Abstract
Abstract An integrated geostatistical methodology for a site-specific reservoir description was used in reservoir simulations to establish additional recovery benefits for gravity drainage infill wells for the Romeo interval for Drill Sites 2 and 5 at Prudhoe Bay. The available geological information varied over multiple scales and included: a detailed chrono-stratigraphic framework relating rocks of similar depositional times, a detailed depositional model with facies associations described at each well from log and core data, and analog outcrop training images obtained using photomosaics. The reservoir description methodology transformed the facies associations at each well into indicator variables and used these variables to analyze vertical and lateral spatial correlation. Conditional truncated gaussian simulation was then used to generate 3D facies association maps within the model area constrained by the chrono-stratigraphic framework. Input from geologists was critical in the evaluation of the geological realism of the computed facies association maps. Porosity and permeability vertical and lateral spatial correlations were then determined for each facies association. Vertical correlation was determined from variogram analysis while lateral correlation was determined from the outcrop training images. Conditional sequential gaussian simulation was then used to distribute 3D porosity and permeability, separately within each facies association, throughout the model area. A pressure solver was used to scale up the 3D fine scale permeability distributions to grid blocks appropriate for direct input into a flow simulation model. The conditional simulation methods allowed the integration of geological information on a number of different scales, thereby capturing the 3D complexity of the Romeo interval. The 3D facies association description generated using this methodology was judged by geologists to depict a very reasonable geological picture of the DS-2,5 Romeo interval. The pressure solver used to scale up the fine scale permeability distributions maintained the major permeability features apparent in the fine scale distribution which were important to reservoir performance.
Published Version
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