University of Houston - United States
AUTHOR PROFILE
EARLY ACADEMIC PURSUITS:
Akinyemi Oluwaseun Daniel embarked on his academic journey at The Federal University of Technology in Akure, Ondo State. His dedication to geoscience and data science emerged during this period, laying the groundwork for his future contributions to these fields.
PROFESSIONAL ENDEAVORS:
With a Master's degree in Exploration Geophysics (Distinction) and a Bachelor's degree in Applied Geophysics (Second Class Upper Division), Akinyemi Oluwaseun Daniel has exhibited a strong academic foundation. His professional endeavors showcase a year of expertise in executing data-driven solutions, particularly in subsurface geology, where he has utilized machine learning models for geological interpretations and dynamic data-driven improvements.
CONTRIBUTIONS AND RESEARCH FOCUS:
Akinyemi's research projects demonstrate a keen focus on integrating machine learning into reservoir geomechanics workflows, predicting CO2 levels in hydrocarbon-bearing reservoirs, and evaluating machine learning models for lithology prediction. His work spans diverse areas such as density and sonic wave transit time predictions, rock physics, and geomechanical analysis for sand production prediction.
IMPACT AND INFLUENCE:
Akinyemi's impact is evident in the range and depth of his research projects, addressing critical aspects of geophysics and reservoir characterization. By integrating advanced techniques like rock physics and seismic inversion, he contributes to enhancing reservoir understanding and characterization, impacting decision-making processes in the oil and gas industry.
ACADEMIC CITES:
Having undertaken extensive research projects, Akinyemi Oluwaseun Daniel has showcased his proficiency in utilizing machine learning techniques for various geophysical applications. His research contributions, characterized by a strong academic foundation, have likely garnered attention within the academic community.
LEGACY AND FUTURE CONTRIBUTIONS:
Akinyemi's legacy is marked by his commitment to advancing geoscience through innovative applications of data science. His future contributions hold the promise of furthering our understanding of subsurface geology, with a particular emphasis on de-risking geological interpretation uncertainties.
NOTABLE PUBLICATION
Machine learning application for prediction of sonic wave transit time - A case of Niger Delta basin. 2023