CT Scan and Neural Network Technology for Construction of Detailed Distribution of Residual Oil Saturation During Waterflooding
Authors: Garg A. , Kovscek A.R. , Nikravesh M. , Castanier L.M. , Patzek T.W. 
Published: December 2007

A. Garg, Department of Materials Science and Mineral Engineering, University of California, Berkeley, A.R. Kovscek and M. Nikravesh, Earth Sciences Division, Lawrence Berkeley National Laboratory, L. M. Castanier, Department of Petroleum Engineering, Stanford University, and T. W. Patzek, Department of Materials Science and Mineral Engineering, University of California, Berkeley and Earth Sciences Division, Lawrence Berkeley National Laboratory

We present an integrated approach to imaging the progress of air displacement by spontaneous imbibition of oil into sandstone. We combine Computerized Tomography (CT) scanning and neural network image processing. The main aspects of our approach are I) visualization of the distribution of oil and air saturation by CT, II) interpretation of CT scans using neural networks, and III) reconstruction of 3-D images of oil saturation from the CT scans with a neural network model. The neural networks developed here construct 3-D images of fluid distribution at any time and/or location within the core. One neural network model interpolates between the CT images for a given position at different time levels and extrapolates beyond the interval of time during which the images were collected. Likewise, the network interpolates spatially between images at a given time. After interpolation and extrapolation, other network models have been developed to reconstruct the three-dimensional distribution of oil in the core. Excellent agreement between the actual images and the neural network predictions is found.

Introduction

An increasing global demand for energy and simultaneous depletion of conventional hydrocarbon reserves impose a formidable challenge for efficient recovery from nonconventional rock systems, such as naturally fractured reservoirs. Fractured petroleum reservoirs provide over 20 % of the world oil reserves. Examples of prolific fractured reservoirs are: the Monterey Shales in California (estimated tens of billions of barrels of oil-in-place); the California Diatomites (estimated fifteen billion barrels of oil-in-place); the West Texas Carbonates; the North Sea Chalks; and the Asmari Limestones in Iran.

Hydrocarbon recovery from naturally fractured reservoirs is not yet fully understood. This is mainly due to the lack of a complete understanding of multiphase flow through fractured porous media. Two- or three-phase flow in a fractured reservoir depends on the combined nonlinear effects of hydraulic connectivity and physicochemical properties of fractures, relative permeabilities to multiphase flow in the fractures, rock-matrix nature, matrix block size, capillary forces and fracture closure stress. The nonlinear interplay of all these factors determines the ultimate hydrocarbon recovery from fractured reservoirs. In contrast, most of the published data have been produced in controlled experiments that have focused on one or more of the above factors considered in isolation. These data are then upscaled in numerical simulators to model the coupled nonlinear behavior of fractured reservoirs. As a result, current numerical simulation models of fractured reservoirs lack firm predictive capability and must be tuned for each field case with the available data.

Thus, it might be helpful to undertake a systematic experimental and theoretical study of joint effects of all the factors governing multiphase fluid flow in a fractured porous rock. Of course, such a study is beyond the scope of this paper. Nevertheless, we have undertaken a study to evaluate the influence of four major factors on hydrocarbon recovery. These are: fracture configuration, rock-matrix block size, wettability characteristics of the rock, and fluid flow rates. This paper reports our progress on a scoping study of spontaneous imbibition of a hydrocarbon (kerosene) into a single air-filled block of rock matrix (Berea sandstone).

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