Intelligent fracture creation for shale gas development

Craig C. Douglas, Guan Qin, Nathan Collier, Bin Gong

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Shale gas represents a major fraction of the proven reserves of natural gas in the United States and a collection of other countries. Higher gas prices and the need for cleaner fuels provides motivation for commercializing shale gas deposits even though the cost is substantially higher than traditional gas deposits. Recent advances in horizontal drilling and multistage hydraulic fracturing, which dramatically lower costs of developing shale gas fields, are key to renewed interest in shale gas deposits. Hydraulically induced fractures are quite complex in shale gas reservoirs. Massive, multistage, multiple cluster treatments lead to fractures that interact with existing fractures (whether natural or induced earlier). A dynamic approach to the fracturing process so that the resulting network of reservoirs is known during the drilling and fracturing process is economically enticing. The process needs to be automatic and done in faster than real-time in order to be useful to the drilling crews.

Original languageEnglish
Pages (from-to)1745-1750
Number of pages6
JournalProcedia Computer Science
Volume4
DOIs
StatePublished - 2011
Externally publishedYes
Event11th International Conference on Computational Science, ICCS 2011 - Singapore, Singapore
Duration: Jun 1 2011Jun 3 2011

Funding

This research was supported in part by NSF grants 1018072 and 1018079, awards from the Center for Fundamentals of Subsurf ace Flow, School of Energy Resources, University of Wyoming, Sinopec, and Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).

FundersFunder number
School of Energy Resources
National Science Foundation
Directorate for Computer and Information Science and Engineering1018072, 1018079
University of Wyoming
Center for Fundamentals of Subsurface Flow
Sinopec Tech Houston Center

    Keywords

    • DDDAS
    • Dynamic data-driven application system
    • Multiscale methods
    • Reservoir simulation
    • Sensor-model feedback

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