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Results
During geophysical exploration, inpainting defective logging images caused by mismatches between logging tools and borehole sizes can affect fracture and hole identification, petrographic analysis and stratigraphic studies. However, existing methods do not describe stratigraphic continuity enough. Also, they ignore the completeness of characterization in terms of fractures, gravel structures, and fine-grained textures in the logging images. To address these issues, we propose a deep learning method for inpainting stratigraphic features. First, to enhance the continuity of image inpainting, we build a generative adversarial network (GAN) and train it on numerous natural images to extract relevant features that guide the recovery of continuity characteristics. Second, to ensure complete structural and textural features are found in geological formations, we introduce a feature-extraction-fusion module with a co-occurrence mechanism consisting of channel attention(CA) and self-attention(SA). CA improves texture effects by adaptively adjusting control parameters based on highly correlated prior features from electrical logging images. SA captures long-range contextual associations across pre-inpainted gaps to improve completeness in fractures and gravels structure representation. The proposed method has been tested on various borehole images demonstrating its reliability and robustness.
- Geology > Geological Subdiscipline > Stratigraphy (0.74)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.46)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Borehole imaging and wellbore seismic (1.00)
- (2 more...)
Fault structure and hydrocarbon prospects of the Palawan basin on the southeastern margin of the South China Sea based on gravity, magnetic, and seismic data
Zhang, Chunguan (Xian Shiyou University, Xian Shiyou University, National Engineering Research Center of Offshore Oil and Gas Exploration) | Liu, Shixiang (CNOOC Research Institute) | Yuan, Bingqiang (Xian Shiyou University, Xian Shiyou University) | Zhang, Gongcheng (CNOOC Research Institute)
In order to study the structural features and hydrocarbon prospects of the Palawan basin in the South China Sea (SCS), the authors collected and collated the existing gravity and magnetic data, and obtained edge recognition information from potential. Combined with the seismic profile data, this paper analyzed the features of the gravity and magnetic anomalies and the edge recognition information of the potential fields, determined the fault system, and delineated favorable areas for oil and gas exploration in the Palawan basin. The results showed that four main groups of faults with NE, NW, near EW, and near SN trends developed in the Palawan basin and adjacent areas in the SCS. The NE-trending fault was the regional fault, while the NW-trending fault was the main fault. The NW-trending fault often terminated at the NE-trending fault, indicating that the NW-trending fault was formed later. This investigation has characterized two different types (Type I and Type II) of exploration favorable areas based on characteristics observed. The most notable characteristic of these exploration favorable areas was that they were located in the high value zones of the local anomaly of Bouguer gravity anomaly, and their development was obviously controlled by the faults. The amplitude of gravity anomalies was higher and the gradient of the gravity anomalies was steeper, and there were oil and gas wells and fields distributed in Type I favorable areas for exploration. Compared with Type I favorable areas, the amplitude of gravity anomalies was relatively small and the gradient of the gravity anomalies was relatively gentle corresponding to Type II favorable areas.
- Asia > China (1.00)
- Asia > Philippines > Palawan (0.28)
- Phanerozoic > Mesozoic (1.00)
- Phanerozoic > Cenozoic > Paleogene (0.46)
- Geology > Structural Geology > Tectonics > Plate Tectonics (1.00)
- Geology > Structural Geology > Fault (1.00)
- Geology > Rock Type (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (1.00)
- Geophysics > Magnetic Surveying (1.00)
- Geophysics > Gravity Surveying > Gravity Acquisition (0.67)
- South America > Venezuela > Caribbean Sea > Tobago Basin (0.99)
- Asia > Philippines > Palawan > South China Sea > Northwest Palawan Basin > West Linapacan Field (0.99)
- Asia > Philippines > Palawan Basin (0.99)
- (2 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- (3 more...)
