Many spatial decision support systems suffer from user adoption issues in practice due to
lack of trust, technical expertise, and resources. Automated machine learning has recently …

The emergence and application of geological models have contributed to new assessment
schemes for oil and gas reservoir development. The model simulates stratigraphic …

Oilfields in high or ultra-high water-cut period are of high nonlinearity and heterogeneity,
thus complicated in its internal physics mechanism. Corresponding production data is often …

This paper proposes a novel approach based on deep learning to improve oil reservoirs'
history matching problem. Deep autoencoders are widely used to solve the oil industry …

One of the challenges related to reservoir engineering studies is working with essential high-
dimensional inputs, such as porosity and permeability, which govern fluid flow in porous …

A novel 2.5 D intelligent gravity inversion technique has been developed to estimate
basement topography. A deep neural network (DNN) is used to address the fundamental …

The properties of crude oil are of great importance for efficient recovery of oil from oil fields.
The properties are primarily used in reservoir simulations for prediction of oil recovery in …

To explore hydrocarbons, it is necessary to interpret seismic data to identify facies and
geological features. Traditionally, this work is performed by visually choosing points …

Offshore oil and gas fields pose significant challenges due to their lower accessibility
compared to onshore fields. To enhance operational efficiency in these deep-sea …

Machine learning (ML) has emerged as a powerful tool in petroleum engineering for
automatically interpreting well logs and characterizing reservoir properties such as porosity …