The research object of oil and gas exploration is gradually shifting toward complex oil and gas reservoirs. The periphery of the Banghu Syncline in the Qianjiang Sag is a typical inland salt lake deposit. The complex thin sand and mudstone interbedded reservoir structure in the Qian-3 section of the Banghu Syncline area requires high-precision and high-resolution exploration technology to support actual production. In view of this, the study conducts forward modeling analysis and complex lithology classification based on well logging data. First, it analyzes and calculates the lithological data based on the original logging data, analyzes the lithological characteristics of sandstones containing different fluids (water-bearing sandstones, oil-bearing sandstones, and dry-layer sandstones), establishes different wedge forward models based on the convolution theory, and investigates the seismic response characteristics of different lithological combinations. Then, lithology curves are reconstructed using the K-means algorithm with known logging lithology data, and density attributes are used to correct natural gamma values for lithology classification. Finally, a geological model of the $ \mathrm{E}{\mathrm{q}}_{3}^{4} $ oil formation is designed to study the effects of changes in reservoir thickness and fluid content on amplitude.Model analysis and the example demonstrate K-means algorithm can effectively divide salt rock, sandstone, and gypsum mudstone with a prediction accuracy of 90.4%, and the forward model based on high-resolution logging information is consistent with actual geological characteristics. Therefore, it is feasible to analyze the reflection characteristics of salt rock and mudstone interbedded layers and thin sandstone layers by the forward model established through lithology curve reconstruction with logging data.

With increasingly complex oil and gas exploration targets, seismic exploration faces challenges such as low signal-to-noise ratio (SNR), low resolution, and difficulties in velocity modeling and imaging of seismic data. Conventional seismic data processing and interpretation methods have certain limitations in accuracy or efficiency when applied to massive seismic data. The artificial intelligence (AI)-based seismic data processing and interpretation methods can effectively improve accuracy and efficiency. To this end, this paper provides an overview of supervised, semi-supervised, and unsupervised deep learning techniques, and summarizes the applications of deep learning in data processing such as first break picking, SNR improvement, data reconstruction, velocity spectrum interpretation, migration, and resolution enhancement. Meanwhile, it discusses the applications of deep learning in identifying geological bodies such as faults, seismic facies, river channels, and salt domes, as well as in wave impedance inversion, AVO inversion, full waveform inversion, lithology identification, reservoir parameter prediction, and fluid identification. The production of training sets, optimization of neural networks, training strategies, and large models are discussed, with an outlook on the development trend of AI-based processing and interpretation methods for seismic data provided. It is pointed out that the generalization of networks should be continuously increased and large models suitable for seismic exploration should be studied.

Intelligent seismic velocity inversion is currently a hot and challenging topic in seismic exploration research. Nevertheless, the complex structure of deep learning networks demands significant computing power from hardware devices, which restricts the application of the model in scenarios with large data volumes and high timeliness requirements. To address these practical issues, in this paper, the U-Net is improved based on the concepts of feature engineering and model lightweighting, and the inversion networks U-Net vG for GPU and U-Net vC for CPU are proposed. Firstly, the characteristics of the velocity inversion network are analyzed to deduce the lightweighting principles of convolutional neural networks. Subsequently, lightweight processing is conducted on the multi-scale module, attention gate module, and feature extraction module to obtain a lightweight convolutional neural network for velocity modeling, which reduces the network volume while maintaining prediction accuracy. Data test results demonstrate that the training process of the proposed network has lower requirements for high-performance hardware resources, and that the network enables efficient velocity inversion, possesses higher seismic velocity inversion accuracy, and exhibits superior noise resistance. It provides a new idea for solving the computing power bottleneck problem in seismic data inversion.

The "stamp-like" acquisition method in complex areas results in fragmented three-dimensional seismic data of a zone, with differences in energy, frequency, phase, and time between datasets. This poses challenges to regional seismic interpretation and comprehensive studies. Conventional pre-stack joint processing helps address boundary splicing problems between 3D blocks but the significant disparities in the quality of original data across different blocks prevent full utilization of high-quality data and ultimately reduce imaging precision. In response to the greatly different seismic data in complex areas, this paper proposes a technical scheme of block and joint seismic data processing based on key parameters of the zone. This method preserves the advantages of native bin processing for individual blocks while possessing the advantages of joint data for facilitating comprehensive studies. Meanwhile, it resolves the problems in conventional joint processing of a large number of existing 3D data, such as "difficult blending of single-block processing" and "insufficient fineness in joint processing". Case studies from the complex mountainous structural belt in Kuche and the Fuman East area of Tarim Basin demonstrate the effectiveness of this technical method. This method constructs joint data volumes by adopting building blocks, which supports the trap well location and basic geological research and provides useful references for the industrial sector.