Adaptive laterally constrained inversion of time-domain electromagnetic data using Hierarchical Bayes
Li, Hai (Chinese Academy of Sciences, Chinese Academy of Sciences) | Di, Qingyun (Chinese Academy of Sciences, Chinese Academy of Sciences) | Li, Keying (Chinese Academy of Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences)
Laterally constrained inversion (LCI) of time-domain electromagnetic (TEM) data is effective in recovering quasi-layered models, particularly in sedimentary environments. By incorporating lateral constraints, LCI enhances the stability of the inverse problem and improves the resolution of stratified interfaces. However, a limitation of the LCI is the recovery of laterally smooth transitions, even in regions unsupported by the available datasets. Therefore, we have developed an adaptive LCI scheme within a Bayesian framework. Our approach introduces user-defined constraints through a multivariate Gaussian prior, where the variances serve as hyperparameters in a Hierarchical Bayes algorithm. By simultaneously sampling the model parameters and hyperparameters, our scheme allows for varying constraints throughout the model space, selectively preserving lateral constraints that align with the available datasets. We demonstrated the effectiveness of our adaptive LCI scheme through a synthetic example. The inversion results showcase the self-adaptive nature of the strength of constraints, yielding models with smooth lateral transitions while accurately retaining sharp lateral interfaces. An application to field TEM data collected in Laizhou, China, supports the findings from the synthetic example. The adaptive LCI scheme successfully images quasi-layered environments and formations with well-defined lateral interfaces. Moreover, the Bayesian inversion provides a measure of uncertainty, allowing for a comprehensive illustration of the confidence in the inversion results.
- Geology > Mineral (0.93)
- Geology > Sedimentary Geology > Depositional Environment (0.34)
- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Exmouth Plateau > WA-1-R > Scarborough Field (0.99)
- Europe > Norway (0.91)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (0.93)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.79)
- (2 more...)
Introduction to special section: South China Sea deep structures and tectonics
Zhang, Ruwei (Guangzhou Marine Geological Survey) | Zhang, Baojin (Guangzhou Marine Geological Survey) | Zhu, Hongtao (China University of Geosciences) | Sibuet, Jean-Claude (Ifremer Centre de Brest) | Briais, Anne (Centre National de la Recherche Scientifique) | Wu, Jonny (University of Houston) | Susilohadi, Susilohadi (National Research and Innovation Agency) | Zeng, Hongliu (The University of Texas at Austin) | Chen, Jianxiong (Anadarko Petroleum Corporation) | Zhong, Guangfa (TongJi University)
E-mail: susi021@brin.go.id 8.The University of Texas at Austin, USA. The South China Sea (SCS) is one of the largest Cenozoic marginal seas in the Western Pacific region. This oceanic basin was opened from the southeastern edge of the Asia continent under the interaction of the Eurasian, Indo-Australian, and Philippine Sea-Pacific plates. Therefore, it provides an exceptional natural laboratory to investigate the genesis of marginal seas and to explore plate-tectonic interactions. It is suggested that the deep structural and tectonic characteristics in the SCS reflect the conditions of the formation and geodynamic evolution of the basin.
- Asia > China (1.00)
- North America > United States > Texas > Travis County > Austin (0.26)
More than 1,000 mound structures have been mapped in shallow marine sediments at the Cretaceous Paleogene boundary in the Rub Al-Khali of Saudi Arabia. Mapping utilized 3D reflection seismic data in a 37,000 square kilometer study area. No wells penetrate the mounds themselves. The mounds are at a present-day subsurface depth of approximately 1 km and are convex-up with diameters of 200 400 m and elevation of 10 15 m. The mounds display spatial self-organization with a mean separation of approximately 3.75 km. Comparison with mound populations in other study areas with known spatial distribution statistics and modes of origin indicates that the mound population in this study has the characteristics of fluid escape structures, and they are interpreted here as mud volcanoes. The observation that the mounds occur at the Cretaceous Paleogene boundary demands a singular trigger at that moment in time. We develop a model of seismic energy related mud volcanism mechanism including the Chicxulub asteroid impact as the energy source that accounts for the timing of the mound structures, and a drainage cell model based on producing water wells that provides a mechanism for spatial self-organization into a regular pattern.
