of downhole image data, allowing subsequent processing steps to more accurately extract key information. High-brightness pixel area identification based on brightness enhancement can quickly locate waterlogged or reflective areas, facilitating target analysis. Edge detection obtains edge data of downhole images, enabling the extraction of complex terrain and structural features, improving the recognition of abnormal features in the image. Performing irregular area statistics on edge data helps identify areas with abnormal shapes and irregular boundaries in the image, which is critical for determining waterlogged and other anomalies. Using the intersection operation of high-brightness pixel area data and irregular edge area data, potential waterlogged areas can be accurately determined, improving the accuracy and coverage of detection. Multi-point laser beam projection simulation is used to perform refined depth analysis of waterlogged areas, thereby improving the understanding and assessment of the spatial structure of downhole waterlogging. Through three-dimensional spatial coordinate conversion, two-dimensional image data can be converted into three-dimensional information, making the detection more three-dimensional and practical. The acquisition of data on the maximum safe depth of waterlogging provides a key reference indicator, ensuring that waterlogging assessments are of substantive significance. Reconstructing a 3D depth distribution map of the flooded area using 3D spatial coordinate data allows analysts to visualize the depth and extent of the flooding, facilitating precise response measures. Ultimately, by comparing this with data on the maximum safe depth for flooding, potential hazards can be identified promptly, improving the safety and efficiency of exploration well operations.

[0028] Optionally, step S13 is specifically as follows:

[0029] Step S131: performing grayscale image conversion on the downhole image data to obtain downhole grayscale image data;

[0030] Step S132: performing gray-level co-occurrence matrix calculation based on the downhole gray-level image data to obtain gray-level co-occurrence matrix data;

[0031] Step S133: performing entropy calculation on the gray-level co-occurrence matrix data to obtain entropy data, and performing high entropy value statistics based on the entropy data to obtain high entropy value data;

[0032] Step S134: performing homogeneity calculation on the gray level co-occurrence matrix data to obtain homogeneity data, and performing high homogeneity statistics based on the homogeneity data to obtain high homogeneity data;

[0033] Step S135: performing fracture area identification on the downhole grayscale image data based on the high entropy data and the high homogeneity data, thereby obtaining fracture area data;

[0034] Step S136: obtaining safety crack length data;

[0035] Step S137: performing a comparison between the abnormal cracks in the exploration well and the crack area data based on the safety crack length data, thereby obtaining abnormal crack data in the exploration well.

[0036] The present invention helps to simplify image data and improve the computational efficiency of subsequent analysis by converting downhole image data into grayscale images. The spatial relationship characteristics between pixels in the image can be captured through grayscale co-occurrence matrix calculation, which helps to reveal potential structural information and image texture characteristics. Entropy calculation can obtain the complexity and randomness of image data. The high entropy value statistics in the entropy data are used to identify areas with complex structures or changes, thereby providing a key indicator for crack detection. Homogeneity calculation of the grayscale co-occurrence matrix can identify areas with high structural consistency in the image. The statistics of high homogeneity data help focus on those parts with relatively consistent textures, which is very important for identifying the edge features of crack areas. Using high entropy data and high homogeneity data to jointly identify crack areas can improve the accuracy of crack detection and reduce missed judgments and misjudgments. Obtaining safe crack length data provides a clear benchmark for subsequent abnormal comparison, allowing the system to automatically identify and mark potential dangerous crack areas. Comparing abnormal fractures in exploration wells with fracture zone data can help quickly discover and assess potential structural risks downhole. This is particularly critical in complex logging environments and can improve data processing efficiency and result reliability, thereby ensuring the safety of exploration operations and providing real-time decision support.

[0037] Optionally, step S2 is specifically:

[0038] Step S21: evaluating the corrosiveness of water accumulation based on abnormal data of the exploration well, thereby obtaining corrosiveness data of water accumulation;

[0039] Step S22: Acquire wellbore rock data and wellbore strain gauge sensing data;

[0040] Step S23: performing water erosion simulation on the well wall rock data according to the water corrosivity data, thereby obtaining well wall rock water erosion data;

[0041] Step S24: performing well wall area statistics based on the well wall rock water erosion data, thereby obtaining well wall rock water erosion area data;

[0042] Step S25: performing stress concentration area statistics based on the wellbore strain gauge sensing data, thereby obtaining stress concentration area data;

[0043] Step S26: performing a wellbore instability region intersection operation based on the wellbore rock water erosion region data and the stress concentration region data, thereby obtaining the wellbore instability region data;

[0044] Step S27: performing stability calculation on the wellbore instability area data to obtain wellbore stability data;

[0045] Step S28: Optimize drilling fluid parameters according to the wellbore stability data, thereby obtaining drilling fluid optimization parameter data.

[0046] The present invention can provide a quantitative analysis of the potential impact of underground water accumulation on the well wall by conducting a water corrosion assessment, laying the foundation for the subsequent well wall structure durability assessment. Acquiring well wall rock data and well wall strain gauge sensor data helps to fully understand the physical properties of the well wall and its stress conditions, ensuring the accuracy of the analysis. Using water corrosion data to simulate water erosion on well wall rock data can predict the potential damage to the well wall under different corrosion environments, thereby discovering weak areas in advance. Performing statistics on well wall areas helps to accurately locate rock areas affected by water erosion and provide a basis for local analysis. Stress concentration area statistics help to identify dangerous areas on the well wall caused by uneven formation pressure or stress distribution, thereby improving the accuracy of well wall instability risk assessment. Combining well wall rock water erosion area data with stress concentration area data to perform intersection operations helps to more accurately identify unstable well wall areas, thereby improving the effectiveness of risk prediction. Performing stability calculations enables the system to evaluate the overall stability of the well wall, helping engineers develop safer drilling plans. Ultimately, optimizing drilling fluid parameters based on stability data helps select the optimal drilling fluid configuration, thereby effectively mitigating the risk of wellbore instability and improving the safety and efficiency of the overall drilling process.

[0047] Optionally, step S28 is specifically as follows:

[0048] Step S281: performing a fracture risk assessment based on the wellbore stability data to obtain fracture risk data;

[0049] Step S282: dividing the crack risk data into high-risk areas, thereby obtaining crack high-risk area data;

[0050] Step S283: increasing the drilling fluid density according to the fracture high-risk area data, thereby obtaining drilling fluid density data;

[0051] Step S284: adjusting the drilling fluid viscosity according to the fracture high-risk area data, thereby obtaining drilling fluid viscosity data;

[0052] Step S285: integrating drilling fluid optimization parameters according to the drilling fluid density data and the drilling fluid viscosity data, thereby obtaining drilling fluid optimization parameter data.

[0053] The present invention can effectively identify potential crack risks in the wellbore through crack risk assessment, providing a scientific basis for engineering personnel to take protective measures in advance. The division of high-risk areas can clearly locate the areas of cracks, making the protective measures more targeted and accurate. The increase in drilling fluid density can improve the downhole pressure balance, reduce crack expansion, and ensure the stability and overall safety of the wellbore. The adjustment of drilling fluid viscosity helps to control the flow characteristics of the drilling fluid downhole, thereby better protecting the wellbore and preventing further development of cracks. Through the integration of density and viscosity data, the generated drilling fluid optimization parameters can achieve comprehensive regulation of the drilling fluid, improve the adaptability of the drilling fluid under complex downhole conditions, effectively reduce the risk of crack expansion and optimize the safety and efficiency of drilling operations.

[0054] Optionally, step S3 specifically includes:

[0055] Step S31: Acquire rock formation data of oil and gas exploration wells;

[0056] Step S32: extracting acoustic wave sensing features based on the oil and gas exploration well sensing data, thereby obtaining acoustic wave sensing data;

[0057] Step S33: calculating the Young's modulus of the formation rock based on the acoustic wave sensing data, thereby obtaining the Young's modulus data of the formation rock;

[0058] Step S34: calculating the Poisson's ratio of the formation rock based on the acoustic wave sensing data, thereby obtaining the Poisson's ratio data of the formation rock;

[0059] Step S35: integrating the rock formation elastic properties according to the Young's modulus data and the Poisson's ratio data of the rock formation, thereby obtaining rock formation elastic data;

[0060] Step S36: Acquire neutron logging data, and perform formation porosity analysis based on the neutron logging data and formation elasticity data, thereby obtaining formation porosity data;

[0061] Step S37: Evaluate the formation fracture pressure based on the acoustic wave sensing data and the rock formation elasticity data, thereby obtaining formation fracture pressure data;

[0062] Step S38: Perform comprehensive production capacity prediction based on the formation fracture pressure data and the formation porosity data, thereby obtaining formation production capacity data.