Seismic facies identification is a crucial link in seismic data interpretation. Deep learning technology can enhance the efficiency and accuracy of automatic seismic facies identification. However, deep learning methods typically rely on large amounts of labeled data, and in practical applications, the labeling cost of seismic data is high, with great difficulty. Additionally, basic logging data cannot be directly utilized. To this end, this paper proposes a semi-supervised automatic seismic facies identification method based on ultra-sparse logging labels. First, based on the HRNet, a seismic facies identification model that uses one-dimensional logging labels is built for for supervision. Second, to preserve the vertical characteristics of seismic data, this paper develops a sparse label sampling module (SLSM) that conducts samples around the logging labels without slicing the seismic data vertically, thus retaining its vertical depth features and laying a solid foundation for subsequent semi-supervised learning tasks. Third, in terms of the lateral correlation of seismic data, the region growing training strategy (RGTS) is proposed, which expands the information from logging labels to the entire seismic volume through an iterative growing process. Experiments on real-world data show that the proposed model achieves a mean intersection over union (MIoU) of 79.64% by using only 32 one-dimensional logging labels, which account for less than 0.5% of the total data volume. This approach provides references for conducting seismic facies identification in areas with sparse and locally distributed logging data, demonstrating promising application potential.

Channel identification is crucial for predicting fluvial facies reservoirs. However, when the P-wave impedance contrast between channel sandstones and surrounding rocks is minimal, it is difficult to use only post-stack P-wave seismic data for channel identification. S-wave data can effectively enhance the reliability of predicting the spatial distribution of channels. However, the combined identification process of P-wave and S-wave involves challenges such as difficult parameter selection, high subjectivity, and extended working cycles, leading to inefficiencies and reduced reliability. This paper proposes an automatic channel identification methodbased on the joint P-wave and S-wave seismic data. First, to address the issue of insufficient sample data, it puts forward a method for automatically generating synthetic forward modeling samples of 3D channel geological models based on actual data interpretation and channel interpretation results, effectively expanding the sample data set. Subsequently, a new 3D automatic channel identification network structure is then designed, which effectively integrates P-wave and S-wave seismic data, enhancing the reliability of the identification results. Finally, the proposed method is applied to identify tight gas channel sandstones in a work area in southwestern China. Compared with traditional seismic attribute analysis and intelligent identification results relying on a single data type, the proposed method demonstrates higher efficiency and reliability, validating its applicability.

The shear wave velocity and thickness of near-surface strata can be obtained through the inversion of Rayleigh wave dispersion curves. However, traditional nonlinear inversion algorithms often have disadvantages such as poor convergence effect and being prone to fall into local extremity. A new improved beluga whale optimization (DWBWO) algorithm is proposed in this paper and applied to the inversion of the Rayleigh surface wave dispersion curve. Based on the beluga whale optimization (BWO) algorithm, this algorithm introduces the Cubic chaotic initialization strategy to improve the uniformity of the initial population. Meanwhile, the dimensional reverse learning strategy is used to improve the convergence efficiency of the algorithm, and the whirlwind foraging strategy (WFS)is adopted to improve the local optimization ability of the algorithm. The DWBWO algorithm is tested by applying the multi-extremum functions, simulated data and measured data, and compared with the grey wolf optimization (GWO) algorithm, sparrow optimization (SSA) algorithm, whale optimization (WOA) algorithm and BWO algorithm. It was proved that the improved algorithm in this paper has higher stability and accuracy.

Karst caves exhibit distinctive "string-of-beads" reflection configurations on seismic profiles, with their spatial distribution governed by intricate fracture networks and thus forming complex fracture-cave systems. Conventional methods, constrained by ambiguities in reservoir architecture and limited sample availability, face challenges in achieving accurate delineation. This study proposes a knowledge graph-guided intelligent identification technique based on coupled fracture-cave modeling, which innovatively integrates geological prior knowledge with deep learning through encoding geological topological relationships into adjacency matrix constraints. The methodology establishes a multi-task learning framework by synergistically combining forward modeling-derived label data volumes with expert-annotated data volumes. The approach employs knowledge graphs to characterize connectivity relationships between fractures and karst caves and designs geologically interpretable loss functions to dynamically adjust model optimization trajectories. Application in the Ordovician Lianglitage Formation of the Tarim Basin demonstrates substantial reduction in manual interpretation workload and significant enhancement in boundary delineation precision for fracture-cave systems. This methodology presents an innovative solution integrating knowledge-driven and data-driven approaches for prediction of strongly heterogeneous carbonate reservoirs.

Continental shale oil has become the major unconventional oil resource in China. The reservoir features include the development of lamellation crack, low porosity, large difference between horizontal and vertical permeability, and complex pore structure. Therefore, clarifying the relationship between the elastic and physical parameters of shale oil reservoirs is important.In addition, there are few studies on the characteristics of shale lamellation crack and its influencing factors. Based on anisotropic background media, a rock physical modeling method for fracture-porosity shale rich in kerogen is proposed in this paper.Through rock physical modeling, the effects of matrix porosity, aspect ratio, connectivity coefficient and the lamellation crack length, width and numbers of the shale are compared and analyzed. The results show that the P wave velocity is greatly affected by the connectivity coefficient, while the S wave velocity is not affected by the connectivity coefficient. The effect of lamellation crack over 100 microns should not be ignored and its induced anisotropy could not be summarized as the effect of intrinsic anisotropy.Vp/Vs positively correlates with the length of lamellation crack, while the relationship with the number of lamellation crack decreases first and then increases. The method in this paper is applied to the logging data of a practical working area. The P‐wave velocity and S‐wave velocity prediction in shale reservoirs through this method are highly consistent with actual data, which verifies the effectiveness and applicability of the proposed method, and the method can be used as a bridge in the characterization of unconventional oil and gas reservoirs.

