This case study shows techniques to map quantitatively a Triassic Halfway reservoir in NE British Columbia, Canada. The sand is at a depth of roughly 1000 meters and has a reservoir thickness that is within the seismic tuning thickness. It demonstrates how significant seismic attributes can be extracted from 20 seismic data . These attributes include amplitudes, instantaneous seismic parameters, inversion results, and AVO parameters including delta p, delta s, fluid stack, and Lame's parameters. In total more than 30 attributes are analyzed and correlated with net sand thickness and fluid content of the reservoir. However, not all attributes are independent. Some significant attributes are selected to predict net sand thickness and fluid content.

Even a single attribute such as amplitude yields a good approach to mapping the sand thickness in a qualitative way. A quantitative estimate is derived with cokriging and simulation techniques. More reliable prediction can be achieved with multiple linear regression or a backpropagation neural network approach. The neural network yields the best results. The derived net sand thickness distribution fits well with the geological understanding of this area.

For the prediction of the fluid content we discriminate between four cases, i.e., d&a , gassy (water with some gas), gas, and oil production (which is basically a mixture of oil, gas, and water). Single and multiattribute regression analysis are ambiguous. This was to be expected because we cannot necessarily assume a linear relation between seismic attributes and fluid content. Only the neural network approach yields useful results. However, only the cases d&a, oil production or gassy can be determined reliably. The identification of the gas only case is not successful, because there is not enough gas only cases in the training set for the neural network approach. The derived map for net sand thickness and fluid content greatly support development and exploration in the investigated area.



About the Author(s)

Rainer Tonn received a Diploma in Geophysics in 1985 and PhD. in Geophysics in 1988 from the University of Kiel, Germany. He was a Research Assistant at the University of Kiel from 1985-89, and worked for Wintershall AG, Kassel, Germany from 1989-1993. Since 1994 he has been employed as a Geophysicist with Wintershall Canada Ltd.

Rainer's interests include Geostatistics and Multi Attribute Seismic Interpretation. He is a member of SEG.

Jonathan Downton Received his B.Sc. in Honors Geophysics from the University of Alberta in 1985 and has continued to supplement his education through university courses and industry sponsored courses. Jon worked as a Geophysicist for a major oil company interpreting in the Canadian Frontier areas and the Western Canadian Plains, then transferring into a Special Projects Group. In 1987, Jon joined Inverse Theory & Applications Inc. as a Processing Geophysicist working primarily on post-stack inversion projects. In 1990, Jon was promoted to Manager, Special Projects for Landmark/I.T.A. Ltd., where he led a team focused on processing to increase stratigraphic information. This consisted mainly of special processing, pre-stack modeling, inversion, A.V.O. and field studies. Jon also worked as a project advisor on the DepthWorks Product with Landmark Graphics Corporation, which included velocity model construction and pre-stack depth imaging. Jon helped found Integra Geoservices Inc. in 1994, where he is a co-owner along with 15 other employees. In 1995, Jon was promoted to Vice President, Geoscience. In this position, Jon provides technical and operational leadership to Integra's production and research teams. As well, Jon is a Director of Data Modeling Inc., a local geophysical software provider. Jon's other activities have included presenting papers at the CSEG, SEG Research Workshop, and SEG Production and Development Workshops. He was also Treasurer for the 1993 CSEG Convention and is a member of APEGGA.



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