Zonal Allocation and Increased Production Opportunities Using Data Mining in Kern River

Abstract

Abstract This paper presents a fast and effective methodology to estimate zonal allocation for commingled producers in a multilayer reservoir using minimal, readily available data (well completion, historical production, sand depths, and location data). A set of data mining tools including regression, neural networks, and fuzzy logic was used to identify candidates for remedial work and the corresponding production increase expected. This approach was applied and executed in a portion of the Kern River field in California with very promising preliminary results.