In the past several decades, machine learning has gained increasing interest in the oil and
gas industry. This paper presents a comprehensive review of machine learning studies for …

In this paper, we investigate large scale computers' capability of speeding up deep neural
networks (DNN) training. Our approach is to use large batch size, powered by the Layer …

Drilling problems such as stick slip vibration/hole cleaning, pipe failures, loss of circulation,
BHA whirl, stuck pipe incidents, excessive torque and drag, low ROP, bit wear, formation …

Oil/gas exploration, drilling, production, and reservoir management are challenging these
days since most oil and gas conventional sources are already discovered and have been …

In this paper, we present how precise deep learning algorithms can distinguish loss
circulation severities in oil drilling operations. Lost circulation is one of the costliest …

Abstract During Drilling and Completion (D&C) operations large volumes of data are
typically collected in oil and gas fields. These datasets typically contain hidden (valuable) …

Text-based tools for reporting technical issues and receiving support are widespread in
commercial applications, such as customer services and internal corporate communication …

There is a large amount of heterogeneous data distributed in various sources in the
upstream of PetroChina. These data can be valuable assets if we can fully use them …

Recently, the use of artificial neural networks (ANN) in the offshore exploration and
production industry to optimize decision-making and reduce costs and non-productive time …

The daily drilling report (DDR) contains the daily activities and parameters during drilling
and completion (D&C) operations that can be used to identify the bottlenecks and improve …