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Optimal Well-Workover Scheduling by Use of Genetic Algorithms

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.

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 The paper has not been peer reviewed.
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Optimal Well-Workover Scheduling by Use of Genetic Algorithms

01 May 2014

Volume: 66 | Issue: 5

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