Browsing Master's Projects by Author "Chou, Irwin"
Prudhoe Bay West End gas lift supply optimizationChou, Irwin; Dandekar, Abhijit; Ning, Samson; Zhang, Yin (2019-12)The western extension of Alaska's Prudhoe Bay, known collectively as Eileen West End (EWE), operates under a gas lift pressure supply constraint. This constraint is largely contributed by two factors: the extensively long gas lift supply line that stretches across the western field and the large number of production wells offtaking gas lift to stay online or enhance production. The gas lift supply line is approximately 18.5 miles long and provides gas lift to 200+ production wells. This results in a pressure drop severe enough to start hindering production on the western most side of the field as low gas lift supply pressure can cause unstable production, reduced production rate, or stop production altogether. Theory suggests that boosting the system's gas lift supply pressure will improve production from the field. In order to quantify the benefit of boosting the gas lift supply pressure and determine the most optimal way to do so, an industry proven physics based multiphase flow simulator was used to construct two models, a production system and a gas lift system. This dual integrated model approach enabled the ability to capture and predict production effects caused by changes in gas lift supply pressure and determine if boosting the pressure will be beneficial from an operator standpoint. The objective of this project is to describe how building an integrated production model can capture and quantify changes in production for a very large and complex interconnected system. Applying these types of models can help steer important operational and economic decisions to minimize risk and expense as an operator. Using the models, several scenarios were evaluated to determine and quantify the most optimal approach to address the low gas lift supply in EWE. It was determined that shutting in the least competitive wells to boost the gas lift supply pressure was the best scenario to implement for several reasons: the scenario still yielded a high production benefit, it did not have any investment requirement, and the actions could be reversed if a negative impact was realized.