Production Analysis in Tight/Shale Reservoirs Via Machine Learning Approaches

Wang, Hai

Production Analysis in Tight/Shale Reservoirs Via Machine Learning Approaches

dc.contributor.advisor	Chen, Shengnan (Nancy)
dc.contributor.author	Wang, Hai
dc.contributor.committeemember	Chen, Zhangxing
dc.contributor.committeemember	Gates, Ian Donald
dc.contributor.committeemember	Shor, Roman J
dc.contributor.committeemember	Zendehboudi, Sohrab
dc.date	2024-05
dc.date.accessioned	2024-04-30T16:04:24Z
dc.date.available	2024-04-30T16:04:24Z
dc.date.issued	2024-04-25
dc.description.abstract	The development of unconventional tight/shale gas reservoirs has undergone a revolutionary transformation, primarily fueled by advancements in horizontal drilling and multi-stage hydraulic fracturing technologies. These innovations have enabled the economical extraction of hydrocarbons from formations characterized by low permeability and complex fracture networks, positioning tight/shale gas as a pivotal component of the global energy mix. The accurate prediction of well production dynamics in these complex formations is a formidable challenge. Traditional empirical and numerical approaches, often fall short due to their inherent simplifications or computational demands. With the surge in data availability from unconventional reservoir developments, leveraging data-driven models for predicting well performance has become increasingly feasible and necessary. This thesis presents a comprehensive suite of machine learning frameworks to predict and enhance well production dynamics in tight/shale gas reservoirs. Initial efforts focus on predicting the first-year cumulative production of infill wells and optimizing their placement and stimulation design. Then, the study delves into the prediction of long-term production dynamics of shale gas wells using a dual-stage attention-based sequence-to-sequence model with some hard physics constraints. By encoding both tabular and time-series data, this model demonstrates superior accuracy and robustness in forecasting well production, outperforming traditional machine learning approaches. Subsequently, a novel physics-informed neural network approach is introduced to deduce the governing partial differential equation for shale gas production decline characterization, integrating Caputo fractional derivatives to capture the heavy-tailed phenomena in production series, thus offering a balance between interpretability and predictive capability. Further, the thesis explores the competitive adsorption of CH4/CO2 in shale formations using a Genetic Algorithm pruned Neural Network. This model robustly predicts the adsorption capacities, offering critical insights for CO2-enhanced shale gas recovery strategies and contributing to carbon capture and storage efforts.
dc.identifier.citation	Wang, H. (2024). Production analysis in tight/shale reservoirs via machine learning approaches (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/118545
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
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.
dc.subject	Unconventional reservoirs
dc.subject	Machine learning
dc.subject	Production forecast
dc.subject	Physics-informed neural network
dc.subject	Time series
dc.subject.classification	Engineering--Petroleum
dc.subject.classification	Energy
dc.subject.classification	Engineering--Chemical
dc.title	Production Analysis in Tight/Shale Reservoirs Via Machine Learning Approaches
dc.type	doctoral thesis
thesis.degree.discipline	Engineering – Chemical & Petroleum
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.