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.
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.
Seismic wave traveltime information required for geophysical inversion such as source localization and tomographic imaging can be obtained by solving the eikonal equation. Common algorithms for this purpose include the fast marching method (FMM) and the fast sweeping method (FSM). Physics-informed neural network (PINN) is a novel mesh-free method that incorporates the constraints of partial differential equations into the loss function of the neural network, thus embedding physical information into the network. Focusing on optimizing node distribution during training, this study adopts an adaptive sampling strategy based on residual distribution to improve the training performance of PINN and proposes a travel-time calculation method using PINN with adaptive node generation. Application tests on the Marmousi model and an irregular topography model show that, compared with the fixed node-generation method, the proposed approach yields a more stable training process and maintains high accuracy in traveltime calculations.
In seismic surveys of complex surfaces such as loess plateau areas, coherent noise greatly reduces the signal-to-noise ratio (SNR) of seismic data, which seriously affects the accuracy of subsequent seismic imaging and physical inversion. To this end, a new coherent noise suppression strategy is proposed in the paper. The method mainly consists of the following core steps. Firstly, anisotropic diffusion filtering is adopted to effectively suppress the incoherent noise components in the data, and initially improve the overall quality of the low SNR data. Next, the dictionary learning method is employed to sparsely characterize the seismic data, and statistical indicators are applied to precisely locate and eliminate the dictionary atoms with a large variance of gradients. These atoms tend to be the main carriers of linear coherent noise and random noise. Then, the dictionary atoms and their corresponding sparse coefficients that can effectively characterize the effective signals are filtered and retained to reconstruct the seismic data. Finally, the effective signals are further extracted from the removed noise through the principle of signal-to-noise local orthogonalization. The simulated data and typical real data tests show that the method ensures the intact preservation of the effective signals while suppressing the coherent and random noises, which further improves the SNR of the data. The method can provide a reference for the treatment of linear coherent noise.
Due to the high and steep underground structure in the limestone outcropping area of the eastern Sichuan Basin, the conventional geometry has some problems, such as uneven illumination and insufficient partial illumination energy.For the high and steep structure of the eastern Sichuan Basin with high-precision seismic ima-ging, seismic illumination analysis is an important method to guide the optimized design of seismic acquisition geometry in the region. By combining the surface and underground geological conditions in limestone outcropping areas of the eastern Sichuan Basin, this paper establishes the demonstration technology and process of geometry encryption based on seismic illumination analysis.By conducting the design and demonstration of geometry encryption of the underlying structure in the limestone outcropping areas, the seismic illumination energy of the local shadow area of the geological target is effectively improved. Firstly, the surface encryption range is determined by adopting the 2D forward model for reverse illumination analysis, and then by employing the 3D geological model for forward illumination modeling analysis combined with the actual seismic data analysis, the acquisition geometry encryption scheme suitable for the limestone outcropping area in eastern Sichuan Basin is obtained. The technological implementation and application show that adding receiving lines at the top of the structure can effectively improve the seismic imaging effect of the high and steep complex structure area in the east Sichuan Basin, and a technically effective and economically feasible encryption scheme for the geometry can be obtained.
With the widespread application of node instruments in seismic exploration, their quality control (QC) problems become increasingly prominent, and their "blind acquisition" mode lacks real-time data transmission capability, making it impossible to monitor and effectively analyze the quality of single-shot data in real time. This results in delays in the synthetic analysis of seismic acquisition data and affects the timeliness of data QC and exploration decision-making responses. Therefore, based on the single-shot acquisition mode combining wired monitoring arrays and node instruments, this paper develops a real-time QC technology for single-shot data acquisition in node instrument "blind acquisition" for the characteristics of seismic data from monitoring arrays. Firstly, the single-shot energy is calculated based on the spherical spreading principle to achieve an accurate analysis of single-shot energy. Secondly, an environmental noise analysis technique based on the number of disturbed channels is employed, with the proportion of disturbed channels calculated to assess environmental noise. Finally, supporting real-time monitoring software is developed to enable real-time monitoring of single-shot excitation energy and noise. This technology is applied to the full-node 3D seismic acquisition project in the NCB area of the Sichuan Basin. The results show a high correlation between the energy of the monitoring array and the full-shot energy, as well as high consistency in the proportion of disturbed channels. This significantly improves seismic exploration efficiency and data quality, provides an effective method for real-time QC of blind acquisition seismic data, and holds significance in the commercial application of 5G real-time data transmission of nodes.
