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 the development of offshore oil and gas exploration and development, the application of dual-sensor ocean bottom receivers is becoming more and more extensive. As a key technology of dual-sensor seismic data processing, the separation of the up-going and down-going wavefield determines data processing qua- lity. In response to the failure of conventional methods to complete separation of up-going and down-going wavefields (up-going wavefield separated include some of the down-going wavefield, and the down-going wavefields separated also include some of the up-going wavefield), this paper proposes a separation method of up-going and down-going wavefields for dual-sensor seismic data based on direct wave calibration. First, the spatial weighted function of the dual-sensor seismic data is calculated by using the dual-sensor direct waves in the frequency-space domain. Then the calibration filter operator of the dual-sensor seismic data is directly computed by using the dual-sensor seismic data with and without direct waves in the time-space domain. Finally, the dual-sensor seismic data is calibrated, and up-going and down-going wavefield are separated. The data examples demonstrate the effectiveness and practicality of the proposed method. The separated up-going wavefield data not only eliminates the interference of the ghost multiples but also improves the signal-to-noise ratio and resolution of seismic data. The method provides high-fidelity up-going and down-going wavefield data for subsequent joint deconvolution and migration imaging processing.

To address the shortcomings of traditional lithology identification methods in missing logging curves handling, accuracy, and model interpretability, this paper proposes an interpretable lithology identification method based on a MSCNN-GRU neural network to complement logging curves, along with Optuna hyper parameter optimization for the XGBoost model. Firstly, to tackle the issue of lost and distorted logging curves in specific layer segments, the paper introduces a curve reconstruction method based on the combination of a multi-scale convolutional neural network (MSCNN) and a gated recurrent unit (GRU) neural network, which provides an accurate data basis for subsequent lithology identification. Secondly, the wavelet packet adaptive thresholding method is employed for denoising and normalizing the data, so as to mitigate the impact of noise on lithology identification. Next, the Optuna framework is utilized to determine the hyperparameters of the XGBoost algorithm, and an efficient lithology identification model is established. Finally, the Shapley additive explanations (SHAP) interpretability method is used for the attribution analysis of the XGBoost model, which reveals the contribution of different features to lithology identification and enhances the interpretability of the model. Experimental results demonstrate that the proposed Optuna-XGBoost model achieves a comprehensive lithology identification accuracy of 79.91%, outperforming traditional methods such as support vector machine (SVM), naive Bayes, and random forest, by margins of 24.89%, 12.45%, and 6.33%, respectively. The proposed lithology identification method based on the SHAP interpretability of the Optuna-XGBoost model has enhanced accuracy and interpretability, aligning well with the practical requirements of lithology identification in production scenarios.

The total organic carbon (TOC) content is an important evaluation index for shale gas exploration and development. Logging data can efficiently assess TOC, but it cannot be used for TOC prediction in inter-well areas. The TOC-sensitive factors extracted from seismic data can achieve three-dimensional (3D) prediction. However, due to the thin thickness and strong heterogeneity of shale reservoirs, it is difficult to achieve the required resolution by relying solely on seismic data. Therefore, it is necessary to comprehensively use multiple data sources to improve the accuracy of TOC assessment. For this purpose, a high-precision quantitative prediction method for shale TOC based on a convolutional neural network (CNN) is proposed. Firstly, the correlation analysis between the measured TOC data of the core from drilling and multiple logging characteristic curves is conducted on the Longmaxi Formation shale in southern Sichuan, and the most representative and sensitive features are selected. Secondly, based on the identified sensitive parameters, a CNN prediction model is constructed. The measured TOC samples and the training samples constructed by sensitive logging parameters are divided into datasets at a ratio of 7:3 for model training and validation. Finally, the high-resolution sensitive parameter inversion results obtained by simulation of seismic waveform indication are used as the feature input for 3D TOC content prediction. The sensitive parameters are rearranged, reorganized, and then input into the CNN model to achieve 3D TOC content prediction. The research results show that CNN has more advantages than multiple regression and back propagation (BP) neural networks in fitting the nonlinear relationship between TOC content and sensitive parameters. The average absolute error and root mean square error are both less than 0.6% between the predicted TOC data and the measured values from drilling. The prediction results are consistent with the actual situation. This method has high accuracy and obvious advantages in 3D TOC content prediction of thin shale reservoirs.

