Smart-Well Production Optimization Using an Ensemble-Based Method

Abstract

Summary Ensemble methods have been applied successfully in assisted history matching and in production optimization. In history matching, the ensemble Kalman filter (EnKF) has been used to estimate the values of hundreds of thousands of variables from various types of data. In production optimization, an ensemble-based method has been used to estimate optimal control settings for problems with thousands of control variables. In both cases, relatively small numbers of random realizations are used to compute update directions for improving estimates. In this paper, we illustrate the application of the ensemble-based optimization on two fairly complex problems that would be difficult to handle by other methods. In the first example, we show its application to optimize inflow-control-valve (ICV) settings on two horizontal wells in a sector model of 200,000 cells. One hundred layers were used in the reservoir model to capture geological heterogeneity. The two wells were drilled parallel to the edgewater boundary. The optimization objective in this example is to minimize cumulative water production over a 10-year production period while maintaining a constant liquid-production rate. Results after only five optimization iterations with improved control-valve settings showed a 50% reduction in cumulative water production. The fully automated optimization process was completed within a few hours under a parallel-computing environment. The ensemble-based method was also applied successfully to a 3D case consisting of 10 multilateral wells with ICVs installed at each lateral junction. The interaction of various laterals is difficult to visualize, but the optimization algorithm was again successful in reducing water production. In this example, we demonstrate that proper choice of control variables can be important to the success of the optimization.

Smart wells and model-based field production optimization

“…Previously these types of model-based optimization strategies (proactive strategies) were presented in several publications (refer, for instance, (Yeten et al, 2004;Obendrauf et al, 2006;Emerick and Portella, 2007;Chen et al, 2009;van Essen et al, 2010;Su and Oliver, 2010) and showed good results. We do believe that it is difficult and inefficient to implement the aforementioned approaches in full field reservoir models directly, because they would result in large, multidimensional and timeconsuming optimization problems.…”

Improved CRM Model for Inter-Well Connectivity Estimation and Production Optimization: Case Study for Karst Reservoirs

“…Therefore, it is urgent to establish a practical methodology for inter-well connectivity estimation while coupling A viable alternative is approximation of the gradient by an ensemble optimization technique known as EnOpt, which has received appealing attention over the past years by reservoir engineers after the pioneering work of Chen et al [24,25]. Using the deterministic or standard EnOpt, hybrid constrained optimization problems were resolved by generalized non-linear programming algorithms [7,[26][27][28]. Do and Reynolds [29] analyzed the deterministic EnOpt and demonstrated its close connection with other stochastic gradient algorithms.…”

Improving the Ensemble-Optimization Method Through Covariance-Matrix Adaptation

“…Predecessors to the EnOpt method were proposed by Lorentzen et al (2006) and Nwaozo (2006), whereafter Chen (2008) and Chen et al (2009) gave systematic descriptions of the method, as mostly used today. Thereafter, several publications addressed applications and computational aspects of the method; see, for example, Chaudhri et al (2009), Chen and Oliver (2010), Su and Oliver (2010), Leeuwenburgh et al (2010), and Chen and Oliver (2012). In a recent paper, Do and Reynolds (2013) demonstrate that EnOpt can be interpreted as a member of a broader class of approximate-gradient methods that also includes the simultaneous-perturbation stochastic approximation method.…”