The typical 3D numerical model and seismic simulation are of great significance for the study of seismic processing and interpretation methods, as they can test and validate the effectiveness of various new technologies and approaches. In the double-complex exploration areas in western China, where both neasurface and subsurface structures present significant complex, there have long been challenges of difficult seismic migration imaging and delineation of structures, making it urgent to build a typical 3D model and forward modeling data that represent the double-complex characteristics of China's foreland basins. By taking the representative double-complex area of the Keshen block in the Kelasu area in western China as an example, this paper combines the typical surface and subsurface geological and geophysical features of foothill areas in the western foreland basins and fully utilizes data of seismic depth migration velocity, acoustic logging, VSP logging, and uphole survey to construct typical numerical models for double-complex exploration zones in western China(BGP-DC²GModels). The BGP-DC²GModels include an acoustic velocity model, an isotropic elastic medium model, TTI/TORT models, and a viscoelastic medium model, typically featuring complex near-surface conditions, complex subsurface structures, a high-speed conglomerate fan body, salt-gypsum formations, detachment layers (coal seams), a Q anomaly, and anisotropy. Based on the BGP-DC²GModels, five sets of high-quality forward modeling data for surface and borehole 3D observation are generated by adopting the spectral-element method, including the isotropic acoustic wave, isotropic elastic wave, isotropic viscoelastic wave, TTI viscoelastic wave, and TORT viscoelastic wave. Additionally, the acoustic reverse time migration and anisotropic reverse time migration results of the data are compared and analyzed, further demonstrating that typical models and numerical simulation data can test and validate the effectiveness of new acquisition, processing, and interpretation methods.

In ocean bottom node (OBN) seismic exploration, significant discrepancies often exist between the actual node positions and the initial surveyed locations due to factors such as ocean currents, tides, topographic variations, and fishing vessel dragging, necessitating secondary positioning. To address this, a high-precision secondary positioning method based on four-quadrant stacking is proposed. Specifically, within a relative coordinate system, shot points are divided into four quadrants according to their azimuth. Linear moveout correction and common receiver point stacking are performed separately for shot points in each quadrant. Subsequently, the displacement of the node from the surveyed position to the actual position is decomposed into two mutually perpendicular components. These two components are determined by analyzing the first arrival time differences from the common receiver points in each quadrant, which thereby enables precise estimation of the node's actual coordinates. Application to field data demonstrates that this method achieves high-precision secondary positioning of nodes. Moreover, the four-quadrant stacking strategy significantly improves the reliability of first arrival picking. Compared with traditional methods, this approach offers higher positioning accuracy and efficiency, showing excellent engineering applicability and promising potential for broader adoption.

The accurate prediction of tight sandstone reservoir parameters is a key scientific issue and technical challenge in unconventional oil and gas exploration. Traditional prediction methods based on linear or nonlinear regression have limitations in characterizing the complex nonlinear relationship between logging curves and reservoir parameters, leading to insufficient prediction accuracy. This study takes the Chang 6 reservoir in the Tang 157 well area of the Ganguyi Production Plant in Yanchang Oilfield as an example. Based on logging data and core analysis porosity data, multi-source data fusion preprocessing is conducted, and a novel neural network architecture (CNN-Transformer Network) that integrates the core advantages of CNN and Transformer is innovatively proposed. The prediction performance of the CNN-Transformer model is comprehensively compared with that of traditional linear regression (LR), TCN-LSTM, GRU, and ResNet models using RMSE, MAE, and R² metrics. Experimental results show that the prediction accuracy of the CNN-Transformer model reaches 96.7%, significantly outperforming the other comparative models. This model effectively captures the unique complex nonlinear mapping relationship between logging curves and porosity in tight sandstone reservoirs, significantly improving the accuracy of reservoir parameter prediction and providing reliable technical support for the efficient exploration and development decision-making of tight sandstone reservoirs.

Deep-learning-based seismic impedance inversion methods have received wide attention due to their ability to handle nonlinear mapping problems. The conventional deep-learning-based seismic impedance inversion methods have the problem of an overwhelming dependence on labeled data, which results in a decrease in the model's ability to extract local features and poor precision of inversion results when training data is insufficient. To address these issues, a new atrous spatial pyramid pooling and U-Net (ASPP-UNet) based seismic impedance inversion method is proposed. The multi-scale feature extraction ability of U-Net is enhanced by the atrous spatial pyramid pooling operation. Based on this, the training datasets were constructed using seismic data and a small amount of logging data. To verify the effectiveness of the proposed method, we conducted two simulation experiments on the Marmousi2 and SEAM public datasets and compared the results with those of CNN, U-Net, and Attention-UNet under the same experimental conditions. The experimental results show that, under the same experimental conditions, the single-trace impedance inversion produced by the proposed method contains richer high-frequency details, and the inverted impedance profile displays smooth vertical continuity between layers and at fault locations. The inversion results also depend less on labeled data and exhibit the least information loss at positions far from the training wells, which is reflected in the strong lateral continuity between traces in the inverted impedance profile. Compared with the comparison methods, the ASPP-UNet inversion results show the best statistical indicators. To further validate the applicability of the ASPP-UNet method, it was applied to real seismic impedance inversion data from East Sichuan Province. The impedance profile obtained by ASPP-UNet is consistent with the actual geological structure. Compared with the three deep-learning methods mentioned above, the inversion results have the highest accuracy, and the impedance profile error is the smallest.

