Optimal Well-Workover Scheduling by Use of Genetic Algorithms
Authors: Carpenter Chris 
Published: February 2015

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 167818, ’Optimal Well-Workover Scheduling: Application of Intelligent-Well Control-Optimization Technology to Conventional Wells,’ by Faraj Zarei, SPE, Khafiz Muradov, SPE, and David Davies, SPE, Heriot-Watt University, prepared for the 2014 SPE Intelligent Energy Conference and Exhibition, Utrecht, the Netherlands, 1-3 April. The paper has not been peer reviewed.

This study develops a workflow to design a proactive workover-optimization workflow by use of genetic algorithms (GAs). The work explored whether a “steered” GA, created by the addition of screening and advanced sampling methods to a “pure” GA, provides sufficient simplification of the problem to make it suitable for routine use in the field. Ultimately, a significant added field value was achieved by the “steered” GA when compared with the “pure” GA.

Introduction

The ultimate goal of field-development optimization is to maximize the objective function while reducing the uncertainty associated with the project value. Efficient reservoir-production planning requires a degree of well-control flexibility. Intelligent wells equipped with downhole flow-control devices and sensors inherently have a greatly increased flexibility to respond to (often unexpected) changes in the well and reservoir performance. Downhole inflow-control valves (ICVs) are used to control the well zonal flow rates. These valves are available in open/close, multiple-position-discrete, or infinitely-variable-position types.

The completion design team is tasked with selecting an appropriate valve type according to the selected production scenario. Model uncertainties and difficulties in finding the optimal ICV control strategy for a dynamic reservoir model often result in the application of reactive control when operating the ICVs, despite the fact that a proactive strategy potentially delivers the highest added value during the field’s life.

A workover operation is often required during the well’s operational life to address well-integrity and flow-assurance problems. The only option available for a conventional well to provide a level of well- and reservoir-management flexibility similar to that achieved by an intelligent well is to carry out an additional type of workover operation (i.e., one that is aimed at improving the well and/or reservoir performance). These are normally carried out as a reaction to observed changes in the well performance. Addressing such problems in a reactive mode carries the risk that the improvement in the well’s performance may not be apparent in the short term or may involve a long-term recovery loss caused by water or gas coning or inefficient reservoir sweep.

Field-workover screening algorithms have been proposed to identify the workovers with the highest added value, but it is clear that a reliable and effective tool able to screen and explore the large search space of the potential workovers and that adds value to the reservoir-management process is not currently available. The search will need to consider the overall performance of the field throughout a specified period while respecting all operational limitations, as well as taking into account the risks and costs of the intervention.

The nature of a workover operation in an interval resembles having an imaginary open/close ICV installed across the interval. This study uses a GA optimization search procedure—one of the most commonly used algorithms in proactive optimization of intelligent wells—to find optimal control strategies by considering both the well and field scale to assign workovers optimally at the full-field level.