Closed-loop reservoir management is a combination of model-based optimization and data assimilation (computer-assisted history matching), also referred to as 'real-time reservoir management', 'smart reservoir management' or 'closed-loop optimization'. The aim is to maximize reservoir performance, in terms of recovery or financial measures, over the life of the reservoir by changing reservoir management from a periodic to a near-continuous process. The key sources of inspiration for our work are measurement and control theory as used in the process industry and data assimilation techniques as used in meteorology and oceanography. We present results of a numerical example to illustrate the scope for closed-loop water flooding using real-time production data under uncertain reservoir conditions. The example concerns a 12-well water flood in a channelized reservoir. Optimization was performed using a reservoir simulator with functionality for adjoint-based life cycle optimization under rate and pressure constraints. Data assimilation was performed using the ensemble Kalman filter. Applying an optimization frequency of respectively once per 4 years, once per 2 years, once per year and once per 30 days resulted in an increase of net present value (NPV) with 6.68, 8.29, 8.30 and 8.71% compared to a conventional reactive control strategy. Moreover, the results for the 30-day cycle were very close (0.15% lower NPV) to those obtained by open-loop optimization using the 'true' reservoir model. We illustrate that for closed-loop reservoir management with a fixed well configuration, the use of considerably different reservoir models may lead to near-identical results in terms of NPV. This implies that in such cases the essential information may be represented with a much less complex model than suggested by the large number of grid blocks in typical reservoir models. We also illustrate that the optimal rates and pressures as obtained by open-or closedloop optimization are often too irregular to be practically applicable. Fortunately, just as is the case for the data assimilation problem, the flooding optimization problem usually contains many more control variables than necessary, allowing for optimization of long-term reservoir performance while maintaining freedom to perform short-term production optimization.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractRecently Ensemble Kalman Filtering (EnKF) has gained increasing attention for history matching and continuous reservoir model updating using data from permanent downhole sensors. It is a sequential Monte-Carlo approach that works with an ensemble of reservoir models. Specifically, the method utilizes cross-covariances between measurements and model parameters estimated from the ensemble. For practical field applications, the ensemble size needs to be kept small for computational efficiency. However, this leads to poor approximations of the cross-covariance matrix, resulting in loss of geologic realism. Specifically, the updated parameter field tends to become scattered with a loss of connectivities of extreme values such as high permeability channels and low permeability barriers, which are of special significance during reservoir characterization.We propose a novel approach to overcome this limitation of the EnKF through a 'covariance localization' method that utilizes sensitivities that quantify the influence of model parameters on the observed data. These sensitivities are used in the EnKF to modify the cross-covariance matrix in order to reduce unwanted influences of distant observation points on model parameter updates. In particular, streamline-based analytic sensitivities are easy to compute, require very little extra computational effort and can be obtained using either a finite difference or streamline-based flow simulator.We show that the effect of the covariance localization is to increase the effective ensemble size. But key to the success of the sensitivity-based covariance-localization is its close link to the underlying physics of flow compared to a simple distance-dependent covariance function as used in the past. This flow-relevant conditioning leads to an efficient and robust approach for history matching and continuous reservoir model updating, avoiding much of the problems in traditional EnKF associated with instabilities, parameter overshoots and loss of geologic continuity. We illustrate the power and utility of our approach using both synthetic and field applications.
In this work we consider model-based optimization of polymer flooding. The reservoir performance is optimized by finding for each injection well optimal values for control variables such as injection and production rates, polymer concentrations, and times when to switch from polymer to water injection (i.e. polymer grading). The same technique can also be applied to optimize other EOR processes such as for example designer water flooding, alkali-surfactant polymer (ASP) flooding and foam flooding. The optimization method that has been used relies on the adjoint implementation in our in-house reservoir simulator to efficiently calculate the gradients. The adjoint method enables the computation of gradients with respect to injection and production rates, injection compositions of each well and switching times of each well at the additional cost of approximately the computation time of a single reservoir simulation. The optimization method uses the adjoint-based gradients to estimate the values of all polymer injection control variables that maximize reservoir performance.The optimization method is demonstrated on a full-field reservoir simulation model. The physics that is modeled includes polymer mixing, hydrodynamic acceleration of the polymer molecules and adsorption of the polymer to the rock. The example shows that the Net Present Value increases significantly as a result of the optimization, mainly due to increased oil production and decreased polymer injection. The obtained optimal control is physically interpreted, so that the learning points from the model-based optimization can be applied to the field and can be used to enhance the polymer flood. IntroductionThe recovery factor of a hydrocarbon reservoir can be significantly increased if water is injected into the reservoir to displace the hydrocarbon towards the producing wells. For hydrocarbon reservoirs with an unfavorable mobility ratio (i.e. the mobility of the displacing fluids is higher than the mobility of the to-be-displaced fluids), the flooding efficiency can be increased by injecting a mixture of polymer and water. As a result the viscosity of the displacing fluids increases, the mobility ratio becomes more favorable and the recovery factor increases.Polymer flooding has been applied already several decades to a significant number of fields; see Kumar 2008 for an evaluation of published field data. The Daqing field is a convincing example for which polymer flooding, combined with improved well and reservoir management, increased the oil recovery with 15% (Shao et al, 2008). With favorable oil prices and modest polymer prices (polymer is used extensively in e.g. the food industry), polymer flooding becomes increasingly interesting for oil companies. Once a reservoir is found to be a suitable candidate for polymer flooding, design variables such as polymer type, polymer slug size, polymer concentrations and injection and production rates are candidates for optimization. In this work the focus is on optimization of the polymer flooding process. T...
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