Reconstruction of Existing Reservoir Model for Its Calibration to Dynamic Data.

Abstract: The increase in computer power and the recent developments in history-matching can motivate the reexamination of previously built reservoir models. To save the time of engineers and the CPU time, four distinct algorithms, which allow for rebuilding an existing reservoir model without restarting the reservoir study from scratch, were formulated. The algorithms involve techniques such as optimization, relaxation, Wiener filtering, or sequential reconstruction. They are used to identify a stochastic function and a set of random numbers. Given the stochastic function, the random numbers yield a realization that is close to the existing reservoir model. Once the random numbers are known, the existing reservoir model can be submitted to a new history-matching process to improve the data fit or to account for newly collected data. A practical implementation is presented within the context of facies reservoirs. This article focuses on a previously built facies reservoir model. Although the simulation procedure is unknown to the authors, a set of random numbers are identified so that when provided to a multiple-point statistics simulator, a realization very close to the existing reservoir model is obtained. A new history-matching procedure is then run to update the existing reservoir model and to integrate the fractional flow rates measured in two producing wells drilled after the building of the existing reservoir model. [Copyright &y& Elsevier]