To address irregular missing seismic data caused by environmental interference during field acquisition，this paper proposes a local learning-based reconstruction method that integrates particle swarm optimization and grey wolf optimizer （HPSOGWO） algorithm with the XGBoost model. The proposed method establishes a nonlinear mapping relationship between seismic trace spatial coordinates （trace number and sampling number） and amplitude values. By adaptively optimizing feature windows using the HPSOGWO algorithm，this method achieves intelligent selection of adjacent trace data and high-precision prediction of missing values. Compared with the traditional convex-set projection method based on the Curvelet transform （Curvelet-POCS），the proposed approach significantly improves reconstruction accuracy in complex structural areas. In contrast to deep learning methods such as U-Net，it reduces the reliance on large training datasets and lowers computational costs. Tests on a three-layer horizontal layered model with 20% random missing traces show that the proposed method achieves a peak signal-to-noise ratio （PSNR） improvement of 11 dB over Curvelet-POCS and 7 dB over U-Net. F-K spectrum analysis further confirms its effectiveness in preserving seismic wavefield characteristics in the frequency domain. Tests on real onshore 2D seismic data show that the reconstructed profile with 20% missing traces achieves a relative amplitude error of 5.72%，demonstrating high amplitude fidelity and phase consistency. The method thus provides an effective and practical solution for seismic data reconstruction under complex geological conditions.

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.

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.

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.

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.

To address the problem of limited generalization ability in traditional models for logging prediction of total organic carbon （TOC） content in the Longmaxi Formation shale reservoirs of the Sichuan Basin， this study systematically evaluates the performance of various deep learning models in both single-well and cross-well prediction tasks and identifies practical models suitable for different scenarios. Based on the acoustic time difference， density， and natural gamma-ray logging data from three wells （well1， well2， and well3） in the Longmaxi Formation， multiple models are constructed， including multiple linear regression （MLR）， support vector regression （SVR）， convolutional neural network （CNN）， bidirectional long short-term memory network （BiLSTM）， and hybrid CNN-BiLSTM and CNN-BiLSTM-Attention models. In the single-well prediction experiment， a random split strategy is applied to well1 for modeling and validation. The results show that the CNN model achieves the best performance， with the coefficient of determination （R²） reaching 0.9519 on the prediction set， demonstrating excellent local feature extraction capability and strong resistance to overfitting. To further evaluate model generalization ability， a leave-one-well-out （LOWO） cross-validation strategy is designed for cross-well prediction. The results indicate that the CNN-BiLSTM-Attention model exhibits the strongest generalization performance， achieving the highest R² of 0.9653 on the prediction set， with mean absolute error （MAE） and root mean square error （RMSE） as low as 0.131% and 0.170%， respectively， which significantly outperforms other models. The attention mechanism effectively integrates the local features extracted by CNN with the long-term sequential dependencies captured by BiLSTM， enhancing the model’s ability to focus on key information and adapt to inter-well variations. This study verifies the effectiveness and robustness of deep learning models integrated with an attention mechanism for TOC prediction under complex geological conditions， emphasizes the importance of cross-well validation in practical applications， and provides a reliable methodological foundation for shale gas sweet-spot prediction.

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.

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.

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.

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.

Accurate prediction of geological sweet spots is a core component for the efficient exploration and development of shale oil and gas. Intelligent prediction of sweet spots of shale oil and gas based on logging data holds significant importance for hydrocarbon development. Within the field of sweet spot prediction， traditional model-based prediction methods suffer from limitations such as weak generalization， insufficient feature representation， and cumulative error propagation. This study integrates novel deep learning frameworks with the aim of achieving accurate prediction of geological sweet spots using logging data. Porosity （POR） and total organic carbon （TOC） are key parameters defining geological sweet spots. This study establishes deep learning models between logging curves and these two critical parameters to achieve intelligent prediction of geological sweet spot parameters. Firstly， a convolutional neural network （CNN） model is constructed. By analyzing and comparing different combinations of input logging curves and varying CNN layer architectures， the model achieves high-precision predictions for both POR and TOC on the test set， with correlation coefficients exceeding 0.96. Secondly， innovatively fusing CNN with an attention mechanism， a composite Transformer architecture termed the CNN-Transformer compound （CTNet） model is proposed. This model possesses the dual capability of capturing local lithological features and global inter-layer dependencies. After validation and testing on the dataset， the CTNet model achieves a correlation coefficient above 0.91 on the test set. Experimental results indicate that the CNN model demonstrates higher overall prediction accuracy than the CTNet model. However， the CTNet model exhibits significantly superior prediction accuracy within limestone intervals compared to the CNN model （e.g.， in the limestone section of well GY6-1， the correlation coefficient of the CTNet model and the CNN model is 0.848 and 0.300， respectively）， showcasing its unique potential for addressing prediction challenges in complex heterogeneous reservoirs. The findings demonstrate that both the proposed CNN and CTNet deep learning mo-dels can effectively achieve high-precision prediction of key geological sweet spot parameters in shale oil and gas， offering new methodologies for intelligent exploration.