[0063] The present invention can provide basic geological information for subsequent analysis by acquiring rock formation data, ensuring the accuracy of the evaluation process. The extraction of acoustic wave sensing features can provide important basic data for the analysis of the physical properties of rocks, ensuring a deep understanding of the rock formation structure and characteristics. The calculation of the Young's modulus and Poisson's ratio of formation rocks can help evaluate the elasticity and deformation characteristics of the rock formation, which is crucial for predicting the behavior of the rock formation under pressure conditions. The integration of rock formation elastic properties helps to fully understand the mechanical properties of the formation, making the analysis of the mechanical behavior of the formation more accurate. Neutron logging data combined with rock formation elastic data for porosity analysis can reveal the internal pore structure of the rock formation and provide key parameters for production capacity assessment. The assessment of formation fracture pressure based on acoustic wave sensing data and rock formation elastic data can predict the risk of formation fracture during drilling and production, and ensure drilling safety. Comprehensive production capacity prediction achieves a comprehensive assessment of the production potential of the formation by combining fracture pressure and rock formation porosity data, providing a reliable basis for production planning.

[0064] Optionally, step S36 is specifically as follows:

[0065] Step S361: Acquire neutron logging data, and perform neutron count feature extraction and neutron attenuation value feature extraction based on the neutron logging data, thereby obtaining neutron count data and neutron attenuation value data;

[0066] Step S362: performing neutron logging porosity calculation based on the neutron counting data and the neutron attenuation value data, thereby obtaining neutron logging porosity data;

[0067] Step S363: performing elastic correction on the neutron logging porosity data according to the formation elasticity data, thereby obtaining neutron logging porosity elasticity correction data;

[0068] Step S364: obtaining density logging data, and performing porosity calculation based on the density logging data, thereby obtaining density logging porosity data;

[0069] Step S365: performing porosity integration on the neutron logging porosity elasticity correction data according to the density logging porosity data, thereby obtaining formation porosity data.

[0070] By acquiring and extracting neutron counts and attenuation characteristics, the present invention can accurately reflect the neutron attenuation behavior of the rock formation, laying a solid data foundation for the calculation of porosity. The calculation of neutron logging porosity helps to reveal the pore structure characteristics of the formation and provide key data for subsequent analysis of the physical properties of the rock formation. Elastic correction further optimizes the accuracy of neutron logging porosity, ensuring that the porosity data can more accurately reflect the actual geological conditions of the rock formation. The introduction of density logging data provides a more comprehensive measurement basis for porosity calculation, thereby enhancing the multidimensionality of porosity data. Porosity integration further improves the comprehensiveness and accuracy of porosity data by combining density logging and neutron logging porosity, providing more complete rock formation porosity data for formation analysis. These steps improve the overall reliability of rock formation physical parameters and provide more accurate data support for formation production potential assessment and drilling decisions.

[0071] Optionally, step S37 is specifically as follows:

[0072] Step S371: extracting shear wave velocity features based on the acoustic wave sensing data to obtain shear wave velocity data;

[0073] Step S372: Obtaining rock formation density data, and performing shear modulus calculation based on the rock formation density data and shear wave velocity data, thereby obtaining rock formation shear modulus data;

[0074] Step S373: Calculate the fracture pressure based on the rock formation shear modulus data and the formation rock Young's modulus data, thereby obtaining the formation fracture pressure data.

[0075] By extracting the shear wave velocity characteristics from the acoustic wave sensing data, the present invention can accurately evaluate the wave propagation characteristics of the formation, thereby providing a reliable basis for the analysis of the physical properties of the rock formation. The acquisition of rock formation density data is combined with shear wave velocity data for shear modulus calculation, which helps to characterize the rigidity and shear resistance of the rock formation and reveals the response behavior of the rock formation under external forces. The shear modulus data is combined with the Young's modulus data of the formation rock to calculate the fracture pressure, which can effectively evaluate the fracture conditions of the formation, provide an in-depth understanding of the strength and stability of the formation, and help determine the downhole pressure balance and safety limit. This process enhances the ability to predict formation fracture pressure by fully integrating the physical properties of the formation, thereby improving the reliability and safety of drilling decisions, helping to reduce the risk of accidents and optimize well site operations.

[0076] The present invention also provides a transmission system for integrated mud logging monitoring video, which is used to execute the above-mentioned transmission method for integrated mud logging monitoring video. The transmission system for integrated mud logging monitoring video includes:

[0077] Exploration well anomaly detection module: used to obtain oil and gas exploration well sensor data, and perform downhole image feature extraction based on the oil and gas exploration well sensor data to obtain downhole image data; perform exploration well anomaly detection based on the downhole image data to obtain exploration well anomaly data;

[0078] Wellbore stability analysis module: used to perform wellbore stability analysis based on abnormal data of exploration wells, thereby obtaining wellbore stability data; optimize drilling fluid parameters based on wellbore stability data, thereby obtaining drilling fluid optimization parameter data;

[0079] Formation production capacity analysis module: used to obtain rock formation data of oil and gas exploration wells; extract acoustic wave sensing features based on the oil and gas exploration well sensor data to obtain acoustic wave sensing data; perform formation production capacity analysis on the oil and gas exploration well rock formation data based on the acoustic wave sensing data to obtain formation production capacity data;

[0080] Formation production video acquisition module: used to evaluate formation production efficiency based on formation production capacity data according to drilling fluid optimization parameter data, thereby obtaining formation production efficiency data; and to acquire formation production video acquisition data based on oil and gas exploration well sensor data.

[0081] Formation production video transmission module: used to encode and compress the formation production video acquisition data to obtain formation production video encoding and compression data; perform 5G network transmission based on the formation production video encoding and compression data to obtain formation production video transmission data, and upload it to the comprehensive logging monitoring platform to perform the formation production video transmission task.

[0082] The transmission system for integrated well logging monitoring video of the present invention can implement any transmission method for integrated well logging monitoring video of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the transmission method for integrated well logging monitoring video. The internal modules of the system cooperate with each other, thereby improving the stability of video transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0084] Figure 1 1. A schematic flow chart of the steps of the method for transmitting integrated well logging monitoring video according to the present invention;

[0085] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0086] Figure 3Detailed flowchart of step S28 in the present invention;

[0087] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0088] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0089] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0090] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0091] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for transmitting integrated logging monitoring video, the method comprising the following steps:

[0092] Step S1: Acquire sensor data of an oil and gas exploration well, and perform downhole image feature extraction based on the sensor data of the oil and gas exploration well to obtain downhole image data; perform exploration well anomaly detection based on the downhole image data to obtain exploration well anomaly data;

[0093] In this embodiment, in oil and gas exploration wells, downhole image data is collected using sensors. The sensors used are high-resolution cameras or laser scanners, which are capable of capturing detailed images or depth data of the downhole. Image processing technology is used to extract features from the collected downhole images. The main features extracted include texture, color, edges, etc., using the gray-level co-occurrence matrix method or the SIFT feature extraction method. These extracted features are used for subsequent exploration well anomaly detection. Anomaly detection is performed using a threshold method or a machine learning algorithm. Typically, a certain pixel value threshold is set. For example, changes in the brightness or edges of the image are detected. Areas exceeding the set threshold are considered abnormal areas. The detection results generate exploration well anomaly data. This data includes cracks, corrosion, subsidence, and other problems in the downhole area, facilitating subsequent processing and decision-making.

[0094] Step S2: performing wellbore stability analysis based on the abnormal data of the exploration well to obtain wellbore stability data; optimizing drilling fluid parameters based on the wellbore stability data to obtain drilling fluid optimization parameter data;

[0095] In this embodiment, a wellbore stability analysis method is used for analysis based on abnormal data of the exploration well. Based on the physical property data of the wellbore rock, such as Young's modulus, Poisson's ratio, density, etc., the stability of the wellbore is calculated by finite element analysis or elastic mechanics model to obtain the stress and deformation distribution of the wellbore. The focus of the wellbore stability analysis is to detect whether there are risks such as slippage and crack expansion in the wellbore. If the stress concentration area exceeds the set safety threshold, the wellbore is considered unstable. According to the wellbore stability data, the parameters of the drilling fluid are optimized, mainly optimizing the density and viscosity of the drilling fluid to enhance the stability of the wellbore. The optimized drilling fluid parameter data is obtained through rheometer testing and calculation, where the density is usually controlled in the range of 1.2-2.5g / cm3 and the viscosity is between 50-120cP. Finally, the optimized drilling fluid parameter data is generated for subsequent drilling operations.

[0096] Step S3: Acquire rock formation data of the oil and gas exploration well; perform acoustic sensing feature extraction based on the oil and gas exploration well sensor data to obtain acoustic sensing data; perform formation production capacity analysis on the rock formation data of the oil and gas exploration well based on the acoustic sensing data to obtain formation production capacity data;

[0097] In this embodiment, rock formation data from oil and gas exploration wells is acquired. Physical parameters of the downhole rock formation, such as rock density, porosity, and lithology, are obtained using equipment such as depth measuring instruments or nuclear magnetic resonance sensors. Simultaneously, acoustic wave signal data is collected by downhole sensors. These signals can be sent and received by the sensors, and the propagation speed of the acoustic wave signals is closely related to the properties of the rock formation. Based on the collected acoustic wave data, acoustic wave features are extracted, including shear wave velocity and longitudinal wave velocity. Standard wave velocity analysis methods, such as the time difference method of reflected waves, are used to calculate the acoustic properties of the rock formation. Next, based on the acoustic wave sensor data, the rock formation's production capacity is analyzed. Specifically, this method involves combining the acoustic wave reflection time with the rock formation's density and porosity to derive a production capacity indicator for the formation. For example, the shear modulus and compression modulus of the rock formation are used to calculate a comprehensive production capacity assessment indicator. This data is used to predict the production capacity and efficiency of oil and gas exploration wells.

