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Collaborating Authors
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Characterizing Landfill Extent, Composition, and Biogeochemical Activity using Electrical Resistivity Tomography and Induced Polarization under Varying Geomembrane Coverage
Ma, Xinmin (Shandong University) | Zhang, Jiaming (Beijing Construction Engineering Group Environmental Remediation Co., Ltd.) | Schwartz, Nimrod (The Hebrew University of Jerusalem) | Li, Jing (Shandong University) | Chao, Chen (Shandong University) | Meng, Jian (Shandong University) | Mao, Deqiang (Shandong University)
Landfill monitoring is essential for sustainable waste management and environmental protection. Geophysical methods can provide quasi-continuous spatial and temporal insights into subsurface physical properties and processes in a non-intrusive manner. The effectiveness of monitoring landfill extent, composition, and degradation under varying geomembrane coverage was evaluated using electrical resistivity tomography (ERT) and induced polarization (IP) methods. Synthetic electrical models for landfill with different geomembrane damage degrees were inverted to assess data reliability. The current conduction channels into the geomembrane during the electrical survey were quantified. Reliable electrical data was obtained when the inverted conduction channel ratio of the geomembrane (representing damage to the geomembrane) was 51.6% or higher. This criterion was validated in a landfill experiencing aeration and anaerobic treatments. ERT and IP data captured construction and domestic waste distribution and identified the landfill boundary. The chargeability of domestic waste proved sensitive to microbial degradation activity, corroborated by characteristic ammonium and nitrate ions and a linear relation between chargeability and subsurface temperature. Temperature variations between the aerobic and anaerobic reaction zones (>20ยฐC and = 12C) were observed to correlate with high chargeability values (>80.4 mV/V), signifying the presence of biogeochemically active zones. IP excels in characterizing geomembrane-covered landfill boundaries and discerning biogeochemical activity, thereby enhancing landfill monitoring and waste management strategies.
- Research Report (0.46)
- Overview (0.46)
- Water & Waste Management > Solid Waste Management (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Information Technology > Artificial Intelligence (0.46)
- Information Technology > Data Science (0.34)
The numerical solution of wave equations plays a crucial role in computational geophysics problems, which forms the foundation of inverse problems and directly impact the high-precision imaging results of earth models. However, common numerical methods often lead to signifcant computational and storage requirements. Due to the heavy reliance on forward modeling methods in inversion techniques, particularly full waveform inversion, enhancing the computational efficiency and reducing storage demands of traditional numerical methods becomes a key issue in computational geophysics. In this paper, we present the deep Lax-Wendroff correction method (DeepLWC), a deep learning-based numerical format for solving two#xD; dimensional (2D) hyperbolic wave equations. DeepLWC combines the advantages of the traditional numerical schemes with a deep neural network. We provide a detailed comparison of this method with representative traditional Lax-Wendroff correction (LWC) method. Our numerical results indicate that the DeepLWC signifcantly improves calculation speed (by more than ten times) and reduces storage space by over 10000 times compared to traditional numerical methods. In contrast to the more popular Physics Informed Neural Network (PINN) method, DeepLWC maximizes the advantages of traditional mathematical methods in solving PDEs and employs a new sampling approach, leading to improved accuracy and faster computations. It is particularly worth pointing that, DeepLWC introduces a novel research paradigm for numerical equation-solving, which can be combined with various traditional numerical methods, enabling acceleration and reduction in storage requirements of conventional approaches.
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)
Numerous surface-felt earthquakes have been spatiotemporally correlated with hydraulic fracturing operations. Because large deformations occur close to hydraulic fractures (HFs), any associated fault reactivation and resulting seismicity must be evaluated within the length scale of the fracture stages and based on precise fault location relative to the simulated rock volumes. To evaluate changes in Coulomb failure stress (CFS) with injection, we conducted fully coupled poroelastic finite-element simulations using a pore-pressure cohesive zone model for the fracture and fault core in combination with a fault-fracture intersection model. The simulations quantify the dependence of CFS and fault reactivation potential on host-rock and fault properties, spacing between fault and HF, and fracturing sequence. We find that fracturing in an anisotropic in-situ stress state does not lead to fault tensile opening but rather dominant shear reactivation through a poroelastic stress disturbance over the fault core ahead of the compressed central stabilized zone. In our simulations, poroelastic stress changes significantly affect fault reactivation in all simulated scenarios of fracturing 50-200 m away from an optimally oriented normal fault. Asymmetric HF growth due to the stress-shadowing effect of adjacent HFs leads to 1.) a larger reactivated fault zone following simultaneous and sequential fracturing of multiple clusters compared to single-cluster fracturing; and 2.) larger unstable area (CFSgt;0.1) over the fault core or higher potential of fault slip following sequential fracturing compared to simultaneous fracturing. The fault reactivation area is further increased for a fault with lower conductivity and with a higher opening-mode fracture toughness of the overlying layer. To reduce the risk of fault reactivation by hydraulic fracturing under reservoir characteristics of the Barnett Shale, the Fort Worth Basin, it is recommended to 1.) conduct simultaneous fracturing instead of sequential; and 2.) to maintain a minimum distance of ~ 200 m for HF operations from known faults.