- Europe (1.00)
- Asia > Middle East > Saudi Arabia (1.00)
- Africa (1.00)
- Phanerozoic > Cenozoic > Paleogene > Paleocene (0.67)
- Phanerozoic > Mesozoic > Cretaceous > Upper Cretaceous (0.46)
- Geology > Sedimentary Geology > Depositional Environment > Marine Environment (1.00)
- Geology > Rock Type > Sedimentary Rock (1.00)
- Geology > Geological Subdiscipline > Volcanology (1.00)
- (2 more...)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation > Well Tie (0.46)
- Oceania > New Zealand > South Island > South Pacific Ocean > Great South Basin (0.99)
- North America > Canada > Saskatchewan > Prairie Evaporite Basin (0.99)
- Europe > Norway > North Sea > Central North Sea > Norwegian-Danish Basin (0.99)
- (6 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
An integrated approach for sewage diversion: Case of Huayuan mine, Hunan Province, China
Kouadio, Kouao Laurent (Central South University, Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Universit Flix Houphout-Boigny) | Liu, Jianxin (Central South University, Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration) | Liu, Wenxiang (Central South University, Guangdong Geological Bureau) | Liu, Rong (Central South University, Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration) | Boukhalfa, Zakaria (Centre de Recherche en Astronomie)
Environment protection is a core priority of many governments in this century. Most environmental problems have diverse causes: emission of greenhouse gases from fossil fuels, resource depletion, or intense mining activities such as the Huayuan manganese mine. The positioning of mining factories and water treatment stations impacts the surrounding groundwater reservoir. As the mine expands, the environmental impact also increases and the previous plan based on monitoring wastewater leakage has become inappropriate. Therefore, to solve this issue, a new study is required to understand the lateral resistivity distribution underground and to define a new station location for water treatment and divert the sewage to that station. In this study, the audio-frequency magnetotelluric method was used. Surveys of two long lines that cross the mining area to its boundaries were carried out. Data was robustly processed and inverted. Based on the inverted models in addition to geological information, drilling inspections, and solid waste distributions map, the integrated interpretation proposed two sites on the top of impermeable layers which constitute a buffer point between the unsafe (high concentration of pollutants) and the safe zones in the northwestern part of the mine. From the resistivity distribution combined with the water quality analysis, a relationship between fault structures reveals an interconnected conductive zone in the southeastern part. Being, the main channels for water circulating underground, these conductive zones delineate the main groundwater reservoir with a clastic aquifer layer. However, close to factories, water from faults contains solid wastes thereby making the groundwater in that zone non-potable, unlike the safety zone located in the northwestern part. To conclude, this workflow could become a field guide to improve the environment of mines and the deployment of hydrogeological drilling in a safe area.
- North America > United States (1.00)
- Asia > China > Hunan Province (0.40)
- Geology > Mineral (1.00)
- Geology > Structural Geology > Fault (0.93)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.66)
- (2 more...)
- Materials > Metals & Mining (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Treatment (0.54)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Health, Safety, Environment & Sustainability > Environment > Water use, produced water discharge and disposal (0.88)
Quantitative characterization of organic and inorganic pores in shale based on deep learning
Yan, Bohong (China University of Petroleum) | Sun, Langqiu (China University of Petroleum) | Zhao, Jianguo (China University of Petroleum) | Cao, Zixiong (Object Research Systems (ORS) Company) | Li, Mingxuan (China University of Petroleum) | Shiba, K. C. (China University of Petroleum) | Liu, Xinze (Yumen Oil Field Branch of China National Petroleum Corporation (CNPC) Exploration and Development Research Institute) | Li, Chuang (China National Petroleum Corporation (CNPC))
ABSTRACT Organic matter (OM) maturity is closely related to organic pores in shales. Quantitative characterization of organic and inorganic pores in shale is crucial for rock-physics modeling and reservoir porosity and permeability evaluation. Focused ion beam-scanning electron microscopy (FIB-SEM) can capture high-precision three-dimensional (3D) images and directly describe the types, shapes, and spatial distribution of pores in shale gas reservoirs. However, due to the high scanning cost, wide 3D view field, and complex microstructure of FIB-SEM, more efficient segmentation for the FIB-SEM images is required. For this purpose, a multiphase segmentation workflow in conjunction with a U-net is developed to segment pores from the matrix and distinguish organic pores from inorganic pores simultaneously in the entire 3D image stack. The workflow is repeated for FIB-SEM data sets of 17 organic-rich shales with various characteristics. The analysis focuses on improving the efficiency and relevance of the workflow, that is, quantifying the minimum number of training slices while ensuring accuracy and further combining the fractal dimension (FD) and lacunarity to study a simple and objective method of selection. Meanwhile, the computational efficiency, accuracy, and robustness to noise of the 2D U-net model are discussed. The intersection over the union of automatic segmentation can amount to 80%–95% in all data sets with manual labels as ground truth. In addition, calculated by the FIB-SEM multiphase segmentation, the organic porosity is used to quantitatively evaluate the OM decomposition level. Deep-learning-based segmentation shows great potential for characterizing shale pore structures and quantifying OM maturity.
