Show simple item record

dc.contributor.advisorTrainor-Guitton, Whitney
dc.contributor.authorYu, Xiaodan
dc.date.accessioned2020-02-03T11:28:56Z
dc.date.accessioned2022-02-03T13:15:56Z
dc.date.available2020-02-03T11:28:56Z
dc.date.available2022-02-03T13:15:56Z
dc.date.issued2019
dc.identifierYu_mines_0052N_11873.pdf
dc.identifierT 8868
dc.identifier.urihttps://hdl.handle.net/11124/174006
dc.descriptionIncludes bibliographical references.
dc.description2019 Fall.
dc.description.abstractHydraulic fracturing has gained its popularity all over the world as more tight geologic formations are developed economically for hydrocarbon resources. While exploring for new unconventional resources such as shale plays, horizontal drilling and multi-stage hydraulic fracturing are required to stimulate the geologic units to increase well production. However, due to the stages' operating complexity, different kinds of disruptions in fracturing operations may occur and even result in great economic loss. Screenout is one of the issues caused by the blockage of proppant inside the fractures. In this project, a horizontal well landing in the Niobrara B shale, Denver-Julesburg (DJ) Basin, is simulated with multiple fracturing stages in a hydraulic fracturing software and various synthetic fracturing treatment data are forward modeled for both screenout and non-screenout scenarios. This thesis describes a screenout classification system based on Gaussian Hidden Markov Models, trained on simulated data, in order to predict screenouts and provide early warning by learning pre-screenout patterns in the simulated surface pressure signals. The classification system consists of two Gaussian Hidden Markov Models (screenout and non-screenout), each of which is fitted and optimized by its respective training set. Both Hidden Markov Models are assigned with two 1D Gaussian probability density functions to represent the distribution of their associated simulated surface pressure signals. During the classification process, once a new surface pressure sequence is observed, the log likelihood is calculated under both fitted models and the model with greater likelihood will be predicted as the class of this new observation. The classification system is validated and verified with a hold-out testing data set from the simulations and the statistics of the performance is presented in a confusion matrix. The results indicate the classification system achieves 86% accuracy for successfully predicting screenout events around 8.5 minutes prior to screenout occurring in the simulation. The described methodology is demonstrated to be a useful tool for early screenout detections and shows its promising feasibility of other time-series analysis such as microseismic data.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.rightsCopyright of the original work is retained by the author.
dc.subjecthydraulic fracturing
dc.subjecttime series
dc.subjectscreenout
dc.subjectHidden Markov
dc.titleHidden Markov approach for screenout detection in unconventional reservoirs, A
dc.typeText
dc.contributor.committeememberShragge, Jeffrey
dc.contributor.committeememberMiskimins, Jennifer L.
thesis.degree.nameMaster of Science (M.S.)
thesis.degree.levelMasters
thesis.degree.disciplineGeophysics
thesis.degree.grantorColorado School of Mines


Files in this item

Thumbnail
Name:
Yu_mines_0052N_11873.pdf
Size:
3.964Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record