TY - GEN
T1 - Modeling permeability prediction using extreme learning machines
AU - Olatunji, Sunday Olusanya
AU - Selamat, Ali
AU - Abdul Raheem, Abdul Azeez
PY - 2010
Y1 - 2010
N2 - In this work, an extreme learning machine (ELM) has been used in predicting permeability from well logs data have been investigated and a prediction model has been developed. The prediction model has been constructed using industrial reservoir datasets that are collected from a Middle Eastern petroleum reservoir. Prediction accuracy of the model has been evaluated and compared with commonly used artificial neural network and support vector machines (SVM). We have applied an extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs). As the ELM has the advantage of fast learning speed and good generalization performance. The simulation results have shown a promising prospect for extreme learning machine in the field of reservoir engineering in particular and oil and gas exploration in general, as it outperforms ANN and SVM.
AB - In this work, an extreme learning machine (ELM) has been used in predicting permeability from well logs data have been investigated and a prediction model has been developed. The prediction model has been constructed using industrial reservoir datasets that are collected from a Middle Eastern petroleum reservoir. Prediction accuracy of the model has been evaluated and compared with commonly used artificial neural network and support vector machines (SVM). We have applied an extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs). As the ELM has the advantage of fast learning speed and good generalization performance. The simulation results have shown a promising prospect for extreme learning machine in the field of reservoir engineering in particular and oil and gas exploration in general, as it outperforms ANN and SVM.
KW - Artificial neural networks
KW - Extreme learning machine
KW - Permeability estimation
KW - Reservoir characterization
KW - Support vector machine
KW - Well logs
UR - https://www.scopus.com/pages/publications/77955196107
U2 - 10.1109/AMS.2010.19
DO - 10.1109/AMS.2010.19
M3 - Conference contribution
AN - SCOPUS:77955196107
SN - 9780769540627
T3 - AMS2010: Asia Modelling Symposium 2010 - 4th International Conference on Mathematical Modelling and Computer Simulation
SP - 29
EP - 33
BT - AMS2010
T2 - Asia Modelling Symposium 2010: 4th International Conference on Mathematical Modelling and Computer Simulation, AMS2010
Y2 - 26 May 2010 through 28 May 2010
ER -