Estimation of cuttings concentration and frictional pressure losses during drilling using data-driven models

• Published in: Volume 10: Petroleum Technology
• Main Authors: ÖzbayoÄŸlu, Murat, ÖzbayoÄŸlu, E., Özdilli, B.G., Erge, O.
• Format: Conference Object
• Language: English
• Published: American Society of Mechanical Engineers (ASME) 2021
• Subjects: Artificial neural networks; Adaboost; Cuttings transport; Machine learning; Random forest; Boreholes; Complex networks; Decision trees; Gas industry; Horizontal wells; Infill drilling; Mean square error; Neural networks; Offshore oil well production; Oil field equipment; Oil wells; Data-driven model; Dimensionless groups; Drilling practices; Frictional pressure loss; Mechanistic models; Mechanistics; Model inputs; Random forests; Wellbore; Adaptive boosting; Arctic
• Online Access: https://hdl.handle.net/20.500.11851/8312; https://doi.org/10.1115/OMAE2021-63653

Ocean, Offshore and Arctic Engineering Division 2021 40th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2021 -- 21 June 2021 through 30 June 2021 -- 172516 Drilling practice has been evolving parallel to the developments in the oil and gas industry. Current supply and demand for oil and gas dictate search for hydrocarbons either at much deeper and hard-to-reach fields, or at unconventional fields, both requiring extended reach wells, long horizontal sections, and 3D complex trajectories. Cuttings transport is one of the most challenging problems while drilling such wells, especially at mid-range inclinations. For many years, numerous studies have been conducted to address modeling of cuttings transport, estimation of the concentration of cuttings as well as pressure losses inside the wellbores, considering various drilling variables having influence on the process. However, such attempts, either mechanistic or empirical, have many limitations due to various simplifications and assumptions made during the development stage. Fluid thixotropy, temperature variations in the wellbore, uncertainty in pipe eccentricity as well as chaotic motion of cuttings due to pipe rotation, imperfections in the wellbore walls, variations in the size and shape of the cuttings, presence of tool joints on the drillstring, etc. causes the modeling of the problem extremely difficult. Due to the complexity of the process, the estimations are usually not very accurate, or not reliable. In this study, data-driven models are used to address the estimation of cuttings concentration and frictional loss estimation in a well during drilling operations, instead of using mechanistic or empirical methods. The selected models include Artificial Neural Networks, Random Forest, and AdaBoost. The training of the models is determined using the experimental data regarding cuttings transport tests collected in the last 40 years at The University of Tulsa – Drilling Research Projects, which includes a wide range of ...