Modeling of Methane and Carbon Dioxide Sorption Capacity in Tight Reservoirs Using Machine Learning (ML) Techniques

Tavakolian, Mohsen

Modeling of Methane and Carbon Dioxide Sorption Capacity in Tight Reservoirs Using Machine Learning (ML) Techniques

dc.contributor.advisor	Kantzas, Apostolos
dc.contributor.author	Tavakolian, Mohsen
dc.contributor.committeemember	Hejazi, Hossein
dc.contributor.committeemember	Shor, Roman
dc.date	2023-06
dc.date.accessioned	2023-01-25T23:55:52Z
dc.date.available	2023-01-25T23:55:52Z
dc.date.issued	2023-01-19
dc.description.abstract	This work examines different machine learning methods, from shallow to deep learning. It investigates their capability to model 489 sets of experiments with 3806 data points where methane (CH4) and/or carbon dioxide (CO2) sorption capacity of shale and coal have been measured at different reservoir conditions. High-accuracy “integrated” shallow and deep learners/predictors were developed, without splitting the dataset into several batches. The best predictors were Random Forest Regressor (RFR) and Artificial Neural Network (ANN) with more than 98% and 96% accuracy, respectively. The developed models worked well with different combinations of potential features, e.g., gas (CH4 and CO2), rock type (either shale or coal family), TOC (total organic carbon), moisture, temperature, porosity, geological properties, etc., resulting in high accuracy (96 to 98%) for 69 sets of “unseen” experimental data with 1384 data points. Furthermore, a sensitivity analysis of the developed predictors showed their alignment with technical expectations of geology and reservoir engineering aspects. Of the best learners, RFR is simpler to apply and quicker to run with higher accuracy and ANN predicts 2% less accuracy on average. However, it operates with less risk of memorizing the data and a higher capability of accurately predicting unseen data. Both models are capable of being applied to lab scale and further developed for their application in reservoir scale simulators. In the end, the developed models are benchmarked against commonly used empirical correlations and pros and cons of each approach are discussed. A new correlation was also developed for CO2 phase change stage and added to this benchmark study.	en_US
dc.identifier.citation	Tavakolian, M. (2023). Modeling of methane and carbon dioxide sorption capacity in tight reservoirs using Machine Learning (ML) techniques (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.uri	http://hdl.handle.net/1880/115774
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/40687
dc.language.iso	eng	en_US
dc.publisher.faculty	Schulich School of Engineering	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Methane Sorption	en_US
dc.subject	CO2 Sorption	en_US
dc.subject	Carbon Dioxide	en_US
dc.subject	Adsorption Capacity	en_US
dc.subject	Tight Reservoirs	en_US
dc.subject	Enhanced Gas Recovery	en_US
dc.subject	Carbon Storage	en_US
dc.subject	Machine Learning	en_US
dc.subject	Artificial Neural Network	en_US
dc.subject.classification	Artificial Intelligence	en_US
dc.subject.classification	Engineering--Environmental	en_US
dc.subject.classification	Engineering--Petroleum	en_US
dc.title	Modeling of Methane and Carbon Dioxide Sorption Capacity in Tight Reservoirs Using Machine Learning (ML) Techniques	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Engineering – Chemical & Petroleum	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US