PREDICTIVE MODELING OF PROPPED FRACTURE CONDUCTIVITY IN SHALE GAS RESERVOIRS

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dc.contributor.authorYegeubayeva, Alin
dc.date.accessioned2024-06-27T09:09:23Z
dc.date.available2024-06-27T09:09:23Z
dc.date.issued2024-04-17
dc.description.abstractHydraulic fracturing is a well completions technique that induces a network of flow channels in a reservoir. These channels are characterized by fracture conductivity, a measure of how easily a liquid or gas flows through the fracture. Fracture conductivity is influenced by several variables including proppant size, proppant concentration, and hydraulic fracture characteristics. The purpose of this research is to present a unique process that incorporates machine learning neural networks in order to predict the fracture conductivity of multi-stage fractured horizontal well in shale gas reservoirs. To accurately predict fracture conductivity using fracture parameters such as width, height, length and orientation, a robust model is necessary. In this study, predictive ability of Multilayer Perceptron algorithm was used in forecasting fracture conductivity. The findings revealed the R-squared value of 0.82, which show a good correlation of these values with the previously conducted researches. Secondly, during validation of algorithm, CMG calculated fracture conductivity at 4.6 md.ft, although the machine learning model came closest at 4.43. Overall, values and other input variable parameters are near, indicating good model performance. Lastly, enhancing the cumulative gas output has been shown to be significantly aided by the process of fine-tuning fracture parameters, which are fracture length, height, and width, inside the CMG program. The obtained results can be used as references in the future examination of parameters that affect fracture conductivity.en_US
dc.identifier.citationYegeubayeva, A. (2024). Predictive Modeling of Propped Fracture Conductivity in Shale Gas Reservoirs. Nazarbayev University School of Mining and Geosciencesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/8043
dc.language.isoenen_US
dc.publisherNazarbayev University School of Mining and Geosciencesen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectType of access: Restricteden_US
dc.subjectpredictionen_US
dc.subjectartificial neural networksen_US
dc.subjectfracture conductivityen_US
dc.subjectmultilayer perceptron neural networken_US
dc.titlePREDICTIVE MODELING OF PROPPED FRACTURE CONDUCTIVITY IN SHALE GAS RESERVOIRSen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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