Deep-learning-based seismic impedance inversion methods have received wide attention due to their ability to handle nonlinear mapping problems. The conventional deep-learning-based seismic impedance inversion methods have the problem of an overwhelming dependence on labeled data, which results in a decrease in the model's ability to extract local features and poor precision of inversion results when training data is insufficient. To address these issues, a new atrous spatial pyramid pooling and U-Net (ASPP-UNet) based seismic impedance inversion method is proposed. The multi-scale feature extraction ability of U-Net is enhanced by the atrous spatial pyramid pooling operation. Based on this, the training datasets were constructed using seismic data and a small amount of logging data. To verify the effectiveness of the proposed method, we conducted two simulation experiments on the Marmousi2 and SEAM public datasets and compared the results with those of CNN, U-Net, and Attention-UNet under the same experimental conditions. The experimental results show that, under the same experimental conditions, the single-trace impedance inversion produced by the proposed method contains richer high-frequency details, and the inverted impedance profile displays smooth vertical continuity between layers and at fault locations. The inversion results also depend less on labeled data and exhibit the least information loss at positions far from the training wells, which is reflected in the strong lateral continuity between traces in the inverted impedance profile. Compared with the comparison methods, the ASPP-UNet inversion results show the best statistical indicators. To further validate the applicability of the ASPP-UNet method, it was applied to real seismic impedance inversion data from East Sichuan Province. The impedance profile obtained by ASPP-UNet is consistent with the actual geological structure. Compared with the three deep-learning methods mentioned above, the inversion results have the highest accuracy, and the impedance profile error is the smallest.
Shear-wave velocity is a key parameter for pre-stack seismic inversion and reservoir characterization. However, due to the technical and cost constraints of both direct and indirect measurement methods, it is quite difficult to obtain in practice. Therefore, a prediction method is proposed based on the broad learning system (BLS). First, appropriate well-log data are selected and pre-processed through denoising and correlation analysis. Second, a BLS neural network structure comprising mapping nodes and enhancement nodes is constructed to complete the BLS process. Finally, well-log data from the two typical wells Y301 and Y302 in the Y block of the Junggar Basin are used to construct a data set of machine learning. Two contrast experiments are designed and compared with curve fitting and deep learning system to verify the stability and generalization of BLS. The actual results show that the proposed BLS-based shear wave velocity prediction method can reduce training time while achieving prediction accuracy, providing a new neural network option for shear wave velocity, petroleum, and relevant reservoir parameter prediction.
Traditional manual cutting logging faces on-site technical challenges, including inability to quantitatively identify cutting components and accurately name the lithology, thereby hindering the comprehensive and precise acquisition of cutting lithology information. To this end, this paper develops an intelligent lithology identification system based on cutting images collected during the logging-while-drilling process. Firstly, by establishing unified standards for image acquisition and annotation, systematic sample annotation is conducted manually to generate standard samples. Secondly, the deep convolutional neural network algorithm YOLOv5 is adopted for sample training, inference, and post-processing, and an attention mechanism for small target identification is added, with the focus on the influence of Fitness function adjustment on target identification accuracy. Finally, the ONNX(Open Neural Network Exchange) model is adopted for cross-platform support, developing an intelligent lithology identification system based on cutting images. Practical applications show that the system can identify six major lithologies (mudstone, sandstone, limestone, dolomite, coal, and carbonaceous mudstone), with the overall accuracy exceeding 85%. Meanwhile, the system can analyze the component contents of each lithology and characteristics of sandstone cuttings including the roundness, grain size, and sorting, thus enabling the accurate description of cutting component characteristics and developing a new lithology identification technology.
Accurate underground velocity information is crucial for seismic imaging in complex area. While existing seismic waveform inversion techniques are highly accurate, they have shortcomings such as high computational amount and reliance on initial models. Currently, deep learning technology experiences rapid advancements in various fields and has successfully been applied to nonlinear seismic inversion. However, conventional end-to-end deep learning networks struggle to establish a multi-scale physical coupling relationship between velocity parameters and seismic records. To this end, this paper proposes a hybrid network AER-UNet, which reorganizes the encode and decoder structures and adds an attention mechanism-based jumping connection module on this basis. This approach effectively obtains key spatial information from seismic records and enhances the representation of the subtle structures in velocity fields, thus accurately capturing the characteristics of underground medium velocity parameters. An appropriate number of random velocity models should be built in the network training phase to simulate the true structure of the underground medium and thus obtain the accurate mapping relationship between velocity models and seismic records. Additionally, developing new loss functions can help improve the computational accuracy of velocity modeling. By carrying out numerical experiments using the SEG/EAGE thrust model, the effectiveness of the hybrid network for velocity modeling is evaluated. Compared to FWI and other deep learning networks, this method can more efficiently and accurately rebuild underground velocity models.
