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Title: Bayesian inference for heterogeneous caprock permeability based on above zone pressure monitoring

Abstract

The presence of faults/ fractures or highly permeable zones in the primary sealing caprock of a CO2 storage reservoir can result in leakage of CO2. Monitoring of leakage requires the capability to detect and resolve the onset, location, and volume of leakage in a systematic and timely manner. Pressure-based monitoring possesses such capabilities. This study demonstrates a basis for monitoring network design based on the characterization of CO2 leakage scenarios through an assessment of the integrity and permeability of the caprock inferred from above zone pressure measurements. Four representative heterogeneous fractured seal types are characterized to demonstrate seal permeability ranging from highly permeable to impermeable. Based on Bayesian classification theory, the probability of each fractured caprock scenario given above zone pressure measurements with measurement error is inferred. The sensitivity to injection rate and caprock thickness is also evaluated and the probability of proper classification is calculated. The time required to distinguish between above zone pressure outcomes and the associated leakage scenarios is also computed.

Authors:
; ;
Publication Date:
Research Org.:
National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States). In-house Research
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1435787
Report Number(s):
NETL-PUB-20753
Journal ID: ISSN 1750-5836; PII: S1750583616305382
Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Greenhouse Gas Control; Journal Volume: 57; Journal Issue: C
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; CO2 storage, geologic carbon storage, GCS, pressure monitoring, seal integrity, above zone monitoring interval, AZMI, Bayesian inference, FRACGEN, risk assessment

Citation Formats

Namhata, Argha, Small, Mitchell J., and Dilmore, Rober. Bayesian inference for heterogeneous caprock permeability based on above zone pressure monitoring. United States: N. p., 2017. Web. doi:10.1016/j.ijggc.2016.12.007.
Namhata, Argha, Small, Mitchell J., & Dilmore, Rober. Bayesian inference for heterogeneous caprock permeability based on above zone pressure monitoring. United States. doi:10.1016/j.ijggc.2016.12.007.
Namhata, Argha, Small, Mitchell J., and Dilmore, Rober. Wed . "Bayesian inference for heterogeneous caprock permeability based on above zone pressure monitoring". United States. doi:10.1016/j.ijggc.2016.12.007. https://www.osti.gov/servlets/purl/1435787.
@article{osti_1435787,
title = {Bayesian inference for heterogeneous caprock permeability based on above zone pressure monitoring},
author = {Namhata, Argha and Small, Mitchell J. and Dilmore, Rober},
abstractNote = {The presence of faults/ fractures or highly permeable zones in the primary sealing caprock of a CO2 storage reservoir can result in leakage of CO2. Monitoring of leakage requires the capability to detect and resolve the onset, location, and volume of leakage in a systematic and timely manner. Pressure-based monitoring possesses such capabilities. This study demonstrates a basis for monitoring network design based on the characterization of CO2 leakage scenarios through an assessment of the integrity and permeability of the caprock inferred from above zone pressure measurements. Four representative heterogeneous fractured seal types are characterized to demonstrate seal permeability ranging from highly permeable to impermeable. Based on Bayesian classification theory, the probability of each fractured caprock scenario given above zone pressure measurements with measurement error is inferred. The sensitivity to injection rate and caprock thickness is also evaluated and the probability of proper classification is calculated. The time required to distinguish between above zone pressure outcomes and the associated leakage scenarios is also computed.},
doi = {10.1016/j.ijggc.2016.12.007},
journal = {International Journal of Greenhouse Gas Control},
number = C,
volume = 57,
place = {United States},
year = {Wed Feb 01 00:00:00 EST 2017},
month = {Wed Feb 01 00:00:00 EST 2017}
}