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Collaborating Authors
Results
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)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Health, Safety, Environment & Sustainability > Environment > Waste management (1.00)
- Information Technology > Artificial Intelligence (0.46)
- Information Technology > Data Science (0.34)
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...)
Aria Abubakar was born in Bandung, Indonesia. He received an MSc degree in Electrical Engineering in 1997 and a PhD in Technical Sciences in 2000, both from the Delft University of Technology, The Netherlands. He joined Schlumberger-Doll Research in Ridgefield, CT, USA in 2003, where he remained for 10 years, ending his tenure as a Scientific Advisor and the Manager of the Multi-Physics Modeling and Inversion Program. From 2013 until mid-2017, he was the Interpretation Engineering Manager at Schlumberger Houston Formation Evaluation in Sugar Land, TX. From mid-2017 until mid-2020, he was Data Analytics Program Manager for Software Technology and then Head of Data Science for the Schlumberger Exploration and Field Development Platform based in Houston, TX. Aria is currently the Head of Data Science for the Digital Subsurface Solutions.
- Europe > Netherlands > South Holland > Delft (0.25)
- North America > United States > Texas > Harris County > Houston (0.25)
- North America > United States > Texas > Fort Bend County > Sugar Land (0.25)
- (2 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Data Science (1.00)
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)
- Information Technology > Artificial Intelligence > Natural Language (0.48)
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...)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)
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)
Petroleum Engineering, University of Houston, 2. Metarock Laboratories, 3. Department of Earth and Atmospheric Sciences, University of Houston) 16:00-16:30 Break and Walk to Bizzell Museum 16:30-17:30 Tour: History of Science Collections, Bizzell Memorial Library, The University of Oklahoma 17:30-19:00 Networking Reception: Thurman J. White Forum Building
- Research Report > New Finding (0.93)
- Overview (0.68)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Mineral (0.72)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- (2 more...)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
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...)
Monitoring stored CO2 in carbon capture and storage projects is crucial for ensuring safety and effectiveness. We introduce DeepNRMS, a novel noise-robust method that effectively handles time-lapse noise in seismic images. The DeepNRMS leverages unsupervised deep learning to acquire knowledge of time-lapse noise characteristics from pre-injection surveys. By utilizing this learned knowledge, our approach accurately discerns CO2-induced subtle signals from the high-amplitude time-lapse noise, ensuring fidelity in monitoring while reducing costs by enabling sparse acquisition. We evaluate our method using synthetic data and field data acquired in the Aquistore project. In the synthetic experiments, we simulate time-lapse noise by incorporating random near-surface effects in the elastic properties of the subsurface model. We train our neural networks exclusively on pre-injection seismic images and subsequently predict CO2 locations from post-injection seismic images. In the field data analysis from Aquistore, the images from pre-injection surveys are utilized to train the neural networks with the characteristics of time-lapse noise, followed by identifying CO2 plumes within two post-injection surveys. The outcomes demonstrate the improved accuracy achieved by the DeepNRMS, effectively addressing the strong time-lapse noise.
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Seismic (four dimensional) monitoring (1.00)
- (2 more...)
LITHOCODIUM MOUND IDENTIFICATION USING LWD IMAGE LOG AND QUANTIFIED CUTTING ANALYSIS VALIDATION WITH ANALOGUES
Perrin, Christian (North Oil Company) | Pointer, Chay (North Oil Company) | Al-Mohannadi, Ghada (North Oil Company) | Sen, Shantanu (North Oil Company) | Buraimoh, Muse Ajadi (QatarEnergy)
Lithocodium mounds are early Cretaceous sedimentary structures described in the literature from outcrops, however, never described in the subsurface. The objective of this work is to identify and characterize Lithocodium mounds in the subsurface along a 25,000ft horizontal well. Drill cuttings sampled at a 100ft interval are observed in thin sections to define and quantify key sedimentary indicators (bioclasts, facies, and texture). Logging-while-drilling (LWD) GR, density, neutron, and resistivity logs are acquired along with the LWD high-resolution borehole image (BHI) log. Bedding dips from BHI data, interpreted along the horizontal well, enabled the reconstruction of the reservoir paleotopography. In particular, the alternation of dip azimuth combined with the facies interpretation from the thin sections supported the interpretation of eight distinct mound structures. An assessment of their overall geometry confirmed the mound shape to be subcircular, consistent with the subcircular geometries observed in Oman at the outcrop. The inferred dimensions of the mounds are comparable with the Aptian Lithocodium mounds in Oman (3040m), and their intermound organization resembles that of the Albian mounds in Texas. This work demonstrates the value of analyzing cuttings to complement image log interpretation and the value of outcrop analogs for interpreting sedimentary structures. For the first time, the subsurface identification and characterization of Lithocodium mounds and intermounds are achieved.
- North America > United States > Texas (0.48)
- Asia > Middle East > Oman (0.45)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- Geology > Sedimentary Geology > Depositional Environment (0.93)
- Geology > Geological Subdiscipline > Stratigraphy (0.66)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.48)
- Well Drilling > Drilling Operations (1.00)
- Well Drilling > Drilling Measurement, Data Acquisition and Automation > Logging while drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
This SPE Live focuses on the critical role of SCAL data in subsurface analysis and modeling within the oil industry. It highlights the development and use of a comprehensive database, integrating extensive analyses done on special core analysis data. This database, featuring data from numerous wells and over a hundred fields, is accessible through standard interfaces and is structured to allow users to analyze data across various parameters like lithostratigraphic units and geological times. The conversation aims to address the complexities in integrating and standardizing SCAL data, which often serve as both input and output across different disciplines in the oil industry. During the conversation, we will also mention the use of machine learning for automated trend analysis and its role in model generation, along with a demonstration of a state-of-the-art workflow in generating SCAL model input for reservoir simulation.
- Reservoir Description and Dynamics > Formation Evaluation & Management > Core analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)