In geological exploration, density and acoustic time difference curves can reflect key physical parameters, such as underground geological structure and reservoir porosity. However, due to the influence of complex geological conditions and other factors, logging data may be incomplete or missing. Therefore, this paper proposes a logging curve reconstruction method based on a Granger causality graph neural network (GCGNN). This method constructs a Granger causality graph by learning the Granger causality between logging curves and uses a graph convolutional network to process and predict missing data. The method is applied to the measured well data in the Gujing area and Jinjing area of the central depression of Songliao Basin in China. The correlation between the density and acoustic time difference curves of Well Gu204 and the original data is 71.70% and 83.76%, respectively, and that is 80.03% and 88.73%, respectively, for Well Gu432. The performance of GCGNN in the reconstruction experiment of the same well is better than that of the lightweight gradient boosting machine, time convolutional network, and long short-term memory network. The method is applied to the reconstruction experiment of different wells. The correlation between the density and acoustic time difference curves and the original data is 77.54% and 87.79%, respectively. Although the model obtained by GCGNN is not the optimal model, the reconstruction effect is still good. The application results on measured data show that the proposed method can effectively predict the missing logging data.
In ocean bottom node (OBN) seismic exploration, significant discrepancies often exist between the actual node positions and the initial surveyed locations due to factors such as ocean currents, tides, topographic variations, and fishing vessel dragging, necessitating secondary positioning. To address this, a high-precision secondary positioning method based on four-quadrant stacking is proposed. Specifically, within a relative coordinate system, shot points are divided into four quadrants according to their azimuth. Linear moveout correction and common receiver point stacking are performed separately for shot points in each quadrant. Subsequently, the displacement of the node from the surveyed position to the actual position is decomposed into two mutually perpendicular components. These two components are determined by analyzing the first arrival time differences from the common receiver points in each quadrant, which thereby enables precise estimation of the node's actual coordinates. Application to field data demonstrates that this method achieves high-precision secondary positioning of nodes. Moreover, the four-quadrant stacking strategy significantly improves the reliability of first arrival picking. Compared with traditional methods, this approach offers higher positioning accuracy and efficiency, showing excellent engineering applicability and promising potential for broader adoption.
Microseismic monitoring technology is of application significance in fields such as unconventional oil and gas reservoir development and mine disaster monitoring. However, its signals are susceptible to noise interference, which results in a low signal-to-noise ratio (SNR), thus severely compromising the accuracy of subsequent seismic source localization and mechanism inversion. Traditional denoising methods such as the complete ensemble empirical mode decomposition (CEEMD) and wavelet modulus maxima (WMM) have limitations in processing non-stationary microseismic signals. To this end, this paper proposes a microseismic denoising method named SSA-VMD-CC-WT, which combines variational mode decomposition (VMD) optimized by the sparrow search algorithm (SSA) with the adaptive wavelet thresholding (WT). Firstly, SSA is employed to optimize key parameters of the VMD algorithm. Secondly, effective modal components are selected by utilizing the cross-correlation coefficient (CC) to suppress noise. Finally, adaptive WT is applied to perform secondary denoising on the effective components, reducing signal distortion. Simulation tests demonstrate that in strong noise conditions, the SSA-VMD-CC-WT method can separate noise from effective signals more accurately than the CEEMD and WMM methods. The processing of actual microseismic data reveals that the proposed method significantly suppresses both low-frequency and high-frequency noise while maintaining the fidelity of weak seismic sources, thereby improving data interpretability and SNR. Meanwhile, compared with the traditional genetic algorithm (GA), SSA demonstrates higher optimization efficiency.
