Stochastic Data Integration: History Matching Production and Time-Lapse-Seismic Data
- Dennis Denney (JPT Senior Technology Editor)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- April 2012
- Document Type
- Journal Paper
- 137 - 139
- 2012. Society of Petroleum Engineers
- 0 in the last 30 days
- 46 since 2007
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This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 146418, "A Comparison of Stochastic Data-Integration Algorithms for the Joint History Matching of Production and Time-Lapse-Seismic Data," by Long Jin, SPE, Faruk O. Alpak, SPE, and Paul van den Hoek, SPE, Shell International E&P, and Carlos Pirmez, Tope Fehintola, SPE, Fidelis Tendo, and Elozino Olaniyan, SPE, Shell Nigeria E&P, prepared for the 2011 SPE Annual Technical Conference and Exhibition, Denver, 30 October-2 November. The paper has not been peer reviewed.
Quantitative integration of spatial and temporal information provided by time-lapse (4D) -seismic surveys into dynamic reservoir models requires efficient and effective data-integration algorithms. The particle-swarm-optimization (PSO) method emerged as more effective compared with the neighborhood algorithm (NA) and very-fast-simulated-annealing (VFSA) methods in the Imperial College Fault Model (ICFM) problem. The PSO method was also effective in the field application. The VFSA method required comparatively more iterations to converge because of its sequential nature, but it has advantages when moderate computing resources are available.
The use of 4D-seismic data is increasingly important for reservoir monitoring and management. However, quantitative integration of 4D-seismic data with historical production data into reservoir-simulation models is a challenging task. A key issue of joint history matching, involving production and time-lapse-seismic data, is proper processing of 4D-seismic data such that physical information (fluid saturations) is captured by geophysical measurements properly. Various types of geophysical measurements have been assimilated in the literature, including inverted impedance, inverted saturation, and fluid fronts. Another important issue centers on appropriate selection of model parameters to be adjusted to obtain a history match. In this work, three stochastic data-integration methods were evaluated: NA, VFSA, and PSO. They were compared on the joint history-matching problem involving production and time-lapse-seismic data.
Stochastic Data-Integration Methods
Data integration in the presence of time-lapse-seismic data consists of updating reservoir dynamic models by jointly assimilating both 4D-seismic and production data. The goal is to use the complementary nature of 4D-seismic data (spatial and temporal information) and production data (temporal information) to produce more-predictive reservoir models, and in turn, make optimal reservoir-management decisions. In this work, the focus was on nonintrusive stochastic data-integration methods. No special information (e.g., adjoint sensitivity coefficients or gradients of the objective function) was needed from the flow and seismic forward models. A generic iterative workflow for joint 4D-seismic and production history matching conducted by a stochastic sampling engine (optimizer) is shown in Fig. 1. The main idea was to perturb the uncertain (model) parameters identified by comparing the synthetic data (production and 4D-seismic) with the measurements simultaneously. The basic idea is also shown in Fig. 2.
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