Establishment of data-driven multi-objective model to optimize drilling performance

Author:

Qu Fengtao,Liao Hualin,Liu Jiansheng,Lu Ming,Wang Huajian,Zhou Bo,Liang Hongjun

Funder

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

China National Petroleum Corporation

National Key Scientific Instrument and Equipment Development Projects of China

Publisher

Elsevier BV

Reference56 articles.

1. The MSE Ratio: the New Diagnostic Tool to Optimize Drilling Performance in Real-Time for Under-reaming Operations;Abbott,2015

2. Computational Intelligence Based Prediction of Drilling Rate of Penetration: A Comparative Study;Ahmed,2019

3. Hybrid data driven drilling and rate of penetration optimization;Alali;J. Pet. Sci. Eng.,2021

4. Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network;Anemangely;J. Geophys. Eng.,2018

5. Missing log data interpolation and semiautomatic seismic well ties using data matching techniques;Bader;Interpretation,2018

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