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Mohammad Mokhtar Abdelmonam Eltrissi

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Mohammad Mokhtar Abdelmonam Eltrissi

PhD Candidate
Department of Geoscience

mohammad.m.a.eltrissi@ntnu.no
+4797358770 Gløshaugen, Trondheim
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About Publications

About

Mohammad is a Wells Engineer, Project Manager, and Machine Learning Developer. He attended Cairo University to study Petroleum Engineering and became the president of the SPE student chapter in 2004. He started his career at Halliburton in 2006, the same year he graduated. In 2009, he moved to Shell, where he spent 14 years as a Senior Wells Engineer, Borehole Survey Manager, and Digitalization Focal Point across several international assignments.

In 2023, Mohammad earned his MSc degree from Cairo University and became a Senior Drilling Advisor for Oliasoft. Currently, he is a PhD researcher at NTNU, focusing on the applications of Bismuth-Tin alloy in Well Plug and Abandonment (P&A).

Beyond his professional endeavors, Mohammad is a squash player, a father of two ladies, and a gentleman.

Publications

Drilling operation optimization using machine learning framework

Optimizing the surface parameters to maximize the rate of penetration (ROP) could be achieved through a machine learning (ML) framework. Hence, predicting ROP is a crucial factor in any optimization problem.

Additional Parameters for Better Vibration Control

The objective of this study is to derive additional options that will improve ROP by providing more flexibility in BHA design than those provided by traditional WOB-rotary speed methods.

Predicting System Surface Parameters Using Artificial Neural Network

The system's (to predict Standpipe Pressure and apparent surface torque) development used non-stochastic gradient decent tools to achieve the global minimum of the solution, contrary to most developed models' approaches to that topic.

Using a Genetic Algorithm to Estimate Bingham Equation Parameters for Rate of Penetration Prediction

This work presents a methodology for estimating localized modified Bingham equation parameters using the Genetic Algorithm (GA). The case study was conducted on a large dataset collected from 260 wells.
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