The accuracy of surface structure investigation greatly influences the quality of seismic field acquisition. However, the commonly used surface structure investigation methods fail to accurately characterize the thin lithological change, and construction under special surface conditions is difficult. To solve this problem, the optical fiber sensor acquisition technology is applied to the investigation of surface structure.The advantages of a high spatial sampling rate and easy construction of optical fiber are used to solve the problems of traditional methods. The feasibility of optical fiber acquisition is verified by comparing the data collected by geophone. At the same time, the same point comparison test of surface velocity inversion and Q-value calculation is carried out. The results show that optical fiber data can identify more lithological change interfaces than geophone data. However, the signal-to-noise ratio of fiber optic data is low, with a narrow effective band range and unstable Q-value calculation results. This study shows that the optical fiber sensor acquisition technology has great potential in the application of surface investigation. Still, more perfect quality control means are needed to improve the quality of data acquisition.
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 is the second part of the research on DAS borehole-surface joint exploration (BSJE) technology in the onshore loess plateau area: data processing section. Aiming at tackling challenges of static correction in complex loess plateau area, low SNR of data, and poor imaging quality, based on the DAS 3D-VSP data acquired by BSJE, this study investigates signal analysis and imaging processing of onshore DAS 3D-VSP data, including DAS 3D-VSP adaptive wavefield separation, DAS 3D-VSP high-resolution processing, and DAS 3D-VSP angle-domain Gaussian beam prestack depth migration, etc. By extracting accurate time-depth relationships, horizon velocities, deconvolution operators, spherical spreading compensation factors, attenuation factors around the well, high-resolution processing of DAS 3D-VSP can be achieved in conjunction with surface seismic data. Through practical applications in a certain block in the eastern part of the Ordos Basin, the effective frequency band of DAS 3D-VSP imaging data reaches 4~85 Hz, and the correlation coefficient of the well-seismic wave group is improved by 9.7% on average, thus, laying a solid data foundation for subsequent seismic geological interpretation, reservoir fine description, and residual oil exploration.
The reverse time migration (RTM) imaging method is highly favored for its ability to conduct imaging on complex geological structures without being constrained by the imaging angle. However, this method has its inherent low-frequency noise, thus significantly degrading the migration imaging quality. Therefore, accurately suppressing the low-frequency noise of RTM is crucial for high‑quality migration imaging results. Based on the conventional RTM approaches, this study conducts a detailed analysis of the mechanisms that generate low-frequency noise in RTM. Specifically, the Laplacian filtering method is currently the most commonly adopted suppression method, but it still retains a portion of the low-frequency noise and alters the amplitude information of the seismic data. To this end, a low-frequency noise suppression method based on X-shaped diffusion filtering is proposed. This method is a global nonlinear iterative filtering approach that introduces a diffusion function derived from thermodynamics into the RTM noise suppression process, with the RTM data containing low-frequency noise taken as the input. Then, by adjusting the diffusion coefficient and the number of iterations, an X-shaped complementary denoising operator for complex dipping structures is introduced, and a damping function for diffusion is set to flexibly control the diffusion extent. This allows for the separation of effective signals and low-frequency noise in relatively amplitude-preserving conditions. Numerical simulations on both simple and complex models demonstrate that the X-shaped diffusion filtering method can quickly and accurately suppress low-frequency noise in RTM and produce high-quality migration results.