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.

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.

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.

The compressed sensing (CS) seismic exploration technique can increase the trace density and improve the image quality at the same cost. The key challenge of the technique is the missing seismic data reconstruction. The reconstruction method based on sparse domain constrained inversion has the advantage of high reconstruction accuracy. However, the low computational efficiency limits its application in the industry. To solve the issue, this paper proposes a fast iterative thresholding 3D seismic data reconstruction method based on curvelet transform by optimizing the conventional reconstruction method based on sparse domain constrained inversion and applied it to industrial data processing. 3D curvelet transform has an excellent sparse signal characterization ability, and the computation redundancy is high. In actual processing, seismic data segmentation is adopted, followed by parallel computation of 3D curvelet transform coefficient, which improves computational efficiency. In addition, a fast iterative thresholding algorithm is designed to improve the convergence speed and the reconstruction accuracy in comparison with the conventional method. Finally, amploying for the CS acquisition data, the proposed method is validated to be effective in data reconstruction and is applicable to data reconstruction in industrial production.

Shale reservoirs have both high-angle natural fractures and horizontal shale bedding fractures, which has significant influences on the expansion of artificial fractures under hydraulic fracturing and fracturing construction. To clarify the interaction between natural fractures and artificial fractures, this study first identified the development characteristics of natural fractures through core and field outcrop observations, electrical imaging logging identification, and seismic attribute prediction and then comprehensively analyzed the influences of natural fractures on the expansion law of artificial fractures and fracturing construction by relying on the downhole microseismic monitoring technology and considering the characteristics of changes in fracturing construction parameters. The results of the case analysis show that there are differences in the influence of natural fractures with different orientations on the hydraulic fracturing effect. High-angle natural fractures are prone to fracturing construction abnormalities such as repeated stimulation between fracturing sections, casing deformation, and casing damage. The influence of natural fractures on the expansion law of artificial fractures changes with different orientations, with EW natural fractures having a promoting effect, NS natural fractures having a blocking effect, and NE/NW natural fractures having a turning effect. The existence of horizontal shale bedding fractures causes artificial fractures to first rupture and extend laterally and then expand upward and downward. The overall performance of artificial fractures is that they do not expand high or extend far, and the construction pressure fluctuates in the high range, which is easy to cause sand blockage. The research results can provide a basis for the subsequent design of horizontal well trajectories and the optimization and adjustment of hydraulic fracturing parameters.

Identifying and analyzing the fault structure in seismic data can reveal the changes of underground structure and rock strata and provide an important basis for resource exploration and geological disaster prevention. However, the collected 3D seismic data contains a great deal of noise, and the proportion of fault bodies is very small. Thus, the results obtained by ant body identification methods have large errors and lack continuity and accuracy. Therefore, this paper proposes a fault identification method based on 3D residual attention network RAtte-UNet for deep learning. The method integrates the residual skip connection and attention mechanism and conducts model training. In the training process, the mixed loss function is used to reduce the influence of the extreme imbalance between faults and non-faults on network training, so that the network has a good identification ability for small faults. Through fault identification of simulated 3D seismic data and real 3D seismic data, it is found that the evaluation indexes such as accuracy, recall, and precision have been improved. Compared with the ant body identification method and other fault identification methods, this method achieves better fault continuity in identification results. It can identify small faults and has a strong model generalization ability, which can be applied to actual seismic data.

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.

This paper addresses the challenge of shallow subsurface geological stratification by investigating the feasibility of joint inversion using direct current (DC) resistivity data and Rayleigh wave data. The study focuses on applying the quantum-behaved particle swarm optimization (QPSO) algorithm enhanced with centroid opposition-based learning (COBL) and chaos search (CS), named the COBL-CS-QPSO algorithm, which integrates multiple optimization strategies, in one-dimensional joint inversion of the two methods. The joint inversion approach enables the extraction of layer thickness information from resistivity data, thereby overcoming the limitations of Rayleigh wave inversion in accurately resolving layer thickness. The incorporation of multi-strategy algorithms mitigates the risk of solutions becoming trapped in local optima during the search process and improves the efficiency of random searches under uncertain conditions. In theoretical model settings, various scenarios are examined, including cases with and without noise, as well as with known and unknown model layer numbers. The inversion is performed over a broad search range, yielding favorable results. Subsequently, the joint inversion algorithm is applied to actual data. The results demonstrate that the joint inversion of the DC resistivity and Rayleigh wave with the COBL-CS-QPSO algorithm produces more accurate outcomes than single-method inversions under field conditions lacking borehole data or detailed subsurface stratification. Furthermore, a comparison with the adaptive particle swarm optimization (APSO) algorithm highlights the advantages of the improved algorithm in inversion performance.

