Artificial Neural Network Modeling of Well Performance in the Garrington Field, Cardium Formation

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dc.contributor.advisorPedersen, Per Kent
dc.contributor.advisorJensen, Jerry L.
dc.contributor.authorKakar, Kushagra
dc.contributor.committeememberChen, Shengnan
dc.contributor.committeememberClarkson, Christopher R.
dc.date2018-06
dc.date.accessioned2019-01-03T16:17:04Z
dc.date.available2019-01-03T16:17:04Z
dc.date.issued2018-12-21
dc.description.abstractA number of studies have reported the use of artificial neural networks (ANNs) to predict tight formation well performance. ANNs are attractive because they do not require pre-conceived models (are “data-driven”), can accommodate numerous inputs, and allow for nonlinear relationships. There are reports of using ANNs in ‘factory mode’ to aid well and stimulation design where fast development of areas precludes devoting the time and effort to individually design each well. The analysis and experience of this study shows, however, that ANNs have limitations which can go unrecognized and lead to faulty predictions: 1) We find that investigators may not have adequately tested the ANN by overlooking the performance for the testing and validation tests, and only reporting the “R” values (correlation coefficients) for the training results. 2) There is a large variability between ANNs trained using the same dataset. We find that, even when we select only those ANNs which give R > 0.85, there can be significant discrepancies between predicted and actual performance. 3) ANN models which exclude the operator may give poor results and make some variables appear more important than they actually are. 4) Among other explanatory variables, the early time linear flow parameters are very important. This thesis illustrates this work using data from the tight oil Garrington Field (Cardium Formation). It makes recommendations to ANN workflows which will guide practitioners in the appropriate development, testing, and application of ANNs in this important topic.en_US
dc.identifier.citationKakar, K. (2018). Artificial Neural Network Modeling of Well Performance in the Garrington Field, Cardium Formation (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/35674
dc.identifier.urihttp://hdl.handle.net/1880/109399
dc.language.isoenen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity 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.subjectArtificial Neural Network Modelingen_US
dc.subjectUncoventional Well Performanceen_US
dc.subjectCompletions Design Evaluationen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.subject.classificationEngineering--Petroleumen_US
dc.titleArtificial Neural Network Modeling of Well Performance in the Garrington Field, Cardium Formationen_US
dc.typemaster thesisen_US
thesis.degree.disciplineEngineering – Chemical & Petroleumen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrue
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