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 …

Drilling wells in challenging oil/gas environments implies in large capital expenditure on
wellbore's construction. In order to optimize the drilling related operation, real-time decisions …

Abstract In this paper, Convolutional Neural Networks (CNNs) are trained to rapidly estimate
several physical properties of porous media using micro-computed tomography (micro-CT) …

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 …

Accurate estimation of permeability is critical for oil and gas reservoir development and
management, as it controls production rate. After assessing numerical techniques ranging …

人工智能技术飞速发展, 在部分行业已取得明显的应用效果, 但在钻井领域的应用尚处于探索
阶段. 为推动人工智能技术在钻井领域的应用, 在简述钻井行业人工智能应用研究情况的基础上 …

Digital rocks obtained from high-resolution micro-computed tomography (micro-CT) imaging
has quickly emerged as a powerful tool for studying pore-scale transport phenomena in …

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 …

Petroleum reservoirs are often associated with multiple target zones or a single zone
adjacent to nonproductive intervals. Real‐time geosteering therefore becomes important to …

The optimization of drilling operations is an ongoing necessity since the major proportion of
the terrestrial hydrocarbon reservoirs has been exhausted. Furthermore, there is a growing …