When dealing with complex seismic-geological condition data, Rayleigh wave exploration technology faces challenges in fast and accurate inversion of formation parameters because the inversion of dispersion curves involves multi-parameters, multi-extremes and nonlinearity. Therefore, a Rayleigh surface wave dispersion curve inversion method is proposed based on improved flying foxes optimization. First, in response to the local optimum of flying foxes optimization in the later stage of iteration, the Lévy flight strategy is introduced and its distribution is applied to replace the existing uniform distribution of flying foxes optimization, thus enhancing the global optimization ability of the algorithm. Second, local optimal perturbation is introduced during the iteration process to develop the improved flying foxes optimization and increase the population diversity, which helps the algorithm quickly approach the global optimal location and direction, avoid local extremum, and improve solution accuracy and approaching speed. Third, a Benchmark function test is carried out on the improved flying foxes optimization to verify its superior performance on issues with different complexities. A typical geologic model is also established to conduct the theoretical dispersion curve inversion with and without noise. Therefore, the effect and advantages of the algorithm in the Rayleigh surface wave dispersion curve inversion are confirmed. Performance test results of two commonly used Benchmark functions indicate that the improved flying foxes optimization is superior to the classical one in global optimization ability. The standard deviation and relative error are further introduced for quantitative analysis. The results show that the improved flying foxes optimization has higher inversion accuracy and stability than the traditional one, showing strong application potential.
In conventional K-means clustering, the number of clusters and the initial values need to be predetermined, and the picking result is the geometric center of the energy cluster and is greatly influenced by the initial values. This paper proposes an automatic velocity picking method based on weighted K-means clustering with threshold constraints. Multiple rectangles of appropriate length are obtained by applying a variable velocity point threshold. The number of clustering centers, initial time, and initial velocity are obtained with the rectangles and prior velocity. At the same time, the prior velocity is used to limit the velocity picking range, and then the constant threshold and adaptive threshold are used to eliminate the velocity points with small amplitudes, reduce the number of velocity points involved in the calculation, and improve calculation efficiency. The weighted K-means clustering algorithm uses the amplitude of velocity points to calculate the weights, and removes the points far away from the center step by step through the distance threshold, so that the cluster center overlaps with the energy cluster center. Finally, multiples are eliminated by comparing with the slope of the prior velocity to make the picking result more accurate. The processing of model and actual data shows that the method proposed in this paper can intelligently pick up seismic velocity under the premise of ensuring accuracy and has high efficiency.
The actual underground medium is very complex. There are inevitable errors in the numerical forward modeling results by employing computer software, and it is difficult to simulate the complex underground media. The dynamic photoelasticity method can obtain the propagation characteristics of wave fields at the seismic scale in the laboratory, and can observe the wave field characteristics in real time. The wave field characteristics in solid media are obtained by dynamic photoelastic physical simulation device experiment, and the propagation law of ultrasonic pulse waves in media is observed. By carrying out the sample model pixel calibration of the instantaneous wave field image and longitudinal pixel sampling of the wave field image, the simulated single-shot seismic record in solid media can be obtained. Comparison of the simulated single-shot seismic records of the non-hole media model and containing-hole media model shows that there are obvious differences in waveform characteristics between the two models. By comparing the simulated single-shot seismic record with the actual field single-shot seismic record, it is found that the two have sound correspondence, which shows that the laboratory high-frequency small-scale and field low-frequency large-scale can also reflect the same internal physical essence without meeting the fixed ratio observation theory.
Buried hill fractured reservoirs are important targets for offshore oil and gas exploration in China, and it is difficult for conventional reflection wave-based imaging results to accurately characterize buried hill fractures. Seismic diffraction waves are the seismic response of underground discontinuous geological bodies, and diffraction imaging is of great significance for identifying small-scale diffraction bodies, such as underground faults, fractures, and vugs. For the three-dimensional (3D) seismic data, common-imaging gathers show reflection wave events as a downward convex surface in the 2D dip-angle domain, with the energy mainly concentrated in the Fresnel zone, while the diffraction wave appears as a plane or cylinder, with the relatively dispersed energy. However, the Fresnel zone in the dip-angle domain varies intricately with the offset and azimuth angle, and zero-offset muting may result in over-muting of diffraction waves. This paper proposes a dip moveout concept and designs an anti-stationary phase weighting function to accurately estimate the Fresnel zone in the dip-angle domain, thereby suppressing reflection waves and achieving 3D diffraction wave separation. The application results of actual data demonstrate that the 3D pre-stack diffraction wave imaging effectively characterizes the buried hills where fractures develop, serving as an effective supplement to conventional reflection wave methods and holding significance for the study of fractured reservoirs in buried hills.
