Closed Loop Reservoir Management

Abstract

Abstract 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 closed-loop 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. Introduction Our work aims at increased reservoir performance, in terms of recovery or financial measures, using a measurement and control approach to reservoir management. This idea has been around for many years in different forms, often centered around attempts to improve reservoir characterization from a geosciences perspective; see e.g. Chierici (1992). Moreover, recently 'closed-loop' or 'real-time' approaches to hydrocarbon production have received growing attention as part of various industry initiatives with names as 'smart fields', 'i-fields', 'e-fields', 'self-learning reservoir management' or 'integrated operations'; see Jansen et al. (2005) for some further references. However, whereas the focus of most of these initiatives is primarily on optimization of short-term production, in our work we concentrate on life-cycle optimization, i.e. on processes at a timescale from years to tens of years. We perform reservoir flooding optimization, based on numerical simulation models, in combination with frequent model updating through data assimilation (computer-assisted history matching). This approach has lately also been referred to as 'closed-loop reservoir modeling' or 'closed-loop production optimization' and some recent references will be discussed below. In contrast to the geosciences-focused approach, we emphasize the need to focus on those elements of the modeling process that can both be verified from measurements and that bear relevance to controllable parameters such as well locations or, in particular, production parameter settings. The underlying hypothesis is that "It will be possible to significantly increase life-cycle value by changing reservoir management from a batch-type to a near-continuous model-based controlled activity."