Failure Predictive Analytics Using Data Mining: How to Predict Unforeseen Casing Failures?

Abstract

Despite numerous studies in the subject matter, industry has yet to resolve casing failure issues. A more interdisciplinary approach is taken in this study integrating seventy-eight land based wells using a data - driven approach to predict the reasons behind casing failure. This study uses a statistical software in collaboration with Python Scikit-learn implementation to apply different Data Mining and Machine Learning algorithms on twenty-four different features on the twenty failed casing data sets. Descriptive analytics manifested in visual 8representations included Normal Distribution Charts and Heat Map. Principal component Analysis (PCA) was used for dimensionality reduction. Supervised and unsupervised approaches were selected respectively based on the response. The algorithms used in this study included Support Vector Machine (SVM), Boot strap, Random Forest, Naïve Bayes, XG Boost, and K-Means Clustering. Nine models were then compared against each other to determine the winner. Features contributing to casing failure were identified based on best algorithm performance.