In response to high-resolution imaging in complex geological structures, surface imaging and vertical seismic profile (VSP) imaging mitigate illumination limitations inherent to either method alone, but are still subject to unbalanced image amplitude and structural distortion due to uneven illumination. To address this issue, a joint VSP-surface Gaussian beam depth migration method is proposed. First, a dynamic complementary mechanism is established for the surface and borehole wavefields, where illumination-based weighting factors are applied to balance their respective contributions and integrate their advantages. Second, to constrain the Gaussian beam propagation paths, a structural dip field is introduced, enabling dynamic adjustment of the initial beam direction for adaptive alignment with the local formation dip. Third, a dip-dependent weighting function is employed during the imaging stack to suppress scattering energy from non-geological directions, thereby enhancing the signal-to-noise ratio of the joint VSP-surface image. Applications to both synthetic and field data demonstrate that the joint VSP-surface Gaussian beam depth migration method produces superior images compared to those obtained using surface data alone and improves the imaging accuracy of complex structure.
The Marchenko method can reconstruct the Green's function at any subsurface position by utilizing the surface reflection response and the background velocity model, which can be applied to structural target-oriented imaging. Compared with traditional seismic interferometry, the imaging is not affected by the overburden medium. However, the imaging quality of the Marchenko method relies on high-density acquisition, which field data sets often fail to provide. To address this issue, this paper proposes the Marchenko imaging method based on CNN-POCS reconstruction. First, sparsely acquired seismic data is reconstructed using the convolutional neural network projection onto convex sets (CNN-POCS) method to generate high-accuracy data with dense spatial sampling. Marchenko imaging is then applied to the target zone using the reconstructed data. This approach not only reduces the high-density acquisition requirement of the Marchenko method but also preserves imaging accuracy. Finally, the method is extended to plane-wave imaging, which improves the computational efficiency of the conventional point-source approach and reduces the computational cost. The model test results show that the proposed method can reduce the receiver deployment by 60%, thus significantly lowering acquisition costs.
Faced with the low effective signal of deep carbonate reservoirs in the Tarim Basin, the seismic response characteristics of the small-scale fracture-cavity reservoirs are unclear, and conventional interpretive processing and reservoir prediction technologies fail to meet the required prediction accuracy. To address this problem, based on the differences between the "string-of-beads" reflection of fracture-cavity reservoirs and the layered stratum reflections and on the principle of feature decomposition, an interpretive processing method based on Tucker decomposition for fracture-cavity anomaly extraction is proposed. First, based on principle analysis, a singular value cumulative ratio is constructed to quantify the key parameters of Tucker decomposition. Its applicability to fracture-cavity bodies of different scales, types, and development positions is evaluated, and the workflow for anomaly extraction is established. Then, a theoretical geological model of the fracture-cavity reservoir was constructed to verify the feasibility and effectiveness of the method, and the parameter selection for practical applications was justified. The results show that when the singular value cumulative ratio ranges from 0.68 to 0.72, the best fracture-cavity anomaly extraction results are obtained. Field seismic data and drilling results demonstrate that the fracture-cavity anomalies extracted by this method exhibit prominent characteristics in both profile and planar attributes. Compared with the conventional interpretive processing and reservoir prediction technologies, the enhancement effect of small-scale fracture-cavity bodies under strong reflection events is more obvious and is also highly correlated with the actual well features. This method provides a practical and effective way to predict small-scale fracture-cavity reservoirs.
The lack of time-frequency resolution of seismic signals in complex reservoirs limits the accuracy of hydrocarbon detection. The limitations of traditional spectral decomposition methods in frequency band selection and time-frequency focusing significantly affect the reliability of key hydrocarbon indicators, such as fluid mobility attributeerties and high-frequency attenuation characteristics, and there is an urgent need for the development of high-resolution temporal-frequency analysis methods. A hydrocarbon detection method based on fractional adaptive superlet transform(FASLT) is proposed. The FASLT optimizes the time-frequency resolution by combining wavelet sets with different bandwidths and using geometric averaging to mitigate the limitation of the uncertainty principle of the traditional method, and introduces the adaptive and fractional-order superlets to achieve the time-frequency super-resolution across frequencies. The algorithm dynamically extracts the dominant frequency bands by combining the adaptive centroid frequency algorithm and constructs a two-parameter joint detection model of fluid mobility attributes and high-frequency energy attenuation gradient to improve the hydrocarbon detection accuracy. Based on the seismic data of an offshore area, the results show that compared with the traditional time-frequency analysis method, the accuracy of this method in identifying gas-bearing reservoirs is significantly improved, and the detection results are basically consistent with the logging data, which verifies the effectiveness and practicality of the method in improving the accuracy of hydrocarbon detection in complex reservoirs.