- Asia > China (1.00)
- North America > United States > Texas (0.68)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Maverick Basin > Eagle Ford Shale Formation (0.99)
- (7 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
Physical property characterization of rocks in the Bayan Obo REE-Nb-Fe deposit, China
Zhang, Lili (Chinese Academy of Sciences) | Fan, Hongrui (Chinese Academy of Sciences, University of Chinese Academy of Sciences) | Wang, Jian (Chinese Academy of Sciences) | Zhao, Liang (University of Chinese Academy of Sciences, Chinese Academy of Sciences) | Yang, Kuifeng (Chinese Academy of Sciences, University of Chinese Academy of Sciences) | Xu, Ya (Chinese Academy of Sciences, University of Chinese Academy of Sciences) | Zhao, Yonggang (Baotou Iron and Steel (Group) Co., Ltd) | Xu, Xingwang (Chinese Academy of Sciences, University of Chinese Academy of Sciences) | Hao, Meizhen (Baotou Steel & Kings Mineral Processing Co., Ltd) | Yang, Zhanfeng (The Chinese Society of Rare Earths, State Key Laboratory of Baiyunobo Rare Earth Resource Researches and Comprehensive Utilization) | Li, Xianhua (University of Chinese Academy of Sciences, Chinese Academy of Sciences)
ABSTRACT Bayan Obo ore deposit is the world’s largest rare-earth element (REE) resource, the second largest niobium (Nb) resource, and also a significant iron (Fe) resource in China. Evaluating resource potential for the deposit has become a focus of global interest. Rock-physical properties bridge geophysical exploration and geologic modeling; variation in these parameters is necessary for successful geophysical application. REE, Nb, iron, and potassium are mainly hosted in dolomite and slate of the Bayan Obo Group, and REE mineralization is genetically associated with carbonatite. Three physical properties (resistivity, polarizability, and magnetic susceptibility [MS]) of iron ore, slate, dolomite, and carbonatite dike outcrop samples at Bayan Obo are measured and statistically analyzed using 3D reconstruction, 1D/2D/3D kernel density estimation, scatterplot matrix, 3D histogram, and Pearson- and maximum information coefficient-based correlation analysis. It is evident that iron ore, iron-mineralized fine-grained dolomite, and iron-mineralized slate are mainly of low resistivity, and iron ore and iron-mineralized fine-grained dolomite have high MS. MS favorably distinguishes iron ore from slate; MS and resistivity distinguish between iron-mineralized fine-grained dolomite and carbonatite dikes. The physical properties and whole-rock geochemistry (major and trace elements) jointly demonstrate that MS of iron ore, slate, and dolomite is positively correlated with TFe2O3 content, polarizability is correlated with TFe2O3, SiO2 content is correlated with K2O, and resistivity is correlated with MS and polarizability, respectively. Resistivity of iron ore and dolomite is negatively correlated with TFe2O3 content. Resistivity of iron ore is negatively correlated with TFe2O3, total REE (), and Nb, respectively, and correlated with thorium. The methods used have intuitive visual expression and reflect the characteristics of the physical properties and their correlation with the mineralogical composition. The results will be beneficial for determining the geometry of ore-hosting rock masses and providing crucial evidence for the resource evaluation.
