Brazos A-105 D-Sand Reservoir Modeling by Integration of Seismic Elastic Inversion Results with Geostatistical Techniques
W. Xu, P. Dooley, K. Wrolstad, K. Domingue, D. Kramer, D. T. Vo, 2006. "Brazos A-105 D-Sand Reservoir Modeling by Integration of Seismic Elastic Inversion Results with Geostatistical Techniques", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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An integrated reservoir modeling study of the Bigenerina humblei (Big Hum) Miocene D-sand at Brazos A-105 field, offshore Texas, was conducted to predict the lateral extent of the reservoir, to build a porosity model for use in flow simulation and reserve evaluation, and to assess the uncertainty of the reserve estimation. Several geostatistical techniques for integrating well-log porosity with quantitative average porosity derived from a forward elastic model-based inversion method for three-dimensional seismic data were applied in this reservoir modeling study. Elastic modeling was necessary to predict the correct porosity-amplitude relationship for this reservoir because it is a class 2 type amplitude-vs.-offset reflection. The results of the study showed that if a reservoir is seismically resolved and properly imaged, elastic model-based inversion of the type employed can be used in conjunction with geostatistical methods to obtain a more complete reservoir description. These techniques were determined to have direct application to reservoir-flow modeling and hydrocarbon reserve volume estimation.
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Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II
Since publication of the first volume of Stochastic Modeling and Geostatistics in 1994, there has been an explosion of interest and activity in geostatistical methods and spatial stochastic modeling techniques. Many of the computational algorithms and methodological approaches that were available then have greatly matured, and new, even better ones have come to the forefront. Advances in computing and increased focus on software commercialization have resulted in improved access to, and usability of, the available tools and techniques. Against this backdrop, Stochastic Modeling and Geostatistics Volume II provides a much-needed update on this important technology. As in the case of the first volume, it largely focuses on applications and case studies from the petroleum and related fields, but it also contains an appropriate mix of the theory and methods developed throughout the past decade. Geologists, petroleum engineers, and other individuals working in the earth and environmental sciences will find Stochastic Modeling and Geostatistics Volume II to be an important addition to their technical information resources.