Huge thick salt-gypsum layers are usually prone to developing into saline aquifers under the influence of sedimentary tectonics. In the process of exploration and development in the high sulfur region of Northeast Sichuan Basin, the gypsum rock in the Jialingjiang Formation of the Tieshanpo area shows significant differences in thickness, and the longitudinal and horizontal distribution of the locally present saline aquifers is unclear, which greatly increases the difficulty and risk of drilling, and seriously affects the safety of downhole operations and the progress of the project. Therefore, this paper summarizes the characteristics of logging response and seismic reflection characteristics of the saline aquifers, and combines the rock physics theory and fluid mobility inversion to predict the saline aquifers. Firstly, the forward model of the saline aquifers is established based on the rock physics theory and actual logging data. Then, the seismic response characteristics of the saline aquifers are clarified, and the feasibility of using fluid mobility to predict the saline aquifers is verified. Finally, fluid mobility inversion is applied to the actual seismic data in the study area, and the longitudinal and horizontal distribution characteristics of the saline aquifers are summarized. The results show that the logging response characteristics of the saline aquifers of the Jialingjiang Formation are characterized by "three highs and two lows": high acoustic interval transit time, high compensated neutron, high natural gamma-ray, low density, and low resistivity. Through the actual seismic data and seismic forward simulation analysis, it is found that the saline aquifers are characterized by internal chaos and intermittent strong amplitudes of reflection in the seismic profile. In addition, the distribution of the saline aquifers predicted by fluid mobility inversion is in good agreement with the actual logging and saline aquifers conditions, which illustrates the effectiveness of this research method. This study has a positive guiding effect on further expanding the prediction of saline aquifers in the high sulfur region of Northeast Sichuan Basin.
The fluvial facies composite sand bodies in Bohai Sea feature intersecting and superimposed channels, rapid horizontal distribution changes, and reservoir thickness smaller than seismic resolution. The existing methods for exploiting the seismic (inversion) attribute and profile variations to predict the sand body boundary and internal structure of composite sand bodies are relatively qualitative, thus making it difficult for them to meet the accuracy requirement in the while drilling phase of horizontal wells. To realize the refined prediction of fluvial facies reservoir structures in the while drilling stage, this paper proposes a reservoir structure prediction method based on seismic waveform feature box (WFB). Firstly, a conceptual model for three-variable (sand body thickness, superimposition degree, and elevation difference) composite sand bodies is developed. Meanwhile, on this basis, the structural patterns of horizontal wells drilling through composite sand bodies are divided into four categories, including the upper sand body (US), lower sand body (LS), middle sand body (MS), and edge sand body (ES). Then, based on the statistical velocity, density, seismic wavelet, and geological characteristics of the study area, a large number of three-variable composite sand body models and forward seismic data are developed. Additionally, three reservoir structure-sensitive attributes including seismic waveform peak amplitude (PA), waveform skewness (WS), and area ratio of peak to trough (APT) are adopted to study the attribute combination response characteristics of different reservoir structure patterns. Finally, on the basis of establishing the composite sand body reservoir structure prediction formula based on the attribute combination, the seismic WFB concept and method are proposed. Additionally, the box-height dimension, box-width dimension, and box-color dimension that reflect seismic waveform PA, seismic WS, and structural pattern of composite sand bodies respectively are defined. The automatic drawing of WFBs along the horizontal well trajectory on seismic sections is realized, and the structural pattern change of reservoirs is visualized by observing box changes to guide the while drilling of horizontal wells. The model experiments show that employing the attribute combination to predict the reservoir structural pattern of composite sand bodies reaches the accuracy of 95.97%. The practical applications in oil fields demonstrate that the structural pattern of composite sand bodies predicted by this method is in sound agreement with the results of while-drilling geosteering boundary detection instruments and logging interpretation results, thereby better guiding the real-time adjustment of the implementation trajectory of development wells. This method holds reference significance for the research on reservoir structure prediction and while drilling of fluvial facies composite sand bodies.
