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Summary The popularity of intelligent wells (I-wells), which provide layer-by-layer monitoring and control capability of production and injection, is growing. However, the number of available techniques for optimal control of I-wells is limited (Sarma et al. 2006; Alghareeb et al. 2009; Almeida et al. 2010; Grebenkin and Davies 2012). Currently, most of the I-wells that are equipped with interval control valves (ICVs) are operated to enhance the current production and to resolve problems associated with breakthrough of the unfavorable phase. This reactive strategy is unlikely to deliver the long-term optimum production. On the other side, the proactive-control strategy of I-wells, with its ambition to provide the optimum control for the entire well's production life, has the potential to maximize the cumulative oil production. This strategy, however, results in a high-dimensional, nonlinear, and constrained optimization problem. This study provides guidelines on selecting a suitable proactive optimization approach, by use of state-of-the-art stochastic gradient-approximation algorithms. A suitable optimization approach increases the practicality of proactive optimization for real field models under uncertain operational and subsurface conditions. We evaluate the simultaneous-perturbation stochastic approximation (SPSA) method (Spall 1992) and the ensemble-based optimization (EnOpt) method (Chen et al. 2009). In addition, we present a new derivation of the EnOpt by use of the concept of directional derivatives. The numerical results show that both SPSA and EnOpt methods can provide a fast solution to a large-scale and multiple I-well proactive optimization problem. A criterion for tuning the algorithms is proposed and the performance of both methods is compared for several test cases. The used methodology for estimating the gradient is shown to affect the application area of each algorithm. SPSA provides a rough estimate of the gradient and performs better in search environments, characterized by several local optima, especially with a large ensemble size. EnOpt was found to provide a smoother estimation of the gradient, resulting in a more-robust algorithm to the choice of the tuning parameters, and a better performance with a small ensemble size. Moreover, the final optimum operation obtained by EnOpt is smoother. Finally, the obtained criteria are used to perform proactive optimization of ICVs in a real field.
- Europe (1.00)
- Asia > Middle East (0.67)
- North America > United States > California (0.28)
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.34)
Abstract One of major field development decision is choosing the number of wells required to efficiently drain an oil or gas reservoir. It is an interactive process in which various development scenarios are chosen and their performance analysed. Formation geology and zone connectivity have a major impact on the choice of well location since they determine well productivity. The industry's current well placement selection process is time consuming and costly. It requires analysing numerous development options by performing a large number of flow simulations. This paper describes a technique to partially automate this well placement process. It has been found that a new map which ranks the reservoir zones based on their productivity potential can speed-up, and hence reduce the cost, of this decision making process. This map, termed the Productivity Potential Map (PPM), is based on fundamental petroleum engineering principles. It is generated from the numerical reservoir models developed from standard data measured during the exploration and appraisal process. It incorporates both static and dynamic properties (e.g. porosity and saturation respectively). A static and stochastic numerical reservoir model are coupled with the PPM and well locations that maximise production are identified. The technique was tested using flow simulation models of two UK reservoirs by generating PPM and identifying the drilling targets that could deliver the maximum, sustained production potential. The first example uses a static reservoir simulation model of a field that had been production history matched for 18 years. Compared to the development plan implemented many years ago, the PPM map reduced the number of development wells by 15% while increasing the cumulative oil production by 2.6% at 2.5 years since production started. The second example employs multiple realisations developed from exploration well data using PETRELâ„¢. Well locations were chosen from a PPM map derived from these multiple realisations. The chosen well locations clearly matched the reservoir geology. Well locations were also chosen from a STOIIP based map. The performance of the STOIIP and PPM based field development were compared - the PPM based well placement consistently performed best. PPM thus reduced the flow simulation effort required, improved the flow forecast and reduced the uncertainty in flow performance. This paper will show that the use of a PPM is a quick and cost-effective technique for analysis of the reservoir production performance and the generation of drilling targets. The technique may also aid reservoir management decisions e.g. water flood front location and selecting the preferred water flood front direction. 1. INTRODUCTION The objective of this paper is to show that a 2D map which ranks reservoir zones based on their productivity potential can be used in the selection of well targets for optimum field development. The productivity potential evaluation method ensures that the locations chosen based on the highest values found in the Productivity Potential Map (PPM) will result in the highest oil production (i.e. longest plateau production time and good sweep efficiency). Basic parameters that define the productivity potential of a reservoir zone are obtained from a 3D reservoir simulation model that is representative of the geological features of the reservoir. This paper will describe the application of PPM for the Maureen field and Field A, both in the United Kingdom. Both fields have been in production for a number of years, thus allowing the comparison of actual production history with the predicted production performance based on PPM. In general, it will be shown that PPM based well location selection resulted in higher oil production with a reduced number of wells. 2. FIELD DEVELOPMENT - WELL PLANNING The typical sequence of events in field development decision-making process shows that it is a multi-disciplinary activity (Fig. 1). Once a field has been sanctioned for development, the key questions that arise to the development design team are: (a) How many wells are to be drilled? (b) Where should they be drilled? and (c) How should the reservoir be produced?
- North America > United States > Texas (1.00)
- Europe > United Kingdom > North Sea > Central North Sea (0.35)