Abstract
In this paper we present the framework and results of a benchmarking study to validate performance of conceptually different methods for computer-assisted history matching (AHM) under reservoir uncertainty on an example of complex waterflooding process.
Traditionally reservoir models were manually reconciled with production data, relying mostly on workflows based on engineering judgment and established best practices. The main disadvantage of the manual HM process is that the reservoir simulation disengages from the geological model and fails to quantify reservoir uncertainty. The oil and gas industry has advanced in developing methods for AHM that enable producing geologically-consistent reservoir simulation models with robust uncertainty quantification and high predictive value. In our study we build distinctive, computationally-intensive AHM workflows covering global multi-objective stochastic optimization and streamline sensitivity-based inversion to perform dynamic real-field model update and history matching.
We use the model of faulted reservoir under a waterflooding improved oil recovery regime. A comprehensive geo-modeling workflow incorporates facies, petrophysical properties, and saturation height function with uncertainty quantification performed on rock types, porosity, permeability, Kv/Kh, water saturation and fault transmissibility. During dynamic model update, the uncertainty workflows were executed as data assimilation on the sufficiently diverse prior ensemble of geomodel realizations, and as a Closed (Big) Loop process, where geomodel realizations are parameterized and updated simultaneously (on-the-fly) using global optimization. The misfit function, subject to minimization, incorporates oil and water rates and static wellhead pressure.
The study concludes that different AHM techniques demonstrate different convergence performance. The overall success dramatically depends on the uncertainty quantification of the initial geomodel. Practical reservoir simulation times were achieved through utilization of a Massive Parallel Processing deployed on an HPC cluster. However, most importantly we demonstrate that, by deploying advanced AHM workflows, the updates of geologically and structurally complex models, with tens of millions grid-cells and large number of matched wells can render geology well-conditioned realizations, without necessity of introducing non-geological features in order to achieve a satisfactory match. Thus, the validated practices add a tangible business value to the process of integrated reservoir modeling by delivering a robust simulation model with high predictive value for future field development planning.
