Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review

S Zendehboudi, N Rezaei, A Lohi - Applied energy, 2018 - Elsevier
Mathematical modeling and simulation methods are important tools in studying various
processes in science and engineering. In the current review, we focus on the applications of …

Application of artificial intelligence techniques in the petroleum industry: a review

H Rahmanifard, T Plaksina - Artificial Intelligence Review, 2019 - Springer
In recent years, artificial intelligence (AI) has been widely applied to optimization problems
in the petroleum exploration and production industry. This survey offers a detailed literature …

Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir

MA Ahmadi, M Ebadi, A Shokrollahi, SMJ Majidi - Applied Soft Computing, 2013 - Elsevier
Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in
numerous numbers of oil production applications like those in remote or unmanned …

Performance forecasting for polymer flooding in heavy oil reservoirs

E Amirian, M Dejam, Z Chen - Fuel, 2018 - Elsevier
As a supply for future fuel and energy demand, 95% of the bitumen deposits in North
America are expected to become a major source. The Steam Assisted Gravity Drainage …

Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches

A Baghban, MA Ahmadi, BH Shahraki - The Journal of supercritical fluids, 2015 - Elsevier
Ionic liquids (ILs) are highly promising for industrial applications such as design and
development of gas sweetening processes. For a safe and economical design, prediction of …

Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs

ARB Abad, S Mousavi, N Mohamadian… - Journal of Natural Gas …, 2021 - Elsevier
Gas condensate reservoirs display unique phase behavior and are highly sensitive to
reservoir pressure changes. This makes it difficult to determine their PVT characteristics …

Neural network-based fuel consumption estimation for container ships in Korea

LT Le, G Lee, KS Park, H Kim - Maritime Policy & Management, 2020 - Taylor & Francis
Due to the outstanding strength of advanced machine-learning techniques, they have
become increasingly common in predictive studies in recent years, particularly in predicting …

[HTML][HTML] Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool

MA Ahmadi, R Soleimani, M Lee, T Kashiwao… - Petroleum, 2015 - Elsevier
Greater complexity is involved in the transient pressure analysis of horizontal oil wells in
contrast to vertical wells, as the horizontal wells are considered entirely horizontal and …

Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO

F Gaxiola, P Melin, F Valdez, JR Castro… - Applied Soft Computing, 2016 - Elsevier
In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms
(GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy …

Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach

MA Ahmadi, M Ebadi, SM Hosseini - Fuel, 2014 - Elsevier
Owing to water coning, water flows into the production wellbore from below the perforated
channels and normally causes several technical issues in wellbore and surface production …