Summary Reservoir management is based on the prediction of reservoir performance by means of numerical-simulation models. Reliable predictions require that the numerical model mimic the production history. Therefore, the numerical model is modified to match the production data. This process is termed history matching (HM). Form a mathematical viewpoint, HM is an optimization problem, where the target is to minimize an objective function quantifying the misfit between observed and simulated production data. One of the main problems in HM is the choice of an effective parameterization—a set of reservoir properties that can be plausibly altered to get a history-matched model. This issue is known as a parameter-identification problem, and its solution usually represents a significant step in HM projects. In this paper, we propose a practical implementation of a multiscale approach aimed at identifying effective parameterizations in real-life HM problems. The approach requires the availability of gradient simulators capable of providing the user with derivatives of the objective function with respect to the parameters at hand. Objective-function derivatives can then be used in a multiscale setting to define a sequence of richer and richer parameterizations. At each step of the sequence, the matching of the production data is improved by means of a gradient-based optimization. The methodology was validated on a synthetic case and was applied to history match the simulation model of a North Sea oil reservoir. The proposed methodology can be considered a practical solution for parameter-identification problems in many real cases until sound methodologies (primarily adaptive multiscale estimation of parameters) become available in commercial software programs. Introduction Predictions of reservoir behavior require the definition of subsurface properties at the scale of the simulation grid cells. At this scale, a reliable description of the porous media requires us to build a reservoir model by integrating all the available sources of data. By their nature, we can categorize the data as prior and production data. Prior data can be seen as "direct" measures or representations of the reservoir properties. Production data include flow measures collected at wells [e.g., water cut, gas/oil ratio (GOR) and shut-in pressure, and time-lapse seismic data]. Prior data are directly incorporated in the setup of the reservoir model, typically in the framework of well-established reservoir-characterization workflows.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir management is based on the prediction of reservoir performance by means of numerical simulation models. Reliable predictions require that the numerical model mimics the known production history of the field. Then, the numerical model is iteratively modified to match the production data with the simulation. This process is termed history matching (HM). Mathematically, history matching can be seen as an optimisation problem where the target is to minimize an objective function quantifying the misfit between observed and simulated production data. One of the main problems in history matching is the choice of an effective parameterization: a set of reservoir properties that can be plausibly altered to get an history matched model. This issue is known as parameter identification problem and its solution usually represents a significant step toward the achievement of an history matched model In this paper we propose a practical implementation of a multiscale approach to identify effective parameterizations in reallife HM problems. The approach is based on the availability of gradient simulators, capable of providing the user with derivatives of the objective function with respect to the parameters at hand. Those derivatives can then be used in a multi-scale setting to define a sequence of richer and richer parameterisations. At each step of the sequence the matching of the production data is improved. The methodology validated on synthetic case and has been applied to history match the simulation model of a North-Sea oil reservoir. The proposed methodology can be considered a practical solution for parameter identification problems in many real cases. This until sound methodologies, primarily adaptive multi scale estimation of parameters, will become available in commercial software programs.