Geostatistics: A Fast-response Tool for Reservoir Management
L. Cosentino, A. Pelgrain de Lestang, J. E. Gonzalez, 2006. "Geostatistics: A Fast-response Tool for Reservoir Management", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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Extensive geostatistical modeling of data from the Bachaquero fields (east coast of Lake Maracaibo, Venezuela) has been conducted within the framework of a large-scale integrated study. These stochastic models have been used for several reservoir-management applications, where a fast and effective response is necessary to satisfy operational requirements. Such requirements include identification of infill drill locations, planning of horizontal well trajectories, and thermal-simulation studies for the optimization of cyclic steam injection projects. Because reservoir management applications commonly concern individual wells (small scale), a specific procedure has been established to extract and statistically analyze the geologic information from geostatistical models (large scale). The procedure has worked very effectively, with most of the operational requirements of corporate field exploitation units being addressed in the required timeframe. In fact, after almost 2 yr of fast-response applications in the Bachaquero fields, most of the operations currently performed by the exploitation unit are supported by geostatistical models. Recommendations based on such fast-response studies have been implemented, and in all cases, very good agreement between predictions and actual results has been observed.
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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.