The absorption and attenuation of seismic wave energy in complex loess plateaus has always been a focal point for geophysicists. This problem makes it difficult to improve the resolution and signal-to-noise ratio of seismic data and satisfy the requirements for describing reservoirs and exploring residual oil in mature oilfields. The distributed acoustic sensing (DAS) multi-well borehole-surface joint exploration (BSJE) technology, characterized by identical-source excitation, consistent reception, small downhole trace distance, and high coverage, can reduce absorption and attenuation of near surface by receiving seismic waves in the well, which is one of the main approaches to solve the aforementioned problem. However, the most critical issue facing BSJE technology is the difficulty in vertical seismic profile (VSP) imaging due to uneven seismic excitation points on complex surfaces. At the same time, there are problems such as the lack of BSJE scheme demonstration method and immature techniques for synchronous reception and quality control of DAS multi-well BSJE. Therefore, based on wide-azimuth, wide-bandwidth, and high-density 3D seismic, a technical study on the DAS BSJE, processing, and interpretation of 3D-VSP is conducted with seven wellsin a block in the eastern part of the Ordos Basin.This paper, as the first part, "Acquisition", of the technical study systematically studies the key issues of DAS BSJE technology in complex loess plateau areas. A sector method-based BSJE excitation scheme demonstration technique aimed at VSP imaging is proposed, and a scheme with uniform excitation, DAS multi-well continuous reception, and quality control for BSJE is developed. High-quality DAS 3D-VSP data are obtained by applying the proposed method, which lays a solid foundation of high-quality data for multi-well BSJE-based 3D-VSP migration imaging and reservoir prediction.

Conventional post-stack seismic data are usually affected by absorption attenuation, resulting ina lower peak frequency, narrower frequency band, low resolution, and poor inversion prediction effect. Therefore, an improved Q estimation and inverse Q-filtering method is proposed to improve the resolution of seismic data and obtain the fidelity and amplitude-preserving seismic data. Firstly, in Q estimation, given the problem that the extracted wavelet amplitude spectrum deviates from the actual situation due to the thin-layer tuning effect that affects the Q estimation accuracy, the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method is introduced, which eliminates the interference of reflection coefficients by decomposing and reconstructing the log amplitude spectrum and thus obtains the wavelet amplitude spectrum that removes the tuning effect. Combined with the centroid frequency shift of the energy spectrum, higher-accuracy Q values are obtained. Then, in terms of inverse Q filtering, the amplitude compensation function of the time-varying gain stabilization factor method is optimized to overcome the density dependence and obtain more stable inverse Q-filtering results. The actual data processing results show that the proposed method can obtain fidelity and amplitude-preserving high-resolution post-stack seismic data, which lays a reliable data foundation for subsequent exploration and development of the study area.

The reservoir space of the Lower Paleozoic carbonate rocks in the Jiyang Depression is mainly composed of fractures and dissolution pores. The reservoirs are highly heterogeneous both vertically and horizontally, which makes the prediction of the buried hill reservoirs quite challenging. Therefore, based on the development characteristics of the reservoir space and the correlation of seismic reflections, this paper proposes a seismic forward modeling method for the buried hill interior based on the dual-porosity medium model, which characterizes the seismic response features of the Lower Paleozoic buried hill interior. Meanwhile, a buried hill reservoir prediction method based on structure-oriented filtering is developed, achieving effective prediction of the favorable reservoir development zones in the Lower Paleozoic buried hill. The research results show that the development of the Lower Paleozoic carbonate reservoirs in the Jiyang Depression is the main factor causing seismic reflection anomalies, and the effective reservoir sections exhibit obvious seismic response anomalies. By comparing the data residuals before and after structure-oriented filtering, the development positions of the main inner reservoirs can be indicated. This method has achieved good results in the Pingnan buried hill and Dawangzhuang buried hill in the Dongying Sag.

Time Frequency Electromagnetic (TFEM) Method is an emerging electromagnetic exploration method that developed and emerged in the field of oil and gas exploration at the beginning of this century. It combines the advantages of time-domain and frequency-domain electromagnetic methods and can provide higher resolution and more accurate underground structure imaging and multi electromagnetic parameter constraints under complex geological conditions. TFEM has played an important role in oil and gas exploration and has been promoted to geothermal and metal mineral exploration fields. This article systematically reviews the development history of TFEM technology: from the limitations of early CSAMT methods, to achieving high-precision detection through instrument innovation (such as wideband transmission systems, node based receiving equipment) and intelligent upgrades (5G cloud acquisition, OpenHarmony system); For complex targets, acquisition techniques such as multi-directional synchronous excitation and joint well ground observation have been proposed, and time-frequency data fusion processing and induced polarization effect inversion methods have been developed, effectively improving the success rate of oil and gas detection (reaching over 75%). In terms of application, TFEM has completed over 47000 kilometers of profiles in more than 150 exploration targets worldwide, successfully applied to various types of reservoir targets such as clastic rocks and lithological traps. In the future, TFEM will make breakthroughs in intelligent equipment, AI interpretation, and multi-field coupling inversion, and expand to the fields of semi aviation electromagnetic, marine exploration, and geothermal/environmental monitoring, providing more efficient and accurate technical support for deep earth resource development.

