A systematic review of data science and machine learning applications to the oil and gas industry
This study offered a detailed review of data sciences and machine learning (ML) roles in
different petroleum engineering and geosciences segments such as petroleum exploration …
different petroleum engineering and geosciences segments such as petroleum exploration …
[HTML][HTML] Application of ML & AI to model petrophysical and geomechanical properties of shale reservoirs–A systematic literature review
Extensive reviews and cross-comparison studies are essential to analyze the emerging
developments in a specific field of research. In the past decade, hydrocarbon exploration …
developments in a specific field of research. In the past decade, hydrocarbon exploration …
Machine learning-A novel approach of well logs similarity based on synchronization measures to predict shear sonic logs
This study proposes a novel approach to predict missing shear sonic log responses more
precisely and accurately using similarity patterns of various wells with similar geophysical …
precisely and accurately using similarity patterns of various wells with similar geophysical …
Reservoir characterization through comprehensive modeling of elastic logs prediction in heterogeneous rocks using unsupervised clustering and class-based …
Geophysical reservoir characterization is a significant task in the oil and gas industry and
elastic logs prediction of subsurface formations is a fundamental aspect of this process …
elastic logs prediction of subsurface formations is a fundamental aspect of this process …
Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning
Awareness of uniaxial compressive strength (UCS) as a key rock formation parameter for the
design and development of gas and oil field plays. It plays an essential role in the selection …
design and development of gas and oil field plays. It plays an essential role in the selection …
A machine learning approach to predict drilling rate using petrophysical and mud logging data
Predicting the drilling rate of penetration (ROP) is one approach to optimizing drilling
performance. However, as ROP behavior is unique to specific geological conditions its …
performance. However, as ROP behavior is unique to specific geological conditions its …
Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs
ARB Abad, S Mousavi, N Mohamadian… - Journal of Natural Gas …, 2021 - Elsevier
Gas condensate reservoirs display unique phase behavior and are highly sensitive to
reservoir pressure changes. This makes it difficult to determine their PVT characteristics …
reservoir pressure changes. This makes it difficult to determine their PVT characteristics …
Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field
Drilling fluid loss of circulation is a challenging issue to resolve for many oil and gas wells as
drilling progresses. It imposes enormous expenses on drilling industry. One of the common …
drilling progresses. It imposes enormous expenses on drilling industry. One of the common …
Machine learning approach to model rock strength: prediction and variable selection with aid of log data
Comprehensive knowledge and analysis of in situ rock strength and geo-mechanical
characteristics of rocks are crucial in hydrocarbon and mineral exploration stage to …
characteristics of rocks are crucial in hydrocarbon and mineral exploration stage to …
Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms
An important requirement of reservoir management is to understand the properties of
reservoir fluids and dependent phase behaviors. This makes it possible to determine the …
reservoir fluids and dependent phase behaviors. This makes it possible to determine the …