Results of bibliometric analysis and a detailed review are reported on the use of supervised
machine learning to study hazardous drilling events. The bibliometric analysis attempts to …

Drilling is one of the most challenging and expensive processes in hydrocarbon extraction
and geothermal well development. Dysfunctions faced during drilling can increase the non …

There is a great deal of interest in the oil and gas industry (OGI) in seeking ways to
implement machine learning (ML) to provide valuable insights for increased profitability …

Tight oil and gas reservoirs have attracted an increasing amount of attentions and have
become one of the focus of research field in recent years. Tight sandstones have complex …

In this study, a Bayesian data assimilation method that fuses physics with motion sensor
data is demonstrated to infer the dynamic states at points of interest on the bottomhole …

The discovery of field T in Mexico represented the largest such find in a generation. Hence,
drilling activity has been focused primarily on economically viable ways to develop these …

Downhole vibration can cause drill string failures and significant economic losses.
Recognizing harmful vibrations in real time is highly significant for optimizing drilling …

Drilling operations are subject to drilling dynamics dysfunctions like bottom hole assembly
(BHA) shocks and vibration (S&V) that can lead to premature downhole tool failures, poor …

This paper presents a medical approach to classify shock waveforms acquired at 31,250
hertz downhole. The shock signals are treated as drilling electrocardiogram (D-ECG). The D …