TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper describes the results of a two-year ECsponsored project which uses new information provided by repeated seismic acquisitions (4D seismic data) jointly with production data in an extended, efficient and consistent history matching process. This process involves a simultaneous minimisation of the mismatch between all types of measured and simulated data. A gradient-based technique has been developed and tested both in a prototype and in commercial computer-aided history matching software. We show results on real cases, located in the North Sea and the Adriatic Sea, and discuss key issues of such seismic history matching. Most applications of time-lapse seismic to date have been qualitative or semi-quantitative. We propose a quantitative workflow. The seismic contribution in the objective function is defined in terms of elastic parameter variations within the reservoir and the data have been properly scaled using an estimate of seismic uncertainty (covariance matrix). The "observed" values are obtained by inversion of the seismic signal. For the "modelled" values, the flow simulator is coupled with a petro-elastic model to convert simulated fluid and static rock properties into simulated elastic properties. The techniques described in this paper allow us to reconcile production history matched models with 4D information, and to reduce the uncertainty in reservoir properties, which haven't a real impact on the well history, but which significantly drive future behaviour of the field. This is a further step towards the necessary integration of available data for better predictive simulations. Focusing on quantitative combined with qualitative use of data enhances the multidisciplinary approach. . ( m J m J m J seis prod

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.

Time-lapse or 4D methodology, that uses repeated 3D seismic surveys to monitor fluids saturations changes in reservoir, is a recommended tool for reservoir management in particular in deepwater fields where additional investments must be carefully evaluated due to the associated costs.This paper presents a real case of a successful rejuvenation project of a deepwater field in West Africa, supported by integrated 4D and reservoir 3D studies. The field, located in 800 m of water depth, has been producing for 10 years with a rate currently equal to the half of the FPSO nameplate capacity. In 2010 a seismic acquisition specifically designed for 4D purposes was executed and, integrated with the historical production data, led to a revision of the levels' potentiality. Reservoir barriers and fluids movements, water and gas in particular generated by the injection, have been modeled based on 4D acoustic impedance differences and were supported by history matching on the dynamic model. The strongly integrated approach supported a volumes in place revision that drove a sidetrack campaign on two wells to reach undrained areas. The integrated study highlighted also the possible risk of water encroachment on one of the targets of the two wells, that was therefore considered as a secondary target in the sidetrack plan. The result of the drilling campaign, combined with near field exploration, confirmed the expectations and contributed to double the production back to the initial peak plateau rate. Lesson learnt on this successful integration among disciplines are presented.

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.

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