Case Study Applied Machine Learning to Optimise PCP Completion Design in a CBM Field

The key objective of multiple Coal Bed Methane (CBM) development operations is to determine cost effective methods to allow sustainable economic production and maximum reserves recovery. The cost of workovers as well as the associated deferred production may overwhelm the economic viability of the field. The primary reason for workovers is progressive cavity pump (PCP) system failures. Here, we demonstrate the use of a machine learning framework that can be used to customise each workover configuration such that it optimises PCP run-life, respecting the well's heterogeneity and age. The framework can be generalised into three major parts: 1) converting the dynamic production data into a stationary surrogate model for the well; 2) the use of Gaussian process regression to create a function that estimates runlife; 3) an optimiser that will search the functional space to recommend the best completion design. A telemetry and completion dataset for PCP run-lives from years 2014-2018 was obtained across the Surat and Bowen basins. After filtering data for completeness, 1499 PCPs remained in the cohort, of which, 895 failed during the observation period. A small portion of the original data was used as a test set. Our work suggests that PCP run-life can be extended by taking a multivariate statistical approach to provide recommendations for customised completions and production strings per well that respect the wells’ geology and production history and thereby improve life of field economics.