A Comparison of Stochastic Data-Integration Algorithms for the Joint History Matching of Production and Time-Lapse-Seismic Data

Abstract

Summary Quantitative integration of spatial and temporal information provided by time-lapse (4D) -seismic surveys to dynamic reservoir models calls for efficient and effective data-integration algorithms. We carry out a comprehensive comparison of stochastic optimization methods using both a synthetic and a field case. Our first case is a challenging synthetic test problem known as the Imperial College Fault Model (ICFM). The methods of very-fast simulated annealing (VFSA), particle-swarm optimization (PSO), and the neighborhood algorithm (NA) are compared in terms of convergence characteristics, data-match quality, and posterior model-parameter distributions. On the basis of the knowledge developed from the ICFM problem, we isolate VFSA and PSO and evaluate their performance further on a field case involving a deepwater reservoir undergoing waterflooding. The field case has a reasonably long production history and good-quality 3D- and 4D-seismic data, allowing the construction of a geologically consistent model by means of dynamic calibration. As such, it constitutes a relevant field test for joint seismic/production history matching. We assess the data-match characteristics and the quality of dynamic forecasts delivered by VFSA and PSO in the field case. Practical guidelines are developed over the course of these studies for selecting a "fit-for-purpose" optimal method for joint history-matching workflows. Our results show that PSO, a population-based method, incurs relatively more computational expense at a given iteration but exhibits good convergence characteristics and provides multiple history-matched models. The PSO method has emerged as more effective compared with the NA and VFSA methods in the ICFM problem. It was also quite effective on the field application. On the other hand, the VFSA method requires comparatively more iterations to converge because of its sequential nature, but it has advantageous features when moderate computing resources are available.