Time-lapse seismic is an important subject in the Petroleum Industry. Going beyond the qualitative interpretation of the seismic data, including quantitative information in the flow simulation model updating process, is a highly desirable goal. 4D seismic data give information far away from the wells, potentially allowing much richer parameterizations of the reservoir model in the history matching process. Since these parameterizations tend to be described by a large number of parameters, efficient algorithms are needed to tackle these problems.

This paper describes some efforts to integrate time-lapse seismic attributes into a derivative-based assisted history matching tool developed in a previous project1 . The implementation of the seismic attributes derivatives using the forward and the adjoint method into a commercial reservoir flow simulator is described. The calculated derivatives are used in an optimization algorithm based on a trust-region Quasi-Newton method to minimize the mismatch between observed and simulated data from production and seismic.

Good results were obtained in several synthetic cases adjusting seismic and production data.

Keywords:
machine learning, optimization problem, Bayesian Inference, Upstream Oil & Gas, objective function, information, calculation, Reservoir Characterization, reservoir simulation, seismic data
Subjects:
Reservoir Characterization, Reservoir Simulation, Seismic processing and interpretation, History matching
Copyright 2007, Society of Petroleum Engineers
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