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.

Bedding fractures are common in tight sandstone reservoirs and shale reservoirs. As the reservoir space and seepage channel of oil and gas, they have a significant impact on oil enrichment and production efficiency. The traditional prediction methods of bedding fractures is limited by the quality of seismic and logging data, as well as the number of actual drilling wells, with some limitations in accuracy and efficiency. In recent years, deep learning technology has been widely used in fracture identification and prediction, but with the increase in model complexity grows, the problems of gradient anomaly and performance degradation are becoming increasingly obvious, and the commonly used models fail to fully adapt to sequence seismic and logging data. Therefore, this paper proposes a new method for bedding fracture prediction, which is based on the convolutional residual bidirectional long short-term memory neural network (BiLSTM). Firstly, pseudo-wells are uniformly deployed in the study area to solve the problem that the number of actual drilling wells is insufficient and it is difficult to fully cover the study area. Combined with the core observation data, the well-side seismic attributes of a variety of real drilling wells and pseudo-wells with statistical information on bedding fractures are extracted to establish training samples and actual prediction data sets. Secondly, through the sample expansion and preprocessing related technical means are adopted to solve the problem of sample quality problem. Finally, the convolutional neural network is used to extract sample features, and the convolution residual connection is established to transmit data to the BiLSTM network with gating mechanism for information selection and forgetting. This effectively alleviates the problems of gradient anomaly and performance degradation in the deep network, and significantly improves the prediction accuracy of the model, with the coefficient of determination reaching up to 91.3 %. The prediction results of bedding fractures in the M region of Subei Basin show that the proposed method can relatively efficiently and accurately predict the development condition of bedding fractures, and the prediction results are consistent with geological understanding. This method provides effective support and practical guidance for on-site oil and gas exploration industry.

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.

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.

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.

Microseismic monitoring is crucial for the quantitative characterization of hydraulically induced fractures during the stimulation of tight oil and gas reservoirs. However, the relatively strong background noise in microseismic signals often leads to relatively low accuracy and reliability of source-parameter inversion methods that only employ first-arrival phase information. Therefore, drawing on the acoustic wave equation full-waveform inversion (FWI) approach and leveraging complete microseismic waveform data, this paper proposes a simultaneous inversion method for microseismic source locations and source wavelets based on sparse regularization FWI and modified Orthant-Wise Limited-memory Quasi-Newton (mOWL-QN). First, to account for the random and continuous nature of microseismic signals, the method of FWI with sparse regularization constraint is adopted to invert the spatiotemporal source function of microseismic events. Through the application of the L₁-norm sparsity constraint as the additional regularization term, the sensitivity of the algorithm to background noise is reduced. Second, the mOWL-QN method is used for optimization to deal with the non-differentiability of the objective function. Finally, sensitivity analysis is performed based on the Marmousi model to investigate the influence of the proposed algorithm on sparse-regularization coefficients, velocity models, background noise, and observation system. Numerical test results show that the proposed algorithm can be accurate and stable under certain background noise without the requirement of prior information on the source wavelet's dominant frequency or wavelet type. The algorithm can simultaneously invert microseismic source locations and the time-domain source wavelet function, which further improves the precision of microseismic source-parameter inversion. This provides technical support for highly accurate quantification of hydraulically induced fracture geometry and post-reservoir-stimulation flow pathways, which provides significant implications for optimizing hydraulic fracturing parameters.

Seismic wave traveltime computation is a critical foundational technology for seismic migration, tomography and subsurface static correction, etc. The third-order weighted essentially non-oscillatory (WENO) finite difference scheme and the Lax-Friedrichs method are used to solve the factored eikonal equation to improve seismic wave traveltime accuracy. First, the general eikonal equation is factorized to avoid singularities near the source point. Then, the factored eikonal equation is rewritten in the Lax-Friedrichs traveltime iterative scheme, and the third-order WENO difference scheme is employed to replace the partial differential items of traveltime along the horizontal and vertical directions in the iterative scheme. Finally, the fast-sweeping iterative algorithm is utilized to solve the factored iterative scheme of traveltime. Calculation accuracy of seismic wave traveltime can be improved effectively due to the adoption of the factorization equation and high-order WENO difference scheme. The numerical solutions of the homogeneous medium, and 2D and 3D constant gradient velocity models show that compared with the conventional eikonal equations, the proposed traveltime calculation method significantly improves the calculation accuracy of seismic wave travel time.