Shale gas has become an important strategic alternative field for China's oil and gas resources. Shale is characterized by low porosity and low permeability, and only after going through large-scale volume fracturing can industrial production capacity be obtained. The fine characterization and quantitative characterization of fracture parameters after shale fracturing are the key to fracturing effect evaluation and development parameter optimization. By taking the three-dimensional CT images of shale cores after fracturing as the research object, this paper conducts intelligent fracture extraction based on the deep learning semantic segmentation model. Firstly, a U-Net deep learning model integrating the pyramid convolution and attention mechanism is built to alleviate the influence of image category imbalance and improve fracture extraction accuracy. Secondly, a digital core model is built based on the semantic segmentation results, and quantitative characterization of the spatial distribution of fractures is realized by combining parameters such as the porosity and tilt index. Finally, the complexity of the fracture network is characterized by the peak and width of the multi-fractal spectrum. The research results show that compared with the traditional image segmentation model, the sensitivity of the improved model is increased by 6.69%, and the intersection over union grows by 0.48%. This study systematically characterizes the three-dimensional fracture features by image segmentation algorithm optimization, digital core modeling, and multi-fractal analysis, which is applicable to the characterization of fracture networks in unconventional reservoirs such as shale and can provide a reference for the evaluation of reservoir stimulation effects after hydraulic fracturing.

Accurately segmenting pores in scanning electron microscope (SEM) images can provide a scientific basis for oil and gas exploration and development, and more. At present, pore segmentation methods mainly rely on data-driven approaches, requiring a large amount of manual annotation of data, which is time-consuming and costly. To this end, this paper proposes the semi-supervised pore segmentation network PoreSeg for SEM images. Firstly, a semi-supervised framework is constructed based on consistency regularization and pseudo labeling. Secondly, a high-intensity combined perturbation strategy is introduced to enhance data diversity. Finally, combined with the pore aware fusion (Pore-CutMix) method, the sparse pore information is fully utilized to improve the segmentation ability of the model for pores. Experimental results show that under the condition of equal labeled samples, PoreSeg improves the pore intersection over union (IoU) by 15.10% compared with the fully supervised network. At the same time, compared with existing semi-supervised methods, PoreSeg is more sensitive to pores and has higher segmentation accuracy. PoreSeg significantly reduces dependence on annotated data while maintaining high accuracy, and has huge application potential.

Full waveform inversion (FWI) is currently the most accurate velocity field inversion technique. FWI employs gradient-based local optimization algorithms to minimize the error between the forward data and the original data, thereby obtaining an accurate subsurface velocity field. The success of FWI on real data relies on the careful processing of original data and the prudent selection of inversion strategies. This paper discusses and experiments with the parameter control and implementation strategies of FWI, considering the characteristics of marine data, and proposes practical techniques for full waveform inversion of marine data. Specifically, the technology is as follows. ① The fraction frequency noise suppression technology is used for turning waves, which primarily improves the signal-to-noise ratio of low-frequency turning waves while avoiding damage to the effective signal. ② The wavelet adjustment techniques to enhance the accuracy of the initial wavelet are applied, which allows for more precise calculation of the error between forward data and actual data and leads to a more accurate calculation of the velocity update gradient. ③ The first-arrival tomography inversion velocity models for near-seafloor velocity modeling improve the accuracy of the initial velocity field, better avoid cycle skipping, and ensure that the objective function converges towards a more accurate direction and at a faster rate. ④ The offset increment strategy is adopted during FWI iterations, progressively updating the velocity field from shallow to deep, reducing the solution multiplicity of FWI, and ensuring more accurate convergence of the FWI velocity field. This technology has achieved relatively good results in practical marine data projects, obtaining more accurate velocity fields and better depth migration imaging effects.

Geophysical logging plays a crucial role in detecting fluid types in subsurface oil and gas reservoirs and evaluating reservoir parameters. Traditional log interpretation methods face significant challenges. Artificial intelligence (AI) algorithms offer advanced capabilities and high accuracy, which makes them highly advantageous for log interpretation. The integration of "logging + AI" has emerged as a new research direction. However, in intelligent log interpretation, the limited sample size and weak generalization ability of training models hinder the widespread application of purely machine learning-based log interpretation methods. Physical models inherently capture the underlying mechanisms that connect logging data to geological targets. Combining data-driven and mechanism-driven approaches provides an effective way to enhance log interpretation accuracy. However, existing joint data-mechanism driving lacks a well-defined paradigm. In view of this, the study focuses on the prediction of intelligent log interpretation parameters, proposes the concept and methodology of joint data-mechanism driving, and presents two key paradigms: data-guided physical modeling, where physical modeling is the primary framework, with data-driven methods assisting in obtaining key steps or parameters, and physics-guided machine learning, where machine learning is the primary approach, while knowledge models or physical mechanisms provide supervision and constraints on input data, loss functions, and training processes. To implement these paradigms, three hybrid models are proposed: physics-augmented datasets, knowledge-driven sample weighting, and rock physics knowledge transfer. These approaches are applied to predict reservoir parameters and mineral composition in tight sandstone and organic shale reservoirs. Compared with purely data-driven machine learning models, the proposed data-mechanism jointly driving paradigms significantly improve the ability of the log interpretation model to learn from small and low-quality samples and make the model have enhanced robustness, generalization ability, and interpretation accuracy.