Traditional manual cutting logging faces on-site technical challenges, including inability to quantitatively identify cutting components and accurately name the lithology, thereby hindering the comprehensive and precise acquisition of cutting lithology information. To this end, this paper develops an intelligent lithology identification system based on cutting images collected during the logging-while-drilling process. Firstly, by establishing unified standards for image acquisition and annotation, systematic sample annotation is conducted manually to generate standard samples. Secondly, the deep convolutional neural network algorithm YOLOv5 is adopted for sample training, inference, and post-processing, and an attention mechanism for small target identification is added, with the focus on the influence of Fitness function adjustment on target identification accuracy. Finally, the ONNX(Open Neural Network Exchange) model is adopted for cross-platform support, developing an intelligent lithology identification system based on cutting images. Practical applications show that the system can identify six major lithologies (mudstone, sandstone, limestone, dolomite, coal, and carbonaceous mudstone), with the overall accuracy exceeding 85%. Meanwhile, the system can analyze the component contents of each lithology and characteristics of sandstone cuttings including the roundness, grain size, and sorting, thus enabling the accurate description of cutting component characteristics and developing a new lithology identification technology.

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.

The transparency of geological condition detection and precise exploration are critical challenges hindering safe and efficient coal mining operations. Coalfield seismic exploration technology plays a vital role as a means to address hidden, potentially hazardous geological issues during the mining process. This technology provides high-precision regional geological structures and coal formation patterns, offering reliable geological basis for coalfield development. The evolution of coalfield seismic exploration technology in China can be divided into three distinct phases: the early stage from the 1950s to the early 1970s marked by technological inception; the digital development era spanning the late 1970s through the 1980 s; and the current phase since the 1990 s, characterized by extensive promotion and practical application. Over the course of more than seventy years, this technology has made significant advancements in coalfield geological surveys and the precise detection of hidden factors contributing to geological hazards. It stands as an indispensable geological support for ensuring safe and efficient coal development. By integrating in-depth research on coalfield exploration techniques with typical engineering practices, the text systematically outlines its technical features and current status across the stages of data acquisition, processing, and interpretation. In response to the urgent need for transparent and intelligent mine construction, future developments in coalfield exploration technology will focus on advancing dense distributed data collection, intelligent data processing and interpretation, and the innovative application and development of multi-attribute integration techniques.

Accurate estimation of coalbed methane (CBM) content plays a crucial role in assessing and efficiently exploiting CBM resources. Deep CBM is influenced by multiple controlling factors and complex genetic mechanisms. Currently, machine-learning approaches for CBM content prediction typically rely on either seismic or logging data. As a result, the complex geological conditions of deep coal seam are not fully accounted for. This study proposes an intelligent prediction method for CBM content, which achieves multi-source data fusion through a multi-scale modeling and deep integration strategy. The approach first extracts multi-scale sensitive attributes or features relevant to CBM content from geological, logging, and seismic sources. For each dataset of the same scale, adaptive modeling is performed using a Bayesian hyperparameter-optimized random forest (RF) algorithm, which enhances model robustness and prevents overfitting. The prediction results from individual scales are subsequently integrated through the least squares method to construct a multi-scale RF composite model. The proposed method is validated using a field dataset and compare its performance with that of conventional approaches, including single-scale RF and linear regression. The results show that, compared with these baseline methods, the proposed method reduces the mean relative error of CBM content prediction on test wells by 3.01% and 4.94%, respectively. This demonstrates that the proposed approach achieves higher accuracy and stronger generalization capability, enabling precise characterization of the spatial distribution of CBM content.