The traditional method of seismic data acquisition involves separate excitation between shots, leading to low construction efficiency. The simultaneous-source acquisition technique, which involves simulta-neous or delayed excitation of multiple shots, has significantly improved construction efficiency. To process multi-source seismic data conventionally, it is essential to separate the mixed data into single-source data. However, traditional data separation methods suffer from low efficiency. On the basis of the separation method using sparse inversion, this paper develops fast projection onto convex sets(FPOCS) algorithm through introdu-cing an inertial parameter into the conventional POCS algorithm.Additionally, to address the problem that random time-delay encoding causes local continuity of aliasing noise and thus reduces separation accuracy, this paper proposes a method of periodic sinusoidal time-delay encoding. During the iterative process, the curvelet transform is used as a sparse basis, the soft thresholding function as the threshold model, and the exponential threshold formula as the threshold update formula. As a result, the blending interference from other sources becomes more discrete, and the aliasing noise is distributed uniformly within a certain delay time, which reduces the difficulty in simultaneous-source data separation. The results obtained from theoretical analysis and processing of measured data demonstrate that the two methods proposed in this paper not only greatly improve the convergence speed but also effectively enhance the accuracy of simultaneous-source data separation and have good anti-noise performance.
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.
Seismic wave impedance inversion is the core of reservoir prediction, and the construction of an accurate seismic forward model is the basis of realizing high-resolution wave impedance inversion. However, it is difficult for the traditional convolution forward models based on one-dimensional seismic wavelet to accurately simulate and characterize the imaging profile in the depth domain when there is a drastic lateral change in the subsurface velocity, which seriously affects the accuracy and reliability of wave impedance inversion results. For this reason, this paper proposes a forward modeling method of convolution in the depth domain of non-stationary space based on point spread function. Firstly, the accurate mapping relationship between seismic ima-ging profile and underground reflection coefficient is derived according to the linear Born forward modeling theory. Then, an accurate convolution forward model is constructed with the point spread function as the seismic wavelet in the multi-dimensional depth domain. Finally, the efficient algorithm of point spread function based on Green's function of the ray theory can greatly improve the computational efficiency of the point spread function. The correctness and effectiveness of the proposed method are verified by the depth-domain convolution forward modeling tests of the simple horizontal layered model and the complex Marmousi model.
In gas-water two-phase flow, phase stratification and the non-uniform distribution of gas and water within the wellbore often lead to significant measurement errors when using conventional instruments under varying flow regimes and phase separation conditions. To overcome these limitations, a modular combination of multiphase flow sensors, such as the Capacitance Array Tool (CAT), Resistance Array Tool (RAT), Spinner Array Tool (SAT), and Gas Array Tool (GAT), can be flexibly configured to enhance the adaptability and efficiency of well logging systems. In particular, the GAT employs optical sensors to perform repeated measurements under various flow conditions, effectively improving measurement robustness and resolution. This study evaluates the gas holdup measurement performance of GAT and RAT under different flow rates and water cut conditions. An inverse distance weighting (IDW) algorithm is applied to interpolate the measured gas holdup distribution for visualization. Experimental results demonstrate that GAT outperforms RAT regarding imaging quality, measurement accuracy, and adaptability under low-to-moderate water cut and high flow rate conditions, providing valuable insights for interpreting gas-water two-phase production profiles in horizontal wells.
Geophysical logging plays a crucial role in detecting fluid types in subsurface oil and gas reservoirs and evaluating reservoir parameters. Traditional log interpretation methods face significant challenges. Artificial intelligence (AI) algorithms offer advanced capabilities and high accuracy, which makes them highly advantageous for log interpretation. The integration of "logging + AI" has emerged as a new research direction. However, in intelligent log interpretation, the limited sample size and weak generalization ability of training models hinder the widespread application of purely machine learning-based log interpretation methods. Physical models inherently capture the underlying mechanisms that connect logging data to geological targets. Combining data-driven and mechanism-driven approaches provides an effective way to enhance log interpretation accuracy. However, existing joint data-mechanism driving lacks a well-defined paradigm. In view of this, the study focuses on the prediction of intelligent log interpretation parameters, proposes the concept and methodology of joint data-mechanism driving, and presents two key paradigms: data-guided physical modeling, where physical modeling is the primary framework, with data-driven methods assisting in obtaining key steps or parameters, and physics-guided machine learning, where machine learning is the primary approach, while knowledge models or physical mechanisms provide supervision and constraints on input data, loss functions, and training processes. To implement these paradigms, three hybrid models are proposed: physics-augmented datasets, knowledge-driven sample weighting, and rock physics knowledge transfer. These approaches are applied to predict reservoir parameters and mineral composition in tight sandstone and organic shale reservoirs. Compared with purely data-driven machine learning models, the proposed data-mechanism jointly driving paradigms significantly improve the ability of the log interpretation model to learn from small and low-quality samples and make the model have enhanced robustness, generalization ability, and interpretation accuracy.
