The Bongor Basin in Chad is located in the western extension zone of the Central African Rift System. Its Cretaceous P reservoir, as the main production layer, has complex lithology and significant heterogeneity, and the oil reservoir resistivity values vary greatly, making saturation evaluation difficult. Based on data from lithology, physical properties, rock electrical experiments, and nuclear magnetic experiments, this study focuses on the Baobab and Daniela blocks in the Bongor Basin of Chad. The study analyzes the complex wettability characteristics of the P reservoir in the Bongor Basin and proposes a saturation calculation model that combines rock wettability with electrical resistivity. Research has shown that the P group sand bodies can be divided into four sets from top to bottom. The first set is composed of thin sandstone layers containing mud, witharesistivity of less than 20 Ω·m, using a mud sandstone saturation model; The second set is partially thick sandstone with a resistivity of less than 100 Ω·m, suitable for hydrophilic models; The third set has an abnormally high resistivity reservoir with a resistivity of 300~1000 Ω·m, which is explained by an oil affinity model; The resistivity of the second set of partial sand bodies and the fourth set of sand bodies is between 100 and 300 Ω·m, suitable for the mixed wetting model. This method achieves accurate calculation of saturation of complex wettability reservoirs in the Bongor Basin of Chad, providing key technical support for efficient exploration and development in the basin in the future.

The JN area is located in the lithogenic syncline of the high and steep fold belt in east Sichuan Basin, The exploration potential of oil and gas resources is great, the first 3D seismic acquisition was carried out in 2007, due to the lack of pertinence of the early 3D acquisition scheme, There are some problems, such as small azimuth, low folds, large rolling line distance and serious acquisition footprints, it is difficult to meet the demand of exploration and development of target layer after repeated reprocessing of acquired data, in recent years, the observation system scheme of several blocks in the adjacent area have been optimized, the imaging effect of the data has been improved significantly, which strongly supports the exploration and evaluation of the target layer. In view of the acquisition difficulties of exploration and development in this work area, combined with the optimization design experience of observation system in neighboring areas, this paper demonstrates the key parameters of the observation system through the observation system design technology based on three-dimensional complex geological model, and puts forward an observation system scheme with small bin, wide azimuth and high fold that meets the exploration and development needs in this area. The feasibility of the observation system is demonstrated by comparing the amplitude and the coherence attribute of different object images, which provides theoretical guidance and basis for the optimization design of the 3D secondary acquisition high-precision seismic observation system in JN area.

A comprehensive statics technique for converted wave data was adopted effectively to solve the problems of low signal-to-noise ratio, serious statics and imaging difficulties in fracturedcarbonate reservoir in the study survey of southern Sichuan Basin. Thisstatics technique combinedthe surfave wave dispersion curves in version, common-detection-point stackingand cross-correlating method and nonlinear global optimization method for residual statics together.The converted wave prestack time migration technique for VTI anisotropic medium was appliedto solve the complex imaging problem, which wasbased on the establishment of migration velocity models and anisotropic parameter models for both PP and converted wave data.The processing results showed that the imaging quality of converted wave data with low signal to noise ratiowas improvedeffectively, with the diffraction energy beingfocused, the far offset imaging gathers being flattenedand the target layersbeing migrated accurately.The final two migration profiles of PP wave and converted wave data correlated with each other clearly with the consistent corresponding features, and the target layer is clearly imigrated, which built a good foundation for gas and water identification and fractured carbonate reservoir detection in the target formaton.

Manual fault interpretation is time-consuming and the experience of the interpreters tends to increase the uncertainty of the fault interpretation. With the development of computers and artificial intelligence, varieties of algorithms based on convolutional neural networks are more widely used in fault recognition. In order to further improve the accuracy of fault identification, a multi attribute convolutional neural network is proposed to identify faults. First, the residual block is introduced to replace the convolutional block in 3D UNet to simplify the learning goal and reduce the difficulty of training. Then the dilated convolution module and multi-layer deep supervision mechanism are introduced to take advantage of multi-scale information more effectively and further improve the accuracy of fault recognition. Finally, the four-channel network is constructed with multiple seismic attributes to learn various response characteristics of faults. The synthetic model test and the application of real seismic data show that this method can effectively identify the fault location, reduce the missed and wrong identification of small faults, and significantly improve the accuracy of fault identification.