[0098] Step S4: performing formation production efficiency evaluation on the formation production capacity data based on the drilling fluid optimization parameter data, thereby obtaining formation production efficiency data; performing video acquisition on the formation production efficiency data based on the oil and gas exploration well sensor data, thereby obtaining formation production video acquisition data;

[0099] In this embodiment, based on the drilling fluid optimization parameter data, a fluid dynamics model is used to simulate and calculate the flow characteristics of the drilling fluid in the rock formation, and the lubrication, cooling, and pressure balance effects of the drilling fluid on the formation are evaluated. Key parameters of this process include the density, flow rate, viscosity of the drilling fluid, and the permeability of the rock formation. The density is generally between 1.2-2.5g / cm3, and the viscosity is controlled between 50-120cP, obtained through rheological testing. Subsequently, the formation production is collected in real time through a video monitoring system, and high-definition cameras or sensors are used to collect images or video data of the production process. This data provides an intuitive basis for subsequent production efficiency evaluation. Video data is collected by regularly filming the production process of the formation, and the collected data is transmitted to the data platform in real time for timely viewing and analysis.

[0100] Step S5: Encode and compress the formation production video acquisition data to obtain formation production video encoding and compression data; perform 5G network transmission on the formation production video encoding and compression data to obtain formation production video transmission data, and upload it to the comprehensive logging monitoring platform to execute the formation production video transmission task.

[0101] In this embodiment, encoding and compression processing is performed on the basis of the formation production video data. Video data compression is performed using the H.265 or VP9 video coding standards. These standards reduce the bandwidth and storage space required for video transmission through efficient compression algorithms. Compression parameters include frame rate, bit rate, resolution, etc., which are usually set to 30 frames per second, with a resolution of 1920×1080 and a bit rate controlled between 500kbps and 2Mbps. The compressed formation production video data is transmitted in real time via the 5G network. The 5G network has the advantages of low latency and high bandwidth, which can ensure high-quality transmission of video data. End-to-end encryption technology is used during the transmission process to ensure data security, while ensuring that data is not lost in the network and that delays are minimized. The video data is transmitted via the 5G network to the integrated logging monitoring platform for storage and further analysis, providing real-time monitoring and decision support to operators. This process ensures that video data during drilling operations can be transmitted in a timely and stable manner, greatly improving the real-time and safety of the operation.

[0102] Optionally, step S1 specifically includes:

[0103] Step S11: acquiring sensor data of the oil and gas exploration well, and performing downhole image feature extraction based on the sensor data of the oil and gas exploration well, thereby obtaining downhole image data;

[0104] In this embodiment, the sensor data of the oil and gas exploration well is collected by downhole equipment such as pressure sensors, temperature sensors, depth sensors and high-definition cameras. The data collected by these devices include the temperature, pressure, depth and high-resolution images of the downhole. During the image acquisition process, the camera uses optical imaging technology to obtain detailed images of the downhole environment, including the well wall, rock formations, surrounding water bodies, etc. Downhole image feature extraction mainly adopts image processing methods, such as edge detection (such as Canny edge detection) and texture analysis (such as grayscale co-occurrence matrix) to extract features in the downhole image. In this process, the image processing parameters, such as edge threshold, resolution of texture features, etc., are set according to the special needs of the downhole environment to ensure that the image details are retained to the greatest extent.

[0105] Step S12: performing water accumulation anomaly detection on the downhole image data, thereby obtaining water accumulation anomaly data of the exploration well;

[0106] In this embodiment, after acquiring the downhole image data, the image is subjected to water accumulation anomaly detection. The presence of water accumulation causes equipment failure during the drilling process or affects operational safety, so it is necessary to detect the water accumulation area in the image. Water accumulation detection utilizes the color and illumination features of the image, and performs threshold segmentation based on the color space conversion method (for example, from RGB to HSV) to identify the water area in the image. During specific implementation, a certain color range and brightness threshold are set. For example, the hue value of the water area is in the range of blue to dark blue, and the brightness value is greater than a certain standard (such as above 80). By performing area and shape analysis on these areas, water accumulation anomalies are determined and water accumulation anomaly data is generated to facilitate subsequent processing and decision-making.

[0107] Step S13: performing fracture anomaly detection on the downhole image data to obtain fracture anomaly data of the exploration well;

[0108] In this embodiment, when performing crack anomaly detection on downhole image data, image edge detection and feature matching methods are used for processing. Cracks usually present special linear features or irregular shapes in the image. First, edge detection is performed on the image using the Sobel operator or the Laplacian operator to identify linear features in the image. Then, morphological operations (such as dilation and corrosion operations) are applied to strengthen the edge area of the crack in combination with the texture and shape features of the image. A certain area threshold is set. If the width or length of the crack area exceeds the preset minimum value (for example, the width of the crack is greater than 0.5 cm and the length is greater than 5 cm), it is determined to be a crack anomaly. The detected crack anomaly data includes the specific location and size of the crack and the risk assessment it brings.

[0109] Step S14: integrating the abnormal features of the exploration wells based on the abnormal water accumulation data and the abnormal fracture data of the exploration wells, thereby obtaining abnormal data of the exploration wells.

[0110] In this embodiment, the water accumulation anomaly data and the fracture anomaly data are integrated to obtain comprehensive exploration well anomaly data. The abnormal feature integration adopts data fusion technology. First, the water accumulation anomaly and fracture anomaly data are matched and compared in time and space to ensure that each anomaly type corresponds to the correct downhole location. During the integration, the two types of data are fused into a complete anomaly data set by combining multiple parameters such as downhole depth, anomaly type, size, and location through weighted averaging, principal component analysis, or decision tree methods. During the fusion process, the severity of water accumulation and fractures is quantified, and a threshold standard is set. When the water accumulation area or fracture width exceeds a certain level (for example, when the water accumulation area exceeds 50cm2 or the fracture width exceeds 1cm), it is calibrated as a high-risk anomaly. The exploration well anomaly data finally generated summarizes all detected downhole problems, facilitating subsequent risk assessment and decision-making.

[0111] Optionally, step S12 is specifically as follows:

[0112] Step S121: performing brightness enhancement on the downhole image data, thereby obtaining brightness enhanced downhole image data;

[0113] In this embodiment, brightness enhancement of downhole image data is achieved through histogram equalization technology in image processing. After acquiring the downhole image data, it is converted into a grayscale image, and the image's brightness distribution is calculated. Based on the image's brightness histogram, the image's pixel value range is expanded to enhance low-brightness areas within the image. During the histogram equalization process, the minimum and maximum values for the expanded pixel value range are set. For example, a pixel range of 0 to 255 is set to enhance detail in darker areas. This method effectively improves image brightness contrast, making downhole images clearer under complex lighting conditions and providing a better foundation for subsequent image processing.

[0114] Step S122: identifying high-brightness pixel areas based on the brightness-enhanced downhole image data, thereby obtaining high-brightness pixel area data;

[0115] In this embodiment, after obtaining the brightness-enhanced downhole image data, high-brightness pixel area identification is performed. Through the threshold segmentation method, a brightness threshold is set (for example, the threshold is set to 180, indicating that pixels with brightness greater than 180 are high-brightness pixels). For each pixel, if its brightness value is greater than the threshold, the pixel is considered to belong to the high-brightness area. This method can identify the highlight areas in the image caused by reflection, light source or water body by judging the pixel values one by one. The data of the identified high-brightness pixel area includes the boundary coordinates, area and relative position of the high-brightness area in the image, providing data support for subsequent water accumulation detection and regional analysis.

[0116] Step S123: performing edge detection based on the brightness enhanced downhole image data to obtain downhole image edge data;

[0117] In this embodiment, edge detection is performed based on the downhole image data after brightness enhancement. The Canny edge detection algorithm is used to identify edge information in the image by calculating the gradient change of the image. In specific implementation, the image is first Gaussian blurred to remove noise, and then the gradient amplitude and direction of the image are calculated. The edge pixels are determined based on the set low threshold (such as 50) and high threshold (such as 150). The Canny algorithm can effectively detect areas with obvious boundaries such as well walls and cracks, ensuring that the main structures in the image are accurately extracted. The obtained downhole image edge data includes the coordinates, length and positional relationship of the edge.

[0118] Step S124: performing irregular region statistics on the downhole image edge data, thereby obtaining the downhole image edge irregular region data;

[0119] In this embodiment, irregular region statistics are performed on the edge data of downhole images. The goal is to identify irregularly shaped regions within the image, typically manifesting as cracks, caves, and the like. First, the shape characteristics of the region are calculated from the edge data. For example, a Hough transform is used to detect straight lines or curves, and irregular shapes are identified through curvature analysis. Shape parameters are set, such as crack widths of no less than 0.5 cm and aspect ratios of no less than 3:1. After identifying these features, their frequency and location are counted. Ultimately, data on irregular regions along the edge of the downhole image is obtained, including their size, shape, distribution, and relationship to other structures.

