Application of an Integrated Subsurface Surface Coupled Model to Optimize the Performance of the Artificial Lift Methods in Matured Oil Fields

Abstract

Abstract Selection of the most economical artificial lift method is necessary for the operators to realize the maximum potential from developing any oil or gas field1. In artificial lift design the engineer is faced with matching facility constraints, artificial lift capabilities and the well productivity so that an efficient lift installation results. Energy efficiency will partially determine the cost of operation, but this is only one of many factors to be considered. Based on the above consideration and some other limitations in any project usually 2-3 methods of artificial lift will be candidate to maintain or increase the production from a particular field and a final decision on the type of lift will be made after doing economic studies on different scenarios and several other non-technical considerations1. Traditionally any decision on engineering design and debottlenecking of surface network and in particular the type of the artificial lift methods are performed by production engineers with their stand-alone tools considering a production forecast profile from a reservoir simulation model. There are lots of simplifications and limitations to generate these profiles, for example well targets in the reservoir simulation model can be unrealistic from production engineering point of view and can cause over or under sizing the production and surface facilities2. Another example can be when different reservoirs are sharing the same network to produce and also there is an artificial lift plan for one of the reservoirs in the field and the production engineer is being asked to evaluate the capacity of the current production network to handle production changes. The answer to these questions if not impossible is a very tedious task to be practiced by traditional field development planning methods. In this paper authors try to review the concept of Integrated Asset modeling with emphasizing on the artificial lift method screening and analysis. An example application of IAM in a field in Mexico will be shown and the benefits of the integration workflow will be demonstrated in bulk of the paper.