Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Society of Exploration Geophysicists
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 (>20°C 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)
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 > Åsgard 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...)
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...)
Sensitivity analysis of S-waves and their velocity measurement in slow formations from monopole acoustic logging-while-drilling
Ji, Yunjia (University of Electronic Science and Technology of China, Guilin University of Electronic Technology, Chinese Academy of Sciences) | Wang, Hua (University of Electronic Science and Technology of China, University of Electronic Science and Technology of China)
Monopole acoustic logging-while-drilling (LWD) enables the direct measurement of shear (S) wave velocity in slow formations, which has been corroborated by recent theoretical and experimental studies. However, this measurement is hampered by the weakness of the S-wave signal and the lack of techniques to amplify it. To address this challenge, we have analytically computed the monopole LWD wavefields, considering both centralized and off-center tools in various slow formations. Modeling analysis reveals that four parameters primarily influence the excitation of the formation S-wave: the formation S-wave velocity, the source-to-receiver distance, the radial distance from receiver to wellbore, and source frequency. S-wave signals can be enhanced by judiciously optimizing these parameters during tool design. Furthermore, our research suggests that the S-wave velocity can be accurately extracted through the slowness-time correlation method only when formation S-wave velocities are in a suitable range. This is because an overly high S-wave velocity causes shear arrivals to be interfered with the inner Stoneley mode, whereas an ultra-slow formation S-wave velocity results in S-wave signals too faint to detect. For the LWD model with an off-center tool, simulations demonstrate that tool eccentricity, especially large eccentricity, can amplify the shear wave and improve its measurement accuracy, provided that waveforms received in the direction of tool movement are used. In a very slow formation, we successfully extracted the S-wave velocity from synthetic full-wave data at that azimuth under conditions of large eccentricity, a task not achievable with a centralized instrument.
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (0.87)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Well Drilling > Drilling Measurement, Data Acquisition and Automation > Logging while drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Facies classification of image logs plays a vital role in reservoir characterization, especially in the heterogeneous and anisotropic carbonate formations of the Brazilian pre-salt region. Although manual classification remains the industry standard for handling the complexity and diversity of image logs, it has notable disadvantages of being time-consuming, labor-intensive, subjective, and non-repeatable. Recent advancements in machine learning offer promising solutions for automation and acceleration. However, previous attempts to train deep neural networks for facies identification have struggled to generalize to new data due to insufficient labeled data and the inherent intricacy of image logs. Additionally, human errors in manual labels further hinder the performance of trained models. To overcome these challenges, we propose adopting the state-of-the-art SwinV2-Unet to provide depthwise facies classification for Brazilian pre-salt acoustic image logs. The training process incorporates transfer learning to mitigate overfitting and confident learning to address label errors. Through a k-fold cross-validation experiment, with each fold spanning over 350 meters, we achieve an impressive macro F1 score of 0.90 for out-of-sample predictions. This significantly surpasses the previous model modified from the widely recognized U-Net, which provides a macro F1 score of 0.68. These findings highlight the effectiveness of the employed enhancements, including the adoption of an improved neural network and an enhanced training strategy. Moreover, our SwinV2-Unet enables highly efficient and accurate facies analysis of the complex yet informative image logs, significantly advancing our understanding of hydrocarbon reservoirs, saving human effort, and improving productivity.
- Geology > Structural Geology > Tectonics > Salt Tectonics (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.67)
- 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...)