INCREASING ANN ACCURACY, BY IMPROVING THE TRAINING DATASET CRITERIA. CASE STUDY: IDENTIFY THE FORMATION DENSITY FROM THE DRILLING SURFACE PARAMETERS IN REAL-TIME

von Gharbi, S.
Veröffentlicht in SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition (2018 : Ad-Dammam) SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition 2018 ; Volume 3 of 6 (2018)

MODELING AND PREDICTION OF RESISTIVITY, CAPILLARY PRESSURE AND RELATIVE PERMEABILITY USING ARTIFICIAL NEURAL NETWORK

von Ba-alawi, M.
Veröffentlicht in International Petroleum Technology Conference (12. : 2020 : Dhahran) International Petroleum Technology Conference 2020 ; Volume 5 of 7 (2020)

A REAL-TIME INDICATOR FOR THE EVALUATION OF HOLE CLEANING EFFICIENCY

von Alawami, M.
Veröffentlicht in SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition (2019 : Nusa Dua) SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2019 ; Volume 3 of 4 (2020)

IMPROVING REAL-TIME DRILLING DATA QUALITY USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUES

von Gharbi, S. H. Al
Veröffentlicht in SPE Middle East Oil & Gas Show and Conference (22. : 2021 : Sanābis) SPE Middle East Oil & Gas Show and Conference 2021 ; Volume 1 of 5 (2022)