Utility of Using Acoustic Impedance Data in the Stochastic Modeling of a Carbonate Reservoir
D. B. Williams, A. A. M. Aqrawi, 2006. "Utility of Using Acoustic Impedance Data in the Stochastic Modeling of a Carbonate Reservoir", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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A Middle Eastern Cretaceous rudist-bearing carbonate reservoir is chosen for this study. This limestone sequence (about 55 m [180 ft] thick) consists of tight argillaceous mudstones and wackestones at the base, grading upward into a more porous section dominated by bioclastic grainstones and packstones.
Applying sequence-stratigraphic concepts, the reservoir is classified into two main sequences. In addition, the upper zone is further divided into two sequences of smaller accommodation cycles. Each of the latter includes a distinctive reservoir type (i.e., dominated by either barrier or shoal facies). The lower main sequence is considered as one unified nonreservoir cycle in the study area of the field. The three-dimensional seismic and sequence-stratigraphic analyses are used in conjunction with designing a deterministic structural model. A three-layer model is used, each layer representing a stratigraphic sequence previously recognized.
Describing the facies and flow units of carbonate reservoirs for reservoirsimulation purposes is a critical task that needs careful study of both the depositional textures and the diagenetic overprints in a sequence-stratigraphic framework. Eight main depositional facies are recognized for facies-modeling purposes. However, only two of them are high-quality reservoirs, dominated by grain-supported textures.
The reservoir architecture is generated using a combination of grid-and object-based simulation techniques to accurately reproduce the facies-and reservoir-type distributions. Petrophysical parameters porosity and permeability are stochastically simulated within facies bodies using a Gaussian cosimulation algorithm. Well data are used as hard information that must be honored, whereas acoustic impedance information is used to define porosity maps, which are used as conditioning trends in the simulation. These trends are believed to represent the effects of diagenesis.
Statistical analysis of the relationship between acoustic impedance and porosity allowed us to use impedance-derived porosity trends as conditioning data in petrophysical simulation. These porosity trends, adjusted to the range of data values from the wells, are believed to represent the effects of diagenetic activity. Using these trends alters the resultant data distribution compared with the distribution as inferred from well data. This is a consequence of the assumption that these porosity trends do reflect diagenesis and, in addition, does so not only at the wells but in theinterwell areas as well.
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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.