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Collaborating Authors
China
Investigating the causes of permeability anisotropy in heterogeneous conglomeratic sandstone using multiscale digital rock
Chi, Peng (China University of Petroleum (East China), China University of Petroleum (East China)) | Sun, Jianmeng (China University of Petroleum (East China), China University of Petroleum (East China)) | Yan, Weichao (Ocean University of China, Ocean University of China) | Luo, Xin (China University of Petroleum (East China), China University of Petroleum (East China)) | Ping, Feng (Southern University of Science and Technology)
Heterogeneous conglomeratic sandstone exhibits anisotropic physical properties, rendering a comprehensive analysis of its physical processes challenging with experimental measurements. Digital rock technology provides a visual and intuitive analysis of the microphysical processes in rocks, thereby aiding in scientific inquiry. Nevertheless, the multiscale characteristics of conglomeratic sandstone cannot be fully captured by a single-scale digital rock, thus limiting its ability to characterize the pore structure. Our work introduces a proposed workflow that employs multiscale digital rock fusion to investigate permeability anisotropy in heterogeneous rock. We utilize a cycle-consistent generative adversarial network (CycleGAN) to fuse CT scans data of different resolutions, creating a large-scale, high-precision digital rock that comprehensively represents the conglomeratic sandstone pore structure. Subsequently, the digital rock is partitioned into multiple blocks, and the permeability of each block is simulated using a pore network. Finally, the total permeability of the sample is calculated by conducting an upscaling numerical simulation using the Darcy-Stokes equation. This process facilitates the analysis of the pore structure in conglomeratic sandstone and provides a step-by-step solution for permeability. From a multiscale perspective, this approach reveals that the anisotropy of permeability in conglomeratic sandstone stems from the layered distribution of grain sizes and differences in grain arrangement across different directions.
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 338 > Block 16/1 > Edvard Grieg Field > ร sgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 338 > Block 16/1 > Edvard Grieg Field > Skagerrak Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 338 > Block 16/1 > Edvard Grieg Field > Hegre Formation (0.99)
- (3 more...)
Xinming Wu joined the USTC (University of Science and Technology of China) as a professor in 2019, where he started the Computational Interpretation Group (CIG). Xinming received an engineering degree (2009) in geophysics from Central South University, an M.Sc. From 2016 to 2019, he was a postdoctoral fellow working with Sergey Fomel at Bureau of Economic Geology, The University of Texas at Austin. Xinming received the J. Clarence Karcher Award from the Society of Exploration Geophysics (SEG) in 2020 and was selected to be the 2020 SEG Honorary Lecturer, South and East Asia. He also received the Shanghai excellent master thesis award in 2013 (Generating 3D seismic Wheeler volumes: methods and applications).[1].
- North America > United States > Texas > Travis County > Austin (0.25)
- Asia > China > Shanghai > Shanghai (0.25)
- Geophysics > Seismic Surveying > Seismic Processing (0.56)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.37)
- Asia > China > Shanxi > Ordos Basin > Changqing Field (0.99)
- Asia > China > Shaanxi > Ordos Basin > Changqing Field (0.99)
- Asia > China > Ningxia > Ordos Basin > Changqing Field (0.99)
- (2 more...)
In recent years, with continuous improvements in ultra-deep oil and gas exploration theory and technology, domestic onshore ultra-deep oil and gas exploration has continued to make breakthroughs, providing an important replacement field for CNPC's upstream business development and large-scale increase of reserves and production. The proven oil and gas reserves in ultra-deep reservoirs in Tarim Basin account for more than 50% of the proven oil and gas in ultra-deep reservoirs in China, and Tarim has become the main field for onshore ultra-deep exploration in China. This is not only due to the innovation of ultra-deep oil and gas geological theory, but also due to the breakthrough of ultra-deep geophysical technology. Tarim ultra deep oil and gas exploration faces many challenges: accurate imaging of steeply ultra-deep structures in complex mountains; better recovery of weak signals; enhanced imaging resolution in the ultra-deep subsalt of large desert areas; ultra-deep imaging in thick loess covered areas and other problems restricts the process and economic development of ultra-deep oil and gas exploration in basin. Therefore, there is an urgent need to study theoretical technologies suitable for ultra-deep geophysical acquisition, weak signal processing and imaging, as well as ultra-deep reservoir prediction and fluid identification under different geological conditions.
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > China Government (0.40)
- Asia > China > Xinjiang Uyghur Autonomous Region > Tarim Basin (0.99)
- North America > United States > Louisiana > China Field (0.95)
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)
Research on focal mechanism of microseismic events and the regional stress during hydraulic fracturing at a shale play site in southwest China
Chen, Xin-Xing (Chengdu University of Technology) | Meng, Xiao-Bo (Chengdu University of Technology) | Chen, Hai-Chao (China University of Petroleum) | Chen, Xin-Yu (Chengdu University of Technology) | Li, Qiu-Yu (Optical Science and Technology (Chengdu) Ltd.) | Guo, Ming-Yu (Chengdu University of Technology)
ABSTRACT We develop a waveform-matching inversion method to determine the focal mechanism of microseismic events recorded by a single-well observation system. Our method uses the crosscorrelation technique to mitigate the influence of anisotropy on the S wave. Then, by conducting a grid search for strike, dip, and rake, we match the observed waveforms of P and S wave with the corresponding theoretical waveforms. A synthetic test demonstrates the robustness and accuracy of our method in resolving the focal mechanism of microseismic events under a single-well observation system. By applying our method to the events that have been categorized into two clusters based on spatial and temporal evolution recorded during the hydraulic fracturing operation in the Weiyuan shale reservoir, we observe that the two clusters have distinct focal mechanism and stress characteristics. The events in the remote cluster (cluster A) exhibit consistent focal mechanisms, with a concentrated distribution of P-axis orientations. The inverted maximum principal stress direction of cluster A aligns with the local maximum principal stress direction (). This implies that events in cluster A occur in a uniform stress condition. In contrast, the other cluster (cluster B) near the injection well exhibits significant variation in focal mechanisms, with a scattered distribution of P-axis orientations. The inverted maximum principal stress direction deviates from local maximum principal stress direction (), indicating that events in cluster B occur in a more complicated stress condition.