Compared with the transversely isotropic (TI) model, the orthorhombic anisotropic (ORT) model with both horizontal and vertical symmetry axes can describe the anisotropic characteristics of underground media better. For seismic wave simulation and imaging in ORT media, the conventional pseudo-acoustic wave equations (PWEs) based on the acoustic assumption are widely used because they are simple and efficient. However, many studies have verified that the acoustic assumption in anisotropic media is only a mathematical simplification and has an unclear physical meaning. Therefore, the conventional PWEs for wave simulation in ORT media may encounter SV-wave artifact contamination and instability. To address the problems of conventional PWEs, this paper develops a new optimized pure P-wave equation (PAWE) and illustrates the corresponding high-accuracy numerical implementation algorithm. First, an optimization strategy is adopted to approximate the original complicated ORT pure P-wave dispersion relation. The expansion equation has simple form and produces high-accuracy approximation effect. Then, a combination of the improved high-order anisotropic Poisson solver and finite difference discretization scheme is used to solve the proposed PAWE effectively. Phase velocity analysis indicates that in comparison with the existing advanced approximation method, the proposed optimization method achieves higher approximation accuracy without increasing any computational cost. Several modeling examples demonstrate that compared with the conventional PWE, the proposed optimized PAWE can significantly suppress SV-wave artifact contamination and numerical instability under different complex media and generate accurate and effective seismic wavefield responses.

In urban subsurface investigations, the characterization of adverse geological structures often relies on two primary methods: passive-source surface wave dispersion curve inversion and horizontal-to-vertical (H/V) spectral ratio inversion. The former uses only vertical-component seismic records, failing to fully incorporate horizontal-component information; the latter is mainly employed to estimate overburden thickness based on the predominant frequency of H/V spectral peaks, with limited application in shear wave velocity structure inversion. To leverage the advantages of both approaches, this study proposes a joint inversion method that integrates dispersion curves and H/V spectral ratios, making comprehensive use of multi-component surface wave information to improve inversion accuracy and stability. A three-layer horizontal layered model representing uniform sedimentary layers and a three-layer vertical fault model simulating a fault zone were established. Comparative analyses of dispersion curve inversion, H/V spectral ratio inversion, and their joint inversion were conducted. Synthetic tests demonstrate that the joint inversion yields more accurate results. Finally, using the F5 ground fissure in Xi'an as a case study, subway seismic data are applied to validate the feasibility and effectiveness of the joint inversion method in detecting and monitoring urban underground structures. The joint inversion of dispersion and H/V spectral curves provides valuable insights for the development and application of urban underground space exploration technologies.

Tight oil is a typical unconventional hydrocarbon resource, characterized by low porosity, complex mineral composition, high clay content, and difficulty in distinguishing between oil and water. At present, the evaluation of tight oil reservoirs relying on acoustic wave data and elastic properties is insufficient for accurately identifying oil-water distributions. In contrast, the combination with electrical properties can better directly reflect them. In this study, the tight oil reservoir in the Qing Ⅱ section of Songliao Basin was selected as the target. A set of 11 rock cores was collected for ultrasonic measurements and fluid sensitivity analysis of elastic parameters. Well-log data from the study area were used to investigate the relationships between the elastic and electrical properties with respect to porosity and clay content. Subsequently, the elastic and electrical rock physics models with the same microstructures were constructed by using the differential effective medium (DEM) theory and the squirt flow model. The effects of porosity, clay content, and water saturation on elastic wave velocities and electrical conductivity were further analyzed. Finally, a 3D joint rock physics template for tight oil reservoirs was established by integrating the elastic and electrical responses of the rock. The template was calibrated and adjusted by using the well-log data and applied to the actual tight oil reservoirs to predict porosity, clay content, and oil saturation. It is demonstrated that the constructed acoustic-electric joint rock physics model of tight oil with both fluid and structural heterogeneity can effectively interpret the acoustic and electrical data of tight oil rocks and predict the oil-water saturation of the reservoir.

