Lost circulation prediction based on machine learning

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

(2020) Ruizhi Zhong et al.   SPE JOURNAL

A New Ensemble Machine-Learning Framework for Searching Sweet Spots in Shale Reservoirs

(2020) Jizhou Tang et al.   SPE JOURNAL

Predict the mud loss in natural fractured vuggy reservoir using discrete fracture and discrete vug network model

(2020) Shiming Wei et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

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

Modeling Lost Circulation Through Drilling-Induced Fractures

(2018) Yongcun Feng et al.   SPE JOURNAL

Using recursive feature elimination in random forest to account for correlated variables in high dimensional data

(2018) Burcu F. Darst et al.   BMC GENETICS

Using data mining to stop or mitigate lost circulation

(2018) Husam H. Alkinani et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Hydrodynamic modeling of mud loss controlled by the coupling of discrete fracture and matrix

(2015) Yang Xia et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Comprehensive methodology for detecting fracture aperture in naturally fractured formations using mud loss data

(2015) Yang Xia et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

A multiple-continuum model for simulating single-phase and multiphase flow in naturally fractured vuggy reservoirs

(2011) Yu-Shu Wu et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Radial flow of yield-power-law fluids: Numerical analysis, experimental study and the application for drilling fluid losses in fractured formations

(2009) Reza Majidi et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING