Optimizing Well Placement and Recovery in Mature Gas Condensate Reservoirs Through the Utilization of Pseudo Pressure Approach

Abstract

This paper overcomes the challenge of reliable prediction by capturing condensate banking effects in the numerical simulation models, and to identify remaining sweet spots for infill drilling. In the rich gas condensate, the heavier components in the gas phase drop out below saturation pressure, it causes: (1) Decline in gas production, (2) Loss of valuable condensate components, both of which manifest in the form of expanding condensate bank incorporated in this study. The case study is of a rich gas condensate field located in the Middle Indus Basin of Pakistan. A detailed compositional simulation model including matched PVT has been developed using commercial simulator to study the effects of condensate banking and its impact in the history matching and forecast. Local grid refinements (LGR) and Generalized pseudo pressure (GPP) approaches have been utilizing and compared in this case study for the improvement of history match along with the reliable predictions. An applied workflow has been developed to locate sweet spots in order to target future development well opportunities in the field. A comparative analysis has been performed in this case study using LGR and GPP approach to study the impact of condensate drop-out on wells productivity. As sector model was developed for the comparison between the techniques and later on the results applied on the full field simulation. GPP approach was much faster with better history matched results as compared to LGR. The forecast generated using GPP technique when later compared with actual field performance was much realistic. As a result, the model was then utilized to identify infill development opportunities in the field. The developed workflows resulted in evaluating two infill wells with incremental recovery of 10-12% of the field. The work is computationally intensive with time & resource constraints challenges. Availability of advanced workflow generation rendered the overall execution in an efficient and timely manner.