Related references
Note: Only part of the references are listed.Application of Rough Neural Network to forecast oil production rate of an oil field in a comparative study
Amin Sheikhoushaghi et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2022)
Toward evaluation and screening of the enhanced oil recovery scenarios for low permeability reservoirs using statistical and machine learning techniques
Mehdi Mahdaviara et al.
FUEL (2022)
A comparative analysis of gradient boosting algorithms
Candice Bentejac et al.
ARTIFICIAL INTELLIGENCE REVIEW (2021)
A data-driven shale gas production forecasting method based on the multi-objective random forest regression
Liang Xue et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2021)
Practical CO2-WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches
Qian Sun et al.
ENERGIES (2021)
Data driven structural dynamic response reconstruction using segment based generative adversarial networks
Gao Fan et al.
ENGINEERING STRUCTURES (2021)
A survey on ensemble learning
Xibin Dong et al.
FRONTIERS OF COMPUTER SCIENCE (2020)
Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model
Xuanyi Song et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2020)
Development and application of a machine learning based multi-objective optimization workflow for CO2-EOR projects
Junyu You et al.
FUEL (2020)
Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions
Raffaele Cioffi et al.
SUSTAINABILITY (2020)
Marine Predators Algorithm: A nature-inspired metaheuristic
Afshin Faramarzi et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
Well predictive performance of play-wide and Subarea Random Forest models for Bakken productivity
Emil D. Attanasi et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2020)
Forecasting oil production using ensemble empirical model decomposition based Long Short-Term Memory neural network
Wei Liu et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2020)
Predicting field production rates for waterflooding using a machine learning-based proxy model
Zhi Zhong et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2020)
Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection
Berihun Mamo Negash et al.
PETROLEUM EXPLORATION AND DEVELOPMENT (2020)
Production prediction at ultra-high water cut stage via Recurrent Neural Network
Wang Hongliang et al.
PETROLEUM EXPLORATION AND DEVELOPMENT (2020)
Insights to fracture stimulation design in unconventional reservoirs based on machine learning modeling
Shuhua Wang et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2019)
Applicability of deep neural networks on production forecasting in Bakken shale reservoirs
Shuhua Wang et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2019)
Time series forecasting of petroleum production using deep LSTM recurrent networks
Alaa Sagheer et al.
NEUROCOMPUTING (2019)
Oil Production Monitoring using Gradient Boosting Machine Learning Algorithm
Timur Bikmukhametov et al.
IFAC PAPERSONLINE (2019)
Gradient boosting to boost the efficiency of hydraulic fracturing
Ivan Makhotin et al.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY (2019)
Correct understanding and application of waterflooding characteristic curves
Dou Hongen et al.
PETROLEUM EXPLORATION AND DEVELOPMENT (2019)
Predicting the oil production using the novel multivariate nonlinear model based on Arps decline model and kernel method
Xin Ma et al.
NEURAL COMPUTING & APPLICATIONS (2018)
Neural Networks for Prediction of Oil Production
Leyla Muradkhanli
IFAC PAPERSONLINE (2018)
Evaluating the critical performances of a CO2-Enhanced oil recovery process using artificial neural network models
Si Le Van et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2017)
New water drive characteristic curves at ultra-high water cut stage
Wang Jiqiang et al.
PETROLEUM EXPLORATION AND DEVELOPMENT (2017)
CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests
Li Ma et al.
BMC BIOINFORMATICS (2017)
Application of Multidimensional Interpolation and Random Forest Regression to Enhanced Oil Recovery Modeling
Fedor Krasnov et al.
CEE-SECR'17: PROCEEDINGS OF THE 13TH CENTRAL & EASTERN EUROPEAN SOFTWARE ENGINEERING CONFERENCE IN RUSSIA (2017)
Multilayer Neural Network with Multi-Valued Neurons in time series forecasting of oil production
Igor Aizenberg et al.
NEUROCOMPUTING (2016)
Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
V. Rodriguez-Galiano et al.
ORE GEOLOGY REVIEWS (2015)
Machine learning: Trends, perspectives, and prospects
M. I. Jordan et al.
SCIENCE (2015)
Anomaly Detection Based on Sensor Data in Petroleum Industry Applications
Luis Marti et al.
SENSORS (2015)
Artificial Neural Network Modeling for the Prediction of Oil Production
S. Elmabrouk et al.
PETROLEUM SCIENCE AND TECHNOLOGY (2014)
Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir
Mohammad Ali Ahmadi et al.
APPLIED SOFT COMPUTING (2013)
Production data analysis of unconventional gas wells: Review of theory and best practices
C. R. Clarkson
INTERNATIONAL JOURNAL OF COAL GEOLOGY (2013)
Derivation of water flooding characteristic curve for high water-cut oilfields
Song Zhaojie et al.
PETROLEUM EXPLORATION AND DEVELOPMENT (2013)
The Application of Artificial Neural Networks for the Prediction of Oil Production Flow Rate
A. Mirzaei-Paiaman et al.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS (2012)
Recent progress on reservoir history matching: a review
Dean S. Oliver et al.
COMPUTATIONAL GEOSCIENCES (2011)
Fault detection and other time series opportunities in the petroleum industry
Roar Nybo
NEUROCOMPUTING (2010)
Using a support vector machine method to predict the development indices of very high water cut oilfields
Zhong Yihua et al.
PETROLEUM SCIENCE (2010)
GSA: A Gravitational Search Algorithm
Esmat Rashedi et al.
INFORMATION SCIENCES (2009)
Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition
Esmaeil Atashpaz-Gargari et al.
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS (2007)
COVNET:: A cooperative coevolutionary model for evolving artificial neural networks
N García-Pedrajas et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2003)
Random forest: A classification and regression tool for compound classification and QSAR modeling
V Svetnik et al.
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES (2003)
Share Paper
