Analysis of Drilling Data and ROP Optimization Using Artificial Intelligence Techniques with Statistical Regression Coupling

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Predictive data‐driven analytics has the potential to successfully predict the downhole environment in Drilling Engineering. In general, rate of penetration (ROP) optimization involves adjustment of the weight on bit (WOB) and rotary speed (RPM) for efficient drilling. ROP has a complex relationship with several other parameters, such as formation properties, mud properties, mud hydraulics, borehole deviation, as well as the size/type of bit. In this study, a new workflow based on statistical regression and artificial intelligence (AI) techniques was designed to forward predict ROP using field data gathered from the North Sea horizontal wells. Several machine‐learning models such as such as step‐wise regression, neural networks, support vector regression, classification‐regression trees, random forests, and boosting, were applied for prediction. A web based prediction app was developed that could perform predictive analytics and uncertainty analysis on any data. The app was further tested on other wells and was shown to predict with significant accuracy.

Rate of penetration (ROP) optimization, Machine learning, Artificial intelligence, Predictive analytics, Drilling, Regression