The accurate prediction of stratum fracture pressure is an important guarantee for the exploration and development of deep oil and gas targets. The limestone strata of the Permian Maokou Formation in the Sichuan Basin have developed dissolution pores, with strong heterogeneity, and the diagenesis is different from that of sandstone and mudstone. The existing fracture pressure prediction methods are all proposed based on mudstone and shale, and there are large errors in predicting the pressure of the Maokou Formation, thus failing to accurately guide the implementation of drilling. The karst stratum of Maokou Formation in central Sichuan Basin was taken as the background, and the limestone of the Maokou Formation was divided into two types: limestone with developed dissolution pores and fractures and dense limestone. On this basis, this paper analyzed the correlation between fracture pressure and logging parameters. Based on Huang's model, a fracture pressure prediction model considering physical property changes and low shale content was proposed. Furthermore, combined with the pre-stack inversion method, an improved fracture pressure prediction method for karst strata was established. The experimental results show that the prediction error of the improved prediction method is less than 4.80%, and the average error of the prediction results is significantly reduced by 21.40% compared with Huang's model. The pressure prediction results have good vertical continuity, and the distribution patterns in both the vertical and horizontal are in line with the actual geological characteristics, which can provide technical support for the efficient oil and gas exploration and development of Maokou Formation.

The echo observation in nuclear magnetic resonance (NMR) logging is a type of weak signal observation. Moreover, due to the limited space of the oil drilling wellbore and the harsh observation environment of high temperature and high pressure, random noises are inevitably embedded into the echo data. The inversion of NMR echoes is a highly ill-posed problem, and random noise interference often leads to instability of inversion results and even significant deviation in the T₂ spectrum distributions. As a result, the T₂ spectrum cannot be used to characterize the true physical properties of reservoirs. Considering the relative stability of conventional logging (including acoustic logging, density logging, and natural gamma logging), this paper proposed a joint inversion algorithm for conventional logging and NMR logging echoes. By utilizing the relative stability of conventional logging data and suppressing the interference of random noises on the NMR echo inversion, the algorithm aims to significantly improve the stability of echo inversion results. Based on the joint inversion of NMR logging and conventional logging, the calculation formula for the reservoir's water saturation was derived according to the physical meaning of the inversion coefficients of the conventional logging constraint equations, and a saturation iteration algorithm was formed. The actual logging data processing results show that the joint inversion of NMR logging and conventional logging effectively improves the stability of NMR echo inversion. Based on the stable inversion of the T₂ spectrum, the water saturation can be obtained in the iterative process, which can be used to check whether the initially calculated saturation is reasonable or not. In addition, the saturation iteration algorithm suggested in this study does not rely on rock resistivity logging and Archie formula parameters, making it promising for evaluating oil reservoirs with low resistance.

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.

The absorption and attenuation of seismic wave energy in complex loess plateaus has always been a challenging issue for geophysicists. The improvement of seismic data resolution and signal-to-noise ratio is particularly difficult, making it hard to satisfy the requirements for describing reservoirs and exploring residual oil in mature oilfields. Based on the interpretation technology for conventional borehole seismic and surface seismic data, supporting interpretation technologies for fine reservoir description were developed by combining DAS 3D-VSP imaging data. These technologies included interpretation preprocessing based on borehole-surface joint exploration data, reservoir prediction, and fracture network characterization. In a certain block in the eastern part of the Ordos Basin, the post-stack adaptive spectrum broadening high-resolution processing technology based on borehole-surface joint exploration data has achieved a 5 Hz increase in main frequency and a 25 Hz bandwidth expansion. The waveform indication technology for thin reservoir inversion based on DAS 3D-VSP data could accurately identify single sand bodies with a thickness of 3~5 m, and the conformity rate of each horizon in the validation well reached 86.32%. The fracture-network identification technology based on inclination azimuth scanning constraint has achieved effective prediction of lower-order faults and fracture networks. Based on DAS borehole-surface joint exploration data, a set of fine reservoir description technologies integrating borehole & surface data+borehole & seismic data has been formed, and the accuracy of reservoir characterization has been improved by 15%. The application results show that the supporting technologies based on the borehole-surface joint exploration have significant promotional value.