In seismic surveys of complex surfaces such as loess plateau areas, coherent noise greatly reduces the signal-to-noise ratio (SNR) of seismic data, which seriously affects the accuracy of subsequent seismic imaging and physical inversion. To this end, a new coherent noise suppression strategy is proposed in the paper. The method mainly consists of the following core steps. Firstly, anisotropic diffusion filtering is adopted to effectively suppress the incoherent noise components in the data, and initially improve the overall quality of the low SNR data. Next, the dictionary learning method is employed to sparsely characterize the seismic data, and statistical indicators are applied to precisely locate and eliminate the dictionary atoms with a large variance of gradients. These atoms tend to be the main carriers of linear coherent noise and random noise. Then, the dictionary atoms and their corresponding sparse coefficients that can effectively characterize the effective signals are filtered and retained to reconstruct the seismic data. Finally, the effective signals are further extracted from the removed noise through the principle of signal-to-noise local orthogonalization. The simulated data and typical real data tests show that the method ensures the intact preservation of the effective signals while suppressing the coherent and random noises, which further improves the SNR of the data. The method can provide a reference for the treatment of linear coherent noise.

Seismic-well tie is an important step in seismic data interpretation. The traditional seismic-well tie method synthesizes seismic records by using well logging data and extracted seismic wavelets and matches them with the seismic traces beside the well by dragging. This method has significant human factors, is highly time-consuming, and can easily cause overstretching. Therefore, a deep learning method based on convolutional neural network (CNN) and gated recurrent unit (GRU) network is proposed to achieve automatic seismic-well tie. Firstly, seismic records are synthesized using typical models, and time correcting values are introduced to correct the records of seismic traces beside the well. Secondly, the relationship between two seismic traces and the time correcting values is established through a trained CNN-GRU network, and the correlation coefficients of the two seismic traces are used as constraint conditions to directly predict the time correcting values by using the synthetic seismic records and seismic traces beside the well. Finally, the neural network is tested using actual data from 30 wells, and the obtained results are compared with manual calibration results. The correlation coefficients between the calibrated synthetic seismic records and the seismic traces beside the wells are calculated. The following findings are obtained. ① The correlation coefficients of automatic calibration with the network are greater than or equal to those of manual calibration for 25 wells and are basically consistent for the other wells. ② Manually calibrating 30 wells takes about 30 min, while calibrating them with the network only takes 5 s. Therefore, compared with the traditional method, the proposed method has higher accuracy and better efficiency in seismic-well tie, which verifies the feasibility and progressiveness of the method.

Current data transmission technologies face challenges such as high coding complexity, low encryption efficiency, and limited data compression, thus restricting the development of efficient and safe communications. To resolve these problems, this paper proposes an innovative ultra-low-traffic data transmission scheme, the Temporal-base data pulse transmission method. The core of this method is the combination of high-accuracy time synchronization and subdivision technology, and the establishment of the "Temporal-base" theoretical framework. By adopting the accurate time datum as the data coding basis, the information is mapped to the specific pulse time sequence or phase via super large binary representation to achieve pulse transmission of data. This mechanism notably simplifies the traditional coding process and improves the confidentiality (due to accurate synchronization requirements of time pulses) and compression potential (due to efficient pulse coding of information in the time dimension) of data. By carrying out case analysis, this study verifies the advantages of the proposed data transmission method in improving the transmission rate, significantly saving communication bandwidth, and strengthening data confidentiality, thus fully proving the huge application potential and value of this method in future efficient and safe data transmission scenarios.

The gravity anomaly inversion, which infers the density distribution of subsurface anomalies from surface gravity data, is an essential tool in geophysical exploration and is widely applied in fields such as oilfields, mineral deposits, geological structures, and underground works detection. Traditional gravity inversion methods face challenges of complex computation, low resolution, and dependence on prior information for inversion results. However, deep learning-based gravity anomaly inversion techniques show significant advantages, particularly in terms of improving inversion accuracy and reducing computation time, without the reliance on initial models or prior information. This paper reviews the development and limitations of traditional gravity anomaly forward and inversion methods and summarizes the current research on deep learning-based gravity inversion methods. Meanwhile, it introduces the improvements and innovations of different gravity inversion problems in four respects, including data preparation, network models, network optimization, and network validation. Additionally, the application effect of various gravity inversion methods on the measured data from Vinton Dome in Louisiana, the USA, and the San Nicolás ore deposit in Mexico. The multi-task framework CDUNet yields the most accurate inversion depth values on data of Vinton Dome, while the 3D U-Net++ network obtains clearer and more accurate inversion results on the data of the San Nicolás ore deposit than the U-Net network.

