Performance Model Analysis for Candidate Recognition

Abstract The main objective of the project was to increase operation efficiency through monitoring production and injection performance in 18 Permian Basin fields (waterflood and natural depletion). A performance model (PM) technique has been developed to efficiently analyze massive amounts of production and injection data from thousands of wells. Using the PM technique, under- performing wells and patterns were rapidly identified and ranked for workover opportunities. Additionally, non-responsive injection areas (composed of several patterns) were also identified to enhance injection efficiency. The PM technique was implemented in 18 fields in the Permian Basin. The largest field having more than 1300 wells was evaluated. The PM technique described here is based on a modified heterogeneity index (MHI) concept. This improvement was necessary since calculations using traditional heterogeneity index (HI) skewed the results and incorrectly quantified the performance comparison. The MHI has successfully corrected and overcome traditional HI weakness. The PM is an improved candidate recognition technique that uses binary codes and personality concepts to effectively monitor wells and injection patterns. The personality characterization process creates and uses several interpretation scenarios to identify problematic wells, patterns or both. PM methodology and personality concepts are discussed in detail and field implementation results are presented.