Microseismic monitoring technology is of application significance in fields such as unconventional oil and gas reservoir development and mine disaster monitoring. However, its signals are susceptible to noise interference, which results in a low signal-to-noise ratio (SNR), thus severely compromising the accuracy of subsequent seismic source localization and mechanism inversion. Traditional denoising methods such as the complete ensemble empirical mode decomposition (CEEMD) and wavelet modulus maxima (WMM) have limitations in processing non-stationary microseismic signals. To this end, this paper proposes a microseismic denoising method named SSA-VMD-CC-WT, which combines variational mode decomposition (VMD) optimized by the sparrow search algorithm (SSA) with the adaptive wavelet thresholding (WT). Firstly, SSA is employed to optimize key parameters of the VMD algorithm. Secondly, effective modal components are selected by utilizing the cross-correlation coefficient (CC) to suppress noise. Finally, adaptive WT is applied to perform secondary denoising on the effective components, reducing signal distortion. Simulation tests demonstrate that in strong noise conditions, the SSA-VMD-CC-WT method can separate noise from effective signals more accurately than the CEEMD and WMM methods. The processing of actual microseismic data reveals that the proposed method significantly suppresses both low-frequency and high-frequency noise while maintaining the fidelity of weak seismic sources, thereby improving data interpretability and SNR. Meanwhile, compared with the traditional genetic algorithm (GA), SSA demonstrates higher optimization efficiency.

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.

Sand body are a kind of common reservoir unit, and their accurate identification and tracking are the key to discovering oil and gas fields and supporting the increase in oil and gas reserves and production. Existing methods such as attribute analysis and deep learning still face challenges such as low boundary identification accuracy, complex parameter selection, and poor noise resistance. To this end, this paper proposes a prompt prediction method based on the Segment Anything Model (SAM), a visual image segmentation foundation model. This method requires no model training, and by simply employing the boundary prompt points of target sand bodies, precise identification and tracking results of the boundaries of target sand bodies can be obtained. To address the prediction error of the prompt encoder in SAM when applied to seismic profiles, this paper proposes a KD-tree search method. By calculating the shortest distance from the prompt points to the potential sand body segmentation blocks, the optimal sand body prediction results are determined. After conducting verification with actual target area data and comparison with the customized training of a U-Net model on the target area data, it is demonstrated that the sand body tracking method based on SAM depicts more lateral changes of sand body boundaries and the boundaries are more consistent with seismic amplitude variations.

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.

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.

Shear-wave velocity is a key parameter for pre-stack seismic inversion and reservoir characterization. However, due to the technical and cost constraints of both direct and indirect measurement methods, it is quite difficult to obtain in practice. Therefore, a prediction method is proposed based on the broad learning system (BLS). First, appropriate well-log data are selected and pre-processed through denoising and correlation analysis. Second, a BLS neural network structure comprising mapping nodes and enhancement nodes is constructed to complete the BLS process. Finally, well-log data from the two typical wells Y301 and Y302 in the Y block of the Junggar Basin are used to construct a data set of machine learning. Two contrast experiments are designed and compared with curve fitting and deep learning system to verify the stability and generalization of BLS. The actual results show that the proposed BLS-based shear wave velocity prediction method can reduce training time while achieving prediction accuracy, providing a new neural network option for shear wave velocity, petroleum, and relevant reservoir parameter prediction.

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.

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.

Accurate underground velocity information is crucial for seismic imaging in complex area. While existing seismic waveform inversion techniques are highly accurate, they have shortcomings such as high computational amount and reliance on initial models. Currently, deep learning technology experiences rapid advancements in various fields and has successfully been applied to nonlinear seismic inversion. However, conventional end-to-end deep learning networks struggle to establish a multi-scale physical coupling relationship between velocity parameters and seismic records. To this end, this paper proposes a hybrid network AER-UNet, which reorganizes the encode and decoder structures and adds an attention mechanism-based jumping connection module on this basis. This approach effectively obtains key spatial information from seismic records and enhances the representation of the subtle structures in velocity fields, thus accurately capturing the characteristics of underground medium velocity parameters. An appropriate number of random velocity models should be built in the network training phase to simulate the true structure of the underground medium and thus obtain the accurate mapping relationship between velocity models and seismic records. Additionally, developing new loss functions can help improve the computational accuracy of velocity modeling. By carrying out numerical experiments using the SEG/EAGE thrust model, the effectiveness of the hybrid network for velocity modeling is evaluated. Compared to FWI and other deep learning networks, this method can more efficiently and accurately rebuild underground velocity models.

