Abstract
This paper proposes a predictive model to help oil workers build a reliable model for identifying oilwell failures. It can help geologists experienced with Machine Learning to improve the accuracy of failure identification and a more accurate approach to well-maintenance planning. This study is based on output data statistics such as per-well daily oil flowmeter readings. The volatility of these indications makes it possible to determine the probability of an oilwell failure. This method makes it possible to rank wells according to the principle of the most probable failures for workers making decisions. The use of predictive diagnostics can help to detect equipment problems early, thereby minimizing unplanned downtime. Unplanned sudden oilwell failures increase the company’s operating costs, as well as increase risks of environmental pollution. Keywords: machine learning; classification algorithms; oil and gas; prediction algorithms; decision-making.
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