In the field of oil exploration, multiple attenuation is an important means to improve the migration imaging quality of seismic data, especially for marine streamer data that is strongly disturbed by free-surface-related multiples. To better improve the multiple attenuation effect and data processing efficiency, this paper proposes a free-surface-related multiple attenuation method using an image translation technology based on deep learning. Firstly, the seismic data processing task is regarded as an image translation task in deep learning, and the Pix2Pix network is used to process the seismic data converted into the image form. Secondly, by improving the form of the target data set from the conventional single form to the combining form, this study carries out multi-task training to improve the output effect of the Pix2Pix network. According to the correlation before and after data processing, an additional loss function is designed to further constrain and improve the output effect of the network. Finally, a layered model and a complex model are established for numerical testing, and additional interference items are added to the input data for quantitative testing. The numerical test results show that the proposed method can achieve multiple identification and attenuation by exploring the common features of data, improve the clarity of migrated images, and more accurately identify horizon information with high computational efficiency in data processing.

Ultra-deep fracture-cavity carbonate oil and gas reservoirs have become an important area for exploration and development in the Tarim Basin, showing promising prospects. The identification of effective ultra-deep fault-controlled fracture-cavity reservoirs in the Fuman area of the Tarim Basin is challenging, which affects the evaluation of reservoir effectiveness and the division of reservoir units. This paper conducts research on identification of fracture-cavity reservoirs with a seismic wave phase reconstruction method. First, the raw seismic data are decomposed in the phase domain. Then, the paper conducts one-dimensional and two-dimensional forward modeling of geological models. The phase component sensitive to the carbonate reservoir is selected. Finally, the component data volume with sensitive phase angle is reconstructed to generate a new phase seismic data volume. The research results show that seismic wave phase reconstruction technology can effectively remove the shielding effects caused by strong seismic reflections due to other factors in the strata, eliminating invalid signals and highlighting weak reflection signals from the reservoir. This technology can effectively delineate the favorable distribution range of fracture-cavity reservoirs, providing valuable information on favorable areas for reservoir evaluation and development planning. The use of seismic wave phase decomposition and reconstruction technology for identifying fracture-cavity carbonate reservoirs can serve as a useful reference for evaluating similar reservoir characteristics in other areas.

Igneous rocks have shielding and absorbing effects on seismic waves, which results in poor seismic data quality of the underlying strata of igneous rocks. Igneous rocks with different lithologies have obvious differences in density and magnetic susceptibility. Thus, the lithology of igneous rocks can be identified by gravity and magnetic data. Combining gravity and magnetic data, this study conducts research on the method of gravity and magnetic data fusion based on a logistic function and applies it to the Qinggelidi Block to improve the reliability of igneous rock identification. Firstly, based on seismic constraints, 3D inversion of gravity and magnetic data is performed to obtain the density and magnetic susceptibility distribution of igneous rocks in the study area. Then, an improved logistic function is used to achieve the fusion of gravity and magnetic inversion data, and the lithology of igneous rocks is imaged. Four groups of igneous rock units are identified, and their spatial distribution characteristics are interpreted. The interpretation results are consistent with the drilling information. The multi-parameter information of igneous rock gravity and magnetism is effectively fused by the logistic function, and the lithology identification results of igneous rocks are more comprehensive and accurate, which shows the effectiveness and reliability of the proposed method.

