abstracts online

 

The Role of an Integrated Asset Model in Implementing Field Management Decisions for Field Development Plans

 

Charles Okafor, Joshua Asagba, Schlumberger

 

While integrated asset modeling continues to gain popularity in the industry, practical application of the concept should be seen more during field development planning (FDP). An oil field can include a number of reservoir units. Each unit may be studied individually and different conclusions may be drawn on the best techno-economical decisions to produce each reservoir. But when these fields are to share common transportation facilities, and top-side facilities, and are subjected to the same global (production or injection) constraints, then determining the best method of producing the field is complex. An asset model can be implemented to reduce uncertainty, for operations optimization and investment cost reduction.

 

This paper presents the usefulness of an integrated asset model (IAM) and its role in typical field management decisions made by asset teams during field project plans. The focus is to show how asset teams can use the IAM as a platform for implementing field management, facility upgrades, and planning decisions during FDP studies. 

 

A discussion on the setup of the IAM model is briefly discussed. With proper setup and preparation, an integrated asset model can be instrumental in making strategic decisions for the optimization of field development projects. 

 

 

Three project case studies support this approach in the form of different FDP scenarios implemented by asset teams. With the first case, three reservoir models are coupled with a global network model and subjected to changing backpressure constraints imposed by the facilities; a new field is to be tied into the facility and the team must capture the back-out effects on production profiles caused by the new tie in. The value gained through an IAM is a fully integrated bidirectional model available for estimating the backed out volumes. The second case details the production facility design for a new gas condensate field. The asset team used the IAM to tackle the task of selecting properly sized surface pipeline and production tubing for the green field, while ensuring that the selected production equipment would be the appropriate size for the range of reservoir estimates (P10, P50 and P90). The third case study focuses on how the IAM addresses a full field asset optimization. Here, the IAM serves as a controller for automatic switching of pipelines and compressors as well as condensate recovery optimization by well prioritization.