Kwame Sarkodie

Petroleum Engineering

About

Dr. Kwame Sarkodie currenly serves as Head of the Department of Petroleum Engineering, KNUST. He is a dynamic and accomplished researcher in the field of flow assurance and multiphase flow metering, with a strong commitment to innovation at the intersection of energy systems, sensor technology, and artificial intelligence. He currently serves as a Senior Lecturer in the Department of Petroleum Engineering at Kwame Nkrumah University of Science and Technology (KNUST), Ghana. With a solid academic foundation, Dr. Sarkodie holds a PhD in Chemical Process and Energy Engineering, an MSc in Petroleum Engineering, and a BSc in Petroleum Engineering, all contributing to his holistic understanding of upstream and midstream oil and gas operations.Dr. Sarkodie is the founder and lead investigator of FLOWSCOPE Research, a pioneering lab dedicated to the development of smart, non-intrusive metering systems for multiphase flow analysis. Through FLOWSCOPE, he has spearheaded cutting-edge research that combines experimental flow loop development with the design and application of acoustic and optical sensors. These sensors are used to characterize gas–liquid flows, detect leakage, and support real-time flow regime identification, particularly under vertical upward conditions—a critical challenge in petroleum production and transport systems.In addition to his work on sensor-based metering, Dr. Sarkodie is actively engaged in broader flow assurance and pipeline transportation projects, including natural gas pipeline expansion modeling and CO2 transport optimization. His contributions span both laboratory-scale innovation and large-scale energy system applications, reflecting his commitment to bridging academic research with practical industry solutions. He is also involved in the design of intelligent well probes and the integration of smart tools for well and reservoir performance monitoring.His professional affiliations include the Society of Petroleum Engineers (SPE), Ghana Institution of Engineers (GhIE), the Energy Institute (EI), and the University Teachers Association of Ghana (UTAG), reflecting his active engagement in both academic and professional engineering communities. At KNUST, he teaches core undergraduate and postgraduate courses such as Drilling and Production Engineering, Reservoir Recovery Techniques, Well Completions, and Natural Gas Engineering. He also supervises MSc and PhD students, guiding them through research in intelligent systems, energy transition technologies, and advanced flow measurement methods.

Research Summary

(inferred from publications by AI)

The researcher's work in physical sciences focuses on innovative techniques for energy storage and flow measurement, which are applied across diverse fields such as oil recovery and reservoir characterization. Techniques like non-intrusive sensing and supervised machine learning are central to advancing methods in hydrocarbon exploration and reservoir simulation. The research emphasizes the practical application of these technologies to address challenges in energy forecasting, geophysical analysis, drilling, corrosion inhibition, thermochemical processes, hydraulic fracturing, and more.

Research Themes

All (33 Papers)

Physical Sciences (33 Papers)

All Papers

Cellulose processing from biomass and its derivatization into carboxymethylcellulose: A review(2021)

A critical review of carbonate reservoir wettability modification during low salinity waterflooding(2022)

Zeta potential prediction of dominant sandstone minerals via surface complexation modelling(2023)

The New EOR Frontiers - Reduced Salinity Waterflooding(2014)

Modeling Mutual Solvent Preflush -The Case of Wettability(2015)

Study of polymer flooding and intelligent well technology for improved oil recovery(2022)

A review of the application of non-intrusive infrared sensing for gas–liquid flow characterization(2018)

Non-Intrusive Passive Acoustic Sensing for Accurate Gas Flow Measurement in Gas Pipelines(2022)

Design and development of a low-cost non-intrusive passive acoustic sensor(2023)

Gas-liquid flow regime identification via a non-intrusive optical sensor combined with polynomial regression and linear discriminant analysis(2022)

Flow Regime Identification in Vertical Upward Gas–Liquid Flow Using an Optical Sensor With Linear and Quadratic Discriminant Analysis(2020)

Improved phase fraction measurement via non-intrusive optical sensing for vertical upward gas -liquid flow(2023)

Novel Investigation of Taylor Bubble and Entrained Bubbles Fractions Using Non-Intrusive Optical Sensors in a Concurrent Upward Slug Flow in Pipes(2025)

Slug Flow Monitoring in Pipes Using a Novel Non-Intrusive Optical Infrared Sensing Technology(2019)

Phase Detection Using Infrared Spectroscopy: A Build up to Inline Gas–Liquid Flow Characterization(2017)

Gas-Liquid Flow Regime Identification Via a Non-Intrusive Optical Sensor Combined with Polynomial Regression and Linear Discriminant Analysis(2021)

Flow regime dynamics in a bubble column reactor(2024)

Novel gas-liquid volumetric flow measurement via non-intrusive optical sensing under upward vertical bubbly flow(2025)

Pyrolysis of municipal food waste: A sustainable potential approach for solid food waste management and organic crop fertilizer production(2023)

The effect of temperature on CO2 injectivity in sandstone reservoirs(2021)

Depositional Behaviour of Highly Macro-Crystalline Waxy Crude Oil Blended with Polymer Inhibitors in a Pipe with a 45-Degree Bend(2019)

Investigation of the Severity of Wax Deposition in Bend Pipes Under Subcooled Pipelines Conditions(2019)

Tannin-Based Deflocculants in High Temperature High Pressure Wells: A Comprehensive Review(2021)

Evaluation and remediation techniques for barite sagging: A review(2023)

Predictive modeling of energy‐related greenhouse gas emissions in Ghana towards a net‐zero future(2023)

Intelligent well Technology- Dealing with Gas Coning Problems in Production wells(2014)

Application of SVC, k-NN, and LDA machine learning algorithms for improved prediction of Bioturbation: Example from the Subei Basin, China(2024)

Application of Supervised Machine Learning Techniques for Improved Prediction of Bioturbation: Example from the Subei Basin, China(2024)

Assessment of Petroleum Reservoir Recovery Factor Using Complexity Scoring and Artificial Neural Network(2015)

Understanding the role of bioturbation in modifying petrophysical properties: a case from well L5 of the third-member Paleocene Funing Formation (E1f3), Gaoyou Sag, Subei Basin, China(2023)

Predicting physico-chemical parameters of Barekese reservoir using feedforward neural network(2025)

Assessing the corrosion inhibition potential of novel green and sustainable plant leaf extracts on mild steel in seawater – Preliminary studies(2025)

Steady-state modelling and simulation of optimum pentane stabilization and recovery options for the Ghana Gas Processing Plant(2025)

Collaboration Network

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Research Collaboration Map

Collaboration Frequency

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About This Profile

This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.

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