Multiples reduce the signal-to-noise ratio of seismic data, affecting the identification of primaries, thereby increasing the difficulty of seismic processing, reducing the authenticity and reliability of seismic imaging, and even forming geological illusions, which affect subsequent seismic exploration and development. The multiple suppression method based on wave theory can better adapt to complex media, which mainly consists of two steps, multiple prediction and adaptive matching subtraction. Both steps impact the final accuracy of multiple suppression. The paper compares three adaptive matching subtraction methods, respectively based on minimum energy principle, pattern recognition, and deep learning. The advantages, disadvantages, and adaptability conditions of each method are also analyzed. The test results of model data containing surface-related multiples and field data with internal multiples show that the adaptive subtraction algorithm based on the principle of minimum energy assumes wavelet consistency, while the pattern recognition based adaptive subtraction technique requires high lateral consistency of seismic data. Compared with the two traditional methods, adaptive matching subtraction based on deep learning can avoid assumed conditions and effectively protect primaries, achieving higher computational accuracy.

Microseismic monitoring is an essential technology in the field of unconventional oil and gas reservoirs exploration. It has been widely used in hydraulic fracturing fracture monitoring, CO₂ storage, and so on. However, the microseismic signal is weak in energy and easy to be polluted by noise. Its low signal-to-noise ratio makes it difficult to obtain good results in subsequent processing. Therefore, microseismic data denoising is a highly important processing step. The denoising effect has a key impact on the accuracy of subsequent source location and the reliability of focal mechanism inversion results. In this paper, a Monte Carlo non-negative dictionary learning (MCNDL) method is proposed for microseismic data denoising. The Monte Carlo block can obtain the initial dictionary containing relatively many effective signal features in a small amount of time. In the process of dictionary updating, non-negativity constraints are used to ensure the sparsity of data transformation and reduce the solution space, thus reducing the computational cost and improving denoising accuracy. This study evaluates the performance of the proposed method by using both synthetic and real-world microseismic datasets, comparing it with band-pass (BP) filtering, frequency-wavenumber (F-K) filtering, and K-singular value decomposition (KSVD) techniques. The findings highlight the superior denoising effect and efficiency of the proposed approach.

With the continuous breakthrough of exploration and development technology, the Qiangtang Basin has been called an important potential area of oil and gas resources in China. The Qiangtang Basin is located in the northern part of the Qinghai-Xizang Plateau. Complex geological structural factors, strong wind interference, unique perennial distribution of plateau permafrost, alpine and anoxic environments have brought many challenges to seismic exploration. The non-uniform distribution of high-speed permafrost can shield seismic wave energy, distort seismic wave travel time, and reduce the signal-to-noise ratio and consistency of seismic data. How to correctly understand the propagation mechanism of seismic waves in permafrost has become a key issue in seismic exploration in a plateau environment. Firstly, in response to the problem of permafrost in the Qiangtang Basin, this study measures the rock physical elastic parameters of permafrost in the field by using rock physical measuring equipment special for plateaus and establishes a complex structure model with permafrost by combining the field understanding and the actual two-dimensional seismic reflection profile. Secondly, the seismic wave field characteristics of permafrost are studied by finite difference elastic wave forward modeling. It is found that permafrost makes the surface wave more developed, which influences seismic wave travel time. When the direct wave field has a "cap" feature, permafrost shields the seismic reflection amplitude, resulting in a weaker seismic reflection amplitude. Finally, by comparing the reverse time migration profiles of the model with permafrost and the model without permafrost, it is found that the seismic reflection imaging energy below permafrost is weaker. The research results of seismic propagation characteristics and mechanism of permafrost provide theoretical support for permafrost identification, field seismic data acquisition, permafrost energy compensation, and migration imaging in Qiangtang Basin.

Time-frequency analysis is an important method that reveals the change regularity of signal spectrum over time. It is of great significance in the fields of seismic signal processing and feature analysis. To improve time-frequency analysis accuracy, this paper proposes a time-frequency analysis method based on local frequency by using the W-transform. The method uses local frequency to obtain the smooth dominant frequency of the signal, which is then applied to the W-transform to obtain the time-frequency spectrum of the signal. By smoothing the dominant frequency using the local frequency approach, this method effectively reduces the impact of noise, yielding a more accurate dominant frequency and improving the reliability of time-frequency analysis. In comparison to the traditional S-transform, this W-transform based on local frequencies partially overcomes dispersion at high frequencies and achieves higher time resolution at low frequencies. Compared with the W-transform, this method makes the spectral energy of the signal better concentrated in the energy center, having better focusing capability. The introduction of local frequencies enhances the W-transform, contributing to increased accuracy in time-frequency analysis. Finally, the effectiveness and superiority of the method are validated through numerical experiments.

