In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry,
with numerous applications which guide engineers in better decision making. The most …

Fluid discrimination is challenging for reservoir prediction, especially for tight sandstones
with special petrophysical properties. In this paper, we first review the effective medium …

This paper presents convolutional neural network architectures for integration of dynamic
flow response data to reduce the uncertainty in geologic scenarios and calibrate subsurface …

This study presents a dynamic risk modeling strategy for a hydrocarbon sub-surface
production system under a gas lift mechanism. A data-driven probabilistic methodology is …

This paper describes a new iterative procedure for probabilistic history matching using a
discrete Latin Hypercube (DLHC) sampling method and nonparametric density estimation …

Bayesian analysis provides a framework for the inverse uncertainty quantification (UQ) of
combustion kinetic models. As the workhorse of the Bayesian approach, the Markov chain …

Offshore drilling operations may not be safe nor available if they are not well maintained.
Dynamic risk-based maintenance (RBM) methodology is a tool for scheduling maintenance …

Reservoir history matching represents a crucial stage in the reservoir development process
and purposes to match model predictions with various observed field data, including …

This work presents an alternative way to combine different types of uncertainty to quantify
risk in petroleum field development. Risk quantification is key in decision analysis. Some …

To address the key problems in the application of intelligent technology in geothermal
development, smart application scenarios for geothermal development are constructed. The …