Petrofacies classification using machine learning algorithms

[1]  Wen Zhou,et al.  Evaluation of machine learning methods for formation lithology identification: A comparison of tuning processes and model performances , 2018 .

[2]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[3]  Srikanta Mishra,et al.  Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian basin, USA , 2018, Journal of Petroleum Science and Engineering.

[4]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[5]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[6]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[7]  Branislav Bajat,et al.  Geological Units Classification of Multispectral Images by Using Support Vector Machines , 2009, 2009 International Conference on Intelligent Networking and Collaborative Systems.

[8]  Tapan Mukerji,et al.  Seismic Lithofacies Classification From Well Logs Using Statistical Rock Physics , 2002 .

[9]  Roseane M. Misságia,et al.  Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information , 2015 .

[10]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[11]  Georges G. Grinstein,et al.  DNA visual and analytic data mining , 1997 .

[12]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[13]  O. Serra,et al.  The Contribution of Logging Data to Sedimentology and Stratigraphy , 1982 .

[14]  Matthias Schmid,et al.  Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages , 2012 .

[15]  Jeffrey L. Baldwin,et al.  Application Of A Neural Network To The Problem Of Mineral Identification From Well Logs , 1990 .

[16]  Jay Alan Rushing,et al.  Rock Typing: Keys to Understanding Productivity in Tight Gas Sands , 2008 .

[17]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[18]  Sebastian Raschka,et al.  Python Machine Learning , 2015 .

[19]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[20]  Stanley Kolodzie,et al.  Analysis Of Pore Throat Size And Use Of The Waxman-Smits Equation To Determine Ooip In Spindle Field, Colorado , 1980 .

[21]  M. Rider,et al.  The Geological Interpretation of Well Logs , 1986 .

[22]  John H. Doveton,et al.  Geologic Log Analysis Using Computer Methods , 1994 .

[23]  E. Pittman Relationship of porosity and permeability to various parameters derived from mercury injection-capillary pressure curves for sandstone , 1992 .

[24]  Hao Luo,et al.  Three-Dimensional Model Analysis and Processing , 2011 .

[25]  Timothy R. Carr,et al.  Neural network prediction of carbonate lithofacies from well logs, Big Bow and Sand Arroyo Creek fields, Southwest Kansas , 2006, Comput. Geosci..

[26]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[27]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[28]  Le Yu,et al.  Towards automatic lithological classification from remote sensing data using support vector machines , 2010, Comput. Geosci..

[29]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[30]  Paulo Johann,et al.  Campos Basin: Reservoir Characterization and Management - Historical Overview and Future Challenges , 2003 .

[31]  Geoffrey I. Webb,et al.  Encyclopedia of Machine Learning , 2011, Encyclopedia of Machine Learning.

[32]  Ali Salehi,et al.  Lithology prediction by support vector classifiers using inverted seismic attributes data and petrophysical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir , 2015 .