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29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536, In this study, our primary aim is to detect different formations, objects on the images taken from various underwater videos. For this purpose, machine learning models such as SVM, multi-layer perceptron, logistic regression that use attributes, image histogram obtained from images were chosen. In addition, Autoencoder and CNN based deep learning models were used directly over images and their performances were compared. According to the results, it was observed that all models were satisfactory and achieved good classification performances. The highest performance was observed in the Autoencoder based deep learning model, which achieved an accuracy level of %95. In the future, we are planning to continue studies to focus on underwater cable tracking and detecting errors and anomalies in underwater cables. © 2021 IEEE.

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 wellbore and pipe sizes, inclinations, ROPs, pipe rotation speeds, flow rates, fluid and cuttings properties. The evaluation of the models is conduct

29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536, In this study, our primary aim is to detect different formations, objects on the images taken from various underwater videos. For this purpose, machine learning models such as SVM, multi-layer perceptron, logistic regression that use attributes, image histogram obtained from images were chosen. In addition, Autoencoder and CNN based deep learning models were used directly over images and their performances were compared. According to the results, it was observed that all models were satisfactory and achieved good classification performances. The highest performance was observed in the Autoencoder based deep learning model, which achieved an accuracy level of %95. In the future, we are planning to continue studies to focus on underwater cable tracking and detecting errors and anomalies in underwater cables. © 2021 IEEE.

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 wellbore and pipe sizes, inclinations, ROPs, pipe rotation speeds, flow rates, fluid and cuttings properties. The evaluation of the models is conduct