In response to high-resolution imaging in complex geological structures, surface imaging and vertical seismic profile (VSP) imaging mitigate illumination limitations inherent to either method alone, but are still subject to unbalanced image amplitude and structural distortion due to uneven illumination. To address this issue, a joint VSP-surface Gaussian beam depth migration method is proposed. First, a dynamic complementary mechanism is established for the surface and borehole wavefields, where illumination-based weighting factors are applied to balance their respective contributions and integrate their advantages. Second, to constrain the Gaussian beam propagation paths, a structural dip field is introduced, enabling dynamic adjustment of the initial beam direction for adaptive alignment with the local formation dip. Third, a dip-dependent weighting function is employed during the imaging stack to suppress scattering energy from non-geological directions, thereby enhancing the signal-to-noise ratio of the joint VSP-surface image. Applications to both synthetic and field data demonstrate that the joint VSP-surface Gaussian beam depth migration method produces superior images compared to those obtained using surface data alone and improves the imaging accuracy of complex structure.

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.

Surface-consistent deconvolution is a useful tool to improve the resolution of seismic data by compressing the seismic wavelets and enhancing the wavelets' waveform consistency. However, the conventional surface-consistent deconvolution mainly uses the least-squares method or the Gauss-Seidel method for spectral decomposition. Their decomposition results are easily affected by noise, which results in an increase of noise energy in the seismic data after deconvolution. Therefore, this paper proposes the three-dimensional (3D) surface-consistent deconvolution method based on the over-relaxation Jacobian iteration algorithm to improve the anti-noise ability of the surface-consistent deconvolution. First, the logarithmic power spectrum is effectively decomposed into five components, including source, receiver, common midpoint (CMP), offset and global term, by using the weighted least-squares objective function under strong noise conditions. Then, the five-component convolution model with the global term is used instead of the conventional four-component model, and the changing near-surface conditions are transformed into surface conditions similar to the global term, which thus effectively eliminates the waveform changes caused by inconsistent near-surface conditions. Test results of two sets of synthetic data and one set of 3D field data show that the over-relaxation Jacobian iteration algorithm has relatively strong anti-noise ability. The algorithm can effectively compress the seismic wavelets, enhance the wavelets' waveform consistency, effectively compress non-surface-consistent noise, and has achieved relatively good deconvolution performance, which shows great significance for enhancing the vertical resolution of seismic data in complex geological structures.

Noise in post-stack seismic data can severely interfere with the identification and interpretation of reflection signals, making efficient denoising essential for improving the accuracy of seismic data interpretation. Owing to its superior multi-scale and multi-directional characteristics, curvelet transform has been widely adopted for post-stack data denoising. However, conventional curvelet transform tends to produce pseudo-Gibbs effects at boundaries, which leads to edge oscillations and spurious reflections. In addition, aggressive noise suppression often results in the loss of valid signals, which limits the practical applicability of these methods. To address these challenges, this paper proposes a curvelet transform approach incorporating multiscale adaptive block curvelet-domain thresholding (MABCDT). First, cycle spinning and MABCDT are introduced in the curvelet domain to enhance the preservation of weak signals and significantly mitigate pseudo-Gibbs phenomena. Then, a fast non-local mean filtering is applied to the data after inverse curvelet transform, which further retains valid signals while removing residual noise. Finally, a directional smoothing diffusion algorithm is introduced, which utilizes gradient direction information to perform directionally weighted smoothing and diffusion, thereby further suppressing noise and enhancing the continuity of valid signals. Both synthetic and field data tests demonstrate that the proposed method outperforms conventional post-stack denoising techniques in terms of signal-to-noise ratio enhancement and waveform fidelity. The method preserves the continuous structural features of seismic signals while suppressing noise and significantly reduces pseudo-Gibbs effects caused by high-frequency truncation.

The Marchenko method can reconstruct the Green's function at any subsurface position by utilizing the surface reflection response and the background velocity model, which can be applied to structural target-oriented imaging. Compared with traditional seismic interferometry, the imaging is not affected by the overburden medium. However, the imaging quality of the Marchenko method relies on high-density acquisition, which field data sets often fail to provide. To address this issue, this paper proposes the Marchenko imaging method based on CNN-POCS reconstruction. First, sparsely acquired seismic data is reconstructed using the convolutional neural network projection onto convex sets (CNN-POCS) method to generate high-accuracy data with dense spatial sampling. Marchenko imaging is then applied to the target zone using the reconstructed data. This approach not only reduces the high-density acquisition requirement of the Marchenko method but also preserves imaging accuracy. Finally, the method is extended to plane-wave imaging, which improves the computational efficiency of the conventional point-source approach and reduces the computational cost. The model test results show that the proposed method can reduce the receiver deployment by 60%, thus significantly lowering acquisition costs.