A gas field in western China is a shallow biogas reservoir. Due to the influence of gas-bearing reservoirs, the quality of P-wave seismic data is poor and cannot provide imaging, thus making the detailed description of the structure and reservoir quite challenging. Therefore, this paper tackles the problems by employing 3D S-wave seismic data to restore the structural morphology of the gas cloud area and characterize high-quality reservoirs. First, an applicability evaluation of the seismic data is conducted to determine the applicable range of different types of seismic data. Then, S-wave logging and S-wave VSP data are adopted for joint calibration of wellbore and surface seismic data, and based on this, fine structural interpretation is conducted. Finally, S-wave attributes and S-wave inversion are utilized to characterize the reservoir distribution, with development data combined to verify the distribution of high-quality reservoirs. The proposed 3D S-wave seismic interpretation workflow produces a high-precision structural map and accurate reservoir prediction results in the study area, effectively solving the structural and reservoir description problems in the gas cloud area. Finally, strong technical support is provided for the exploration and development planning of the gas cloud area.
The carbonate reservoirs of the Middle-Lower Ordovician in the Gucheng area of the Tarim Basin are controlled by multiple factors such as high-energy reef-shoal facies, karsts, and faults. Fractured-vuggy reservoirs formed by the superposition of reef-shoal bodies, fracture fragmentation, and karst corrosion are developed. Therefore, it is difficult for traditional reservoir prediction methods such as single seismic attribute analysis and conventional seismic inversion to effectively predict their spatial distribution characteristics, thus restricting oil exploration in this area. Given the characteristics that the reservoirs are controlled by multiple factors, a "three-facies control" seismic inversion technology that fully integrates the characteristics of "high-energy reef-shoal facies, fracture fragmentation facies, and karst fractured-vuggy facies" is proposed, thus greatly improving the reservoir prediction accuracy. Firstly, based on the well-seismic characteristics, the seismic response characteristics of the "three-facies zone" are clarified. Secondly, the seismic facies volume attributes, the structure-oriented smoothing filter volume constrained by faults, the maximum likelihood attribute volume, and the background modeling highlight volume are respectively employed to characterize the envelopes of the high-energy reef-shoal facies, fracture fragmentation facies, and karst fractured-vuggy facies. Finally, the envelopes of the "three-facies zone" are integrated and calibrated to build a "three-facies control" constrained low-frequency trend model, with sparse spike inversion is carried out to predict the reef-beach fault-karst composite reservoirs. The inversion results show that the coincidence rate of reservoir prediction for the posteriori well GT1 reaches 87.5%. This technology provides an effective technical means for the high-precision prediction of reef-shoal fault-karst composite reservoirs.
Distinct from horizontally layered sedimentary reservoirs, fracture-cavity reservoirs feature vertical strike slip and dissolution. In seismic exploration, seismic signals received at different angles contain different reservoir information, which provides advantages for a fine description of fracture-cavity reservoirs. To this end, this study proposes a novel method for fine identification of fracture-cavity reservoirs by utilizing angle domain information. By extracting angle domain data, this approach expands the data sources and directions for seismic-based detailed descriptions of such reservoirs. Firstly, the seismic reflection characteristics of fracture-cavity reservoirs in the study area are analyzed by adopting full-azimuth data slices, followed by a comparison of the cross-sectional features of the reservoirs across different azimuthal data. Next, the internal architecture is delineated by employing the maximum amplitude attribute from small-angle data, while fault connectivity is assessed by leveraging the dominant frequency attribute derived from full-azimuth data. Finally, the maximum amplitude attribute corresponding to the energy cluster center in large-angle data is extracted to predict the oil-bearing property and connectivity.The results are as follows. (1) The coherence attributes of full-azimuth data can more clearly delineate the contours of fracture-cavity reservoirs. (2) Compared with large-angle data, small-angle data can delineate the internal architecture of the reservoirs. (3) Frequency domain information can effectively identify fault connectivity and hydrocarbon connectivity. In China, deep and ultra-deep fracture-cavity reservoirs are typically explored by adopting large-array acquisition, which provides favorable angle-domain data conditions. The proposed method can serve as a reference for application in similar exploration areas.
