Application of machine learning and well log attributes in geothermal drilling

Author:

Kiran Raj,Dansena PrabhatORCID,Salehi Saeed,Rajak Vinay Kumar

Publisher

Elsevier BV

Subject

Geology,Geotechnical Engineering and Engineering Geology,Renewable Energy, Sustainability and the Environment

Reference73 articles.

1. Predicting the amount of lost circulation while drilling using artificial neural networks: An example of southern Iraq oil fields;Abbas,2019

2. Learning activation functions to improve deep neural networks;Agostinelli,2015

3. Prediction of pore and fracture pressures using support vector machine;Ahmed S,2019

4. Ahmmed, B., Vesselinov, V., 2021. Prospectivity Analyses of the Utah FORGE Site using Unsupervised Machine Learning. In: Proceedings, Geothermal Rising, Vol. 1. San Diego, CA..

5. Al-Khdheeawi, E.A., Mahdi, D.S., Feng, R., 2019. Lithology Determination from Drilling Data Using Artificial Neural Network. In: U.S. Rock Mechanics/Geomechanics Symposium. All Days.

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