Seismic inversion, a critical method for inferring subsurface petrophysical parameters (acoustic impedance) from seismic data, plays a vital role in hydrocarbon exploration and geological research. However, conventional seismic inversion methods heavily rely on low-frequency background models, exhibit sensitivity to noise interference, and suffer from low computational efficiency, making them inadequate for high-precision inversion under complex geological conditions.In recent years, Convolutional Neural Networks (CNN) have been widely adopted in seismic impedance inversion due to their strong nonlinear modeling capabilities and end-to-end learning approaches, enabling effective mapping of seismic data to impedance values. Nevertheless, deep CNN architecture still face challenges such as gradient vanishing, gradient explosion, and overfitting, which compromise the stability and accuracy of inversion results. To address these limitations, this study proposes a ResUNet-based inversion approach by integrating residual blocks into a UNet framework, thereby mitigating gradient vanishing and information loss issues while enhancing inversion accuracy and stability. Synthetic seismic data generated through forward modeling were used to train the ResUNet model, and real seismic data were subsequently input into the trained network to derive impedance inversion results. However, the intelligent inversion outputs may exhibit discrete artifacts and "spiking" phenomena near complex geological features (fault), impairing the smoothness and continuity of the results. To optimize the inversion outputs, a post-processing smoothing technique was further introduced to suppress anomalies and improve stability. Theoretical model testing and real-data validation demonstrate that the ResUNet combined with smoothing effectively enhances the precision and stability of seismic acoustic impedance inversion, offering a feasible novel method for intelligent seismic inversion applications.

In both natural and industrial settings, the issue of underwater gas leakage has received increasing attention. This encompasses leaks resulting from ocean engineering endeavors as well as those that occur naturally. Such leaks have emerged as a major challenge for the protection of marine environments. To accurately obtain information from these emergencies and implement prompt responses, experts across various disciplines have been suggesting and investigating novel, dependable methods for detecting underwater gas leaks over the last few decades. Building upon thorough and detailed research of existing technologies, this article examines carrier detection and marine environmental detection, systematically evaluates the current leading technologies for detecting seabed gas leaks. Furthermore, the article offers a detailed analysis of the various challenges currently faced by gas leak detection and anticipates future development trends, with the aim of providing reference and insights for research and practice in related areas.

Submarine mud volcanoes are uplifted landforms formed by the escape of submarine fluids. Studying their evolution process is of great significance for the exploration of marine oil and gas resources, the protection of marine biodiversity, and the prevention of marine geological disasters. Based on the existing research, this paper systematically reviews and summarizes the identification characteristics of submarine mud volcanoes under different acoustic detection methods using submarine acoustic instruments and platforms. It also summarizes the morphological features, causes, and evolution stages of submarine mud volcanoes, and analyzes and discusses the progress of quantitative research on the morphology of submarine mud volcanoes. The investigation results show that theacoustic data obtained by combining multiple acoustic methods can not only be used to obtain the morphological and profile characteristics of submarine mud volcanoes, but also serve as supporting data for quantitative research on the morphology of submarine mud volcanoes. Due to the low detection resolution and narrow detection range, the submarine mud volcanoes identified in China at present are mainly large and medium-scale and dome-shaped. Small-scale and complex-shaped submarine mud volcanoes are difficult to identify. The morphological parameters of submarine mud volcanoes are mainly water depth, diameter and slope. There is less research on other parameters such as the slope of the two wings and the depth of the depression. It is suggested to continuously improve the detection technology to enhance the detection resolution, and to use multiple carrier platforms to expand the detection range of the seabed to obtain higher-resolution and more abundant seabed topographic and geomorphic data. At the same time, on the basis of the existing detection technology and detection resolution, the quantitative research on the morphology of submarine mud volcanoes should be deepened to obtain rich morphological quantitative parameters and achieve precise quantitative research. Automatic identification methods such as deep learning should be introduced to realize the automatic identification of submarine mud volcanoes and improve the efficiency of identifying submarine mud volcanoes.