Although the southwestern margin of Xuefeng Uplift is endowed with sound material foundations for Cambrian shale gas accumulation, significant breakthroughs have yet to be made in exploration efforts due to poor structural preservation conditions. To identify the key controlling factors of shale gas preservation in this area, this paper studies the preservation conditions of shale gas by taking Majiang Uplift showing shale gas indications as the research object. Firstly, based on 2D seismic, magnetotelluric, and gravity data, refined interpretation is conducted on the structural characteristics of the study area, with secondary structural units delineated and fault systems identified. Secondly, evaluations are carried out on factors influencing the structural preservation of shale gas, including shale self-sealing capacity, structural deformation, and caprock development. Finally, a multi-factor superposition evaluation method is employed to quantify and integrate various factors according to their influence on preservation conditions. The results indicate that Majiang Uplift can be divided into three secondary structural units of the Majiang Gentle Syncline, Jidong Anticline, and Shiban Syncline. Additionally, the Cambrian base interface shows shallower depths in the south, deeper depths in the north, deeper depths in the east-west direction, and shallower depths in the central region. Structural stimulation effect is the dominant controlling factor for spatial variations in shale gas preservation conditions, with significant influence exerted by parameters such as basement faults, stratum fragmentation, distance to outcrops, stratum occurrence, and burial depth on preservation quality. Two favorable areas of the central Jidong Anticline and the western flank of the Majiang Gentle Syncline for shale gas preservation are identified via comprehensive evaluation. The proposed multi-factor superposition evaluation method for structural preservation conditions provides ideas and reference for similar research on shale gas preservation conditions at the margins of similar paleo-uplifts.
Investigation of the detection range and depth under varying geological conditions is a critical issue in the application of the ground-airborne frequency-domain electromagnetic (GAFDEM) method. Moreover, the horizontal magnetic component Hy, which carries stratigraphic information, is often overlooked. Based on an analysis of electromagnetic sources in a horizontally layered model, this paper compares and discusses the detection range and depth of the Hy component in an axial receiver configuration and the Hz component in an equatorial receiver configuration across different geological models. The results indicate that the GAFDEM system achieves its maximum detection range in the mid-frequency band, whereas the targets cannot be effectively detected in the near-source region. Although the overall detection range of Hy exceeds that of Hz, its detection depth is generally lower. When the target layer manifests as a low-resistivity anomaly, its detection depth surpasses that of a high-resistivity anomaly. As the resistivity contrast between the target layer and the surrounding rock, or the layer thickness, increases, the detection depth of Hy increases in all cases except for low-resistivity anomalies, where it initially increases and then slightly decreases with further thickness increments. Results from practical engineering applications indicate that forward modeling and the appropriate choice of transmitter–receiver offsets and frequency ranges have practical guiding significance for field surveys.
Total harmonic distortion (THD) is a key comprehensive indicator that reflects the signal acquisition capability and quality of seismic data acquisition equipment. THD detection is an important means to ensure that the technical performance of the instrument meets the standard, the equipment is stable and reliable, and the acquired seismic data are qualified. Firstly, this paper analyzes the THD source according to the detection process and internal structure of the seismic instrument. Secondly, an ultra-low distortion signal source specific to seismic applications is developed to provide a high-purity benchmark for testing. Finally, by integrating the 7th-order Blackman-Harris window spectrum leakage suppression and Zoom-FFT spectrum refinement techniques, fast and high-precision distortion calculation is achieved based on only a small dataset. By carrying out the development of hardware and algorithms, high-precision distortion detection for seismic data acquisition equipment is realized, thereby reducing the detection time. Currently, this technology has been integrated into the eSeis Neo node detection system to achieve industrial production and applications. The application in multiple key oil and gas exploration projects in North China and Xinjiang shows that the technology features stable operation, high detection efficiency, and reliable performance of qualified nodes, gaining widespread recognition within the industry.
Seismic exploration is one of the important application areas of high-performance computing. With the widespread application of the "wide-azimuth, wide-bandwidth, and high-density" technology in seismic exploration, the amount of data processed by seismic imaging algorithms has reached the TB or even PB level, and the processing program often needs to run for tens or even hundreds of hours. Once a failure occurs, it will cause serious resource waste. In order to efficiently develop seismic data processing programs with fault-tolerant capabilities, this paper designs an active message model AMFT with dynamic communication and runtime fault-tolerant mechanisms based on the actual needs of high-performance computing in seismic exploration. It includes three communication primitives and one fault-tolerant primitive, which can easily design and express complex seismic data processing algorithms in a fault-tolerant environment. This paper presents an efficient implementation method for AMFT and designs and implements a parallel and distributed programming framework GPP for seismic exploration, which combines process management, communication management, and other practical tool libraries necessary for distributed high-performance computing programs. The GPP framework has better stability and usability than mainstream frameworks such as MPI. After more than 10 years of development and production verification, GPP has been used to implement all core offset imaging modules within BGP Inc., progressively replacing MPI and other foreign commercial software.
