Dr Clement Etienam


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Clement is a researcher in data assimilation, Inverse problems, applied math, Bayesian theory, machine learning, reservoir engineering & artificial intelligence (A.I). He has Bachelors in Chemical Engineering from the University of Lagos, Masters in Petroleum Engineering from Robert Gordon University, Aberdeen and a PhD in Petroleum Engineering (with emphasis on Inverse problems & Machine Learning) from the University of Manchester. He obtained his PhD under the supervision of Dr Rossmary Villegas, Dr Oliver Dorn and Dr Masoud Babaei, with his thesis titled “Structural and Shape Reconstruction Using Inverse problems and Machine Learning Techniques with application to Hydrocarbon Reservoirs. His PhD research focused on subsurface Inverse/Data assimilation problems termed “History matching” and was one of the pioneers in using ensemble based data assimilation with unsupervised learning for characterizing reservoir models. He recently completed a 2-year Post-doc in applied mathematics & artificial intelligence under the supervision of Professor Kody Law. His current research interest includes but not limited to using several supervised machine and deep learning techniques for developing surrogate models unique to fusion simulation data and /or large scale physics problem, health datasets, materials finger printing and classification using several computer vision techniques such as convolution neural networks (CNN), Bayesian and statistical methods for solving inverse problems with keen application to Bayesian deep learning approach to neural networks, improving Natural Language models (NLP) for semantics and text classification with limited voice recognition scenarios ,speech synthesis, Mixture of Experts using Deep Gaussian processes and reservoir history matching with ensemble based approach infusing extra source of data ,in particular 4D Seismic and electromagnetic data. Clement has several papers published in high impact journals focusing on petroleum reservoir inverse problems and machine learning. His hobbies are coding (for fun in Matlab and Python), playing Chess, reading medical science journals, playing FIFA and watching videos on space exploration and the Cosmos. His long term career goal is to be world renowned in the field of A.I and hopefully win the noble prize in his lifetime 


Clements' research interest includes and not limited to :

1) Supervised and Unsupervised learning algorithms for large data sets

2) Reservoir history matching methods in particuar ensemble based methods (Ensemble Kalman filter)

3) Uncertainty quantification and Markov Chain Monte Carlo methods

4) Inverse problems for conductivity estimation (Electromagnetic induction tomography)

5) Non-convex optimisation

6) Data assimilation and 4D-VAR methods

7) Bayesian Deep neural networks

8) Natural language processing, Semantics modelling and Speech synthesis

9) Computer Vision

10) Brain-Computer Interface

11) Gaussian Processes

12) Mixture of Experts for supervised learning


Bachelors in Chemical Engineering from the University of Lagos,Lagos 2011

National Youth Service Corps(Para-military service) with ExxonMobil Nigeria -Disclipined as a Drilling Engineer-2012

Masters in Petroleum Engineering (with distinction) from the Robert Gordon University,Aberdeen in 2014

PhD in Petroleum Engineering (with emphasis on Inverse problems and Machine Learning) from the University of Manchester in 2018. 

Memberships of committees and professional bodies

Europen Association of Geoscientist and Engineers(EAGE)

Society of indusrial and applied mathematics (SIAM)

Society of petroleum engineers(SPE)

Institute of pure and applied mathematics (IPAM)


  • Reservoir History Matching, Data Assimilation, Machine Learning, deep learning ANNs, Uncertainty quantification, Inverse problems, Ensemble Kalman filter, Supervised learning, high performance computing, Non-convex optimisation, imaging science, Unsupervised learning, Python, MATLAB, C++, C, FORTRAN 90, Software, Eclipse, Petrel, Microsoft office, NumPy, Keras, TensorFlow, XGboost, OpenCV, Scikit-Learn, Matplotlib, SciPy, Theano, Ubuntu, LaTeX, Pandas, PyTorch, PyMC, CNN, NLP, Computer-Vision, Markov-chain Monte Carlo, Artificial Inteligence

Education / academic qualifications

  • 2019 - Doctor of Philosophy, Structural and Shape Reconstruction using Inverse Problems and Machine Learning Techniques with Application to Hydrocarbon Reservoirs, The University of Manchester (2015 - 2018)
  • 2014 - Master in Science, Dynamic modelling for Haptic Feedback in Well Drilling Operations, The Robert Gordon University (2013 - 2014)

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