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China
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...)
Data-driven double-focusing resolution analyses for seismic imaging
Fu, Li-Yun (China University of Petroleum (East China), Laoshan Laboratory) | Tang, Cong (PetroChina Southwest Oil & Gas Field Company) | Wei, Wei (Chinese Academy of Sciences) | Du, Qizhen (China University of Petroleum (East China), Laoshan Laboratory)
Seismic imaging requires a supporting tool to measure its resolution characteristics as a basis for seismic interpretation. However, traditional focal-beam resolution analyses are usually applied to acquisition geometries by calculating the impulse response of a single point in a reference velocity model. Seismic data to directly estimate the spatial resolution of migrated images remains unaddressed. We address this data resolution by incorporating weighted focal beams into the prestack migration process to develop a data-driven double-focusing (DF) resolution analysis method for complex media. Unlike traditional resolution analyses that define the system resolution of acquisition geometries using a unit point reflector, the data-driven resolution analysis for seismic imaging uses angle-trace gathers that contain all the information of acquisition geometries, migration velocities, propagation effects, and reflectivities. The data-driven resolution analysis consists of the detector- and source-focusing processes using common-shot and common-detector gathers, respectively, followed by a multiplication of weighted focal detector and source beams. The resulting resolution function can be used to calculate the horizontal and vertical resolution and sharpness of a given imaging point. It is implemented along with prestack migration to share the same wavefield extrapolation without invoking extra computational cost. We benchmark the data-driven method for a homogeneous medium containing single-point and double-point targets by conventional point-spread and focal-beam methods. Numerical experiments with wedge-model synthetic data and field data show the performance of the DF resolution analysis, demonstrating the effects of propagation attenuation, incorrect migration velocity, and noise contamination, which significantly reduce the system resolution of acquisition geometries.
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling (1.00)
- Asia > China > Sichuan > Sichuan Basin > Southwest Field > Longwangmiao Formation (0.99)
- North America > United States > Louisiana > China Field (0.97)
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...)
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...)
We present a new alternative for the joint inversion of well logs to predict the volumetric and zone parameters in hydrocarbon reservoirs. Porosity, water saturation, shale content, kerogen and matrix volumes are simultaneously estimated with the tool response function constants with a hyperparameter estimation assisted inversion of the total and spectral natural gamma-ray intensity, neutron porosity and resistivity logs. We treat the zone parameters, i.e., the physical properties of rock matrix constituents, shale, kerogen, and pore-fluids, as well as some textural parameters, as hyperparameters and estimate them in a meta-heuristic inversion procedure for the entire processing interval. The selection of inversion unknowns is based on parameter sensitivity tests, which show the automated estimation of several zone parameters is favorable and their possible range can also be specified in advance. In the outer loop of the inversion procedure, we use a real-coded genetic algorithm for the prediction of zone parameters, while we update the volumetric parameters in the inner loop in addition to the fixed values of zone parameters estimated in the previous step. We apply a linearized inversion process in the inner loop, which allows for the quick prediction of volumetric parameters along with their estimation errors from point to point along a borehole. Derived parameters such as hydrocarbon saturation and total organic content show good agreement with core laboratory data. The significance of the inversion method is in that zone parameters are extracted directly from wireline logs, which both improves the solution of the forward problem and reduces the cost of core sampling and laboratory measurements. In a field study, we demonstrate the feasibility of the inversion method using real well logs collected from a Miocene tight gas formation situated in the Derecske Trough, Pannonian Basin, East Hungary.
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.71)
- Europe > Slovakia > Pannonian Basin (0.99)
- Europe > Serbia > Pannonian Basin (0.99)
- Europe > Romania > Pannonian Basin (0.99)
- (9 more...)
- 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)
Introduction to special section: Borehole image data applications in reservoir characterization ? Case studies and updates on new developments
Li, Bingjian (Blackriver Geoscience LLC) | Egorov, Vsevolod (GeoExpera) | Perona, Ricardo (Repsol) | Haddad, David (Apache Corporation) | Sementelli, Katy (Woodside Energy) | Xu, Chicheng (Aramco Americas Company) | Mardi, Chrystianto (BPX Energy)
Borehole image data have played an important role in the oil and gas industry for decades, providing invaluable insights into hydrocarbon exploration, reservoir appraisal, and development. Recent advancements in borehole image technologies, encompassing data acquisition, processing, and interpretation, have ushered in a new era of possibilities. Geoscientists have expanded the applications of image data, progressing from basic natural fracture detection to comprehensive reservoir characterization. This special section explores significant advances in sedimentological and structural interpretation, full-scale fracture and fault analysis, wellbore geomechanics, reservoir heterogeneity evaluation, and 3-D reservoir modeling. Applications of borehole image log data have transcended reservoir types, spanning clastics, carbonates, naturally fractured basements, and unconventional shales. With these developments in mind, we have invited submissions that showcase studies utilizing borehole image log data for the successful characterization of any reservoir type, along with related case studies of interest to the exploration and development community. The overwhelming response to our call-for-papers resulted in the selection of seven high-quality contributions for inclusion in this special publication. Mohebian et al. revolutionize fracture identification by employing the YOLOv5 deep learning algorithm on borehole image logs, shifting from manual to automated processes.