Pre-stack amplitude variation with offset (AVO) inversion is a crucial method for obtaining petrophysical properties. Traditional pre-stack AVO inversion methods often rely on the approximate reflection coefficient equation, which usually lose accuracy in specific geological environments or under large incident angles. In response, this paper introduces a pre-stack nonlinear inversion method for joint PP-PS wave based on the exact Zoeppritz equation. This method combines a multi-objective global optimization algorithm with the PP-PS joint inversion and can simultaneously optimize the two objective functions of PP and PS, thereby achieving fully nonlinear parameter inversion. To overcome the challenge of assigning weight coefficients for PS data in traditional PP-PS joint inversion methods, this paper establishes a multi-objective function for PP-PS joint inversion within the Bayesian framework and employs a multi-objective intelligent optimization algorithm called strength Pareto evolutionary algorithm 2 (SPEA2) to solve the constructed multi-objective function for inversion. Tests are conducted on single-well synthetic seismic records, synthetic seismic records based on the Marmousi model, and seismic records of an actual field. The results show the method proposed in this paper, based on the exact Zoeppritz equation, can accurately estimate the elastic parameters of strata. It has better inversion results than traditional AVO inversion methods when dealing with seismic data for complex strata and large incident angles.

The sedimentary water body of Enping Formation in Huibei Area of the Pearl River Mouth Basin is relatively shallow. It is mainly in a sedimentary environment of shallow water delta-lacustrine swamp-shore and shallow lake facies, among which the floodplain swamps of the delta plain and lacustrine swamps are the main sedimentary facies belts where coal measures source rocks develop. Peripheral drilling has confirmed that a large number of coal seams have developed in Enping Formation. The maximum thickness of a single coal seam is 4.5 m, and the minimum thickness is 0.5 m, which is far less than the minimum thickness that can be identified by conventional seismic inversion. Therefore, how to predict the development scale of thin coal seams is the key to the evaluation of the coal measures source rocks in this area. Thus, based on a comprehensive analysis of the geological and sedimentary backgrounds of coal measures source rocks, a "three-step method" is explored to achieve the prediction of thin coal seams in coal measures source rocks. Firstly, through forward modeling and analysis, it is confirmed that thin coal seams in different sedimentary environments all have the characteristics of strong amplitude seismic reflection, while sandstone and mudstone show weak amplitude characteristics. The root-mean-square amplitude attribute is extracted as a sensitive attribute to distinguish the development range of coal seams from that of sandstone and mudstone. Secondly, the frequency band of seismic data is broadened through high-resolution seismic processing techniques to obtain broadband seismic data, which provides a high-precision data foundation for seismic inversion. Finally, by using the root-mean-square amplitude attribute, a stepwise fusion inversion method of low-frequency, medium-frequency, high-frequency dominant frequency bands is adopted to gradually characterize coal seams of different thicknesses. Finally, the prediction of the development range of thin coal seams in coal measures source rocks is completed. The actual application results in Block L of Huibei Area show that the predicted development range of coal seams have a relatively high degree of consistency with the drilling results, and this method can effectively improve inversion accuracy.

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.

Refraction and the first arrival wave traveltime tomography are two mainly used methods for near-surface model building and static correction. Refraction is appropriate to be applied in the area where the refraction layer is relatively stable, but cannot be directly applied to the case of prestack depth migration because the provided model is a layered one. The first arrival wave traveltime tomography can demonstrate the vertical and horizontal variation trends of the near-surface relatively well. However, due to factors such as insufficient actual data sampling and dramatically rugged topography, there exist problems of low accuracy and local illogicality of the inversion model. Therefore, this paper proposes a first arrival wave traveltime tomography method with refraction model constraint. As for the specific practice of the method, firstly the delay time and refraction velocity are calculated by the refraction method, and a geological refraction model is established based on information such as surface survey, first arrival, and near-surface structure. Then the refraction model is discretized in the principle of vertical traveltime equivalence, according to the vertical and horizontal variation rules of the actual surface velocity. Lastly, constrained tomography is applied to obtain the final near-surface velocity model. The theoretical model verifies the effectiveness of the proposed method. The actual data shows that the near-surface velocity model inverted by the proposed method is more reasonable and the application effect is better than that of the unconstrained tomography.

