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Multi‐dimensional analyses of the SEAM controlled source electromagnetic data—the story of a blind test of interpretation workflows
Author(s) -
Tseng HungWen,
Stalnaker Jack,
MacGregor Lucy M.,
Ackermann Rolf V.
Publication year - 2015
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/1365-2478.12327
Subject(s) - petrophysics , workflow , magnetotellurics , geology , inversion (geology) , geophysics , environmental geology , reservoir modeling , data processing , data mining , computer science , electrical resistivity and conductivity , seismology , database , petroleum engineering , geotechnical engineering , engineering , porosity , telmatology , electrical engineering , tectonics
Using a subset of the SEG Advanced Modeling Program Phase I controlled‐source electromagnetic data, we apply our standard controlled‐source electromagnetic interpretation workflows to delineate a simulated hydrocarbon reservoir. Experience learned from characterizing such a complicated model offers us an opportunity to refine our workflows to achieve better interpretation quality. The exercise proceeded in a blind test style, where the interpreting geophysicists did not know the true resistivity model until the end of the project. Rather, the interpreters were provided a traditional controlled‐source electromagnetic data package, including electric field measurements, interpreted seismic horizons, and well log data. Based on petrophysical analysis, a background resistivity model was established first. Then, the interpreters started with feasibility studies to establish the recoverability of the prospect and carefully stepped through 1D, 2.5D, and 3D inversions with seismic and well log data integrated at each stage. A high‐resistivity zone is identified with 1D analysis and further characterized with 2.5D inversions. Its lateral distribution is confirmed with a 3D anisotropic inversion. The importance of integrating all available geophysical and petrophysical data to derive more accurate interpretation is demonstrated.