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Title: Measuring and Modeling Fault Density for Plume-Fault Encounter Probability Estimation

Abstract

Emission of carbon dioxide from fossil-fueled power generation stations contributes to global climate change. Storage of this carbon dioxide within the pores of geologic strata (geologic carbon storage) is one approach to mitigating the climate change that would otherwise occur. The large storage volume needed for this mitigation requires injection into brine-filled pore space in reservoir strata overlain by cap rocks. One of the main concerns of storage in such rocks is leakage via faults. In the early stages of site selection, site-specific fault coverages are often not available. This necessitates a method for using available fault data to develop an estimate of the likelihood of injected carbon dioxide encountering and migrating up a fault, primarily due to buoyancy. Fault population statistics provide one of the main inputs to calculate the encounter probability. Previous fault population statistics work is shown to be applicable to areal fault density statistics. This result is applied to a case study in the southern portion of the San Joaquin Basin with the result that the probability of a carbon dioxide plume from a previously planned injection had a 3% chance of encountering a fully seal offsetting fault.

Authors:
; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
Earth Sciences Division
OSTI Identifier:
1016011
Report Number(s):
LBNL-4538E
TRN: US201112%%377
DOE Contract Number:
DE-AC02-05CH11231
Resource Type:
Technical Report
Resource Relation:
Related Information: Journal Publication Date: 2011
Country of Publication:
United States
Language:
English
Subject:
54; 58; CAP ROCK; CARBON; CARBON DIOXIDE; CLIMATES; CLIMATIC CHANGE; GEOLOGIC STRATA; MITIGATION; PLUMES; POWER GENERATION; PROBABILITY; SIMULATION; SITE SELECTION; STATISTICS; STORAGE

Citation Formats

Jordan, P.D., Oldenburg, C.M., and Nicot, J.-P. Measuring and Modeling Fault Density for Plume-Fault Encounter Probability Estimation. United States: N. p., 2011. Web. doi:10.2172/1016011.
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Jordan, P.D., Oldenburg, C.M., & Nicot, J.-P. Measuring and Modeling Fault Density for Plume-Fault Encounter Probability Estimation. United States. doi:10.2172/1016011.
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Jordan, P.D., Oldenburg, C.M., and Nicot, J.-P. Sun . "Measuring and Modeling Fault Density for Plume-Fault Encounter Probability Estimation". United States. doi:10.2172/1016011. https://www.osti.gov/servlets/purl/1016011.
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@article{osti_1016011,
title = {Measuring and Modeling Fault Density for Plume-Fault Encounter Probability Estimation},
author = {Jordan, P.D. and Oldenburg, C.M. and Nicot, J.-P.},
abstractNote = {Emission of carbon dioxide from fossil-fueled power generation stations contributes to global climate change. Storage of this carbon dioxide within the pores of geologic strata (geologic carbon storage) is one approach to mitigating the climate change that would otherwise occur. The large storage volume needed for this mitigation requires injection into brine-filled pore space in reservoir strata overlain by cap rocks. One of the main concerns of storage in such rocks is leakage via faults. In the early stages of site selection, site-specific fault coverages are often not available. This necessitates a method for using available fault data to develop an estimate of the likelihood of injected carbon dioxide encountering and migrating up a fault, primarily due to buoyancy. Fault population statistics provide one of the main inputs to calculate the encounter probability. Previous fault population statistics work is shown to be applicable to areal fault density statistics. This result is applied to a case study in the southern portion of the San Joaquin Basin with the result that the probability of a carbon dioxide plume from a previously planned injection had a 3% chance of encountering a fully seal offsetting fault.},
doi = {10.2172/1016011},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun May 15 00:00:00 EDT 2011},
month = {Sun May 15 00:00:00 EDT 2011}
}
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Technical Report:
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DOI:  10.2172/1016011
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