Evolving strategies for shear wave velocity estimation: smart and ensemble modeling approach
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Abdul Azeez Abdul Raheem | Zeeshan Tariq | Mohamed Mahmoud | Teslim Olayiwola | Teslim Olayiwola | Zeeshan Tariq | A. Abdulraheem | M. Mahmoud
[1] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[2] Robert C. Glen,et al. Random Forest Models To Predict Aqueous Solubility , 2007, J. Chem. Inf. Model..
[3] Abdulazeez Abdulraheem,et al. An intelligent data-driven model for Dean–Stark water saturation prediction in carbonate rocks , 2019, Neural Computing and Applications.
[4] J. P. Castagna,et al. Shear-wave velocity estimation in porous rocks: theoretical formulation, preliminary verification and applications , 1992 .
[5] A. Ramezanzadeh,et al. Machine learning technique for the prediction of shear wave velocity using petrophysical logs , 2019, Journal of Petroleum Science and Engineering.
[6] G. R. Pickett,et al. Acoustic Character Logs and Their Applications in Formation Evaluation , 1963 .
[7] S. Ji,et al. Shear wave properties and Poisson's ratios of ultrahigh-pressure metamorphic rocks from the Dabie-Sulu orogenic belt, China: Implications for crustal composition , 2005 .
[8] H. Memarian,et al. Shear wave velocity prediction using Elman artificial neural network , 2017, Carbonates and Evaporites.
[9] Børge Arntsen,et al. Imaging through gas-filled sediments using marine shear-wave data , 1999 .
[10] T. Brocher. Empirical relations between elastic wavespeeds and density in the Earth's crust , 2005 .
[11] Mohamed Mahmoud,et al. Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction , 2019, Neural Computing and Applications.
[12] A. Najibi,et al. Prediction of seismic-wave velocities in rock at various confining pressures based on unconfined data , 2014 .
[13] M. Zoveidavianpoor. An integrated approach in determination of elastic rock properties from well log data in a heterogeneous carbonate reservoir , 2017 .
[14] Kaveh Ahangari,et al. Modeling of shear wave velocity in limestone by soft computing methods , 2017 .
[15] Mohamed Mahmoud,et al. New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network , 2017, Neural Computing and Applications.
[16] Alexander J. Smola,et al. Support Vector Regression Machines , 1996, NIPS.
[17] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[18] M R Rezaei,et al. SHEAR WAVE VELOCITY ESTIMATION UTILIZING WIRELINE LOGS FOR A CARBONATE RESERVOIR, SOUTH-WEST IRAN , 2003 .
[19] Guiling Wang,et al. Modeling seasonal vegetation variation and its validation against Moderate Resolution Imaging Spectroradiometer (MODIS) observations over North America , 2005 .
[20] A. Garrouch,et al. Investigating the dependence of shear wave velocity on petrophysical parameters , 2016 .
[21] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[22] Mojtaba Asoodeh,et al. ACE stimulated neural network for shear wave velocity determination from well logs , 2014 .
[23] Amir Nourafkan,et al. Shear wave velocity estimation from conventional well log data by using a hybrid ant colony–fuzzy inference system: A case study from Cheshmeh–Khosh oilfield , 2015 .
[24] S. Butt,et al. Data-driven shear wave velocity prediction model for siliciclastic rocks , 2019 .
[25] Mattia Aleardi,et al. Seismic velocity estimation from well log data with genetic algorithms in comparison to neural networks and multilinear approaches , 2015 .
[26] Tohru Watanabe,et al. Compressional and shear wave velocities of serpentinized peridotites up to 200 MPa , 2007 .
[27] B. Xia,et al. Handbook of seismic properties of minerals, rocks and ores , 2002 .
[28] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[29] J. Friedman. Stochastic gradient boosting , 2002 .
[30] N. Christensen,et al. Compressional wave velocities in possible mantle rocks to pressures of 30 kilobars , 1974 .
[31] Salaheldin Elkatatny,et al. Estimation of Rock Mechanical Parameters Using Artificial Intelligence Tools , 2017 .
[32] E. Fjaer,et al. Petroleum Related Rock Mechanics , 1992 .
[33] Bahman Bohloli,et al. Intelligent approaches for prediction of compressional, shear and Stoneley wave velocities from conventional well log data: A case study from the Sarvak carbonate reservoir in the Abadan Plain (Southwestern Iran) , 2010, Comput. Geosci..
[34] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[35] R. D. Carroll. The determination of the acoustic parameters of volcanic rocks from compressional velocity measurements , 1969 .
[36] M. Zoback,et al. Empirical relations between rock strength and physical properties in sedimentary rocks , 2006 .
[37] Behzad Tokhmechi,et al. Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour Oilfield , 2017 .
[38] Robert P. Sheridan,et al. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..
[39] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[40] David Zhang,et al. Feature selection and analysis on correlated gas sensor data with recursive feature elimination , 2015 .
[41] C. Furlanello,et al. Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products , 2006 .
[42] D. Grana,et al. Seismic Reflections of Rock Properties , 2014 .
[43] M. Zoback,et al. Empirical relationships among seismic velocity, effective pressure, porosity, and clay content in sandstone , 1989 .