This paper discusses about the various approaches that has been taken to reduce downtime in Coal bed methane (CBM) wells by predicting the string integrity failure in advance. Various approaches such as the Principal component analysis (PCA) based T-statistics approach and the Bag of Features approach have been taken to find a solution. These approaches fall under the classical classification approach of supervised learning. The power and usefulness of these approaches are fuelled and limited by the number and richness of annotations and sensors at the Well site. These approaches used on the CBM wells gave very encouraging results, thereby proving the helpfulness of this approach in enhancing the well efficiency and decreasing the well downtime by planning based on the failure prediction of the model.

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