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1 ExxonMobil Upstream Research Company, P.O. Box 2189, Houston, Texas 77252-2189; yuhong.liu{at}ExxonMobil.com
2 ChevronTexaco Energy Technology Company, San Ramon, California; AndrewHarding{at}chevrontexaco.com
3 ChevronTexaco Overseas Petroleum Company, San Ramon, California; WilliamAbriel{at}chevrontexaco.com
4 ChevronTexaco Energy Technology Company, San Ramon, California; stsb{at}chevrontexaco.com
Yuhong Liu holds a Ph.D. in geological and environmental science from Stanford University (2003). She worked at the University of Petroleum, China, as an assistant professor and a lecturer from 1995 to 1999, working on industry projects related to petroleum geology and reservoir characterization. She joined ExxonMobil in 2003 and now works on geological modeling and geostatistics as a research geologist.Andrew Harding holds an M.A. degree in physics from Trinity College, Cambridge, in Great Britain. He has worked in the oil industry since 1975, the last 23 years with ChevronTexaco. He presently is a consultant in reservoir modeling in San Ramon, California, and works on projects from around the world. Andrew is a chartered geologist of the Geological Society of London.
William Abriel (Bill) received his B.S. and M.S. degrees in geophysics (1978) from Pennsylvania State University. Joining Chevron Oil Company from 1978 to the present, he has been involved in many interesting projects in both operations and seismic research offshore and onshore in North and South America, Europe, Asia, Australia, and Africa. Bill is a member of the Society of Exploration Geophysicists (SEG), European Association of Exploration Geophysicists, and AAPG and the 2004 SEG Spring distinguished lecturer.
Sebastien Strebelle earned his Ph.D. at Stanford University in geological and environmental sciences in 2000. His research focuses on reservoir geomodeling and geostatistics. He is currently employed as a research geoscientist with ChevronTexaco.
There are two significant challenges in building a reservoir model integrating all available information. One challenge is that wells and seismic data measure the reservoir at different scales of resolution. The other challenge lies in how to account for conceptual geological knowledge with resolution at multiple scales.
In this paper, we present a case study of integrating well data, seismic data, and conceptual geologic models. The well and seismic data are of good quality, but conventional well-seismic data calibration indicates that the seismic data are unable to fully differentiate sand from shale. The reason for this poor well-seismic data calibration is that well log and seismic data measure the reservoir at different scales. Well logs are able to differentiate sand from shale, whereas seismic data are better at detecting larger scale depositional geometries.
A new workflow is presented to deal with this problem. First, principal component analysis clustering is used to identify characteristic patterns of certain depositional facies, from which sandy and shaly channels are interpreted. Next, multiple-point geostatistical simulation is performed to build a depositional-facies model, which integrates both hard and soft data but also incorporates realistic depositional-facies geometries provided by our geological knowledge of this reservoir. Finally, different lithofacies (sand and shale) indicators and corresponding petrophysical properties are simulated honoring the limited well data.
The results show that not only are the geological features better reproduced, but also is the uncertainty about the reservoir significantly reduced because of a better integration of corresponding three-dimensional seismic data.
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  O. Falivene, P. Arbues, A. Gardiner, G. Pickup, J. A. Munoz, and L. Cabrera Best practice stochastic facies modeling from a channel-fill turbidite sandstone analog (the Quarry outcrop, Eocene Ainsa basin, northeast Spain) AAPG Bulletin, July 1, 2006; 90(7): 1003 - 1029. [Abstract] [Full Text] [PDF]
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