Rock fabric contains abundant geological information， and lithology identification from rock thin sections is of great importance for oil and gas exploration， mineral extraction， and related fields. To address issues such as low accuracy and high labor costs in rock image classification， this paper proposes a ConvNext V2-VMamba-based method for lithology identification in rock thin sections. First， based on the ConvNext V2 model， grouped convolution and channel shuffle strategies are adopted to combine the ConvNext module with the VSS module. Then， a Conv-VMamba module is proposed to replace the original ConvNext module， enabling the model to possess both a global receptive field and excellent local feature extraction capabilities. Finally， a spatial attention EMA module is integrated into the model to enhance cross-channel spatial information aggregation and improve the capture of texture information in different rock images. Experimental results show that the model achieves an accuracy of 81.5%， precision of 81.1%， recall of 81.4%， specificity of 96.3%， and F1-score of 81.2% on the test set. Compared with the original model， the accuracy is improved by 5%. This method demonstrates the highest algorithmic accuracy and best classification performance， providing a new approach and method for the field of lithology identification.

With the depletion of conventional oil and gas resources and the increase of water content in oilfields in the east of China, geothermal energy development has become the key to the green and low-carbon transformation of old oilfields, and thermal reservoir identification is the core of geothermal field research. The existing thermal reservoir identification algorithms fail to employ the hidden sample relationships between logging data as inputs for conducting training and tests, and a single view is insufficient for the extraction of depth sequence information and spatial features embedded in it. To this end, a thermal reservoir logging identification method based on dual-view GraphSAGE (dv-GraphSAGE) is proposed. Firstly, the depth distance map and feature similarity map are constructed by depth sequence and feature similarity, and then features are extracted by adopting GraphSAGE and the feature self-attention mechanism (FSAtt) to retain the information richness and complex associations of the views. Finally, the view features are fused by an adaptive feature fusion module and fed into a multilayer perceptron (MLP) network to achieve thermal reservoir identification. The experimental results of logging data from 30 geothermal wells show that the overall identification accuracy of the dv-GraphSAGE model for the mudstone layer, dense layer, dry layer, oil layer, and water layer reaches 95.4%, of which the identification rate for the water layer is 96.9%. The experimental comparison results also indicate that dv-GraphSAGE has a better thermal reservoir identification effect, which provides a new idea for geothermal development of oilfields.

Fault-controlled fractured-vuggy reservoirs are currently a hotspot in exploration and development due to their abundant hydrocarbon resources. These relatively deep-buried reservoirs exhibit coupled seismic reflection patterns with sedimentary strata, which makes their accurate identification difficult with conventional methods. This study proposes a new hydrocarbon prediction method for fault-controlled fracture-vuggy reservoirs to solve identification problems for hydrocarbon in such reservoirs. First, horizontally or sub-horizontally layered strata are treated as background noise, and the K-L transform is introduced to enhance vertical fracture-vuggy features through isochronous stratigraphic units, embedding and denoising reconstruction. Second, after stratigraphic information is removed, a well-localized generalized S-transform for time-frequency spectrum analysis is used to extract maximum spectral energy clusters and identify subtle fracture-vuggy structures not detectable by conventional amplitude slices. Subsequently, high-frequency attenuation-rate attributes are employed to distinguish between dry wells and high-yield oil wells. Finally, through analysis of fault-control factors in water-cut wells and eliminating non-strike-slip faults unrelated to hydrocarbon accumulation, an integrated fault-control attribute method is employed to predict hydrocarbon-bearing distribution within fracture-vuggy bodies. Both numerical simulations and field data analysis demonstrate the proposed method's value for large-scale exploration and development of fracture-vuggy reservoirs.

