A deep learning workflow for petro-mechanical facies predictions in unconventionals
Author:
Affiliation:
1. ExxonMobil Upstream Integrated Solutions Company
2. XTO Energy Inc.
3. ExxonMobil Technology & Engineering Company
Publisher
Society of Exploration Geophysicists and American Association of Petroleum Geologists
Link
https://library.seg.org/doi/pdf/10.1190/image2023-3907068.1
Reference13 articles.
1. Caf, A., D. Lubo-Robles, M. J. Pranter, H. Bedle, K. Marfurt, and R. Zulfiquar, 2022, CO2 injectivity and storage potential of the Arbuckle Group using supervised machine learning and seismic-constrained reservoir modeling and simulation, Wellington Field, Kansas: Second International Meeting for Applied Geoscience & Energy, SEG/AAPG, Expanded Abstracts, 462–466, doi: 10.1190/image2022-3751017.1.
2. Seismic reservoir characterization of Bone Spring and Wolfcamp Formations in the Delaware Basin — A case study: Part 1
3. Artificial intelligence techniques and their application in oil and gas industry
4. Claerbout, J., 1983, Ground roll and radial traces: Stanford Exploration Project Report, 43–54.
5. Seismic inversion for organic richness and fracture gradient in unconventional reservoirs: Eagle Ford Shale, Texas
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