The increasing demand for hydrocarbon resources makes deep and ultra-deep hydrocarbon reservoirs one of the important exploration fields. In the Junggar Basin, many sets of high-quality source rocks and reservoirs have developed in Permian. Notably, hydrocarbon reservoirs closely related to unconformity have been discovered in the Mahu Sag, Shawan Sag, Fukang Sag, and other areas in the basin. However, previous hydrocarbon exploration in Junggar Basin primarily concentrates on the medium and shallow layers, and there remains a lack of clarity regarding the development characteristics and scale of the deep and ultra-deep unconformities in central Junggar Basin and their effect on controlling hydrocarbon accumulation. Therefore, under the current exploration situation of deep target layers and relatively scarce research data, a detailed study is conducted to analyze the development characteristics, scale, and accumulation effect of the deep and ultra-deep Middle/Upper Permian (P2/P3) unconformity in central Junggar Basin by analyzing the logging data of 40 wells, measured element data, and regional seismic profiles. The results indicate that the Middle/Upper Permian unconformities in the central Junggar Basin are widely developed in the deep and ultra-deep layers, thus supporting the accumulation of hydrocarbon reservoirs. There are five types of unconformity developed in the study area, including the parallel-parallel type, parallel-truncation type, overlap-parallel type, overlap‑truncation type, and parallel-fold type, all of which are developed in both deep and ultra-deep layers. Additionally, there are six lithological combinations of unconformity structures, including the sand-mud-sand, sand-mud-mud, sand-mud, sand-sand, mud-sand, and mud-mud types, among which the first three types are primarily distri-buted in the deep and ultra-deep layers. Based on these findings, a total of 14 structural types in the unconformity of the Middle/Upper Permian are identified, with 11 types mainly distributed in the deep and ultra-deep layers. Finally, two zones (Types A and B) favorable for hydrocarbon accumulation are selected, with the favorable zone Type A exhibiting superior hydrocarbon accumulation capacity within their unconformities. This study holds guiding significance for deep and ultra-deep hydrocarbon explorations.
With the advancement of geological theory and geophysical prospecting technology, shale reservoirs have emerged as the crucial target of oil and gas exploration and development. Research indicates that the development of lamination or stratification can conspicuously enhance the physical property conditions of the reservoir. Drilling outcomes also affirm that stratification characteristics such as the density of lamination tend to determine the development of oil and gas. Nevertheless, the study of lamination or stratification remains in its nascent stage. Therefore, it holds significant importance for the characterization of shale reservoirs to clarify the micro rock physical mechanism for shale stratification and search for inversion parameters directly characterizing the stratification. Based on the prevailing rock physics theory, the stratification inducing factors, such as lamination density, mineral shape, and random arrangement of clay blocks, are studied respectively. The corresponding rock physical models of each factor are summarized and the degree of influence is analyzed. On this basis, a shale rock physical model considering stratification is established and successfully applied to the shear wave prediction of actual logging data. To achieve the direct inversion of shale stratification characteristics, the stratification indicators are constructed by employing the concept of upper and lower limits in rock physics and utilized as the criterion to measure the stratification. At the same time, the elastic impedance form of three approximate equations of reflection coefficients of VTI media is derived. The relationship between the stratification indicators and the model parameters in the equation is retrieved by a machine learning algorithm, thereby enabling the stable inversion of the stratification indicators. The application of the actual data demonstrates that the inversion results are in excellent agreement with the actual drilling, which effectively delineates the distribution of the reservoir and is conducive to guiding the horizontal evaluation of the shale reservoir.
