Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea
Authors:
- Nikolay O. NikitinNNNikolay O. Nikitin
- Ilia RevinIRIlia Revin
- Alexander HvatovAHAlexander Hvatov
- Pavel VychuzhaninPVPavel Vychuzhanin
- Anna V. KalyuzhnayaAKAnna V. Kalyuzhnaya
- 18 February 2022
- journal article
- research article
- Published by Elsevier in Computers & Geosciences
- Vol. 161
Abstract
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Funding Information
- Ministerstwo Edukacji i Nauki (FSER-2021-0012)
- Ministry of Science and Higher Education of the Russian Federation
This publication has 35 references indexed in Scilit:
- An integrated framework that combines machine learning and numerical models to improve wave-condition forecastsJournal of Marine Systems, 2018
- A learning-based data-driven forecast approach for predicting future reservoir performanceAdvances in Water Resources, 2018
- Cost-optimal design of pressure-based monitoring networks for carbon sequestration projects, with consideration of geological uncertaintyInternational Journal of Greenhouse Gas Control, 2018
- Deep Convolutional Neural Networks for Image Classification: A Comprehensive ReviewNeural Computation, 2017
- Full field reservoir modeling of shale assets using advanced data-driven analyticsGeoscience Frontiers, 2016
- New Approach To Identify Analogous ReservoirsSPE Economics & Management, 2014
- Non-linear Heterogeneous Ensemble Model for Permeability Prediction of Oil ReservoirsArabian Journal for Science and Engineering, 2013
- Field Applications of Capacitance-Resistance Models in WaterfloodsSPE Reservoir Evaluation & Engineering, 2009
- A Capacitance Model To Infer Interwell Connectivity From Production and Injection Rate FluctuationsSPE Reservoir Evaluation & Engineering, 2006
- Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather predictionNeural Networks, 2006