: Recently, gas was obtained from Well Dongba 1 in the Longwangmiao Formation of Penglai Gas Field in the northern paleo-uplift of central Sichuan, which indicates that there is potential for new discoveries in the Longwangmiao Formation of Penglai Gas Field. Therefore, it is of great significance to clarify the distribution scale of reservoirs for predicting the exploration potential of the Longwangmiao Formation in Penglai Gas Field. However, the Longwangmiao Formation in the study area also faces challenges such thin strata, rapid lithological changes in overlying strata, and difficulty in identifying seismic response of reservoirs. Therefore, based on fully considering the influencing factors of reservoir identification in the Longwangmiao Formation, this paper conducts high-resolution data processing, establishes seismic response models for different zones with the help of zoned forward modeling analysis, and forms qualitative and quantitative prediction techniques for reservoirs. Finally, a comprehensive evaluation is made on the distribution characteristics of reservoirs in the Longwangmiao Formation of Penglai Gas Field. The research results show the followings: ① Based on real drilling data and forward modeling analysis, it is clear that the thinning of strata in the Longwangmiao Formation will lead to unclear seismic response characteristics of its reservoirs, while high-fidelity high-resolution data processing can effectively improve the reservoir identification ability for thin strata. ② The change of overlying strata in the Longwangmiao Formation has a certain influence on the seismic response characteristics of reservoirs, and different zones have different seismic response characteristics. Four types of seismic response models are established through zoning. ③ Through petrophysical analysis, it is believed that the facies-controlled quantitative characterization method for reservoirs can effectively reduce the influence of complex lithology on reservoir prediction and improve prediction accuracy. The high-resolution data processing technology and the zoned reservoir prediction method can effectively improve the reservoir identification ability and accuracy in the Longwangmiao Formation. A total of 4200 km² of reservoir area in the Longwangmiao Formation of Penglai Gas Field is predicted, which clearly indicates that favorable reservoir areas in the Longwangmiao Formation of Penglai Gas Field have developed in a large area.

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.

Kriging interpolation is a modeling method that can be combined with empirical knowledge, in which the accuracy of variogram determines the effect of the interpolation, thus affecting the construction of low-frequency model of seismic inversion based on Kriging interpolation. It is difficult for the traditional Kriging interpolation method to use multiple different theoretical models of variogram at the same time to improve the accuracy of low-frequency model construction, and only using a single theoretical model to solve the variogram leads to the uncertainty of theoretical model selection, the smoothing effect with a low fitting value of the variogram, and the hole effect caused by a long well distance. To solve the above problems, a neural network CNN-GRU model is introduced to adaptively fit the complex semi-variance relationship between the vector and the corresponding well and further realize the effective fusion of the spherical model, the Gaussian model, the exponential model, and the hole effect model, so as to address the uncertainty, the smoothing effect, and the hole effect of the variogram. The model takes into account the correlation between wells and conveniently realizes the point-by-point variation analysis with a convenient processing process, which can well match the randomness of the parameters selected by the variogram. The actual data show that the Kriging method based on the CNN-GRU model can establish a high-precision low-frequency model of seismic inversion, and it has a better effect than the traditional method.

As the rotational components of seismic waves capture both the rotational motion of particles and spatial gradient field information, they offer certain advantages in seismic source localization, P-and S-wave field separation, and subsurface velocity inversion imaging. However, the high cost and limited availability of rotational seismometers have resulted in scarce observational data on rotational components, making it difficult to conduct in-depth research on their application value using limited data. Consequently, many traditional methods and techniques developed for translational seismic components are restricted in the study of rotational components.This paper, based on simulated and measured data, conducts experimental comparisons of the conversion of rotational components in dense seismic arrays using the differential and wavefield gradient methods. The results reveal that the wavefield gradient method exhibits superior conversion accuracy compared to the differential method. Furthermore, this study places particular emphasis on analyzing the impact of inter-array spacing and subsurface velocity on the conversion accuracy and discusses the optimal frequency range for array-based rotational component calculations.Additionally, this paper focuses on analyzing the influence of inter-array spacing and site phase velocity on conversion accuracy and derives a relationship for calculating the frequency range of translational-to-rotational component conversion. The accuracy of converting rotational components in dense arrays is affected by array spacing, site phase velocity, and target frequency. Given the conditions of the observational system, the derived relationship can be used to calculate the target frequency range, within which the computed rotational components show a high degree of fit with the observed rotational components, with a maximum correlation coefficient reaching 0.98.

