Mohammad Chahardowli
Faculty member of Petroleum Engineering
Amirkabir University of Technology
Dr. Mohammad Chahardowli is an accomplished scholar, educator, and executive with a distinguished career spanning nearly twenty years at the forefront of petroleum engineering and sustainable energy systems. Currently serving as an Associate Professor at Amirkabir University of Technology (Tehran Polytechnic), one of Iran's premier engineering institutions, he possesses a unique profile that integrates deep academic research, high-level university leadership, and strategic advisory roles within the national energy sector. His professional journey is characterized by a consistent commitment to advancing both the science and practical application of energy technologies.
His academic foundation was solidified with a PhD from the renowned Delft University of Technology (TU Delft) in the Netherlands, a global epicenter for energy research. This strong base propelled a career marked by significant leadership positions. In these roles, he has overseen academic and research strategy for specialized national institutions and helped shape research priorities for the country's core energy industry. This breadth of experience has provided him with an unparalleled, holistic perspective on the entire energy ecosystem.
Dr. Chahardowli’s research portfolio is strategically aligned with the pressing challenges of modern resource management and energy transition. It is built upon three interconnected, forward-looking pillars. First, he advances Dimethyl Ether (DME) Technology, investigating its production and application as a clean, sustainable fuel alternative to mitigate environmental impact. Second, he pioneers Data-Driven Reservoir Management, where he develops and applies sophisticated machine learning algorithms, computational modeling, and simulation frameworks to optimize hydrocarbon recovery, enhance production forecasting, and improve operational decision-making. Third, his work in Underground Gas Storage focuses on the secure and efficient design of geological formations for storing natural gas and future fuels like hydrogen, a critical enabler for energy security and the integration of renewable sources.
His scholarly output, evidenced by a robust publication record in peer-reviewed journals and consistent citation impact, demonstrates a commitment to foundational science. However, his work is distinguished by its translational nature, consistently seeking to bridge the gap between theoretical innovation and field-ready solutions. This ethos stems from his direct experience in leadership roles that demand practical outcomes. Dr. Mohammad Chahardowli stands as a prominent figure whose career exemplifies the synergistic power of academic excellence, institutional leadership, and a dedicated focus on developing sustainable, intelligent, and secure energy solutions for the future.
Participates in
TECHNICAL PROGRAMME | Primary Energy Supply
Initially, the Reservoir Similarity Index (RSI) is computed utilizing historical oil production data and oil saturation metrics. In this context, unsupervised clustering techniques—including k-means, k-medoids, c-means, as well as metaheuristic algorithms such as Genetic Algorithms, Particle Swarm Optimization, and Simulated Annealing—are employed to ascertain the centroid of the predominant cluster. Subsequently, more operational criteria—including the number of existing applicable wells, interfering wells and adjacent wells, average distances between these wells and the center of the candidate pilot area, and the average distance from facilities—are evaluated for each alternative candidate. The corresponding calculated weights of the considered criteria are equal to 4.6%, 10.63%, 22.93%, 16.70%, 1.98%, 9.6%, 2.70% and 30.85%, respectively.
Following this assessment, a decision matrix is constructed and Multi-Criteria Decision Making (MCDM) methodologies—specifically, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Analytical Hierarchy Process (AHP), and VIKOR—are applied to compute the pilot opportunity index for each area. The results indicate that the pilot opportunity index assigned via the hierarchical analysis method yields values of 11.50%, 10.46%, and 6.97%, whereas the Shannon entropy method produces indices of 11.68%, 9.80%, and 6.80% for the three highest-ranking pilot areas, respectively. Ultimately, the area with the highest score is designated as the primary candidate for pilot implementation. Moreover, prioritization strategies such as mean rank, Borda’s count, and Copeland’s method are utilized to aggregate and identify the most favorable pilot area. To meet the validation point, the representative sectors were determined by comparing the geostatic properties, e.g., rock porosity and permeability distributions and dynamic performance between each pilot area and entire field based on simulation outputs, e.g., water cut and oil recovery factor. Finally the high computational load field scale simulation results verify and confirm the obtained results from this AI-based pilot study. Hence, this novel systematic decision making tool are proposed to utilize in enhanced oil recovery field development purposes.
Co-author/s:
Hossein Kheirollahi, Researcher, Sahnad University of Technology.
Mohammad Simjo, Associate Professor, Sahand University of Technology.