In recent years, the Keping fault uplift in Tarim Basin has emerged as a significant breakthrough zone for hydrocarbon exploration, where well-developed high-quality source rocks coexist with complex structural settings, creating a unique exploration environment. The combination of intense surface relief, pronounced velocity contrasts from shallow high-velocity rock masses, and multi-phase superimposed fault systems has led to technical bottlenecks in conventional seismic imaging methods, including substantial static correction errors, low velocity modeling accuracy, and structural distortion. This study systematically investigates key pre-stack depth migration (PSDM) technologies tailored for piedmont zones based on geological characteristics of the 3D seismic survey in the Kepingnan area. Three main innovations are presented: ① Integrated shallow layer modeling technology. To address near-offset data deficiencies caused by complex surface acquisition geometries, this paper develops a constrained tomographic inversion algorithm integrating uphole survey data with wide-azimuth seismic information was developed. This approach effectively enhances shallow velocity model accuracy while overcoming velocity-thickness coupling limitations inherent to conventional methods. ② True surface migration datum construction technology. By optimizing surface-consistent static correction schemes and implementing high-frequency static correction time-difference correction, the paper establishes a dynamic matching mechanism between true surface elevation and migration datum was established. This innovation significantly mitigates topographic effects on wavefield continuation and improves structural fidelity in steeply dipping stratigraphic imaging. ③ Multi-scale velocity iterative inversion technology. The paper develops a multi-azimuth grid tomography method incorporating structural constraints. Through azimuth-dependent hierarchical inversion strategies, this technique achieves synergistic improvements in both vertical and lateral velocity resolution, providing precise velocity models for deep nappe structures and buried fault systems. Field applications demonstrate that this technology system substantially enhances imaging quality of depth-migrated seismic data in Kepingnan area. Target horizons exhibit improved signal-to-noise ratios with clearer fault imaging and more accurate structural configurations. The proposed methodology provides an effective technical solution for hydrocarbon exploration in complex piedmont zones, offering practical value for advancing regional exploration progress.

In seismic exploration, multiples pose a significant issue, particularly in marine exploration. This study developed a method for suppressing interbed multiples based on the inverse scattering series method in the curvelet domain. The curvelet transform (CT), as a multi-scale and multi-directional transformation method, can sparsely represent seismic data, facilitating the capture of the main features of seismic data. By combining CT with the inverse scattering series method, the seismic data was first converted to the curvelet domain using CT. Then, the interbed multiples were predicted in the curvelet domain through the inverse scattering series method. This approach did not require building a model of the subsurface medium, offering high applicability and practicality. Furthermore, CT can sparsely represent seismic data, reducing the computational load of the inverse scattering series method. By performing an inverse CT on the predicted multiples, they were reconstructed in the time-space domain and subtracted from the original seismic data, ultimately obtaining the effective signal with suppressed multiples. The numerical experiments demonstrate that this method not only significantly improves computational efficiency but also reduces memory consumption while ensuring accuracy. This study provides an innovative approach for suppressing multiples in seismic data processing and is expected to play an important role in practical seismic exploration.

The seismic data of the Lower Cambrian Qiongzhusi Formation in the Sichuan Basin has weak reflected energy without distinct characteristics of interlayer wave groups and clear description of faults, which makes it difficult to meet the requirements for fine prediction of high-quality shale reservoirs and the accurate design of horizontal well trajectories. In response, taking the 3D seismic project for shale gas in Well Z201 as an example, this paper proposes a high-precision seismic acquisition technology of deep shale gas in Qiongzhusi Formation of Sichuan Basin. First, the observation system parameter optimization technology for pre-stack inversion of reservoirs is applied to design the observation system. Then, the intelligent layout of physical points in the obstacle area based on the contribution degree is conducted to improve the uniformity of coverage times in the target zone. Finally, the surface velocity and lithology constrained modeling technology is used to characterize the near-surface structure and spatial distribution of lithology within the survey area. The application results of the proposed method in seismic acquisition of shale gas in Qiongzhusi Formation demonstrate that high-resolution, broadband seismic data can be obtained using the technology, which provides a data basis for the subsequent high-resolution processing and fine reservoir description.

Diffraction imaging is an important method to improve the imaging accuracy of underground small-scale geologic bodies. However, conventional seismic surveys are mainly based on reflection imaging, with weak-energy diffraction suppressed, as a result of which diffraction need to be separated and imaged separately. At present, the localized damped rank-reduction with adaptively chosen ranks (LDRRA) method widely used improves the separation accuracy of diffraction by damping the adaptively chosen singular value matrix with a damping operator, but its damping factor is mainly given manually, and all local window data use the same given damping factor. Different local windows contain different seismic data, and using the same damping factor will reduce the separation accuracy of diffraction. Therefore, a localized adaptive damped rank-reduction (LADRR) method for diffraction separation is proposed. First, based on the LDRRA framework, the Hankel matrix undergoes singular value decomposition (SVD) to truncate singular values. Second, a squared ratio of singular value is introduced to adaptively compute a damping factor for each localized data window, through which the optimal damping factor is selected to apply damping effects to the truncated singular values, thereby preserving the reflection components. Finally, the damped localized window data is subjected to inverse Hankelization and inverse Fourier transform, and then subtracted from the original wavefield to yield the separated diffraction. Theoretical simulation and field data test results demonstrate that the proposed method can separate diffraction with high accuracy, and the imaging results of the separated diffraction can get more accurate location of the underground small-scale geologic body.

