Determination of In-Situ Stress and Geomechanical Properties from Borehole Deformation
Abstract
This paper proposes a cost-effective technique to determine geomechanical properties and in-situ stress from borehole deformation data. In this approach, an artificial neural network (ANN) is applied to map the relationship among in-situ stress, borehole size, geomechanical properties, and borehole displacements. The genetic algorithm (GA) searches for the set of unknown stresses and geomechanical properties that matches the objective borehole deformation function. Probabilistic recapitulation (PR) analysis is conducted after each ANN-GA modeling cycle and will be repeated with a reduced number of unknowns for the next ANN-GA modeling cycle until unequivocal results are achieved. The PR-GA-ANN method has been demonstrated by a field case study to estimate borehole size, Young’s modulus, Poisson’s ratio, and the two horizontal stresses using borehole deformation information reported from four-arm caliper log of a vertical borehole. The methodology effectively solves the issue of the multiple solutions (various rock mechanical properties and in-situ stresses combinations) for a certain borehole deformation. The case study also indicated that the calculated horizontal stresses are in reasonable agreement with the filed hydraulic fracture treatment observations and the reported regional stress study of the area.
- Authors:
-
- Univ. of Wyoming, Laramie, WY (United States). Dept. of Petroleum Engineering
- Publication Date:
- Research Org.:
- Univ. of Wyoming, Laramie, WY (United States)
- Sponsoring Org.:
- USDOE Office of Fossil Energy (FE)
- OSTI Identifier:
- 1509846
- Grant/Contract Number:
- FE0026825
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Energies
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 1996-1073
- Publisher:
- MDPI AG
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; in-situ stress; geomechanical properties; caliper log; borehole deformation; borehole size; bit size; probabilistic recapitulation
Citation Formats
Han, Hong, and Yin, Shunde. Determination of In-Situ Stress and Geomechanical Properties from Borehole Deformation. United States: N. p., 2018.
Web. doi:10.3390/en11010131.
Han, Hong, & Yin, Shunde. Determination of In-Situ Stress and Geomechanical Properties from Borehole Deformation. United States. https://doi.org/10.3390/en11010131
Han, Hong, and Yin, Shunde. Fri .
"Determination of In-Situ Stress and Geomechanical Properties from Borehole Deformation". United States. https://doi.org/10.3390/en11010131. https://www.osti.gov/servlets/purl/1509846.
@article{osti_1509846,
title = {Determination of In-Situ Stress and Geomechanical Properties from Borehole Deformation},
author = {Han, Hong and Yin, Shunde},
abstractNote = {This paper proposes a cost-effective technique to determine geomechanical properties and in-situ stress from borehole deformation data. In this approach, an artificial neural network (ANN) is applied to map the relationship among in-situ stress, borehole size, geomechanical properties, and borehole displacements. The genetic algorithm (GA) searches for the set of unknown stresses and geomechanical properties that matches the objective borehole deformation function. Probabilistic recapitulation (PR) analysis is conducted after each ANN-GA modeling cycle and will be repeated with a reduced number of unknowns for the next ANN-GA modeling cycle until unequivocal results are achieved. The PR-GA-ANN method has been demonstrated by a field case study to estimate borehole size, Young’s modulus, Poisson’s ratio, and the two horizontal stresses using borehole deformation information reported from four-arm caliper log of a vertical borehole. The methodology effectively solves the issue of the multiple solutions (various rock mechanical properties and in-situ stresses combinations) for a certain borehole deformation. The case study also indicated that the calculated horizontal stresses are in reasonable agreement with the filed hydraulic fracture treatment observations and the reported regional stress study of the area.},
doi = {10.3390/en11010131},
journal = {Energies},
number = 1,
volume = 11,
place = {United States},
year = {Fri Jan 05 00:00:00 EST 2018},
month = {Fri Jan 05 00:00:00 EST 2018}
}
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Works referencing / citing this record:
Inference of in situ stress from thermoporoelastic borehole breakouts based on artificial neural network
journal, August 2019
- Zhang, Hua; Yin, Shunde
- International Journal for Numerical and Analytical Methods in Geomechanics, Vol. 43, Issue 16