[0120] Step S125: performing a water accumulation area intersection operation based on the high-brightness pixel area data and the irregular edge area data of the downhole image, thereby obtaining water accumulation area data;

[0121] In this example, the waterlogged area is obtained by overlaying the data of the high-brightness pixel area with the data of the irregular edge area. During the calculation process, the spatial coordinates of the high-brightness area and the irregular edge area are first determined. The overlap between the two areas is then found through spatial intersection. Image coordinate transformation technology is used to ensure spatial alignment of the two sets of data. The resulting waterlogged area data includes the shape and size of the waterlogged area and its relationship to the well wall, cracks, and other structures.

[0122] Step S126: performing a multi-point laser beam projection simulation on the waterlogged area data to obtain multi-point laser beam projection data;

[0123] In this embodiment, multi-point laser beam projection simulation is performed on waterlogged area data to simulate the three-dimensional morphology of the waterlogged area. Multiple laser beam sources are set within the waterlogged area, firing laser beams at the waterlogged area from different angles and positions to simulate the reflection of the laser beams from the waterlogged surface. The laser beam emission angle and distance are set based on the actual downhole conditions. For example, the laser beam emission interval is set to 10 degrees, and the effective laser projection range is 5 meters. Based on the reflected laser signals, the surface morphology of the waterlogged area is calculated through triangulation, generating multi-point laser beam projection data, including elevation information and surface undulations of the waterlogged area.

[0124] Step S127: performing three-dimensional space coordinate conversion according to the multi-point laser beam projection data, thereby obtaining three-dimensional space coordinate data;

[0125] In this embodiment, the coordinate data obtained during the laser beam projection process is typically based on a two-dimensional plane system and needs to be converted into three-dimensional space coordinates. Classic coordinate conversion algorithms are used, such as converting from polar coordinates to Cartesian coordinates or using a perspective transformation algorithm to map the three-dimensional space. Coordinate calculations are performed based on the emission angle, distance, and three-dimensional position of the laser beam, converting the data in the two-dimensional coordinate system into coordinate data in three-dimensional space. The three-dimensional space coordinate data includes the elevation, depth, and relative position of the waterlogged area, ensuring that the spatial characteristics of the waterlogged area are accurately reconstructed.

[0126] Step S128: Obtaining data on the maximum safe depth of accumulated water;

[0127] In this embodiment, data on the maximum safe depth for water accumulation is obtained, determined based on the design standards and safety requirements of oil and gas exploration wells. Real-time depth data acquired by downhole sensors is combined with engineering standards to set the maximum safe depth for water accumulation areas. For example, based on the pressure resistance of downhole equipment, a safe depth of 300 meters is set. Water accumulation above this depth will affect wellbore stability. In actual operation, this parameter is dynamically adjusted through real-time monitoring of depth sensors and safety standards to ensure the safety of downhole operations.

[0128] Step S129: reconstruct the three-dimensional depth distribution map of the waterlogged area based on the three-dimensional spatial coordinate data to obtain the three-dimensional depth distribution map data of the waterlogged area, and compare the three-dimensional depth distribution map data of the waterlogged area with the waterlogging anomaly of the exploration well based on the maximum safe depth data of the waterlogged area to obtain the waterlogging anomaly data of the exploration well.

[0129] In this embodiment, a three-dimensional graphic of the waterlogged area is drawn using spatial coordinate data to ensure the accuracy of the graphic and the integrity of the data. A three-dimensional modeling software (such as AutoCAD, SolidWorks, etc.) is used to convert the three-dimensional coordinate data projected by the laser beam into a visual depth distribution map to mark the specific location of the waterlogged area. Combined with the maximum safe depth data of the waterlogged area, the reconstructed map is compared and analyzed to determine whether the waterlogged area exceeds the safe depth and to identify potential high-risk areas. Finally, the exploration well waterlogging anomaly comparison is performed based on the three-dimensional depth distribution map data of the waterlogged area to generate exploration well waterlogging anomaly data, providing a basis for subsequent decision-making and processing.

[0130] Optionally, step S13 is specifically as follows:

[0131] Step S131: performing grayscale image conversion on the downhole image data to obtain downhole grayscale image data;

[0132] In this embodiment, the grayscale image conversion of downhole image data is achieved by converting the original color image into a grayscale image. This process uses the standard RGB to grayscale value conversion formula: Grayscale value = 0.2989*R + 0.5870*G + 0.1140*B, where R, G, and B represent the pixel values of the red, green, and blue channels of the original image, respectively. The red, green, and blue values of each pixel in the image are replaced with the calculated grayscale value, thus generating downhole grayscale image data. This step provides the foundation for subsequent image feature extraction, ensuring uniformity in image processing and simplifying computational complexity, making it suitable for further texture analysis.

[0133] Step S132: performing gray-level co-occurrence matrix calculation based on the downhole gray-level image data to obtain gray-level co-occurrence matrix data;

[0134] In this embodiment, the grayscale co-occurrence matrix is obtained by counting the grayscale levels of each pair of pixels in the image and their relative position relationship, and is usually calculated using a sliding window with a window size of 3×3. First, the grayscale values of each pair of pixels in the window are selected, and the frequency of the pixel pairs is calculated according to the specified direction (such as horizontal, vertical, diagonal, etc.) and step size (such as 1 pixel) to generate a co-occurrence matrix. For example, in the horizontal direction, if the grayscale values of two adjacent pixels are g1 and g2, the count is increased at the (g1, g2) position of the co-occurrence matrix. In this way, the grayscale co-occurrence matrix data describing the image texture can be obtained, which provides a basis for subsequent statistical feature calculations.

[0135] Step S133: performing entropy calculation on the gray-level co-occurrence matrix data to obtain entropy data, and performing high entropy value statistics based on the entropy data to obtain high entropy value data;

[0136] In this embodiment, entropy calculation is performed on the calculated grayscale co-occurrence matrix data. Entropy is an indicator to measure the complexity of image texture and the amount of information, and the formula is: Entropy = -ΣP(i,j)*log(P(i,j)), where P(i,j) is the probability value of position (i,j) in the grayscale co-occurrence matrix. By calculating the corresponding probability of each element of the grayscale co-occurrence matrix and calculating the entropy value, the texture complexity of the image can be quantified. The larger the entropy value, the more complex the image texture, and vice versa. Based on the entropy data, high entropy statistics are further performed, and the entropy threshold is set (for example, an entropy value greater than 2.5 is considered a high entropy value) to screen out parts with complex textures, cracks or abnormal areas.

[0137] Step S134: performing homogeneity calculation on the gray level co-occurrence matrix data to obtain homogeneity data, and performing high homogeneity statistics based on the homogeneity data to obtain high homogeneity data;

[0138] In this embodiment, homogeneity calculation is performed based on the calculated grayscale co-occurrence matrix data. Homogeneity is an indicator for measuring the uniformity of image texture, and the calculation formula is: Homogeneity = ΣP(i,j) / (1+|ij|), where P(i,j) represents the probability of the (i,j) position in the grayscale co-occurrence matrix. By calculating the grayscale difference of each pixel pair in the matrix and taking its weighted average, the homogeneity value of the image can be obtained. The higher the homogeneity, the more uniform the image texture, which usually corresponds to a flat area or an area without obvious cracks. The calculation result of the homogeneity value is used for subsequent high homogeneity statistics, and a threshold is set (for example, a homogeneity greater than 0.8 is considered high homogeneity) to identify areas with uniform texture as a comparison standard for crack areas.

[0139] Step S135: performing fracture area identification on the downhole grayscale image data based on the high entropy data and the high homogeneity data, thereby obtaining fracture area data;

[0140] In this embodiment, high-entropy data and high-homogeneity data are combined to identify fracture regions in downhole grayscale image data. Based on the high-entropy and high-homogeneity data obtained in the previous step, the fracture region is determined through an intersection operation. Specifically, high-entropy regions and low-homogeneity regions are first screened out. These regions contain large texture variations, indicating the presence of fractures. By further superimposing the data of high-entropy and low-homogeneity regions, the location and shape of the fracture region are identified. This method accurately identifies the fracture locations in the image by integrating the complexity and uniformity of the texture and generates fracture region data, including the spatial location, size, and shape of the fracture.

[0141] Step S136: obtaining safety crack length data;

[0142] In this embodiment, safe crack length data is obtained. The safe crack length is an evaluation indicator based on downhole engineering standards and the impact of cracks on wellbore stability. By analyzing downhole design documents and historical monitoring data, the maximum safe length of each crack is set (for example, cracks longer than 2 meters are considered unsafe). This data can be obtained through manual setting or historical data analysis, and is continuously adjusted according to the load and safety standards of the downhole equipment. In actual operation, it is necessary to dynamically adjust the standard of safe crack length based on different downhole operating conditions, such as pressure, temperature, etc.

[0143] Step S137: performing a comparison between the abnormal cracks in the exploration well and the crack area data based on the safety crack length data, thereby obtaining abnormal crack data in the exploration well.