Pre-stack inversion is an important means to obtain the elastic parameters of underground media. The Markov chain Monte Carlo (MCMC) algorithm is a classical method for pre-stack inversion. Compared with the traditional numerical optimization algorithm and linear inversion method, the MCMC inversion algorithm has higher accuracy, but there are still problems such as dependence on the initial model, long calculation time, and large uncertainty. Therefore, the conventional MCMC inversion algorithm was improved, and a BLI-MCMC pre-stack stochastic inversion method based on structural dip constraints was proposed. Firstly, the dip angle of the geological structure was added to the prior constraint information to improve the sampling efficiency of inversion and reduce the uncertainty of inversion results. Then, the Bayesian linear inversion (BLI) algorithm was used to provide a good initial model for MCMC inversion and served as the starting point of iteration. the combustion time of the Markov chain was shortened, and the efficiency of inversion was improved from the perspective of the initial model. The application results of simulated data and actual data show that the improved method can significantly improve the inversion accuracy and efficiency and maintain the high lateral continuity of underground media. It provides technical support for the inversion of undulating underground media.

The "catchment ridge"based reservoir control model has made a major breakthrough in oil and gas exploration in the Bohai Sea. As a typical "catchment ridge" pattern of steep slope sand, it is of great significance to evaluate the main controlling factors of oil and gas enrichment in this type of reservoir. Physical simulation experiments and geological data statistics were carried out to verify the main influencing factors of oil and gas accumulation and enrichment in the "catchment ridge" of steep slope sand. Grey correlation method and multiple linear regression method were used to clarify the primary and secondary relations of each factor, and a quantitative evaluation model of the "catchment ridge" of steep slope sand was established. The results show that the main factors affecting the degree of oil and gas enrichment are ridge porosity, ridge area, ridge thickness, and hydrocarbon generation intensity of source rock, and the oil and gas enrichment is mainly controlled by the coupling of source-reservoir factors. Based on the comprehensive consideration of various influencing factors, a quantitative evaluation model of the "catchment ridge" of steep slope sand is constructed based on the multiple linear regression method to evaluate the convergence ability of the typical oil and gas reservoir, namely the BZ 28-X structure. The evaluation results are consistent with the actual exploration results, indicating that the evaluation model is reasonable and reliable. It is of great practical significance to guide the shallow oil and gas accumulation law of the "catchment ridge" of deep steep slopes in the steep slope zones of the Bohai Sea.

Target processing or attribute extraction is the foundation of reservoir and fluid prediction, which directly restricts the accuracy of reservoir and fluid prediction. Fluid prediction is a difficult point in oil and gas exploration and development in the periphery of the Gangxi Oilfield. Accurately predicting oil and gas enrichment areas and effectively identifying oil and gas interfaces are the keys to selecting favorable areas for oil and gas field exploration and development and successfully deploying well locations. In response to the above challenges, this paper conducted research on fluid prediction technology driven by attribute fusion and waveform simulation in sequence based on post-stack time offset seismic data. Firstly, oil and gas reservoir prediction was achieved based on the fusion of root mean square amplitude and arc length attributes. Secondly, waveform indication simulation technology was used to finely predict the oil and gas enrichment area of the reservoir, or in other words, fluid prediction was carried out through rock physics analysis of the target area. Based on this, the resistivity and wellbore diameter curves were used to reconstruct the acoustic waves, and the common structure between the pseudo wave impedance curve and seismic reflection waves was established to identify the fluid in the reservoir. The two technologies were used to carry out fluid prediction in the target area, achieving a coincidence rate of 90.9%. This provides technical support for predicting the fluid in the reservoir in the periphery of the Gangxi Oilfield, and the results can provide reference for the exploration and development of similar lithologic oil and gas reservoirs.

Lithology-fluid identification is a crucial step in reservoir prediction and geological modeling, playing a significant role in guiding oil and gas exploration and development. Although machine learning models have achieved remarkable success in this field, their decision-making processes lack interpretability and face challenges related to the non-uniqueness of classification results and uncertainty assessment. To address these issues, this study proposed a high-accuracy and interpretable lithology-fluid identification method by integrating the heterogeneous time series ensemble learning algorithm HIVE-COTE 2.0 (HC2) with SHAP interpretability analysis. HC2 enhanced prediction accuracy and generalization ability by incorporating multiple base classifiers and adopting a cross-validation accuracy-weighted probability ensemble strategy. Additionally, the probability values generated by HC2 quantified the uncertainty of prediction results, providing risk assessment support for decision-making. SHAP further analyzed the importance of input parameters, optimizing feature selection to improve classification performance. Experimental results demonstrate that this method surpasses traditional machine learning and deep learning models in terms of accuracy, generalization ability, and interpretability, offering a novel approach for precise reservoir prediction and efficient oil and gas development.

