Evaluating the Effectiveness of Machine Learning Technologies in Improving Real-Time Drilling Data Quality

Due to high demand for energy, oil and gas companies started to drill wells in remote environments conducting unconventional operations. In order to maintain safe, fast, and more cost-effective operations, utilizing machine learning (ML) technologies has become a must. The harsh environments of drilling sites and the transmission setups are negatively affecting the drilling data, leading to less than acceptable ML results. For that reason, a big portion of ML development projects was actually spent on improving the data by data-quality experts. The objective of this paper is to evaluate the effectiveness of ML on improving the real-time drilling-data quality and compare it to human expert knowledge. To achieve that, two large real-time drilling datasets were used; one dataset was used to train three different ML techniques: artificial neural network (ANN), support vector machine (SVM), and decision tree (DT); the second dataset was used to evaluate it. The ML results were compared with the results of a real-time drilling-data-quality expert. Despite the complexity of ANN and good results in general, it achieved a relative root-mean-square error (RRMSE) of 2.83%, which was lower than DT and SVM technologies that achieved RRMSE of 0.35% and 0.48%, respectively. The uniqueness of this work is in developing ML that simulates the improvement of drilling-data quality by an expert. This research provides a guide for improving the quality of real-time drilling data.