Please use this identifier to cite or link to this item:
Authors: Erge, O.
Ozbayoglu, M.
Ozbayoglu, E.
Keywords: Deep learning
drilling rate prediction
genetic algorithm
Deep learning
Infill drilling
Learning algorithms
Learning systems
Oil well drilling
Oil wells
Parameter estimation
Rotation rate
Deep learning
Drilling parameters
Drilling rate prediction
Drilling rates
Learning models
Mechanical specific energies
Rate of penetration
Rate predictions
Weight on bits
Well constructions
Genetic algorithms
Issue Date: 2022
Publisher: American Society of Mechanical Engineers (ASME)
Abstract: In this study, a deep learning model is proposed that can accurately predict the rate of penetration during geothermal or oil and gas well construction operations. Also, a genetic algorithm is applied and used together with the deep learning model to determine the optimum values for the drilling parameters: weight on bit (WOB) and drillstring rotation rate (RPM). It is vital to estimate the optimal set of values for drilling parameters to construct wellbores quickly and efficiently. Traditionally, drill-off tests are conducted by halting the normal drilling operation and manually changing the WOB and RPM values to search for the highest ROP output. This operation can be repetitive and can lead to an inaccurate estimation of parameters because only a few different parameters are tried. The proposed learning algorithm estimates the optimum WOB and RPM, based on the historical values and can keep learning as the drilling proceeds, which is essential for fully automated well construction. The proposed deep learning model is trained with actual drilling datasets that showed an accurate prediction of the rate of penetration and mechanical specific energy (MSE). This model is used together with the genetic algorithm and the optimum drilling parameters are determined that yield minimum MSE. The results showed a significant performance improvement compared to the historical values. The proposed model can be used as an advisory system to the driller or the output can be used within the control system to automate the drilling process. The proposed learning model showed the capability to further optimize the drilling-rate and mitigate any invisible lost time (ILT) and potential nonproductive time (NPT) by completing the well as soon as possible. Copyright © 2022 by ASME.
Description: Ocean, Offshore and Arctic Engineering Division
ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022 -- 5 June 2022 through 10 June 2022 -- 183473
ISBN: 9780791885956
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering

Show full item record

CORE Recommender

Page view(s)

checked on Oct 2, 2023

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.