[0144] In this embodiment, crack screening is performed based on the crack lengths in the crack region data, combined with the safety crack length data (for example, crack lengths greater than 2 meters are considered abnormal). By comparing crack lengths with the safety crack lengths, abnormal crack regions are identified. Specifically, if the crack length in a particular area exceeds a set safety threshold, the area is marked as abnormal. This step effectively identifies areas with potential wellbore stability issues, ensuring the safety of downhole operations and providing a basis for subsequent treatment measures.

[0145] Optionally, step S2 is specifically:

[0146] Step S21: evaluating the corrosiveness of water accumulation based on abnormal data of the exploration well, thereby obtaining corrosiveness data of water accumulation;

[0147] In this embodiment, the assessment of the corrosiveness of accumulated water is achieved by analyzing the type of accumulated water in the exploration well, its chemical composition, and the corrosive effect of the accumulated water on the well wall rock. First, the accumulated water is classified according to the location of the accumulated water and the water quality components (such as pH value, dissolved oxygen concentration, chloride ion concentration, etc.) identified in the abnormal data of the exploration well. Then, the corrosion assessment is performed using a corrosion assessment model (for example, by calculating the rate at which the acidity and alkalinity of water react with the mineral surface). In specific operations, a threshold is set based on experimental data or existing corrosion standards. For example, if the pH value of the water is lower than 5, it is considered to be highly corrosive, and vice versa. This model can be used to obtain the corrosiveness data of accumulated water, reflecting the degree of corrosion of the well wall rock.

[0148] Step S22: Acquire wellbore rock data and wellbore strain gauge sensing data;

[0149] In this embodiment, wall rock data is derived from wellbore sampling and analysis. By analyzing rock samples for mineral composition, porosity, density, and other parameters, basic data such as rock physical properties and compressive strength are obtained. Furthermore, wellbore strain gauges are placed at key locations on the wellbore to monitor strain data in real time. Strain gauges can measure minute deformations of rock formations under stress, and the electrical signals output by the sensors are converted to strain data. This data provides a basis for subsequent wellbore water erosion simulations and analysis of stress concentration areas.

[0150] Step S23: performing water erosion simulation on the well wall rock data according to the water corrosivity data, thereby obtaining well wall rock water erosion data;

[0151] In this embodiment, water erosion simulation uses a numerical simulation method. By establishing a coupled model of the wellbore rock and the accumulated water, the corrosive effect of the corrosive components in the accumulated water on the rock is simulated. In the simulation, the model parameters of the corrosion rate are first determined based on the corrosive data of the accumulated water, such as pH value, redox potential, etc. Then, the finite element method (FEM) is used to calculate the spatiotemporal evolution of the corrosion on the rock surface. Based on the interaction between the physical properties of the rock (such as hardness, toughness, etc.) and the corrosive medium, the wellbore rock water erosion data is obtained. This process can provide the corrosion depth and corrosion rate of the rock and its impact on the stability of the wellbore.

[0152] Step S24: performing well wall area statistics based on the well wall rock water erosion data, thereby obtaining well wall rock water erosion area data;

[0153] In this embodiment, the statistical analysis of wellbore rock erosion data is achieved by performing a spatial distribution analysis of the degree of erosion in each wellbore region. Based on the wellbore rock erosion data, the wellbore is divided into different regions (such as the upper, middle, and lower parts) for analysis, and indicators such as the average erosion depth and corrosion rate of each region are calculated. Regional statistics are performed by segmenting the wellbore region and performing a weighted average of the erosion data within each region to obtain the regional distribution of wellbore rock erosion. Based on these statistical results, it is possible to determine which areas of the rockbore have more severe erosion, thereby affecting the stability of the wellbore.

[0154] Step S25: performing stress concentration area statistics based on the wellbore strain gauge sensing data, thereby obtaining stress concentration area data;

[0155] In this embodiment, statistical analysis of concentrated areas is performed by processing the data sensed by the strain gauges on the wellbore wall. The strain gauge data can reflect the stress distribution at different locations on the wellbore wall. By sampling the strain gauge data at multiple points, the stress gradient on the wellbore wall can be calculated, and the areas of stress concentration can be identified. In specific operations, a stress concentration threshold is set (for example, the strain value at a certain location exceeds a certain standard value, such as 1.5%). When the stress in a certain area exceeds this threshold, it can be identified as a stress concentration area. The spatial location and range of these areas are statistically analyzed to obtain stress concentration area data.

[0156] Step S26: performing a wellbore instability region intersection operation based on the wellbore rock water erosion region data and the stress concentration region data, thereby obtaining the wellbore instability region data;

[0157] In this example, areas of severe water erosion are identified using data from wellbore rock water erosion. Then, areas of stress concentration are identified based on data from stress concentration areas. An intersection operation is performed to overlay the water erosion and stress concentration areas, identifying those areas experiencing both water erosion and stress concentration. These areas are at high risk for wellbore instability. The intersection operation compares the spatial position and intensity of the two datasets to identify overlapping areas and mark them as unstable regions. The resulting data on wellbore instability areas provides a basis for subsequent stability assessment and treatment.

[0158] Step S27: performing stability calculation on the wellbore instability area data to obtain wellbore stability data;

[0159] In this embodiment, the wellbore stability calculation adopts a comprehensive analysis method based on the effects of stress and rock erosion. By comprehensively considering the stress concentration of the wellbore, the water erosion of the rock, and the physical and mechanical properties of the rock, the calculation is performed using a stability analysis model. Classical static analysis methods, such as the limit equilibrium method or the finite element analysis method, can be used to evaluate the stability of the wellbore under existing stress and water erosion conditions. The input parameters in the model include the compressive strength of the rock, the strain value, the corrosion depth, the wellbore shape, etc. The calculation results will provide a stability index of the wellbore, such as the stability factor or the safety factor, and then determine whether the wellbore has a potential risk of instability.

[0160] Step S28: Optimize drilling fluid parameters according to the wellbore stability data, thereby obtaining drilling fluid optimization parameter data.

[0161] In this embodiment, the drilling fluid parameter optimization is performed based on the wellbore stability data. The optimization process is mainly based on the wellbore stability requirements, and a suitable drilling fluid formula is selected to enhance the stability of the wellbore. By comparing the performance indicators of different drilling fluid formulas (such as viscosity, rheology, density, etc.), combined with factors such as the water erosion depth and stress concentration of the wellbore, the optimal drilling fluid parameters are determined. In actual operation, the optimization goals of the drilling fluid are set (for example, reducing corrosion and enhancing anti-penetration ability), and the component ratio and performance of the drilling fluid are optimized through experiments or simulations, so that the drilling fluid can improve work efficiency while ensuring the stability of the wellbore. The drilling fluid optimization parameter data finally obtained is used for subsequent drilling operations.

[0162] Optionally, step S28 is specifically as follows:

[0163] Step S281: performing a fracture risk assessment based on the wellbore stability data to obtain fracture risk data;

[0164] In this embodiment, the fracture risk assessment is achieved by using the wellbore stability data through a comprehensive analysis of rock characteristics, stress concentration and water erosion. In the specific operation, firstly, the stress distribution in different areas is evaluated based on the stress data obtained from the wellbore stability data, and the stress concentration area is calculated in combination with the physical properties of the rock (such as compressive strength, tensile strength and friction coefficient). For the water erosion data, the impact of water erosion on rock strength is evaluated by calculating the water erosion depth. All data are input into the fracture risk assessment model, and the risk assessment threshold is set. For example, if the stress value exceeds the maximum bearing capacity of the rock, or the water erosion depth exceeds the set safety value, it is determined to be a high-risk area for fractures. Finally, fracture risk data is generated to reflect the probability of cracks in various areas of the wellbore.

[0165] Step S282: dividing the crack risk data into high-risk areas, thereby obtaining crack high-risk area data;

[0166] In this example, the risk level of crack occurrence is determined based on crack risk data. A threshold for high-risk areas is set at 70% or above. Regions with risk values exceeding 70% are designated as high-risk areas. Based on this, a spatial distribution analysis of the risk data is performed, and areas with risk above the threshold are designated as high-risk zones. This process utilizes a geographic information system (GIS) for spatial data analysis, combined with the geological structure and stress distribution maps of the wellbore wall, to accurately delineate high-risk areas. Ultimately, data on high-risk areas is output, providing a basis for subsequent processing.

[0167] Step S283: increasing the drilling fluid density according to the fracture high-risk area data, thereby obtaining drilling fluid density data;

[0168] In this embodiment, after selecting a high-risk area for cracks, the increased drilling fluid density is calculated based on the stress distribution and water erosion conditions in the area. The amount of density increase is determined by comparing the pressure demand in the crack risk area with the downhole pressure balance. Generally speaking, in areas with high crack risk, the density of the drilling fluid needs to be increased to provide sufficient hydrostatic pressure to prevent crack expansion. The density of the drilling fluid is usually increased by adding weighting agents such as barite or bazite. Depending on the specific well depth and pressure requirements, the density value is adjusted in the range of 1.5g / cm3 to 2.2g / cm3. Finally, the density data of the drilling fluid is obtained to provide parameter support for optimizing operating conditions.

