My technical background is rooted in 2D and 3D seismic interpretation, structural geology, reservoir characterization, seismic attribute analysis, well-to-seismic integration, and field-development support. I have worked across complex basins and petroleum systems in Egypt, Algeria, Libya, Canada, and the UAE, including rifted basins, fractured carbonate reservoirs, tight gas reservoirs, offshore gas systems, unconventional reservoirs, and oil sands operations.
Through SaherLabs.dev, I present selected digital geoscience projects that reflect my current professional direction: building practical tools that connect subsurface expertise with modern data analytics, machine learning, AI, and web technologies.
The purpose is not technology for its own sake. The purpose is to improve how geoscience and reservoir data are reviewed, quality-controlled, visualized, and communicated.
Professional Background
My career has included geophysics and consulting roles with Halliburton Consulting, Tharwa Petroleum, PetroBel, Terracon Geotechnique with Suncor Energy, and industry-linked research work with ARC Resources in Canada.
I have worked on seismic interpretation, structural framework modeling, depth conversion, fault and fracture interpretation, seismic facies classification, AVO/DHI analysis, attribute extraction, natural fracture characterization, reservoir property mapping, and integrated subsurface studies.
I also have extensive experience with commercial geoscience platforms including Landmark OpenWorks, DecisionSpace / DSG, and Petrel. In parallel, I have developed Python-based workflows and prototype applications to automate technical tasks, integrate datasets, improve QC, and test machine learning approaches for reservoir characterization.
Education
I hold two Master of Science degrees: an MSc in Reservoir Characterization from the University of Calgary, Canada, and an MSc in Data Analytics from Western Governors University, USA. I also hold a BSc in Geology and Geophysics from Ain Shams University, Egypt.
This combination of geoscience, reservoir characterization, and data analytics is the foundation of my work in applied machine learning, AI-assisted geoscience workflows, and digital transformation for upstream oil and gas.
Digital Transformation Focus
My current technical direction is focused on practical digital transformation in upstream oil and gas.
I work on projects that convert seismic, well, reservoir, production, map, and grid-based data into more usable digital workflows. These include Python automation tools, machine learning workflows, technical dashboards, web-based field visualization, ZMAP/grid processing utilities, and early-stage AI interaction with structured project data.
I am also currently developing workflows that use open-source seismic interpretation packages such as OpendTect, together with Python-based geoscience libraries, to improve the automation of structural and stratigraphic seismic interpretation. These workflows aim to combine machine learning with geometric and geophysical attribute analysis to support fault detection, horizon interpretation, seismic facies analysis, and reservoir-scale interpretation QC.
My strongest professional interest is the intersection of geophysics, reservoir characterization, Python automation, machine learning, AI-assisted workflows, field data visualization, and decision-support applications.
I believe digital transformation in oil and gas should be practical, data-grounded, and directly tied to technical decisions. It should reduce repetitive manual work, improve interpretation QC, integrate disconnected datasets, and make complex subsurface information easier to understand.