Electromagnetic methods are naturally superior in identifying whether deep targets contain fluids. It is of great significance for oil and gas field exploration and development to study high-precision electromagnetic detection technology for reservoir fluids. Based on the capillary model and electrochemical theory, we consider that the polarized electric field is closely related to fluid concentration, microscopic charge migration, and accumulation, and has time and frequency dependencies. The essence of polarization effects can be explained through the electric diffusion hypothesis and thin film polarization theory. Based on Maxwell's equations and the equivalent complex resistivity model, we derive the basic formula for electromagnetic hydrocarbon detection. The accurate characterization of electrical anomalies in oil and gas reservoirs by relevant parameters is further analyzed, especially the performance of induced polarization effects related to oil and gas content in electromagnetic field amplitude and phase curves. In practice, the induced polarization effect can be effectively extracted and the distribution of oil and gas reservoirs can be identified only by optimizing parameters such as transmission and reception distance and frequency. At last, we analyze the characteristics of the induced polarization effect on electromagnetic field phase and differential curves through numerical simulation, which is consistent with theoretical mathematical characterization. We clarify the effective parameters and critical frequency for data acquisition of the induced polarization effect in reservoirs. Finally, we discuss the applicability of this method in fluid detection and propose a construction method technology based on characteristic curves, providing theoretical support for the practical application of electromagnetic hydrocarbon detection.
The gravity anomaly inversion, which infers the density distribution of subsurface anomalies from surface gravity data, is an essential tool in geophysical exploration and is widely applied in fields such as oilfields, mineral deposits, geological structures, and underground works detection. Traditional gravity inversion methods face challenges of complex computation, low resolution, and dependence on prior information for inversion results. However, deep learning-based gravity anomaly inversion techniques show significant advantages, particularly in terms of improving inversion accuracy and reducing computation time, without the reliance on initial models or prior information. This paper reviews the development and limitations of traditional gravity anomaly forward and inversion methods and summarizes the current research on deep learning-based gravity inversion methods. Meanwhile, it introduces the improvements and innovations of different gravity inversion problems in four respects, including data preparation, network models, network optimization, and network validation. Additionally, the application effect of various gravity inversion methods on the measured data from Vinton Dome in Louisiana, the USA, and the San Nicolás ore deposit in Mexico. The multi-task framework CDUNet yields the most accurate inversion depth values on data of Vinton Dome, while the 3D U-Net++ network obtains clearer and more accurate inversion results on the data of the San Nicolás ore deposit than the U-Net network.
The marineintegrated navigation systems are limited in functionality, having problems such as the inability to be released across operation system platforms, inability to adapt to the unique displays and operations in various spatial locations such as instrument rooms, source gun control system operation rooms, bridges, and aft decks of field operation fleets.These limitation saffect production operation efficiency and make it difficult to conduct subsequent maintenance and development, and function expansion. To this end, this paper systematically summarizes the software functional modules of the marine integrated navigation system, data interaction and collaborative work of each module, safety management of the operation fleet, management of key basic information, and specific navigation operation requirements of each operation team. By comparing the differences between REST and gRPC in interface protocol conventions, transmission protocols, streaming processing and request response, the strong type and serialization, and system performance, the technical advantages of gRPC in high-concurrency and low-latency scenarios of ocean exploration are demonstrated. Meanwhile, a framework for a distributed marine integrated navigation system based on the gRPC framework is designed, and the main program and remote procedure call service program of the integrated navigation system are developed, thereby realizing dynamic management and monitoring of other modules running in the system by a system service program.The main program is responsible for the system configuration, startup, and shutdown management of thesubmodule program of the integrated navigation system. The development practice proves that the integrated navigation system based on the distributed system architecture has realized the deployment of the system modules in the local and remote areas, and can meet the needs of navigation operation, navigation display, and quality control of various operating locations of the fleets.
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.
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