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.

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.

The mechanism of land internal multiples is complicated, and internal multiple attenuation is a big challenge. Additionally, unequal source and receiver sampling in pre-stack seismic data of land does not satisfy the theoretical assumption of internal multiple prediction, which is an important problem encountered in land exploration. In response, this paper proposes a pre-stack internal multiple prediction method based on a four-dimensional data index tree real-time interpolation. First, to address the problem that the acquisition mode is not in line with the theoretical assumption, the paper comprehensively considers the center point position, azimuth, and offset of each seismic data to construct a four-dimensional data index tree to manage pre-stack seismic data. Then, data interpolation is carried out to obtain seismic data meeting the theoretical requirement in real time for internal multiple prediction, which avoids regularization of pre-stack seismic data. The model data and actual data both show that this method is effective in internal multiple prediction. After pre-stack internal multiple attenuation, the phenomenon of reverse velocity in the velocity spectrum is significantly improved for actual 3D data, and breakpoints and fractures become clearer on the imaging profile.

High-quality three-dimensional seismic exploration data in the field forms the basis for subsequent data processing and interpretation. However, traditional manual or semi-automatic methods for assessing the quality of seismic records can no longer meet the efficiency requirements of high-density three-dimensional seismic exploration, nor can they locate the sources of seismic noise. Utilizing deep learning techniques supplemented by cosine similarity algorithms, this paper proposes a classification method of seismic data quality based on deep learning. First, the study automatically classifies the quality of seismic records into six categories: normal traces, strong seismic source interference traces, industrial electrical interference traces, instrument problems traces(poor coupling between the geophone and the earth, blank traces), weak interference traces, and co-channel & anti-channel traces. A well-trained convolutional neural network achieves a speed of less than 3 seconds for quality assessment of single-shot seismic records (over 8000 traces), with an accuracy of 86% compared to manually classified results, and the evaluation results are objective. The results of model training show that this approach not only facilitates the rapid identification of different types of noise or instrument problems in seismic records, thereby improving the efficiency and quality of seismic construction, but also provides an important decision-making basis for comprehensive assessment of seismic data quality by grades and zones, suitable for qua-lity monitoring in mass seismic acquisition fields.

Dense drilling platforms, coral reefs, docks, pipelines, and other obstacles lead to the necessity of obstacle avoidance for source and receiver points in the design of marine seismic data acquisition by the ocean bottom node (OBN). Planning a path with obstacle avoidance is a key to the success of marine seismic exploration. Reasonable obstacle avoidance method and path planning scheme can minimize the influence of obstacles on bin attributes in the acquisition area, avoid the invalid work, and reduce the acquisition risk. Traditional obstacle avoidance methods generally move the source and receiver points in the obstacle area laterally to the outside without considering the influences of the turning radius of the source vessel and the width of the expander. The manual obstacle avoidance method leads to technical problems such as low obstacle avoidance efficiency, a large error, serious loss in the coverage area, and potential safety hazards caused by inaccurate turning radius design of vessel. This paper proposes an intelligent obstacle avoidance method for complex sea area observation system. The shot line of the observation system is fitted by the trajectory theory, which is then combined with algorithms of multi-obstacle tangent circle against trajectory, obstacle merging, and dynamic feedback correction of trajectory. In this way, the paper forms an intelligent obstacle avoidance technique based on a backtracking algorithm and real-time calculation and adjustment of various parameters, greatly improving the efficiency and accuracy of obstacle avoidance. The production practice shows that the method can improve the efficiency and accuracy of exploration in complex sea areas and provide technical support for safe and efficient exploration in complex marine obstacle areas.