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Supporting Data: An inversion algorithm to estimate maximum and minimum horizontal stress based on field test data for sleeve fracturing

Huang, yao and Zolfaghari, Navid and Ohanian, John and Bunger, Andrew (2021) Supporting Data: An inversion algorithm to estimate maximum and minimum horizontal stress based on field test data for sleeve fracturing. [Dataset] (Submitted)

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Abstract

An algorithm is developed to estimate maximum and minimum horizontal stress using experimental data from sleeve fracturing experiments. The algorithm is developed by analyzing the crack initiation, propagation and interaction behavior between primary and secondary cracks based on the Finite Element Method with Cohesive Zone elements. Five key quantities can be obtained from data curves and are related to comprise the basis of the algorithm in a manner consistent with a proxy model based on stress analysis around the borehole. The algorithm has been validated by 100 synthetic data cases covering various parameter combinations. Moreover, quantifying the degree of uncertainty in the stress prediction indicates strongest dependence on detecting when the borehole deformation from the first fracture(s) is impacted by growth of secondary fracture(s). Validity based on simulated data motivates new methods for high resolution strain sensing during sleeve fracturing especially for the purpose of accurately detecting secondary fracture initiation.


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Details

Item Type: Dataset
Status: Submitted
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Huang, yaoyah54@pitt.eduyah54
Zolfaghari, Navidnaz43@pitt.edunaz43
Ohanian, Johnohanianj@lunainc.com
Bunger, Andrewbunger@pitt.edubunger000-0002-0696-7066
Date: 2021
Schools and Programs: Swanson School of Engineering > Civil and Environmental Engineering
Uncontrolled Keywords: Sleeve fracturing; in-situ stress testing
Funders: Shell Exploration and Production
Type of Data: Database
Copyright Holders: The authors
Date Deposited: 10 Feb 2021 21:43
Last Modified: 10 Feb 2021 21:43
URI: http://d-scholarship.pitt.edu/id/eprint/40239

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