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.56)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Asia > Kazakhstan > West Kazakhstan > Uralsk Region > Precaspian Basin > Karachaganak Field (0.99)
- Asia > China > Bohai Basin (0.99)
Using seismic petrophysical modeling and prestack simultaneous inversion to provide insights into the physical properties of uranium-bearing reservoirs: Implications for favorable sites of sandstone-hosted uranium deposits
Wu, Qubo (China University of Geosciences (Beijing), Beijing Research Institute of Uranium Geology) | Wang, Yanchun (China University of Geosciences (Beijing)) | Huang, Yucheng (Beijing Research Institute of Uranium Geology) | Qiao, Baoping (Beijing Research Institute of Uranium Geology) | Cao, Chengyin (Beijing Research Institute of Uranium Geology) | Li, Ziwei (Beijing Research Institute of Uranium Geology) | Yu, Xiang (China National Uranium Corporation)
ABSTRACT Seismic prospecting has been accepted as one of the most widely available methods for exploring sandstone-hosted uranium deposits (SUDs). However, conventional seismic interpretation faces a challenge in the identification and characterization of a uranium reservoirโs complexity. How to characterize in detail a uranium reservoirโs physical complexity and effectively improve uranium reservoir prediction accuracy remain unresolved problems. To address this, we develop a novel combination of petrophysical modeling and prestack simultaneous inversion to understand in detail the physical properties of uranium-bearing reservoirs and efficiently predict favorable SUD sites. First, we develop a workflow of rock-physics modeling for SUD logs using the Xu-White method to calculate the modulus of elasticity of the grain matrix; subsequently, we extend the Walton model for the modulus prediction of the dry rocks and the Gassmann equation for one of the saturated rocks after a massive calculation test; and then, we predict the S-wave data used for the following inversion. Second, we execute a prestack simultaneous inversion to obtain the petrophysical parameters (e.g.,ย P-impedance, density [], shear modulus [], Lamรฉ coefficient [], and Youngโs modulus) that can provide insights into the physical properties of a uranium metallogenic environment. Accordingly, we discover that sites bearing uranium mineralization strongly correspond to areas with low elastic-parameter values (especially and ), whereas nonuranium anomalies occur in high-value sites. This indicates that weakened elastic characteristics are caused by the enhancement of the total organic content and total clay mineral volumes of the uranium-bearing layers. In summary, the developed combination approach can yield an effective and accurate characterization of the geologic properties of uranium-bearing formations, and it can provide prediction factors (e.g.,ย parameters related to the shear modulus) for uranium mineralization.
- Asia > China (1.00)
- North America > Canada (0.68)
- Geology > Mineral > Silicate (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.47)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.37)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Asia > Pakistan > Upper Indus Basin > Potwar Basin (0.99)
- Asia > China > Xinjiang Uyghur Autonomous Region > Junggar Basin (0.99)
- Asia > China > South China Sea > Zhujiangkou Basin (0.99)
- (7 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)
- Health, Safety, Environment & Sustainability > Environment > Naturally occurring radioactive materials (1.00)
ABSTRACT Although trial-and-error modeling may give some level of interpretation about the subsurface while sacrificing certainty, it is a viable alternative for precise 3D interpretation of real ground-airborne frequency-domain electromagnetic (GAFEM) data. In this sense, a semiautomatic trial-and-error modeling approach is developed. Specifically, we first develop the 3D GAFEM forward-modeling code. Its accuracy is demonstrated using a 3D synthetic model with topography and a tilted anomalous body. Second, an initial model is established based on known geologic constraints. Then, the code is conducted repeatedly, and the parameters of the model are renewed semiautomatically based on a predefined geometry-resistivity combination list. Finally, the model that can achieve the minimum error between the computed response and the collected GAFEM data is selected as the final model. Furthermore, we apply the presented semiautomatic trial-and-error modeling approach to the geothermal resources survey at the Yishu Faulting Basin, China. The purpose of the survey is to interpret the resistivity structure of the subsurface and evaluate the potential development of the geothermal resources in the survey area. As a result, the final model obtained by the trial-and-error modeling, which is constrained by the known geologic information and subsurface geoelectric structures inferred from 2D models inverted by the magnetotelluric and controlled-source audio-frequency magnetotelluric data measured at the same location, indicates the existence of the geothermal resources. This indication is proven by the drilling result of a well site located on the survey line. To further verify the reliability, a comparative analysis is conducted between the model obtained by the trial-and-error modeling and the models obtained by 3D inversion of a GAFEM data set and apparent resistivity calculation using the same data. The results indicate that different approaches can achieve similar subsurface geometry and resistivity distribution of the faulting basin structure.
- Asia > China (0.85)
- North America > Canada > Newfoundland and Labrador > Newfoundland (0.28)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource (0.65)
- North America > Canada > Saskatchewan > Athabasca Basin (0.99)
- North America > Canada > Alberta > Athabasca Basin (0.99)
- North America > Canada > Newfoundland and Labrador > Newfoundland > North Atlantic Ocean > Atlantic Margin Basin > Grand Banks Basin > Flemish Pass Basin (0.95)
- Asia > China > Shandong > Yishu Basin (0.95)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Non-Traditional Resources > Geothermal resources (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)