Pay Sand Mapping using Seismic multi-attribute calibration proved to be effective from Development to Near Field Exploration stages:
A Case Study from De Soto Canyon, Gulf of Mexico
Authors: E. Pavanel, M. Fervari and A. Corrao
ENI E&P, Milan
M. Gallagher and B. Ciurlo
ENI US Operating Co. Inc., New Orleans
Speaker: Mike Gallagher
Biography:
Michael Gallagher is a Geologic Advisor with Eni Petroleum. He has worked twenty-eight years with various companies as an exploration and development geologist in the onshore, shelf, and deep-water Gulf of Mexico. He has widely varied experience in exploration technologies and play types. He started his career working the Cretaceous/Jurassic trends of the onshore Mississippi Salt Basin, moved to prospect generation and field development on the shelf in the Western, Central and Eastern Gulf of Mexico, and most recently, worked field development and prospect generation in the Central and Eastern GOM Deep Water. He is a member of AAPG, NOGS, SEG and SGS. He has chaired and served as a trustee of several NOGS committees and the NOGS Memorial Scholarship Fund. He served as NOGS Secretary, Vice President and President (1995-1996). He served as Program Chair / Oral Sessions for the 1993 AAPG convention, and presently serves as Chair of the Ad Hoc Committee on University Support. Mike earned his Bachelor of Science Degree in Biology in 1974 from Tulane University and his Master of Science Degree in Geology in 1989 from University of New Orleans.
Abstract:
Reservoir characterization using elastic attributes has become a routine practice in the oil industry. Our experience in the GOM has shown that P-impedance derived by acoustic impedance (AI) processing followed by a multi-attribute calibration, can be effectively used to assess pay thickness and characterization of reservoir quality. This methodology requires a minimum time-cost expenditure and gives quite accurate results.
A robust petro-acoustic model can be defined when enough well data are available, to represent a significant ‘training dataset’. A multi-attribute approach is then adopted to characterize the reservoir in terms of pay sand thickness, within the ‘modeled field’. The same model, applied to stratigraphically equivalent productive targets outside the original ‘modeled field’, (‘blind test’ approach), resulted in reasonable pay thickness estimations, even if no substantial DHI’s were present.