Efficient Optimization Strategies for Developing Intelligent-Well Business Cases
Authors: Carpenter Chris 
Published: June 2017

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 181062, “Efficient Optimization Strategies for Developing Intelligent-Well Business Cases,” by Adam Vasper, Jon Endre Seljeset Mjos, and Tran Thi Thuy Duong, Schlumberger, prepared for the 2016 SPE Intelligent Energy International Conference and Exhibition, Aberdeen, 6–8 September. The paper has not been peer reviewed.

The complex paper evaluates optimization techniques to develop, or support, business cases for intelligent or smart wells. Recommendations are made for the methods most appropriate for large or small numbers of flow-control valves (FCVs) and other parameters. Closed-loop and model-based methods are compared in terms of computational cost.

Introduction

Downhole FCVs that are surface or remotely controlled were introduced in the late 1990s to allow operators to control fluid flow downhole without the need for interventions. Their use and industry acceptance have grown to the point at which, in certain locations, a significant fraction of the wells completed include downhole flow control and are therefore considered to be intelligent or smart wells.

To determine whether a well is to be completed as an intelligent well can be a complex process. A key component of that process is determining the financial implications associated with the alternatives. Reservoir simulation is commonly used to determine the production profiles used in economic calculations. In certain cases, such as ones in which an intelligent well replaces two or more conventional wells, the requirements for reservoir simulation may be relatively simple. However, when the intelligent well is expected to facilitate the control and optimization of produced fluids vs. time, more-complex modeling is required.

Control Variables

Control variables or decision variables are the properties over which one may have physical control and whose values one wishes to optimize by maximizing the objective function. Properties such as oil price, permeability, and porosity are not control variables because one does not have control over them. They can, however, be treated as uncertainty variables to understand their effect on the project economics and the optimal solution. In an intelligent-well-optimization problem, the control variables will often include some or all of the following:

Number and location of FCVs

Location of packers

Flow area of the FCVs

The majority of FCVs installed today are multipositional devices with closed, fully open, and choking positions. To maximize the value of these devices and to get closer to the optimal solution, it is necessary to optimize their flow area vs. time. When the flow area of an FCV is a control variable, a separate control variable is required for each optimization step. For example, to optimize a single FCV’s flow area annually for 5 years, five control variables are required for that FCV. In practice, a set of control variables are defined, and each affects the FCV only for a specific period of time and is inactive for the remaining time.