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Laura Bandura advises on strategy and performance improvement opportunities across the value chain within the Chevron Gulf of Mexico Business Unit. Laura has had a diverse career in her nine years at Chevron pioneering applications of machine learning to seismic imaging and interpretation, and cross-functional digital portfolio management. Prior to Chevron, Laura was a physicist at Argonne National Lab and the Facility for Rare Isotope Beams (FRIB) at Michigan State University, specializing in charged particle beam dynamics with applications to nuclear physics. She co-designed the fragment separator at FRIB, which is used to isolate and discover new isotopes. Laura has published research articles and patented inventions across a variety of fields, including geophysics, machine learning, nuclear science, and charged particle beam dynamics.
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)
- Information Technology > Artificial Intelligence > Machine Learning (0.49)
John T. Etgen received a Bachelor of Science degree in Geophysical Engineering from the Colorado School of Mines in 1985 and a PhD in Geophysics from Stanford University in 1990. During his studies, he had the good fortune to work on a wide variety of topics in seismic imaging and data processing while learning from his mentors, Jon Claerbout and Norm Bleistein, along with many talented colleagues and fellow students. His thesis studied new-at-the-time prestack migration-driven tomographic techniques for velocity estimation. That experience taught him the true difficulties of inverse problems. Leaving Stanford behind, he began his industrial career in late 1990 at the Amoco Production Research Company in Tulsa, Oklahoma.
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)
Sergey Fomel is Wallace E. Pratt Professor of Geophysics at The University of Texas at Austin and the Director of the Texas Consortium for Computational Seismology (TCCS). At UT Austin, he is affiliated with the Bureau of Economic Geology, the Department of Geological Sciences, and the Oden Institute for Computational Engineering and Sciences. Sergey received a PhD in Geophysics from Stanford University in 2001. Previously, he worked at the Institute of Geophysics in Russia (currently Trofimuk Institute of Petroleum Geology and Geophysics), Schlumberger Geco-Prakla, and the Lawrence Berkeley National Laboratory. For his contributions to exploration geophysics, he has been recognized with a number of professional awards, including the J. Clarence Karcher Award from SEG in 2001, Best SEG Poster Presentation Awards in 2007 and 2011, and the Conrad Schlumberger Award from EAGE in 2011.
- Asia (0.51)
- North America > United States > Texas > Travis County > Austin (0.26)
- Geophysics > Seismic Surveying > Seismic Processing (0.73)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.31)
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.49)
Mark Willis discusses his upcoming Distinguished Instructor Short Course, "Distributed acoustic sensing for seismic measurements โ what geophysicists and engineers need to know." In this conversation with host Andrew Geary, Mark helps geoscientists and engineers build intuition and understanding of DAS seismic technology's value, limitations, and applications. Mark also discusses the most common objection to DAS, when DAS is better than conventional seismic acquisition, and tips for someone planning their first DAS seismic survey. Mark will be teaching this course for the first time at IMAGE, and this is a great preview of the valuable, insightful, and helpful tools and resources you will gain from this course.
Roel Snieder holds the W.M. Keck Distinguished Chair of Professional Development Education at the Colorado School of Mines [1]. In 1984, he received in a Master's degree in Geophysical Fluid Dynamics from Princeton University and in 1987 a PhD in seismology from Utrecht University. In 1993 he was appointed as Professor of Seismology at Utrecht University, where from 1997โ2000 he served as Dean of the Faculty of Earth Sciences. Roel served on the editorial boards of Geophysical Journal International, Inverse Problems Journal, Reviews of Geophysics, the Journal of the Acoustical Society of America, and the European Journal of Physics. In 2000 he was elected as a Fellow of the American Geophysical Union.
- Energy > Oil & Gas > Upstream (1.00)
- Education > Educational Setting > Higher Education (1.00)
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)
Fabien Allo highlights his award-winning article, "Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks." Fabien shares the potential of deep neural networks (DNNs) in integrating seismic data for reservoir characterization. He explains why DNNs have yet to be widely utilized in the energy industry and why utilizing a training set was key to this study. Fabien also details why they did not include any original wells in the final training set and the advantages of neural networks over seismic inversion. This episode is an exciting opportunity to hear directly from an award-winning author on some of today's most cutting-edge geophysics tools.
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
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)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
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...)
- 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)