M
Mohamed Mahmoud
Researcher at King Fahd University of Petroleum and Minerals
Publications - 453
Citations - 6666
Mohamed Mahmoud is an academic researcher from King Fahd University of Petroleum and Minerals. The author has contributed to research in topics: Drilling fluid & Chemistry. The author has an hindex of 30, co-authored 385 publications receiving 4066 citations. Previous affiliations of Mohamed Mahmoud include Suez University & Cairo University.
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Cooling load prediction for buildings using general regression neural networks
TL;DR: In this paper, the authors used General Regression Neural Networks (GRNNs) to optimize HVAC thermal energy storage in public buildings as well as office buildings using climate records in Kuwait.
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Development of a New Correlation of Gas Compressibility Factor (Z-Factor) for High Pressure Gas Reservoirs
TL;DR: In this paper, a new correlation has been developed using regression for more than 300 data points of measured Z-factor using matlab in addition to other data points at low pressure and temperature from standing-Katz charts and DAK correlation.
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Oilfield scale formation and chemical removal: A review
Muhammad Shahzad Kamal,Ibnelwaleed A. Hussein,Mohamed Mahmoud,Abdullah S. Sultan,Mohammed A. Saad +4 more
TL;DR: In this paper, a review of different types of scales that are common in oil and gas production operations, their sources and formation mechanisms are discussed, and several alternatives to HCl that are more environment-friendly in removing oilfield scale deposits are discussed.
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Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network
Ahmed Abdulhamid Mahmoud,Ahmed Abdulhamid Mahmoud,Salaheldin Elkatatny,Mohamed Mahmoud,Mohamed Mahmoud,Mohamed Abouelresh,Mohamed Abouelresh,Abdulazeez Abdulraheem,Abdulwahab Ali +8 more
TL;DR: In this article, the authors developed an empirical correlation to determine the total organic carbon (TOC) for Barnett and Devonian shale formations based on conventional logs using artificial neural network (ANN).
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Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box)
TL;DR: In this paper, a mathematical model that obtained from the weights, biases, and the transfer functions used in the Artificial Neural Networks (ANNs) was converted to white box to obtain a visible mathematical model which can be used to predict the drilling fluid rheological properties only using Marsh funnel viscosity, solid content, and density.