Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA

Z Zhong, TR Carr, X Wu, G Wang - Geophysics, 2019 - library.seg.org
Permeability is a critical parameter for understanding subsurface fluid flow behavior,
managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In …

A multiple-input deep residual convolutional neural network for reservoir permeability prediction

M Masroor, ME Niri, MH Sharifinasab - Geoenergy Science and …, 2023 - Elsevier
Permeability plays an essential role in reservoir-related studies, including fluid flow
characterization, reservoir modeling/simulation, and management. However, operational …

Unlocking the power of artificial intelligence: Accurate zeta potential prediction using machine learning

R Muneer, MR Hashmet, P Pourafshary, M Shakeel - Nanomaterials, 2023 - mdpi.com
Nanoparticles have gained significance in modern science due to their unique
characteristics and diverse applications in various fields. Zeta potential is critical in …

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

F Anifowose, J Labadin, A Abdulraheem - Applied Soft Computing, 2015 - Elsevier
The ensemble learning paradigm has proved to be relevant to solving most challenging
industrial problems. Despite its successful application especially in the Bioinformatics, the …

Fracture density estimation from core and conventional well logs data using artificial neural networks: The Cambro-Ordovician reservoir of Mesdar oil field, Algeria

RS Zazoun - Journal of African Earth Sciences, 2013 - Elsevier
Fracture density estimation is an indisputable challenge in fractured reservoir
characterization. Traditional techniques of fracture characterization from core data are costly …

Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme

EA El-Sebakhy - Journal of Petroleum Science and Engineering, 2009 - Elsevier
PVT properties are very important in the reservoir engineering computations. There are
numerous approaches for predicting various PVT properties, namely, empirical correlations …

Prediction of oil PVT properties using neural networks

EA Osman, OA Abdel-Wahhab… - SPE Middle East Oil and …, 2001 - onepetro.org
Reservoir fluid properties are very important in reservoir engineering computations such as
material balance calculations, well test analysis, reserve estimates, and numerical reservoir …

An ROP predictive model in nile delta area using artificial neural networks

MM Amer, AS Dahab, AAH El-Sayed - SPE Kingdom of Saudi Arabia …, 2017 - onepetro.org
Bit performance is a key factor to improve drilling performance and reduce drilling costs;
however, factors controlling bit performance are many and the interactions between these …

Application of artificial neural networks in a history matching process

LAN Costa, C Maschio, DJ Schiozer - Journal of Petroleum Science and …, 2014 - Elsevier
Reservoir simulation is an important tool for reservoir studies because it enables the testing
of production strategies and to perform forecasts. To obtain a reliable production prediction …

[HTML][HTML] Development of new correlations for the oil formation volume factor in oil reservoirs using artificial intelligent white box technique

S Elkatatny, M Mahmoud - Petroleum, 2018 - Elsevier
Oil formation volume factor (OFVF) is considered one of the main parameters required to
characterize the crude oil. OFVF is needed in reservoir simulation and prediction of the oil …