Ensuring strong correlation among samples of the same category in seismic facies images and determining the number of seismic facies categories are the core of unsupervised pre-stack seismic facies analysis. This paper proposes an unsupervised pre-stack seismic facies analysis algorithm with correlation threshold constraints for category determination. First, the pre-stack seismic trace set data is transformed into two-dimensional images, and the high-level nonlinear, discriminative, and invariant features of the images are extracted using unsupervised deep learning networks, which can highlight strongly concealed information. Subsequently, the threshold for the number of seismic facies categories is determined based on the cross-correlation values of deep features of pre-stack seismic images corresponding to different categories of seismic facies within the study area, ensuring that samples within the same class in the obtained pre-stack seismic facies images exhibit highly strong correlation, and the number of seismic facies categories is determined based on discriminant thresholds. Finally, the obtained pre-stack seismic facies images are calibrated using existing drilling information to provide a basis for geological experts to infer sedimentary environments and reservoir distributions. Theoretical model testing confirms that this method not only determines the number of pre-stack seismic facies depending on discriminant thresholds but also ensures strong correlation among samples within the same class in the seismic facies images, demonstrating greater robustness compared to other methods. Application of actual data shows that this method improves the accuracy of predicting seismic facies of fracture-cavity reservoirs in the Permian Maokou Formation and provides a reliable scientific basis for well deployment and the discovery of undrilled fracture-cavity reservoirs.

Seismic wave impedance inversion is the core of reservoir prediction, and the construction of an accurate seismic forward model is the basis of realizing high-resolution wave impedance inversion. However, it is difficult for the traditional convolution forward models based on one-dimensional seismic wavelet to accurately simulate and characterize the imaging profile in the depth domain when there is a drastic lateral change in the subsurface velocity, which seriously affects the accuracy and reliability of wave impedance inversion results. For this reason, this paper proposes a forward modeling method of convolution in the depth domain of non-stationary space based on point spread function. Firstly, the accurate mapping relationship between seismic ima-ging profile and underground reflection coefficient is derived according to the linear Born forward modeling theory. Then, an accurate convolution forward model is constructed with the point spread function as the seismic wavelet in the multi-dimensional depth domain. Finally, the efficient algorithm of point spread function based on Green's function of the ray theory can greatly improve the computational efficiency of the point spread function. The correctness and effectiveness of the proposed method are verified by the depth-domain convolution forward modeling tests of the simple horizontal layered model and the complex Marmousi model.

Electromagnetic methods are naturally superior in identifying whether deep targets contain fluids. It is of great significance for oil and gas field exploration and development to study high-precision electromagnetic detection technology for reservoir fluids. Based on the capillary model and electrochemical theory, we consider that the polarized electric field is closely related to fluid concentration, microscopic charge migration, and accumulation, and has time and frequency dependencies. The essence of polarization effects can be explained through the electric diffusion hypothesis and thin film polarization theory. Based on Maxwell's equations and the equivalent complex resistivity model, we derive the basic formula for electromagnetic hydrocarbon detection. The accurate characterization of electrical anomalies in oil and gas reservoirs by relevant parameters is further analyzed, especially the performance of induced polarization effects related to oil and gas content in electromagnetic field amplitude and phase curves. In practice, the induced polarization effect can be effectively extracted and the distribution of oil and gas reservoirs can be identified only by optimizing parameters such as transmission and reception distance and frequency. At last, we analyze the characteristics of the induced polarization effect on electromagnetic field phase and differential curves through numerical simulation, which is consistent with theoretical mathematical characterization. We clarify the effective parameters and critical frequency for data acquisition of the induced polarization effect in reservoirs. Finally, we discuss the applicability of this method in fluid detection and propose a construction method technology based on characteristic curves, providing theoretical support for the practical application of electromagnetic hydrocarbon detection.

Clastic rock reservoirs are widely distributed in the Barbados accretionary wedge basin with the primary reservoir type of deepwater turbidite sandstone and the hydrocarbon type of biogas reservoir. Since gas-bearing sandstones and water-bearing sandstones in the basin are both presented as "bright spot" anomalies in seismic data, it is difficult for conventional post-stack hydrocarbon detection methods to accurately predict the distribution of gas reservoirs, which leads to a low success rate of early exploration. Thus, after analyzing logging response characteristics of drilled reservoirs, this paper proposes a new fluid detection method based on post-stack relative amplitude decoupling for "bright spot" sandstone reservoirs. Firstly, the main controlling factors of the seismic response of reservoirs are quantitatively analyzed by means of fluid substitution and forward modeling. Secondly, the post-stack relative amplitude decoupling factor is designed for "bright spot" sandstone reservoirs, and the relative amplitude relationship template is established for different lithologies and fluids. Finally, gas-bearing sandstones and water-bearing sandstones are distinguished utilizing the relative amplitude relationship template to realize reservoir prediction and fluid detection with multi-age post-stack seismic data. The application results show that the new methodology has a stronger ability to distinguish gas-bearing sandstones and water-bearing sandstones in the study area, which overcomes the blind area of traditional post-stack hydrocarbon detection methods, significantly improves the prediction accuracy of biogas reservoirs, and provides an important basis for exploration decision.