[0169] Step S284: adjusting the drilling fluid viscosity according to the fracture high-risk area data, thereby obtaining drilling fluid viscosity data;

[0170] In this embodiment, the rheological properties of the wellbore in the area are evaluated by using the data of the high-risk area of cracks. During specific implementation, the high-risk area is selected and the viscosity of the drilling fluid is adjusted according to the potential impact of the occurrence of cracks. Increasing the viscosity helps to improve the suspension ability of the drilling fluid and reduce the accumulation of drilling fluid on the wellbore wall, thereby reducing the probability of cracks. According to the stress concentration and water erosion of the wellbore wall, the range of viscosity increase is set, usually controlled between 20-40cP. To this end, a thickener (such as bentonite or high molecular polymer) is added to the drilling fluid, and the change in viscosity is tested by a rheometer to ensure that the prepared drilling fluid has good fluidity and stability in the high-risk area of cracks. Finally, the drilling fluid viscosity data is obtained to ensure stable downhole operations.

[0171] Step S285: integrating drilling fluid optimization parameters according to the drilling fluid density data and the drilling fluid viscosity data, thereby obtaining drilling fluid optimization parameter data.

[0172] In this embodiment, the density and viscosity data of the drilling fluid are combined for integrated optimization. At this time, by comprehensively considering the actual conditions of different high-risk areas of fractures, the density and viscosity ratio of the drilling fluid is adjusted to meet the requirements of wellbore stability. During the integration process, physical modeling and fluid dynamics simulation are used to ensure the optimal ratio of density and viscosity to achieve fracture suppression and wellbore stability. During the integration of the optimized parameters of the drilling fluid, the adjustment of density and viscosity must not only consider the fluid dynamics properties, but also the rheological properties of the drilling operation and the protective effect on the wellbore. By optimizing the ratio, the optimized parameter data of the drilling fluid, including density, viscosity and other related properties, is obtained to ensure the stability and safety of the wellbore during the drilling operation.

[0173] Optionally, step S3 specifically includes:

[0174] Step S31: Acquire rock formation data of oil and gas exploration wells;

[0175] In this embodiment, geological exploration tools such as core sampling, seismic sounding, or drilling data are used to obtain rock formation data for oil and gas exploration wells. This data includes the depth, thickness, composition, physical properties (such as density, porosity, and compaction), and rock formation type (sandstone, shale, limestone, etc.). Specific downhole rock formation data is obtained through core sampling, wellbore imaging, and real-time monitoring systems on drilling equipment. This data must be accurately recorded to ensure the accuracy of subsequent data processing and provide a foundation for subsequent calculations.

[0176] Step S32: extracting acoustic wave sensing features based on the oil and gas exploration well sensing data, thereby obtaining acoustic wave sensing data;

[0177] In this embodiment, acoustic wave sensing data is collected using acoustic wave sensors deployed in oil and gas exploration wells. Acoustic wave sensors are used to measure the propagation velocity of sound waves in a formation, which is related to the density and elastic properties of the formation. First, the acoustic wave sensor transmits an acoustic wave pulse of a certain frequency and records the echo signal. The propagation velocity of the sound wave in the formation is calculated based on the propagation time of the echo signal. Acoustic wave sensing feature extraction extracts propagation velocity, amplitude, and waveform characteristics through data analysis, thereby deriving acoustic wave sensing data. This data plays an important role in the subsequent analysis of the formation's rock physical properties.

[0178] Step S33: calculating the Young's modulus of the formation rock based on the acoustic wave sensing data, thereby obtaining the Young's modulus data of the formation rock;

[0179] In this embodiment, Young's modulus of the formation rock is calculated based on the propagation velocity in the acoustic wave sensor data. Young's modulus is an important parameter for measuring the elasticity of the rock formation and is closely related to the longitudinal wave velocity (Vp) of the acoustic wave. The Young's modulus can be calculated using the following formula based on the propagation velocity of the acoustic wave in the rock formation:

[0180] E=ρ·Vp2;

[0181] Where E is the Young's modulus, ρ is the rock density, and Vp is the longitudinal wave velocity. Using the acoustic wave propagation velocity and rock density data obtained in the previous step, the Young's modulus of the rock formation is calculated. This data describes the rock formation's ability to resist deformation under stress.

[0182] Step S34: calculating the Poisson's ratio of the formation rock based on the acoustic wave sensing data, thereby obtaining the Poisson's ratio data of the formation rock;

[0183] In this embodiment, the Poisson's ratio of the formation rock is calculated using the shear wave velocity (Vs) and longitudinal wave velocity (Vp) of the acoustic wave sensor data. The Poisson's ratio describes the degree of deformation in the vertical direction when a material is subjected to a force in one direction. The Poisson's ratio is calculated using the following formula:

[0184]

[0185] Where v is the Poisson's ratio, Vp is the longitudinal wave velocity, and Vs is the shear wave velocity. Acoustic sensor data provides longitudinal and shear wave velocity data, which can be substituted into the formula to determine the Poisson's ratio of the rock formation. Poisson's ratio data is used to assess the elastic deformation behavior of the formation and is particularly important in determining the likelihood of fracture initiation and its propagation.

[0186] Step S35: integrating the rock formation elastic properties according to the Young's modulus data and the Poisson's ratio data of the rock formation, thereby obtaining rock formation elastic data;

[0187] In this embodiment, the elastic properties of the rock formation reflect the stress-strain relationship of the rock formation under the influence of seismic waves and pressure. By combining Young's modulus and Poisson's ratio, a more comprehensive picture of the rock formation's elastic properties is obtained. A common integration approach is to use stress-strain relationship models to predict the elastic response of the rock formation under different stress conditions and assess whether the rock formation is susceptible to fracture or other deformation. By integrating elastic properties, the comprehensive performance of the rock formation under various loads can be obtained, providing the necessary parameters for assessing the stability of the formation.

[0188] Step S36: Acquire neutron logging data, and perform formation porosity analysis based on the neutron logging data and formation elasticity data, thereby obtaining formation porosity data;

[0189] In this example, neutron logging data primarily reflects the hydrogen content of the rock formation, thereby inferring porosity. Neutron logging instruments utilize the scattering of neutrons by hydrogen atoms in the rock formation to obtain porosity data. Combined with the formation's elasticity data, this data is analyzed using a porosity-elasticity relationship model. This helps assess the formation's pore structure and its relationship to rock physical properties. Ultimately, through data integration, formation porosity data is obtained, providing support for subsequent fracture pressure assessment and production capacity prediction.

[0190] Step S37: Evaluate the formation fracture pressure based on the acoustic wave sensing data and the rock formation elasticity data, thereby obtaining formation fracture pressure data;

[0191] In this embodiment, formation fracture pressure assessment is achieved by combining acoustic sensor data with rock formation elasticity data. Fracture pressure refers to the minimum pressure at which a rock formation begins to fracture under water pressure or other external forces. By analyzing elastic properties of the rock formation, such as Young's modulus, Poisson's ratio, and porosity, and combining them with velocity variations in acoustic sensor data, the fracture pressure of the rock formation at different depths and locations can be estimated. A common assessment method is to calculate the critical stress and fracture stress of the rock formation and then predict the fracture pressure. This assessment can provide formation fracture pressure data, ensuring safe production.

[0192] Step S38: Perform comprehensive production capacity prediction based on the formation fracture pressure data and the formation porosity data, thereby obtaining formation production capacity data.

[0193] In this example, formation productivity prediction is based on formation breakdown pressure data and formation porosity data. By assessing the formation's breakdown pressure and combining it with porosity data, the production potential of oil and gas exploration wells can be analyzed. Porosity reflects the volume of space within the formation that can store oil and gas, while breakdown pressure affects the feasibility of downhole fluid flow. Combining this data, a fluid dynamics model can be used to calculate the formation's productivity, including fluid flow rate, pressure distribution, and produced fluid volume. Ultimately, formation productivity data is generated, providing a viable assessment of oil and gas extraction.

[0194] Optionally, step S36 is specifically as follows:

[0195] Step S361: Acquire neutron logging data, and perform neutron count feature extraction and neutron attenuation value feature extraction based on the neutron logging data, thereby obtaining neutron count data and neutron attenuation value data;

[0196] In this embodiment, neutron logging data is typically acquired using a neutron logging instrument. This instrument emits a neutron beam into the formation and measures the intensity of the signal returned after the neutrons scatter off hydrogen atoms in the formation. The signal intensity is directly related to the porosity and hydrogen content of the formation. By collecting this data, the neutron count feature and neutron attenuation feature are first extracted. The neutron count feature is based on the number of neutrons received by the instrument and reflects the hydrogen content of the formation. The neutron attenuation feature analyzes the attenuation of the neutron beam to assess the pore structure and fluid distribution of the formation. Based on this raw data, neutron count data and neutron attenuation data are calculated. These data provide the basis for subsequent porosity analysis and assessment of the physical properties of the formation.

