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 …

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 …

Accurate numerical modeling of multiphase flow and transport mechanisms is essential to
study varied, complex physical phenomena including flow in subsurface oil and gas …

In reservoir simulation, proxy models have been used to explore relationships between
explanatory variables (eg, porosity, permeability, well locations and constraints) and …

A machine learning approach is considered to develop a Seq2Seq LSTM-based learning
framework for oil production forecasting in the Eagle Ford shale employing encoder-decoder …

Gradient-based optimization is now ubiquitous across graphics, but unfortunately can not be
applied to problems with undefined or zero gradients. To circumvent this issue, the loss …

A methodology to construct deep neural network-(DNN) and recurrent neural network-(RNN)
based proxy flow models is presented; these can reduce computational time of the flow …

In order to improve the design of advanced wells, the performance of such wells needs to be
carefully assessed by taking the reservoir uncertainties into account. This research aimed to …

Computerized solutions for field management optimization often require reduced-order
models to be computationally tractable. The purpose of this paper is to compare two different …

Integrated reservoir studies for performance prediction and decision-making processes are
computationally expensive. In this paper, we develop a novel linearization approach to …