Download the full-sized PDF of Development of multiscale microbial kinetic-transport models for prediction and optimization of biogenic coalbed methane productionDownload the full-sized PDF



Permanent link (DOI):


Export to: EndNote  |  Zotero  |  Mendeley


This file is in the following communities:

Graduate Studies and Research, Faculty of


This file is in the following collections:

Theses and Dissertations

Development of multiscale microbial kinetic-transport models for prediction and optimization of biogenic coalbed methane production Open Access


Other title
kinetics-transport coupling
microbially enhanced coalbed methane
multiscale modeling
model reduction
robust optimization
stochastic proxy modeling
Type of item
Degree grantor
University of Alberta
Author or creator
Senthamaraikkannan, Gouthami
Supervisor and department
Gates, Ian (Chemical and Petroleum Engineering)
Prasad, Vinay (Department of Chemical and Materials Engineering)
Examining committee member and department
Gates, Ian (Chemical and Petroleum Engineering)
Sauvageau, Dominic (Department of Chemical and Materials Engineering)
Rajendran, Arvind (Department of Chemical and Materials Engineering)
Sandoval, Luis Ricardez (Chemical Engineering)
Prasad, Vinay (Department of Chemical and Materials Engineering)
Department of Chemical and Materials Engineering
Process Control
Date accepted
Graduation date
Doctor of Philosophy
Degree level
The fundamental objective of this research project is to develop an enzymatic reaction kinetic model for coal bioconversion which, on integration with multiscale transport models, would allow simulation and optimization of field scale biogenic coalbed methane production. Biogenic coalbed methane (CBM) is an unconventional source of natural gas produced by microbial anaerobic breakdown of coal. Given the many advantages of converting coal to natural gas, much research has been conducted on the enhancement of this natural process at the laboratory scale, and some field scale tests have also been conducted. Commercialization of any such technology requires conceptualization and optimization of field scale strategies. This is a challenge given the complexity and variability of coal, the associated transport processes and the microbial processes involved. In this study, we have used a scaling-up approach starting with the development of reaction kinetics at the smallest scale and the addition of appropriate transport effects at each successive scale to build a model for simulation of CBM in coalbed reservoirs. The first challenge is to develop a suitable microbial kinetic model with reasonable predictive capability. To this end, microbial reaction networks involved in coal bioconversion were extensively reviewed and complemented with analysis and interpretation of data from laboratory experiments to propose a simplified reaction pathway. An enzymatic reaction kinetic model based on simple and modified Monod models was then derived using lumped species, an approach common in kinetic descriptions of complex reaction mixtures such as those found in fluid catalytic cracking. The model was then validated by nonlinear regression of data from various coal enrichment cultures. The kinetic model was next applied to a coreflooding experiment, which is a laboratory scale representation of field conditions, with the inclusion of gas diffusion and sorption behaviour. The model was simplified using computational singular perturbation analysis and an optimal model-based experimental design was devised. Next, a set of partial differential equations were derived to model the multiple gas transport/storage processes occurring in a coalbed reservoir characterized by dual porosity. After discretization using forward difference formulas and non-dimensionalization, the stiff transport model was solved using the Levenberg-Marquardt algorithm. Dimensionless numbers derived in the course also allow analysis of dominant processes at changing scales. History matching of the transport model was performed against gas production data from Manville wells in Alberta. Finally, gas transport and reaction kinetics were coupled for simulation of biogenic coalbed methane flow and then advanced to multiphase, multicomponent reservoir simulations in CMG STARS for estimation and optimization of commercial biogenic coalbed methane production. Polynomial chaos expansion (PCE) was used to quantify the effect of parametric uncertainty in the model on estimates of methane production. Legendre orthogonal polynomials were applied in conjunction with PCEs to generate stochastic proxy models for coalbed methane production. In an alternate approach, coefficients of time series models (AR/ARMAX/BJ) were expanded in PCEs to account for dynamic operating variables. Since reservoir models are large and complex with multiple parameters and operating variables, sparse meta models can be employed in proxy model development. The proxy model was then applied for robust optimization based on computationally efficient evaluation of statistical metrics of the costs function at varying operating conditions.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Senthamaraikkannan, G. and Prasad, V. Stochastic proxy modelling for coalbed methane production using orthogonal polynomials. In Proc. 9th IFAC Symposium on Advanced Control of Chemical Processes. June 2015.

File Details

Date Uploaded
Date Modified
Audit Status
Audits have not yet been run on this file.
File format: pdf (PDF/A)
Mime type: application/pdf
File size: 5937445
Last modified: 2016:06:24 18:39:36-06:00
Filename: Senthamaraikkannan_Gouthami_201509_PhD_pdfa.pdf
Original checksum: 4535f19fffbaac696a66157a2f6203ff
Activity of users you follow
User Activity Date