Improving Efficiency and Accuracy in Estimating Well Potential Using an Integrated Asset Operations Model

This paper describes an efficient approach for estimating well potential using advanced, automated workflows for a large field with more than a thousand well strings from multi-layered reservoirs having different characteristics. This paper provides insight into reservoir guidelines, well performance, and surface facility constraints using the integrated asset operations model (IAOM) to compute well potential. The IAOM tool automates an engineering approach in which reservoir management guidelines, in conjunction with calibrated wells and a network model, are used to estimate well potentials. This process incorporates the interaction among various components including wellbore dynamics (Inflow performance and well performance), surface network backpressure effects and well performance key parameters, such as GOR and water cut. This engineered workflow computes the well potential corresponding to each guideline and constraint. This engineered workflow has reduced the time to compute the well potential rate from 3-4 weeks to just 2 hours for this large field, reducing computation time by more than 95%. This workflow helped engineers to avoid tedious manual calculations on a well-by-well basis and allowed them to focus on engineering, analytical, and optimization problems. The confirmation of calculated well potential rates using the updated surface network model helped in finalizing the business scenarios such as field-capacity tests. For example, the accuracy of predicted results in a zonal capacity test was approximately 98% using this engineered workflow approach. The value derived from this engineering logic using validated physical models supported the business plan and further identified key candidates for production optimization without heavy dependence on drilling additional wells, leading to cost optimization. This automated workflow ensures the use of updated physical models and maintains higher accuracy of results. This digital system-based data-management process supports data governance objectives. This enhanced workflow supports corporate objectives of standardization for a work process to set well allowable, in line with the operator's integrated reservoir management (IRM) initiative.