- North America > Canada (1.00)
- North America > United States > Texas > Travis County > Austin (0.28)
- North America > United States > Texas > Tarrant County > Fort Worth (0.24)
- Geology > Structural Geology > Tectonics > Plate Tectonics > Earthquake (1.00)
- Geology > Structural Geology > Fault (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- (2 more...)
- South America > Argentina > Patagonia > Neuquรฉn > Neuquen Basin > Vaca Muerta Shale Formation (0.99)
- North America > United States > Wyoming > Green River Basin > Jonah Field (0.99)
- North America > United States > West Virginia > Appalachian Basin (0.99)
- (51 more...)
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...)
Seismic Characterization of the Individual Geological Factor with Disentangled Features
Fei, Yifeng (University of Electronic Science and Technology of China (UESTC)) | Cai, Hanpeng (University of Electronic Science and Technology of China (UESTC)) | Zhou, Cheng (University of Electronic Science and Technology of China (UESTC)) | He, Xin (University of Electronic Science and Technology of China (UESTC)) | Liang, Jiandong (University of Electronic Science and Technology of China (UESTC)) | Su, Mingjun (PetroChina) | Hu, Guangmin (University of Electronic Science and Technology of China (UESTC))
Seismic attributes are critical in understanding geological factors, such as sand body configuration, lithology, and porosity. However, existing attributes typically reflect a combined response of multiple geological factors. The interplay between these factors can obscure the features of the target factor, posing a challenge to its direct seismic characterization, particularly when the factor is subtle. To address this, we develop an innovative neural network designed to disentangle and characterize the individual geological factor within seismic data. Our approach divides the geological information in the seismic data into two categories: the single geological factor of interest and an aggregate of all other information. A novel feature-swapping mechanism within our network facilitates the disentanglement of these two categories, providing an interpretable representation. We employ a triplet loss function to differentiate data samples with similar waveforms but varying subtle geological details, thus enhancing the extraction of distinct features. Additionally, our network employs a co-training strategy to integrate synthetic and actual field data during the training process. This strategy helps mitigate potential performance degradation arising from discrepancies between simulated and actual field data. We apply our method to synthetic data experiments and field data from two geologically distinct areas. Current results indicate that our method surpasses traditional approaches such as a deep autoencoder and a convolutional neural network classifier in extracting seismic attributes with more explicit geophysical implications.
- Geology > Rock Type > Sedimentary Rock (0.46)
- Geology > Geological Subdiscipline > Geomechanics (0.45)
Velocity errors and data noise are inevitable for seismic imaging of field datasets in current production; therefore, it is desirable to improve the seismic images as part of the migration process to mitigate the influence of such errors and noise. To address this, we have developed a new method of adaptive merging migration (AMM). This method can produce migrated sections of equal quality to conventional migration methods given a correct velocity model and noise-free data. Additionally, it can ameliorate the seismic image quality when applied with erroneous migration velocity models or noisy seismic data. AMM employs an efficient recursive Radon transform to generate multiple p-component images, representing migrated sections associated with different local plane slopes. It then adaptively merges the subsections from those p-component images that are less distorted by velocity errors or noise into the whole image. Such merging is implemented by computing adaptive weights followed by a selective stacking. We use three synthetic velocity models and one field dataset to evaluate the AMM performance on isolated Gaussian velocity errors, inaccurate smoothed velocities, velocity errors around high-contrast and short-wavelength interfaces, and noisy seismic data. Numerical tests conducted on both synthetic and field datasets validate that AMM can effectively improve the seismic image quality in the presence of different types of velocity errors and data noise.
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.46)
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...)