The theoretical and technological innovation in the exploration of conglomerate oil reservoirs has driven the discovery of a billion-ton super-large conglomerate oilfield in the Mahu Sag. To further enrich and refine the theory of reservoir formation and enrichment in large conglomerate oil areas, this paper uses the exploration practice of the Mahu Sag as a representative case, focusing on the geophysical needs of large-scale fan-delta conglomerate oil reservoir exploration. It systematically summarizes advances in seismic interpretation technology for exploring large fan-delta conglomerate oil reservoirs from five key aspects: micro fault characterization, seismic facies analysis, high-precision reservoir prediction, fracture prediction, and oil and gas detection. It also analyzes the situation and needs faced by efficient exploration and development of conglomerate oil reservoirs, and points out the development trend of seismic interpretation technology for complex conglomerate reservoir exploration, which is advancing towards intelligent interpretation and quantitative reservoir prediction. The study reveals that, given the strong heterogeneity and complex seismic response of conglomerate reservoirs, customized multi-technology integration schemes (e.g., multi-attribute fusion, dual-parameter simultaneous inversion) are key to improving exploration accuracy. Furthermore, seismic interpretation must be deeply integrated with development needs. Particularly in optimizing volume fracturing for deep conglomerates, geo-engineering integrated interpretation results can effectively mitigate development risks. This work provides technical support and successful experience for exploring and developing large-scale conglomerate oil reservoirs in depression areas.
The transparency of geological condition detection and precise exploration are critical challenges hindering safe and efficient coal mining operations. Coalfield seismic exploration technology plays a vital role as a means to address hidden, potentially hazardous geological issues during the mining process. This technology provides high-precision regional geological structures and coal formation patterns, offering reliable geological basis for coalfield development. The evolution of coalfield seismic exploration technology in China can be divided into three distinct phases: the early stage from the 1950s to the early 1970s marked by technological inception; the digital development era spanning the late 1970s through the 1980 s; and the current phase since the 1990 s, characterized by extensive promotion and practical application. Over the course of more than seventy years, this technology has made significant advancements in coalfield geological surveys and the precise detection of hidden factors contributing to geological hazards. It stands as an indispensable geological support for ensuring safe and efficient coal development. By integrating in-depth research on coalfield exploration techniques with typical engineering practices, the text systematically outlines its technical features and current status across the stages of data acquisition, processing, and interpretation. In response to the urgent need for transparent and intelligent mine construction, future developments in coalfield exploration technology will focus on advancing dense distributed data collection, intelligent data processing and interpretation, and the innovative application and development of multi-attribute integration techniques.
In geophysical exploration, the resistivity method and the induced polarization (IP) method are crucial electrical exploration techniques, yet each has limitations when used independently. This study presents a systematic review of the current research status of joint inversion of resistivity and chargeability. Starting from the development status of single resistivity inversion, single chargeability inversion, and the constitutive relationship between chargeability and resistivity, it provides an overview of the developmental history of inversion techniques for both the resistivity method and the IP method. The paper focuses on analyzing various joint inversion methods, including those based on spatial structure coupling constraints, petrophysical relationship coupling constraints, multi-parameter constraints, and deep learning. It discusses the principles, advantages, and shortcomings of each method. Research findings indicate that joint inversion can integrate the information from the two methods, reduce non-uniqueness, and improve the interpretation accuracy of complex geological bo- dies. Meanwhile, the paper proposes future development directions for the joint inversion of resistivity and chargeability. These directions include constructing initial models using artificial intelligence, accelerating inversion through GPU parallel computing, building constraint models via clustering and petrophysical analysis, innovating the inversion process by integrating 3D visualization and human-computer interaction technology, and developing multi-physics simultaneous acquisition instruments to drive the upgrading of joint inversion. The purpose is to improve the accuracy and efficiency of inversion, thus providing better support for fields such as geological exploration and resource prospecting.
