Prediction of Critical Multiphase Flow Through Chokes by Using A Rigorous Artificial Neural Network Method

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

“…In the area of production, ML models and its hybrids are mainly used for the monitoring, prediction, forecasting, selection and detection of various characteristics and components critical for optimal production. This includes, choke valve flow-rates, production at ultra-high water cut stages, precipitation of asphaltene, sand production, separator selection, faulty events, production forecasting and predictive maintenance [20][21][22][23][24][25][26][27][28][29].…”

“…learning -Supervised learning & Genetic algorithm]; Hybrid [Machine learning -Supervised learning & Swarm intelligence] Mohamadian et al (2021) [14] Field development (Drilling) Machine learning -Supervised learning; Fuzzy logic; Swarm intelligence; Genetic algorithm; Hybrid (Multiple types) Ossai and Duru (2020) [15] Field development (Drilling) Machine learning -Multigene genetic programming Agwu et al (2021) [16] Field development (Drilling) Machine learning -Supervised learning Agwu et al (2018) [17] Field development (Reservoirs) Hybrid [Machine learning -Supervised learning & Swarm intelligence]; Fuzzy logic; Hybrid [Genetic algorithm & Fuzzy logic] Ahmadi et al (2014) [18] Field development (Reservoirs) Machine learning -Reinforcement learning Hourfar et al (2019) [19] Production Machine learning -Supervised learning; Genetic algorithm used as an optimizer Rashid et al (2019)[20] …”

Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inflow control devices

“…On the other hand, mathematical models can only be developed for one valve, and when there are several valves in a well, a separate model must be developed for each valve. In recent years, many soft computing techniques and machine-learning methods, some hybridized with efficient optimization algorithms, have been adopted as powerful approaches to predict various parameters associated with complex systems in the oil and gas industry (Rabiei et al, 2015;Jovic et al, 2016;Wood, 2018;Yavari et al, 2018;Ashfari et al, 2019;Barbosa et al, 2019;Rashid et al, 2019;Sabah et al, 2019;Yilmaz et al, 2019;Elkatatny, 2020;Gamal et al, 2020;Ghorbani et al, 2020;Mehrad et al, 2020;Moazzeni and Khamehchi, 2020;Ossai and Duru, 2020;Somehsaraei et al, 2020;Abad et al, 2021;Hazbeh et al, 2021;Mardanirad et al, 2021;Mohamadian et al, 2021).…”

PCNN: A physics-constrained neural network for multiphase flows

“…Based on the given experimental and theoretical velocity data, Valero and Bung [51] identified the characteristic shapes of the fluid phases and explored the behaviors of the air-water flow. Rashid et al [44] developed a radial basis function NN to predict MPFs under critical conditions and discovered the relationships between the upstream pressure and gas-liquid ratio, and between the choke bin size and liquid flow rate. Serra et al [47] proposed a randomized hough transform with an NN to predict the void fraction with given image samples, and their results are consistent with the actual void fraction values in natural circulation-based systems of nuclear power plants.…”