Identifying applications of machine learning and data analytics based approaches for optimization of upstream petroleum operations
Over the past few years, machine learning and data analytics have gained tremendous
attention as emerging trends in the oil and gas industry. The usage of modern tools and high …
attention as emerging trends in the oil and gas industry. The usage of modern tools and high …
Modelling oil and gas flow rate through chokes: A critical review of extant models
Oil and gas metering is primarily used as the basis for evaluating the economic viability of oil
wells. Owing to the economic implications of oil and gas metering, the subject of oil and gas …
wells. Owing to the economic implications of oil and gas metering, the subject of oil and gas …
A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning
N Mohamadian, H Ghorbani, DA Wood… - Journal of Petroleum …, 2021 - Elsevier
The casing-collapse hazard is one that drilling engineers seek to mitigate with careful well
design and operating procedures. However, certain rock formations and their fluid pressure …
design and operating procedures. However, certain rock formations and their fluid pressure …
Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms
Shear wave velocity (VS) data from sedimentary rock sequences is a prerequisite for
implementing most mathematical models of petroleum engineering geomechanics …
implementing most mathematical models of petroleum engineering geomechanics …
Optimized machine learning models for natural fractures prediction using conventional well logs
Identifying and characterizing natural fractures is essential for understanding fluid flow and
drainage in many oil and gas reservoirs, particularly carbonate. The presence of fractures …
drainage in many oil and gas reservoirs, particularly carbonate. The presence of fractures …
Towards design of Internet of Things and machine learning-enabled frameworks for analysis and prediction of water quality
The degradation of water quality has become a critical concern worldwide, necessitating
innovative approaches for monitoring and predicting water quality. This paper proposes an …
innovative approaches for monitoring and predicting water quality. This paper proposes an …
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 …
Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data
One of the challenges in reservoir management is determining the fracture density (FVDC)
in reservoir rock. Given the high cost of coring operations and image logs, the ability to …
in reservoir rock. Given the high cost of coring operations and image logs, the ability to …
Predicting formation pore-pressure from well-log data with hybrid machine-learning optimization algorithms
Accurate prediction of pore-pressures in the subsurface is paramount for successful
planning and drilling of oil and gas wellbores. It saves cost and time and helps to avoid …
planning and drilling of oil and gas wellbores. It saves cost and time and helps to avoid …
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 …