Consistent Structural Model Simulations for Volumetric Uncertainty Study
M. A. Lecour, P. Thore, R. Cognot, 2006. "Consistent Structural Model Simulations for Volumetric Uncertainty Study", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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Uncertainty studies become increasingly important as reservoir complexity increases. Although such studies are commonly a key factor in oil and gas exploration, projects are commonly restricted to simulation of facies and petrophysical properties. The structure of the reservoir itself is commonly considered to be a deterministic parameter, and yet it may contain uncertainty that has a major impact on the reserve estimations.
A new methodology based on the P-field technique and focusing on reservoir geometry uncertainty is proposed to tackle this problem. With this method, not only horizon but also fault geometries are simulated around a given reference model.
Because simulating fault geometries is more difficult than simulating horizon geometries, a new data structure has been designed to produce efficient computation of geometries. The simulation method tries to preserve the initial geometry at best. It may affect the location, dip, and shape in map view of all faults or any combination of these three basic modifications. Then, for each fault network realization, several horizon geometry simulations may be performed. The modeling and simulation system keep the geological model consistent after each simulation loop, allowing volumetric studies to proceed in a consistent fashion.
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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.