- Materials > Metals & Mining > Iron (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.88)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (0.66)
ABSTRACT Igneous rocks are widely developed in various Mesozoic and Cenozoic continental and marine basins. Igneous reservoirs are the key reservoirs for current oil and gas development. Accurate prediction of lithology and lithofacies is a prerequisite for the effective exploration of igneous reservoirs. Igneous lithology and lithofacies are complex and correlated. The existing single-label igneous rock identification methods only consider the prediction of individual properties, and less consideration is given to the correlation of reservoir properties. Therefore, lithology and lithofacies prediction based on conventional logging data is regarded as a typical class-imbalanced multilabel classification problem when considering both attribute correlation in algorithms and evaluation metrics. To solve this problem, an ensemble method of data optimization combined with multigrained cascade forest (CF) is used in this study to develop a new multilabel lithology and lithofacies prediction model based on data from nine conventional logs in the eastern depressional reservoirs of the Liaohe Basin, and satisfactory results are obtained. The imbalance problem of the conventional logging data sets is first solved by using K-means and synthetic minority oversampling technique methods; then, the model is trained by scenario transformation and stripping with multigrained CF; and next, a multilabel classification evaluation index with multiple perspectives is introduced. The differences between the model and typical intelligent algorithms such as CF, adaptive boosting, random forest, and support vector machine are compared in simulated wells, and the new model is found to have obvious advantages. The model is finally applied to an actual well, and the accurate prediction results illustrate that the new model designed and trained for the class imbalance multilabel classification problem in this paper has application value in the lithology and lithofacies multilabel prediction of igneous rocks and also provides a theoretical basis for more complex multilabel reservoir evaluation using machine learning in the future.
- Overview (0.54)
- Research Report > New Finding (0.35)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Abstract The Central Sumatra Basin is a vast sedimentary basin that has been proven to produce hydrocarbon. The basin comprises several subbasins that are not sufficiently imaged by conventional seismic reflection profiles and limited well-log data, particularly in the nearshore area to the east. This research aims to delineate sedimentary subbasins, interpret the subsurface geologic model, and identify geologic structures beneath the eastern part of the Central Sumatra Basin using integrated geophysical gravity, seismic profiles, and well-log data. Three-dimensional gravity inversion modeling results indicate that the pre-Tertiary granitic basement is a continental crust with a mass density value of 2.67 gr/cc. The modeling results indicate that the sedimentary rock is composed of Early Oligocene–Middle Miocene sedimentary rock, with a mass density of 2.35 gr/cc, arranged from bottom to top. The residual gravity anomaly model identifies 13 sedimentary subbasins with structural features such as basement height, graben, and fault mapped in a relatively northwest–southeast direction. Moreover, based on the graben pattern and the basement high beneath the eastern Central Sumatra Basin, many structural patterns support the development of petroleum systems similar to that of the western part of the basin, which has already produced hydrocarbon. Our research also revealed the thickness of the Sihapas Formation in the eastern part of the basin, which shows great potential as a hydrocarbon reservoir. The results show that integrated analysis of many geophysical data sets can substantially decrease the uncertainty associated with individual data sets and produce more reliable imaging of subsurface geology.
- Phanerozoic > Cenozoic > Neogene > Miocene (0.69)
- Phanerozoic > Cenozoic > Paleogene > Oligocene (0.54)
- Geology > Structural Geology > Tectonics > Plate Tectonics (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (0.68)
- Geology > Structural Geology > Tectonics > Compressional Tectonics (0.68)
- (2 more...)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Gravity Surveying > Gravity Modeling > Gravity Inversion (1.00)
- Geophysics > Borehole Geophysics (1.00)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Serbin Field (0.99)
- Asia > Indonesia > Sumatra > South Sumatra > South Sumatra Basin (0.99)
- Asia > Indonesia > Sumatra > Riau > Central Sumatra Basin > Rokan Block > Menggala Formation (0.99)
- (6 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)