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TitleMachine learning-assisted production data analysis in liquid-rich Duvernay formation
AuthorKong, BORCID logo; Chen, ZORCID logo; Chen, S; Qin, T
SourceJournal of Petroleum Science & Engineering vol. 200, 108377, 2021 p. 1-18,
Alt SeriesNatural Resources Canada, Contribution Series 20200645
PublisherElsevier B.V.
Mediapaper; on-line; digital
File formatpdf; html
ProvinceBritish Columbia; Alberta
Lat/Long WENS-120.0000 -110.0000 57.0000 51.0000
SubjectsScience and Technology; fossil fuels; Economics and Industry; petroleum industry; petroleum resources; production; hydrocarbon recovery; oil; gas; drilling; reservoir rocks; wells; bedrock geology; lithology; sedimentary rocks; shales; models; modelling; Duvernay Formation; machine learning; Artificial intelligence; Methodology
Illustrationslocation maps; tables; diagrams; charts; graphs; cross-plots
ProgramGeoscience for New Energy Supply (GNES) Shale-hosted petroleum resource assessment
Released2021 01 16
AbstractThe production of gas and oil from the unconventional tight and shale reservoirs is the outcome through a series of cooperative efforts of drilling, completion, and production operations. This study aims to optimize these operations to enhance the well productivity and oil recovery, and ultimately to reduce the development footprint on the level of individual wells. More specifically, a comprehensive data set is collected and analyzed for the Duvernay reservoir, including geology, drilling, completion, production operations, and production data. A customized stacked model is created to train production models with an extreme gradient boosting regressor as the base model and linear regressor as the meta model. The models achieve robust predictive ability with a determination coefficient of up to 0.80. The Shapley values reveal that the producing time, condensate/gas ratio, and the completion section length are the most important features to the early time production. The Bayesian optimization method is adopted to optimize the production using the trained models. This study shows the potential of the machine learning approach to model oil and gas production and provides insights for optimizing production in the tight and shale reservoirs.
Summary(Plain Language Summary, not published)
Oil and gas production from shale requires hydraulic fracking to improve rock permeability that may cause additional environment impacts. This study used machine learning and big data methods to determine the major factors that affect the production performance. Based on the findings, the authors proposed operational optimization to enhance production performance and reduce environmental footprint.

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