[0197] Step S362: performing neutron logging porosity calculation based on the neutron counting data and the neutron attenuation value data, thereby obtaining neutron logging porosity data;

[0198] In this embodiment, neutron logging porosity is calculated based on neutron count data and neutron attenuation data. Porosity is an important parameter that reflects the ratio of voids to solid matter in a rock formation. Porosity is calculated using the following formula:

[0199]

[0200] Where φ is the porosity, N0 is the baseline neutron count (the non-porous portion of the formation), N is the actual neutron count, and Nmin is the background count (typically the low-porosity portion of the formation). Neutron attenuation characteristics can also be used to estimate porosity. By normalizing data with different attenuation rates, the porosity data of the formation can be obtained. This calculation obtains porosity values based on neutron logging data, providing a basis for subsequent elastic corrections.

[0201] Step S363: performing elastic correction on the neutron logging porosity data according to the formation elasticity data, thereby obtaining neutron logging porosity elasticity correction data;

[0202] In this embodiment, the rock formation elasticity data is a key parameter reflecting the deformation of the rock formation under different stress states. The rock formation elasticity data is combined with the neutron logging porosity data to perform elastic correction, aiming to improve the accuracy of porosity calculations, especially in highly elastic rock formations. The elasticity correction process is typically adjusted using an elasticity correction factor, which is calculated based on elastic parameters such as the rock formation's Young's modulus and Poisson's ratio. This is performed using the following correction formula:

[0203]

[0204] Where φelastic is the corrected porosity, φmeasured is the original porosity, Eactual and Ereference are the Young's moduli of the actual and reference rock formations, respectively. This correction eliminates porosity errors caused by differences in rock formation elasticity, improving data accuracy.

[0205] Step S364: obtaining density logging data, and performing porosity calculation based on the density logging data, thereby obtaining density logging porosity data;

[0206] In this embodiment, density logging data is usually obtained by measuring using a gamma ray source or an X-ray source. The instrument emits rays at different depths in the well and receives echoes. The density of the rock formation is estimated by analyzing the intensity of the echo signal. In actual operation, the signal recorded by the instrument is processed to obtain the density value of the rock formation. These density values reflect the material composition of the rock formation and its pore structure. The density logging data needs to be further applied to the porosity calculation. Specifically, the porosity of the rock formation is calculated by comparing the total density, skeleton density and pore fluid density of the rock formation. The density logging porosity data can reflect the spatial structure of the rock formation and provide a basis for further analysis. In order to ensure the accuracy of the porosity calculation, it is necessary to use the rock density and fluid density values obtained by field experiments. These data need to be accurately calibrated during the actual measurement process to avoid the influence of environmental or equipment errors.

[0207] Step S365: performing porosity integration on the neutron logging porosity elasticity correction data according to the density logging porosity data, thereby obtaining formation porosity data.

[0208] In this embodiment, the final rock formation porosity data is obtained by integrating the density logging porosity data with the neutron logging porosity elastic correction data. Density logging porosity data generally reflects the physical properties of the rock formation, while the neutron logging porosity elastic correction data takes into account the influence of the elastic properties of the rock formation on the porosity. Therefore, a certain weighting strategy needs to be adopted when integrating these two sets of data. The setting of the weighting coefficient is allocated based on the reliability and applicability of the two data. The weights of different data are usually determined based on the type of rock formation, the distribution of fluids, and the existing experimental data. The integration process obtains a comprehensive porosity result by weighting the correction values of the density logging data and the neutron logging data. This process is especially important for complex formations because a single logging data cannot fully reflect the pore characteristics of the rock formation. Through integration, the accuracy of the porosity data can be improved, providing more reliable parameter support for subsequent geological analysis and production prediction.

[0209] Optionally, step S37 is specifically as follows:

[0210] Step S371: extracting shear wave velocity features based on the acoustic wave sensing data to obtain shear wave velocity data;

[0211] In this embodiment, the shear wave velocity data is usually obtained by measuring the shear wave propagation velocity of the rock formation using an acoustic wave sensor. The acoustic wave sensor is usually composed of multiple sensor arrays, arranged on the well wall, and transmits and receives acoustic wave signals. By analyzing the relationship between the propagation time of the shear wave signal and the physical properties of the downhole rock formation, the shear wave velocity can be calculated. The shear wave propagation velocity is affected by the elastic properties of the rock formation. Therefore, the shear wave velocity characteristics can be extracted using the shear wave propagation time data obtained by the acoustic wave sensor. During the feature extraction process, it is necessary to perform time domain and frequency domain analysis on the acoustic wave signal to extract the signal's peak value, frequency, propagation time and other characteristics, and then calculate the shear wave velocity. These data are the basis of geological analysis and are used for subsequent shear modulus and fracture pressure calculations.

[0212] Step S372: Obtaining rock formation density data, and performing shear modulus calculation based on the rock formation density data and shear wave velocity data, thereby obtaining rock formation shear modulus data;

[0213] In this embodiment, the rock formation density data is usually obtained through a density logging instrument, which measures the density of the rock formation by emitting gamma rays or X-ray signals and calculates the mass value per unit volume. The shear modulus is an important parameter of the elastic properties of the rock formation, which reflects the elastic response of the rock formation under stress. The calculation of the shear modulus depends on the density and shear wave velocity data of the rock formation, and the shear wave velocity and shear modulus are closely related. By combining the shear wave velocity and rock formation density data, the shear modulus can be calculated using relevant physical theories. In this process, it is necessary to ensure the consistency of the rock formation density data and the shear wave velocity data within the same depth range to avoid inaccurate calculation results due to inconsistent data.

[0214] Step S373: Calculate the fracture pressure based on the rock formation shear modulus data and the formation rock Young's modulus data, thereby obtaining the formation fracture pressure data.

[0215] In this embodiment, the fracture pressure is an important parameter for evaluating the stability of the wellbore and production safety. It reflects the critical conditions for the rock formation to fracture when subjected to pressure. In this step, the shear modulus and Young's modulus are used as elastic characteristic parameters of the rock formation, combined with the physical properties of the rock formation (such as density, porosity, etc.), and can be calculated using a known fracture pressure model. The shear modulus data of the rock formation can provide the ability of the rock formation to resist shear stress, while the Young's modulus reflects the response of the rock formation when it is stretched or compressed. The combination of the two data helps to accurately estimate the fracture pressure of the rock formation under different stresses. In order to ensure the accuracy of the calculation results, it is necessary to use the actual measurement data of the rock formation and the empirical model to evaluate the fracture pressure. In addition, it is necessary to take into account the complexity of the underground geological conditions and make necessary corrections to the data during the calculation process.

[0216] Optionally, this specification also provides a transmission system for integrated mud logging monitoring video, which is used to execute the above-mentioned transmission method for integrated mud logging monitoring video. The transmission system for integrated mud logging monitoring video includes:

[0217] Exploration well anomaly detection module: used to obtain oil and gas exploration well sensor data, and perform downhole image feature extraction based on the oil and gas exploration well sensor data to obtain downhole image data; perform exploration well anomaly detection based on the downhole image data to obtain exploration well anomaly data;

[0218] Wellbore stability analysis module: used to perform wellbore stability analysis based on abnormal data of exploration wells, thereby obtaining wellbore stability data; optimize drilling fluid parameters based on wellbore stability data, thereby obtaining drilling fluid optimization parameter data;

[0219] Formation production capacity analysis module: used to obtain rock formation data of oil and gas exploration wells; extract acoustic wave sensing features based on the oil and gas exploration well sensor data to obtain acoustic wave sensing data; perform formation production capacity analysis on the oil and gas exploration well rock formation data based on the acoustic wave sensing data to obtain formation production capacity data;

[0220] Formation production video acquisition module: used to evaluate formation production efficiency based on formation production capacity data according to drilling fluid optimization parameter data, thereby obtaining formation production efficiency data; and to acquire formation production video acquisition data based on oil and gas exploration well sensor data.

[0221] Formation production video transmission module: used to encode and compress the formation production video acquisition data to obtain formation production video encoding and compression data; perform 5G network transmission based on the formation production video encoding and compression data to obtain formation production video transmission data, and upload it to the comprehensive logging monitoring platform to perform the formation production video transmission task.

[0222] The transmission system for integrated well logging monitoring video of the present invention can implement any transmission method for integrated well logging monitoring video of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the transmission method for integrated well logging monitoring video. The internal modules of the system cooperate with each other, thereby improving the stability of video transmission.

[0223] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0224] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for transmitting integrated logging monitoring video, characterized in that: The following steps are involved: Step S1: Acquire sensor data of an oil and gas exploration well, and perform downhole image feature extraction based on the sensor data of the oil and gas exploration well to obtain downhole image data; perform exploration well anomaly detection based on the downhole image data to obtain exploration well anomaly data; Step S2: performing wellbore stability analysis based on the abnormal data of the exploration well to obtain wellbore stability data; optimizing drilling fluid parameters based on the wellbore stability data to obtain drilling fluid optimization parameter data; Step S3: Acquire rock formation data of the oil and gas exploration well; perform acoustic sensing feature extraction based on the oil and gas exploration well sensor data to obtain acoustic sensing data; perform formation production capacity analysis on the rock formation data of the oil and gas exploration well based on the acoustic sensing data to obtain formation production capacity data; Step S4: performing formation production efficiency evaluation on the formation production capacity data according to the drilling fluid optimization parameter data, thereby obtaining formation production efficiency data; Video acquisition of formation production efficiency data is performed based on oil and gas exploration well sensor data, thereby obtaining formation production video acquisition data; Step S5: performing encoding and compression on the formation production video acquisition data, thereby obtaining formation production video encoding and compression data; The 5G network transmission is performed based on the formation production video coding compression data to obtain the formation production video transmission data, and upload it to the comprehensive logging monitoring platform to perform the formation production video transmission task.