In seismic exploration, owing to the viscoelastic properties of subsurface media, the seismic wavefield experiences substantial attenuation, loss of high-frequency components, and phase distortion during propagation. This is because energy is absorbed, which ultimately results in a reduction of the resolution and imaging accuracy of seismic data. Precisely acquiring the Q-value of the near-surface media and conducting inversion processing is a crucial approach to address this issue. Consequently, an adaptive near-surface Q-value estimation method based on the asymmetric wavelet spectrum is herein proposed. Initially, a synthetic wavelet is generated by taking the geometric mean of the input and output signals. Subsequently, an adaptive algorithm is introduced in light of the synthetic wavelet to determine the key parameters, thereby minimizing the errors arising from subjectively selecting constant parameters. Finally, a formula is derived to compute the near-surface Q-value, enabling precise estimation of the Q-value. Both theoretical model validation and practical application outcomes demonstrate that the method presented in this study can automatically adapt to the changes in spectral asymmetry during the absorption attenuation of seismic wave propagation. It can more accurately fit various asymmetric source amplitude spectra, enhance the accuracy of Q-value modeling, and offer technical support for high-precision seismic data processing.

Seismic impedance inversion is a technical means to quickly estimate the thickness of underground rock layers and evaluate the quality of reservoirs by using post-stack seismic data. Howerver, limited by the inappropriateness of the inversion itself, the process is highly susceptible to the influence of the observed data and the model bias, which leads to the instability of the inversion results. Instability arising from noise or the forward model during the solution process can be suppressed by appropriately introducing smoothness, sparsity or structural assumptions in the objective function. The total-variation regularization optimization method can preserve formation edge features, but may also lead to blocky structures of the inversion results. To overcome this defect, this paper introduces hybrid fractional-order total-variation regularization constraints to characterize the inter- and intra-stratum variability characteristics in a more reasonable way. First, an objective function with hybrid total-variation regularization constraints is built. Then, the orthogonal finite-memory quasi-Newton method is used to solve this complex objective function, so as to improve the resolution and stability of seismic impedance inversion. Test results and real data application demonstrate that the hybrid fractional-order total-variation regularization can be better adapted to the inversion of lumped and smooth hybrid models than total-variation regularization and $ {\mathrm{L}}_{2} $ regularization. It is verified that the proposed inversion method has the features of high accuracy, strong noise immunity, and fast convergence speed.

During waterflooding, evaluating interwell connectivity is essential for reservoir management, production strategy optimization, and improved hydrocarbon recovery. Existing methods, those are based on graph neural networks fail to characterize the temporal and spatial relationships within well networks through graph structures, limiting the accurate description of delayed dynamic responses. To bridge this gap, this study proposes a multi-level graph-structured temporal network model to capture dynamic relationships from time-series well network data, and enable precise interwell connectivity evaluation. Specifically, considering the injection-production response delay of a well network, a multi-level graph-structured temporal-spatial dependence representation method is proposed, which integrates production time-series responses with the spatial structure information of the well network is introduced. Subsequently, a multi-level temporal graph neural network model is established; Next, an attention mechanism based hierarchical information interaction and update method is developed. These together enable the model to explore dynamic interactions between injection and production wells and achieve accurate inversion of interwell connectivity. Experimental results demonstrate that the proposed model exhibits superior accuracy over conventional temporal models, with a consistency of 93.8% with tracer test results. Moreover, it conforms to the gradual evolution of connectivity in accordance with physical principles, demonstrating the method's strong practical applicability in engineering.

The development of horizontal drilling technology has greatly improved the efficiency of oil and gas reservoir exploitation. In recent years, this technology has been widely used in coalbed methane development and has achieved good application results. With the deepening of coalbed methane development, geological conditions have become increasingly complex, and horizontal well drilling faces engineering risks such as off-target and leakage through target windows. In response to such issues, this article proposes a real-time guidance technology system integrated well-seismic analysis, which combines target control technology, fine interpretation technology for micro amplitude structures combined with well-seismic analysis, iterative prediction technology for reservoirs with artificial intelligence, and horizontal segment model guidance technology. This technology system has the advantages of timeliness, accuracy, and intuitiveness, and can effectively support drilling tracking. This technology was applied in the H1 tracking process of the coalbed methane horizontal well in the Benxi Formation in the Mizhi area of the Ordos Basin. Based on the geological conditions encountered during drilling, the prediction results were continuously iterated, and the guidance model was corrected to ensure that the drilling tool smoothly drilled into high-quality reservoirs within the target window. With the help of real-time guidance technology integrating well-seismic analysis, the coal rock drilling encounter rate of this well is 100%, and the total hydrocarbon content in the horizontal section reaches 50%~90%, with excellent results. Through practical examples, it has been verified that the real-time guidance technology integrating well-seismic analysis studied in this article is effective in tracking coalbed methane horizontal wells, and has good reference significance for tracking shale gas horizontal wells.