High-Resolution Reservoir Models Integrating Multiple-Well Production Data

Abstract

SummaryThis paper presents a method to generate maps of high resolution permeability from multiple well single-phase flow rate and pressure data. The dynamic (i.e., temporal) production data contain important information about the interwell permeability distribution that should be integrated with static data, such as well and seismic data, to generate reservoir models to provide reliable input to reservoir simulation and reservoir management. A two-step procedure is proposed for such data integration: establish the spatial constraints on large-scale permeability trends caused by the production data by means of an inverse technique and construct the detailed geostatistical reservoir models subject to those spatial constraints by means of geostatistical techniques. The single-phase pressure and production data could be provided by permanent pressure gauges, simultaneous multiple well tests, or flow rates under primary depletion.Production data and reservoir petrophysical properties, specifically permeability, are nonlinearly related through flow equations. Establishing the spatial constraints on permeability resulting from production data calls for the solution of a difficult inverse problem. This paper adapts the sequential self-calibration (SSC) inverse technique to single-phase multiple-well transient pressure and production rate data. The SSC method is an iterative geostatistically based inverse method coupled with an optimization procedure that generates a series of coarse grid two-dimensional (2D) permeability realizations whose numerical flow simulations correctly reproduce the production data. Inverse results with two synthetic data sets show that this SSC implementation is flexible, computationally efficient, and robust.Fine-scale models generated by downscaling the SSC generated coarse-scale models (by simulated annealing) are shown to preserve the match to the production data at the coarse scale. Finally, reservoir performance prediction results show how the integration of production data can dramatically improve the accuracy of production forecasting with significantly less uncertainty.