Predicting Fluvial Reservoir Facies by Upscaling Seismic Inversion with 3D Geocellular Modeling: Pinedale Field Case Study | Schlumberger
Tech Paper
Location
United States, North America, Onshore
Byline
S.K. Logan and E. LaBarre, Ultra Petroleum; C. Dorion, Schlumberger; P.R. Clarke, Ultra Petroleum; M. Ahmed, CGG; A. Hartley, University of Aberdeen
Society
URTeC
Paper Number
2123
Presentation Date
20-22 July 2020
Products Used
Premium

Predicting Fluvial Reservoir Facies by Upscaling Seismic Inversion with 3D Geocellular Modeling

Pinedale Field case study



Abstract

Over the past twenty-five years, more than 3,500 wells have been drilled in the giant Pinedale Field in the northern Green River Basin of Wyoming, at spacing intervals as tight as 5-acres and completed in the 6,000 ft. reservoir column of stacked, tight-gas, fluvial sandstones. Even with this high density of well control, geologic uncertainties remain regarding the geometry and architecture of the highly heterogeneous fluviatile basin-fill. The goal of this project was to predict the distribution and size of the reservoir facies using geocellular-modeling techniques.

A process-based depositional framework formed the starting point for our characterization study. Examples from modern analogs and the rock record were used to condition the model inputs as related to channel size, scaling relationships (including net/gross), and overall reservoir architecture; the fluvial depositional sequences comprise a distributary fluvial system. At a range of scales, a seismic inversion facies volume, supplemented with a dense population of well logs, and core data helped constrain the size, geometry and distribution of reservoir facies at multiple, geologically distinct intervals. The resulting geocellular model honors all input data. Vertical upscaling is primarily constrained by well logs and core. Lateral facies distributions are primarily constrained by the seismic inversion column paired with well data.

By combining sedimentologic interpretations from seismic and well logs, this integrated study was able to differentiate fluvial reservoir sandstones from overbank siltstones and mudstones. This technology-driven reservoir characterization study was undertaken to improve our understanding of resource-in-place and to optimize wellbore placement and construction, for ongoing field development.

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