- North America > Canada > Alberta (0.47)
- North America > United States > Texas (0.47)
- Asia > China > Sichuan Province (0.29)
- Geology > Geological Subdiscipline > Geomechanics (0.94)
- Geology > Structural Geology > Tectonics > Plate Tectonics > Earthquake (0.70)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.68)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (0.50)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin > West Pembina Field (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin > Pembina Field > Viking Formation (0.99)
- (2 more...)
ABSTRACT Deep learning is prevalent in many fields and attempts have been made to use it in nonbidirectional mapping problems, such as seismic inversion. These nonbidirectional mapping problems have two special issues, that is, insufficient labels and uncertainty of solution. Therefore, current deep-learning structures are not suitable for handling this kind of problem. A distinctive knowledge-embedded close-looped (KECL) deep-learning framework is developed, tuned to the characteristics of the seismic inverse problem. The KECL deep-learning framework is composed of a reservoir parameter generator (RPG) and a reservoir parameter updater (RPU). The former half-loop is RPG, which takes the seismic data as input to generate the initial reservoir parameters. The latter loop is RPU, which takes the initial parameters as input to output synthetic seismic data. Through the training by well data, the difference between field seismic data and synthetic seismic data modeled by the RPU is used to optimize the RPG and RPU. In this deep-learning framework, knowledge of the Robinson convolutional model is embedded to address the problem of insufficient labels. Furthermore, semisupervised learning is used as prior information to reduce the uncertainty of solution. After the training, with the help of prior geologic information data, the RPU is used to update the initial reservoir parameters generated by RPG for final reservoir parameter inversion. Numerical models and field data are used to test the feasibility of our deep-learning framework. We find that intelligent inversion results using data from one well to train the KECL network are consistent with results using multiple well data. Experiments demonstrate that it is adaptable to situations in which insufficient well data are available and is able to achieve reliable intelligent inversion.
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 worldโs 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.
- 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)
MT2DInv-Unet: A 2D magnetotelluric inversion method based on deep-learning technology
Pan, Kejia (Shenzhen Research Institute of Central South University, Central South University) | Ling, Weiwei (Central South University, Jiangxi College of Applied Technology) | Zhang, Jiajing (Jiangxi College of Applied Technology, Ministry of Natural Resources) | Zhong, Xin (Jiangxi College of Applied Technology, Ministry of Natural Resources) | Ren, Zhengyong (Central South University) | Hu, Shuanggui (China University of Mining and Technology) | He, Dongdong (The Chinese University of Hong Kong) | Tang, Jingtian (Central South University)
ABSTRACT Traditional gradient-based inversion methods usually suffer from the problems of falling into local minima and relying heavily on initial guesses. Deep-learning methods have received increasing attention due to their excellent nonlinear fitting ability. However, given the recent application of deep-learning methods in the field of magnetotelluric (MT) inversion, there are currently challenges associated with achieving high inversion resolution and extracting sufficient features. We develop a neural network model (called MT2DInv-Unet) based on the deformable convolution for 2D MT inversion to approximate the nonlinear mapping from the MT response data to the resistivity model. The deformable convolution is achieved by adding an offset to each sample point of the conventional convolution operation, which extracts hidden relationships and allows the flexible adjustment of the size and shape of the feature region. Meanwhile, we design the network structure with multiscale residual blocks, which effectively extract the multiscale features of the MT response data. This design not only enhances the network performance but also alleviates issues such as vanishing gradients and network degradation. The results of synthetic models indicate that our network inversion method has stable convergence, good robustness, and generalization performance, and it performs better than the fully convolutional neural network and U-Net network. Finally, the inversion results of field data show that MT2DInv-Unet can effectively obtain a reliable underground resistivity structure and has a good application prospect in MT inversion.
- Research Report > New Finding (0.93)
- Research Report > Experimental Study (0.68)
The numerous applications, I secured a role as Duncan, Rocco Detomo, Joseph Ebeniro, continent's unique position necessitates a a geologist/mudlogger for a small oil and Fela Aromolaran, Estella Atekwana, and continuous influx of proficient geoscientists, gas servicing company. Many geophysics Larry Lines became invaluable. Lines particularly geophysicists, to effectively graduates who were less fortunate found expressed astonishment at my journey from extract and commercialize these subsurface employment in financial institutions or Nigeria solely to attend the SEG Annual treasures. In this regard, the continent holds took menial jobs to make ends meet. I have served SEG isolated and sometimes overlooked in scientific issue for geophysics talent in Africa. in various roles for more than 17 years, research, especially in the field of Additionally, political views often prioritize including as student cochair of the geophysics.