The BZ block is located in Kuqa Depression, with a buried depth of more than 6000m and developed fractures. The quality of existing seismic data is low, and the conventional ant tracking workflow has problems such as small ant values, poor consistency of ant values, and inconspicuous fracture characteristics. As a result, it is difficult to meet the requirements of blocks' characterization for fracture systems. Therefore, according to the geological characteristics of the study block and the existing data, an improved scheme of ant tracking workflow is put forward. Firstly, based on the global optimization and the interpretation of subtle geological features, the ant attribute volume is generated on the basis of post-stack attributes of inclination consistency and curvature consistency. Secondly, by comparing and analyzing the characteristics of the single ant volume and multiple ant volumes under default parameters on low-level fracture characterization and ant value consistency, the iteration times of ant attributes volume are determined. Finally, by conducting sensitivity analysis of different ant tracking parameters combinations, the characterization accuracy of fractures at the block boundaries is further optimized to obtain the final ant attribute volume. The practical application shows that the ant volume generated by the proposed method is obviously better than the conventional ant tracking workflow in terms of the fracture characterization effect, ant value, and ant value consistency. Meanwhile, 321 fracture combinations in the block are completed, which effectively improves the interpretation accuracy of the fracture system. The extracted fracture fragments have a high matching degree with the artificially interpreted internal and boundary faults, thus solving the problem of the unsatisfactory fracture system caused by low seismic data quality in the ultra-deep BZ block. This method can provide a reference for the characterization of fault system characterization in blocks with similar geological conditions.

The use of time-lapse seismic method can identify the waterflood front under water injection development, but conventional methods lack analysis of the lateral coupling changes of water saturation and pore pressure in terms of reservoir changes, as well as that of the impact of simultaneous changes in multiple factors such as water saturation, pore pressure, and effective pressure on rock' elastic parameters in terms of rock physics. There is also a lack of forward simulation research under different development conditions. In response to the above issues, the article first uses the Buckley-Leverett equation to establish the characteristics of the water- flood front and their lateral coupling changes when the water saturation and pore pressure of the reservoir change under water injection development. Then, in terms of rock physics, the Hertz-Mindlin contact model, Batzle-Wang fluid model, and Gassmann fluid replacement theory were used to establish a rock physics model for for time-lapse seismic events, clarifying the characteristics of elastic parameters caused by changes in water saturation and pore pressure at the waterflood front and lateral variations under water flooding conditions. Finally, in terms of forward modeling analysis, five scenarios were designed: original state, natural energy development, water injection development, over-pressure situation, and strong over-pressure situation. The time-lapse seismic response characteristics and lateral variation characteristics of the five scenarios at the waterflood front were clarified, and the quantitative standard for identifying the waterflood front using the time-lapse seismic amplitude change rate under water injection development was further determined. Practical oilfield applications have shown that the use of the time-lapse seismic method can effectively identify the impact range of water flooding. By comparing time-lapse seismic data from different periods, the waterflood path and history can be determined. Therefore, the time-lapse seismic method can effectively guide the fine development of deepwater reservoirs through water injection.

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.

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.

In recent years, ocean bottom seismic exploration has become a highly efficient operating mode in the field of offshore oil and gas exploration. Ocean bottom seismic exploration requires real-time navigation and monitoring of the exploration vessel and source array, and it is necessary to determine the spatial position of the source array and seafloor geophone at the moment the gun is fired. The positioning accuracy of the source array and seafloor geophone directly affects the imaging quality and reliability of seismic data. Based on the needs of ocean bottom seismic exploration operations, this paper presented a detailed design of key algorithms for node placement, acoustic secondary positioning, and source excitation. It also developed an imported navigation system for ocean bottom seismic exploration based on these algorithms. The actual measurement results show that the developed integrated navigation system has a shot prediction accuracy better than 0.3 meters, and the transponder positioning result differs by about 1 meter from the current mainstream imported navigation systems. This level of accuracy meets the needs of ocean bottom seismic exploration and navigation operations. The system has been successfully deployed in exploration operations in both the South China Sea and the Bohai Sea, delivering impressive results in its practical application.

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.