Petrofacies classification using machine learning algorithms
暂无分享,去创建一个
Roseane M. Misságia | Abel Carrasquilla | Adrielle Silva | Mônica Tavares | Marco Ceia | A. Carrasquilla | M. Ceia | R. Misságia | Adrielle Silva | Mônica W. Tavares
[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 .