Daniel Otoo

Background

About Daniel Otoo

Skilled in integrating geological and geophysical data for subsurface characterization. Efficient in applying subsurface software and machine learning tools to enhance geological evaluation and decision-making in the exploration and production sector. Committed to continual learning in order to contribute effectively towards attaining set goals. Skills: Geological Modeling and Stratigraphic Correlation: Petrel, LeapFrog3D, GPM, Vulcan3D, and DionisosFlow.| Reservoir Dynamic Simulation: Eclipse, and PumaFlow.| Spatial Analysis: ArcGIS, and MapInfo.| Data Analysis and Machine Learning: Python Programming, Tableau, SQL. Accomplishment:(i) Geological modeller in the investigation of connectivity and its influence of flow and heat exchange in a geothermal reservoir.(ii) Project geologist on regional greenfield exploration that enhanced decision-making on project Published researcher with focus on greener energy alternatives and the use of machine learning algorithms to enhance subsurface prediction.(iv) Industry and Academic Technical Meetings (JIP meeting on GPM software at Schlumberger, London, EAGE Petroleum Geostatistics Conference, Florence, SIS Global Forum 2019, Monaco). Present and Future Interest:(i) Reservoir Data Analysis and Machine Learning.(ii) Renewable Energy Resource Exploration & Production Business Analysis (Geothermal, Mining, and Hydrocarbon). Education: Ph.D.(University of Manchester). MSc. Petroleum Geoscience (Development and Production, University of Manchester). BSc. Geological Engineering (KNUST, Ghana) Email: dpkwes@gmail.com