Distributed Acoustic Sensing (DAS) technology leverages the linear response of Rayleigh backscattered light in optical fibers to external acoustic fields, enabling distributed sensing and collection of vibrations. This technology has seen extensive application in seismic exploration. However, conventional straight optical fibers exhibit sensitivity primarily along the fiber axis, limiting their effectiveness in detecting vertically incident seismic waves, thus constraining their use in surface seismic surveys. To address this limitation, a Helically Wound Cable-Based Distributed Acoustic Sensing (HWC-DAS) technique has been proposed. In this approach, the fiber is helically wrapped around a deformable elastic medium, improving directional sensitivity and enhancing its ability to detect vertically incident reflections, significantly boosting sensitivity. Nevertheless, systematic comparisons between HWC-DAS and traditional seismic geophones remain sparse, and the differences in their sensitivity, Signal-to-Noise Ratio (SNR), and frequency-dependent responses are not yet clearly understood. To fill this knowledge gap, we conducted controlled laboratory experiments, including shaking table and vibration-sensing tests, to systematically compare the performance of HWC-DAS with conventional seismic geophones. Results from shaking table experiments indicate that HWC-DAS exhibits vibration response capabilities comparable to those of geophones across various frequency bands. Furthermore, vibration-sensing tests demonstrated that HWC-DAS achieves higher sensitivity and improved SNR compared to single-point geophones. Our study validates the effectiveness and feasibility of HWC-DAS for seismic exploration through laboratory experimentation, laying foundational groundwork for its practical deployment in industry.

Aiming at the problem of invisible diseases of tower foundation concrete in power transmission and transformation projects caused by improper operation and related environmental factors during construction, this paper proposes a GPR image recognition method for hidden diseases of tower foundation in power transmission and transformation projects based on improved Faster R-CNN. Firstly, the traditional Faster R-CNN model is optimized, and RseNet-50 is used as the feature extraction backbone network, combined with the attention mechanism (SE module). By adding the SE module to different levels of the network and placing the SE module in different positions for comparative experiments, the optimal position in the model is judged, so that the model can reduce the redundant calculation burden on the basis of extracting the key information in the feature map. Secondly, the soft-NMS algorithm is used to improve the detection ability of closely connected targets. Finally, based on the measured data of the rigid straight column foundation, the simulation is carried out by gprMax, and the GPR image is processed by the generative adversarial network to expand the data set and enhance the learning ability of the model. The experimental results show that the average accuracy of the optimized model is 84.49%, and the F-Score is 77.58%. Compared with the traditional Faster-RCNN target detection model, the recognition accuracy is improved by 6.37% on the basis of the same data set.

In shallow geological exploration, drilling and geophysical prospecting are two distinct technical methods that play different roles in the acquisition of geological and geophysical data. With the continuous advancement of modern science and technology, the direction is towards green, intelligent, and precise exploration. Integrating drilling and geophysical prospecting technologies can achieve the acquisition of multiple information parameters, such as the structure and physical properties of rock and soil layers, in a single borehole. This integration provides a foundation for the construction of transparent geological models and the precise identification of underground space information. This paper combines the current state of research and development on integrated drilling and geophysical prospecting technologies and equipment to analyze future development trends of intelligent integrated drilling and geophysical prospecting technologies and equipment for shallow layers. It posits that the current period is crucial for the transition from traditional exploration to green and intelligent exploration. By integrating artificial intelligence, big data, and intelligent transmission technologies, and improving traditional drilling processes, the advantages of combined drilling and geophysical prospecting technologies can be effectively leveraged. This can enhance the level of automated and intelligent exploration of shallow geological conditions, providing new technical support for geological disaster prevention, complex condition engineering exploration, and construction. The technical research and engineering practice significance are substantial. Additionally, the paper offers insights and understandings based on drilling-while-prospecting methods, technologies, equipment, and engineering practices to provide a reference for the future development of precise and intelligent geological exploration.