Analysis of rate of penetration prediction in drilling using data-driven models based on weight on hook measurement

[1]  H. Jalalifar,et al.  Half a century experience in rate of penetration management: Application of machine learning methods and optimization algorithms - A review , 2021, Journal of Petroleum Science and Engineering.

[2]  Hossein Jalalifar,et al.  Fifty years of experience in rate of penetration management: Managed pressure drilling technology, mechanical specific energy concept, bit management approach and expert systems - A review , 2021, Journal of Petroleum Science and Engineering.

[3]  Mohammadhadi Shateri,et al.  Experimental measurement and modeling of water-based drilling mud density using adaptive boosting decision tree, support vector machine, and K-nearest neighbors: A case study from the South Pars gas field , 2021 .

[4]  Mohsen Hadian,et al.  A formation-based approach for modeling of rate of penetration for an offshore gas field using artificial neural networks , 2021, Journal of Natural Gas Science and Engineering.

[5]  A. Diveev,et al.  Machine Learning Control by Symbolic Regression , 2021 .

[6]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[7]  Chinedu I. Ossai,et al.  Applications and theoretical perspectives of artificial intelligence in the rate of penetration , 2020 .

[8]  H. Jalalifar,et al.  The effect of formation thickness on the performance of deterministic and machine learning models for rate of penetration management in inclined and horizontal wells , 2020 .

[9]  A. Aladejare Evaluation of empirical estimation of uniaxial compressive strength of rock using measurements from index and physical tests , 2020, Journal of Rock Mechanics and Geotechnical Engineering.

[10]  Applications of Artificial Intelligence Techniques in the Petroleum Industry , 2020 .

[11]  Mauro Hugo Mathias,et al.  Machine learning methods applied to drilling rate of penetration prediction and optimization - A review , 2019 .

[12]  Brandon M. Greenwell,et al.  Hands-On Machine Learning with R , 2019 .

[13]  A. Abbas,et al.  Drilling Rate of Penetration Prediction of High-Angled Wells Using Artificial Neural Networks , 2019, Journal of Energy Resources Technology.

[14]  Hu Yule,et al.  Prediction of drilling rate of penetration (ROP) using hybrid support vector regression: A case study on the Shennongjia area, Central China , 2019, Journal of Petroleum Science and Engineering.

[15]  Seyed Babak Ashrafi,et al.  Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field , 2019, Journal of Petroleum Science and Engineering.

[16]  David A. Wood,et al.  A machine learning approach to predict drilling rate using petrophysical and mud logging data , 2019, Earth Science Informatics.

[17]  Kenneth E. Gray,et al.  Real-time predictive capabilities of analytical and machine learning rate of penetration (ROP) models , 2019, Journal of Petroleum Science and Engineering.

[18]  Ahmed A. Adeniran,et al.  Computational intelligence based prediction of drilling rate of penetration: A comparative study , 2019, Journal of Petroleum Science and Engineering.

[19]  Salaheldin Elkatatny,et al.  New Approach to Optimize the Rate of Penetration Using Artificial Neural Network , 2017, Arabian Journal for Science and Engineering.

[20]  Z. Wen,et al.  Structural characteristics and main controlling factors on petroleum accumulation in Zagros Basin, Middle East , 2018, Journal of Natural Gas Geoscience.

[21]  Chiranth Hegde,et al.  Evaluation of coupled machine learning models for drilling optimization , 2018, Journal of Natural Gas Science and Engineering.

[22]  Behzad Tokhmechi,et al.  Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network , 2018 .

[23]  David A. Wood,et al.  Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling Rate , 2018 .

[24]  Kwang Yeom Kim,et al.  Drilling data from an enhanced geothermal project and its pre-processing for ROP forecasting improvement , 2018 .

[25]  M. S. Momeni,et al.  An optimum drill bit selection technique using artificial neural networks and genetic algorithms to increase the rate of penetration , 2018 .

[26]  Mostafa Sharifzadeh,et al.  Performance assessment of rotary drilling using non-linear multiple regression analysis and multilayer perceptron neural network , 2019, Bulletin of Engineering Geology and the Environment.

[27]  Harry R. Millwater,et al.  Analysis of rate of penetration (ROP) prediction in drilling using physics-based and data-driven models , 2017 .

[28]  Aurélien Géron,et al.  Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems , 2017 .

[29]  Behzad Tokhmechi,et al.  Effect of Rock Properties on ROP Modeling Using Statistical and Intelligent Methods: A Case Study of an Oil Well in Southwest of Iran , 2017 .

[30]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[31]  Kwang Yeom Kim,et al.  Rate of penetration ( ROP ) forecast based on artificial neural network with online learning , 2017 .

[32]  Melvin B. Diaz,et al.  On-Line Prediction Model for Rate of Penetration (ROP) With Cumulating Field Data in Real Time , 2017 .

[33]  H. J. Skadsem,et al.  Accuracy and Correction of Hook Load Measurements During Drilling Operations , 2015 .

[34]  Liu Tianyu,et al.  A New Model of ROP Prediction for Drilling Engineering with Data Mining Technology , 2013 .

[35]  Seyed Reza Shadizadeh,et al.  Modeling and Optimizing Rate of Penetration Using Intelligent Systems in an Iranian Southern Oil Field (Ahwaz Oil Field) , 2011 .

[36]  Mohamad Hasan Bahari,et al.  Drilling rate prediction using an innovative soft computing approach , 2010 .

[37]  Jerry Osmond,et al.  Sophisticated ROP Prediction Technology Based on Neural Network Delivers Accurate ResultsSophisticated ROP Prediction Technology Based on Neural Network Delivers Accurate Results , 2010 .

[38]  Karim Salahshoor,et al.  Comparison of the Penetration Rate Models Using Field Data for One of the Gas Fields in Persian Gulf Area , 2010 .

[39]  Geir Hareland,et al.  Improved Drilling Efficiency Technique Using Integrated PDM and PDC Bit Parameters , 2010 .

[40]  Celal Karpuz,et al.  Estimating Drilling Parameters for Diamond Bit Drilling Operations Using Artificial Neural Networks , 2008 .

[41]  Tiago Cardoso da Fonseca,et al.  Applying a genetic neuro-model reference adaptive controller in drilling optimization , 2007 .

[42]  Ivan Rizzo Guilherme,et al.  A Genetic Neuro-Model Reference Adaptive Controller for Petroleum Wells Drilling Operations , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

[43]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

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

[45]  H. Rabia,et al.  Dynamic conditions complicate weight indicator interpretation , 1988 .

[46]  F. S. Young,et al.  A Multiple Regression Approach to Optimal Drilling and Abnormal Pressure Detection , 1974 .

[47]  J. Eckel Microbit Studies of the Effect of Fluid Properties and Hydraulics on Drilling Rate , 1967 .