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4D signal enhancement using singular‐value decomposition: application to mapping oil–water contact movement across the Nelson Field
Author(s) -
Reid Fiona J.L.,
Bertrand Alexandre,
McInally Alan T.,
MacBeth Colin
Publication year - 2005
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/j.1365-2478.2005.00466.x
Subject(s) - singular value decomposition , signal (programming language) , decomposition , field (mathematics) , regional geology , environmental geology , geology , economic geology , noise (video) , signal processing , algorithm , gemology , singular value , computer science , telmatology , engineering geology , mathematics , artificial intelligence , hydrogeology , physics , digital signal processing , seismology , chemistry , geotechnical engineering , image (mathematics) , quantum mechanics , programming language , eigenvalues and eigenvectors , organic chemistry , pure mathematics , computer hardware , tectonics , volcanism
A new method for time‐lapse signal separation and enhancement using singular‐value decomposition is presented. Singular‐value decomposition is used to separate a 4D signal into its constituent parts: common geology, time‐lapse response and noise. Synthetic tests which demonstrate the advantages of the singular‐value decomposition technique over traditional differencing methods are also presented. This signal separation and enhancement technique is used to map out both the original and moved oil–water contacts across the Nelson Field. The singular‐value decomposition technique allows the oil–water contact to be mapped across regions which would have been missed using traditional differencing methods. In particular, areas toward the edges of the field are highlighted by the technique. The oil–water contact is observed to move upwards across the field, with the largest movements being associated, as anticipated, with natural production. The results obtained are broadly consistent with those predicted by the reservoir simulator model. Singular‐value decomposition is demonstrated to be a useful tool for enhancing the time‐lapse signal and for gaining confidence in areas where traditional differencing fails.