Machine learning for drilling applications: A review

Practical Machine-Learning Applications in Well-Drilling Operations

(2021) T. A. Olukoga et al.   SPE DRILLING & COMPLETION

Combining physics-based and data-driven modeling in well construction: Hybrid fluid dynamics modeling

(2021) Oney Erge et al.   Journal of Natural Gas Science and Engineering

Downhole data correction for data-driven rate of penetration prediction modeling

(2021) Mauro A. Encinas et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Generating pseudo density log from drilling and logging-while-drilling data using extreme gradient boosting (XGBoost)

(2020) Ruizhi Zhong et al.   INTERNATIONAL JOURNAL OF COAL GEOLOGY

Using Machine Learning Methods To Identify Coal Pay Zones from Drilling and Logging-While-Drilling (LWD) Data

(2020) Ruizhi Zhong et al.   SPE JOURNAL

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

(2020) Javed Akbar Khan et al.   Energies

A new methodology for kick detection during petroleum drilling using long short-term memory recurrent neural network

(2020) Augustine Osarogiagbon et al.   PROCESS SAFETY AND ENVIRONMENTAL PROTECTION

Predicting seismic-based risk of lost circulation using machine learning

(2019) Zhi Geng et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field

(2019) Mohammad Sabah et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Intelligent decisions to stop or mitigate lost circulation based on machine learning

(2019) Ahmed K. Abbas et al.   ENERGY

Classification of drilling stick slip severity using machine learning

(2019) Chiranth Hegde et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

A machine learning approach to filtrate loss determination and test automation for drilling and completion fluids

(2019) Sercan Gul et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Machine learning methods applied to drilling rate of penetration prediction and optimization - A review

(2019) Luís Felipe F.M. Barbosa et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Big Data Analysis for Monitoring of Kick Formation in Complex Underwater Drilling Projects

(2018) Haiyan Xie et al.   JOURNAL OF COMPUTING IN CIVIL ENGINEERING

Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network

(2018) Mohammad Anemangely et al.   Journal of Geophysics and Engineering

Kick control reliability analysis of managed pressure drilling operation

(2018) Idris Sule et al.   JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES

Artificial intelligence techniques and their applications in drilling fluid engineering: A review

(2018) Okorie E. Agwu et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Performance Comparison of Algorithms for Real-Time Rate-of-Penetration Optimization in Drilling Using Data-Driven Models

(2018) Chiranth Hegde et al.   SPE JOURNAL

Predicting the pressure losses while the drillstring is buckled and rotating using artificial intelligence methods

(2018) Evren M. Ozbayoglu et al.   Journal of Natural Gas Science and Engineering

Time series data analysis for automatic flow influx detection during drilling

(2018) Hewei Tang et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Real-time predictive capabilities of analytical and machine learning rate of penetration (ROP) models

(2018) Cesar Soares et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Data integration modeling applied to drill hole planning through semi-supervised learning: A case study from the Dalli Cu-Au porphyry deposit in the central Iran

(2017) Moslem Fatehi et al.   JOURNAL OF AFRICAN EARTH SCIENCES

Development of an ANN-based soft-sensor to estimate the apparent viscosity of water-based drilling fluids

(2017) Vitor Diego da Silva Bispo et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Analysis of rate of penetration (ROP) prediction in drilling using physics-based and data-driven models

(2017) Chiranth Hegde et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box)

(2016) Salaheldin Elkatatny et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Estimation of Pressure Loss of Herschel–Bulkley Drilling Fluids During Horizontal Annulus Using Artificial Neural Network

(2014) Reza Rooki   JOURNAL OF DISPERSION SCIENCE AND TECHNOLOGY

A New Support Vector Machine and Artificial Neural Networks for Prediction of Stuck Pipe in Drilling of Oil Fields

(2014) Habib Rostami et al.   JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME

Artificial neural network-based prediction and geomechanical analysis of lost circulation in naturally fractured reservoirs: a case study

(2013) Reza Jahanbakhshi et al.   European Journal of Environmental and Civil Engineering

Artificial Neural Network Modeling of Plastic Viscosity, Yield Point, and Apparent Viscosity for Water-Based Drilling Fluids

(2013) Meisam Mirarab Razi et al.   JOURNAL OF DISPERSION SCIENCE AND TECHNOLOGY

Using Bayesian networks in reliability evaluation for subsea blowout preventer control system

(2012) Baoping Cai et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

Intelligent Prediction of Differential Pipe Sticking by Support Vector Machine Compared With Conventional Artificial Neural Networks: An Example of Iranian Offshore Oil Fields

(2012) Reza Jahanbakhshi et al.   SPE DRILLING & COMPLETION

Improved Drilling Efficiency Technique Using Integrated PDM and PDC Bit Parameters

(2010) H.R. Motahhari et al.   JOURNAL OF CANADIAN PETROLEUM TECHNOLOGY