2. The transmission method for comprehensive logging monitoring video according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring sensor data of the oil and gas exploration well, and performing downhole image feature extraction based on the sensor data of the oil and gas exploration well, thereby obtaining downhole image data; Step S12: performing water accumulation anomaly detection on the downhole image data, thereby obtaining water accumulation anomaly data of the exploration well; Step S13: performing fracture anomaly detection on the downhole image data to obtain fracture anomaly data of the exploration well; Step S14: integrating the abnormal features of the exploration wells based on the abnormal water accumulation data and the abnormal fracture data of the exploration wells, thereby obtaining abnormal data of the exploration wells.

3. The transmission method for comprehensive logging monitoring video according to claim 2 is characterized in that: Step S12 is specifically as follows: Step S121: performing brightness enhancement on the downhole image data, thereby obtaining brightness enhanced downhole image data; Step S122: identifying high-brightness pixel areas based on the brightness-enhanced downhole image data, thereby obtaining high-brightness pixel area data; Step S123: performing edge detection based on the brightness enhanced downhole image data to obtain downhole image edge data; Step S124: performing irregular region statistics on the downhole image edge data, thereby obtaining the downhole image edge irregular region data; Step S125: performing a water accumulation area intersection operation based on the high-brightness pixel area data and the irregular edge area data of the downhole image, thereby obtaining water accumulation area data; Step S126: performing a multi-point laser beam projection simulation on the waterlogged area data to obtain multi-point laser beam projection data; Step S127: performing three-dimensional space coordinate conversion according to the multi-point laser beam projection data, thereby obtaining three-dimensional space coordinate data; Step S128: Obtaining data on the maximum safe depth of accumulated water; Step S129: reconstruct the three-dimensional depth distribution map of the waterlogged area based on the three-dimensional spatial coordinate data to obtain the three-dimensional depth distribution map data of the waterlogged area, and compare the three-dimensional depth distribution map data of the waterlogged area with the waterlogging anomaly of the exploration well based on the maximum safe depth data of the waterlogged area to obtain the waterlogging anomaly data of the exploration well.

4. The transmission method for comprehensive logging monitoring video according to claim 2, characterized in that: Step S13 is specifically as follows: Step S131: performing grayscale image conversion on the downhole image data to obtain downhole grayscale image data; Step S132: performing gray-level co-occurrence matrix calculation based on the downhole gray-level image data to obtain gray-level co-occurrence matrix data; Step S133: performing entropy calculation on the gray-level co-occurrence matrix data to obtain entropy data, and performing high entropy value statistics based on the entropy data to obtain high entropy value data; Step S134: performing homogeneity calculation on the gray level co-occurrence matrix data to obtain homogeneity data, and performing high homogeneity statistics based on the homogeneity data to obtain high homogeneity data; Step S135: performing fracture area identification on the downhole grayscale image data based on the high entropy data and the high homogeneity data, thereby obtaining fracture area data; Step S136: obtaining safety crack length data; Step S137: performing a comparison between the abnormal cracks in the exploration well and the crack area data based on the safety crack length data, thereby obtaining abnormal crack data in the exploration well.

5. The transmission method for comprehensive logging monitoring video according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: evaluating the corrosiveness of water accumulation based on abnormal data of the exploration well, thereby obtaining corrosiveness data of water accumulation; Step S22: Acquire wellbore rock data and wellbore strain gauge sensing data; Step S23: performing water erosion simulation on the well wall rock data according to the water corrosivity data, thereby obtaining well wall rock water erosion data; Step S24: performing well wall area statistics based on the well wall rock water erosion data, thereby obtaining well wall rock water erosion area data; Step S25: performing stress concentration area statistics based on the wellbore strain gauge sensing data, thereby obtaining stress concentration area data; Step S26: performing a wellbore instability region intersection operation based on the wellbore rock water erosion region data and the stress concentration region data, thereby obtaining the wellbore instability region data; Step S27: performing stability calculation on the wellbore instability area data to obtain wellbore stability data; Step S28: Optimize drilling fluid parameters according to the wellbore stability data, thereby obtaining drilling fluid optimization parameter data.

6. The transmission method for comprehensive logging monitoring video according to claim 5, characterized in that: Step S28 is specifically as follows: Step S281: performing a fracture risk assessment based on the wellbore stability data to obtain fracture risk data; Step S282: dividing the crack risk data into high-risk areas, thereby obtaining crack high-risk area data; Step S283: increasing the drilling fluid density according to the fracture high-risk area data, thereby obtaining drilling fluid density data; Step S284: adjusting the drilling fluid viscosity according to the fracture high-risk area data, thereby obtaining drilling fluid viscosity data; Step S285: integrating drilling fluid optimization parameters according to the drilling fluid density data and the drilling fluid viscosity data, thereby obtaining drilling fluid optimization parameter data.

7. The transmission method for comprehensive logging monitoring video according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: Acquire rock formation data of oil and gas exploration wells; Step S32: extracting acoustic wave sensing features based on the oil and gas exploration well sensing data, thereby obtaining acoustic wave sensing data; Step S33: Calculating the Young's modulus of the formation rock based on the acoustic wave sensing data, thereby obtaining Young's modulus data of the formation rock; Step S34: calculating the Poisson's ratio of the formation rock based on the acoustic wave sensing data, thereby obtaining the Poisson's ratio data of the formation rock; Step S35: integrating the rock formation elastic properties according to the Young's modulus data and the Poisson's ratio data of the rock formation, thereby obtaining rock formation elastic data; Step S36: Acquire neutron logging data, and perform formation porosity analysis based on the neutron logging data and formation elasticity data, thereby obtaining formation porosity data; Step S37: Evaluate the formation fracture pressure based on the acoustic wave sensing data and the rock formation elasticity data, thereby obtaining formation fracture pressure data; Step S38: Perform comprehensive production capacity prediction based on the formation fracture pressure data and the formation porosity data, thereby obtaining formation production capacity data.

8. The transmission method for comprehensive logging monitoring video according to claim 7, characterized in that: Step S36 is specifically as follows: Step S361: Acquire neutron logging data, and perform neutron count feature extraction and neutron attenuation value feature extraction based on the neutron logging data, thereby obtaining neutron count data and neutron attenuation value data; Step S362: performing neutron logging porosity calculation based on the neutron counting data and the neutron attenuation value data, thereby obtaining neutron logging porosity data; Step S363: performing elastic correction on the neutron logging porosity data according to the formation elasticity data, thereby obtaining neutron logging porosity elasticity correction data; Step S364: obtaining density logging data, and performing porosity calculation based on the density logging data, thereby obtaining density logging porosity data; Step S365: performing porosity integration on the neutron logging porosity elasticity correction data according to the density logging porosity data, thereby obtaining formation porosity data.

9. The transmission method for comprehensive logging monitoring video according to claim 8, characterized in that: Step S37 is specifically as follows: Step S371: extracting shear wave velocity features based on the acoustic wave sensing data to obtain shear wave velocity data; Step S372: Obtaining rock formation density data, and performing shear modulus calculation based on the rock formation density data and shear wave velocity data, thereby obtaining rock formation shear modulus data; Step S373: Calculate the fracture pressure based on the rock formation shear modulus data and the formation rock Young's modulus data, thereby obtaining the formation fracture pressure data.

10. A transmission system for integrated logging monitoring video, characterized in that: For executing the transmission method for comprehensive logging monitoring video according to claim 1, the transmission system for comprehensive logging monitoring video comprises: Exploration well anomaly detection module: used to obtain oil and gas exploration well sensor data, and perform downhole image feature extraction based on the oil and gas exploration well sensor data to obtain downhole image data; perform exploration well anomaly detection based on the downhole image data to obtain exploration well anomaly data; Wellbore stability analysis module: used to perform wellbore stability analysis based on abnormal data of exploration wells, thereby obtaining wellbore stability data; optimize drilling fluid parameters based on wellbore stability data, thereby obtaining drilling fluid optimization parameter data; Formation production capacity analysis module: used to obtain rock formation data of oil and gas exploration wells; extract acoustic wave sensing features based on the oil and gas exploration well sensor data to obtain acoustic wave sensing data; perform formation production capacity analysis on the oil and gas exploration well rock formation data based on the acoustic wave sensing data to obtain formation production capacity data; Formation production video acquisition module: used to evaluate formation production efficiency based on formation production capacity data according to drilling fluid optimization parameter data, thereby obtaining formation production efficiency data; and to acquire formation production video acquisition data based on oil and gas exploration well sensor data. Formation production video transmission module: used to encode and compress the formation production video acquisition data to obtain formation production video encoding and compression data; perform 5G network transmission based on the formation production video encoding and compression data to obtain formation production video transmission data, and upload it to the comprehensive logging monitoring platform to perform the formation production video transmission task.

Images