Statistical approaches to leak detection for geological sequestration

Geological sequestration has been proposed as a way to remove CO₂ from the atmosphere by injecting it into deep saline aquifers. Detecting leaks to the atmosphere will be important for ensuring safety and effectiveness of storage. However, a standard set of tools for monitoring does not yet exist. The basic problem for leak detection - and eventually for the inverse problem of determining where and how big a leak is given measurements - is to detect shifts in the mean of atmospheric CO₂ data. Because the data are uncertain, statistical approaches are necessary. The traditional way to detect a shift would be to apply a hypothesis test, such as Z- or t-tests, directly to the data. These methods implicitly assume the data are Gaussian and independent. Analysis of atmospheric CO 2 data suggests these assumptions are often poor. The data are characterized by a high degree of variability, are non-Gaussian, and exhibit obvious systematic trends. Simple Z- or t-tests will lead to higher false positive rates than desired by the operator. Therefore Bayesian methods and methods for handling autocorrelation will be needed to control false positives. A model-based framework for shift detection is introduced that is capable of coping with non-Gaussian data and autocorrelation. Given baseline data, the framework estimates parameters and chooses the best model. When new data arrive, they are compared to forecasts of the baseline model and testing is performed to determine if a shift is present. The key questions are, how to estimate parameters? Which model to use for detrending? And how to test for shifts? The framework is applied to atmospheric CO₂ data from three existing monitoring sites: Mauna Loa Observatory in Hawaii, Harvard Forest in central Massachusetts, and a site from the Salt Lake CO₂ Network in Utah. These sites have been chosen to represent a spectrum of possible monitoring scenarios. The data exhibit obvious trends, including interannual growth and seasonal cycles. Several physical models are proposed for capturing interannual and seasonal trends in atmospheric CO₂ data. The simplest model correlates increases in atmospheric CO₂ with global annual emissions of CO₂ from fossil fuel combustion. Solar radiation and leaf area index models are proposed as alternative ways to explain seasonality in the data. Quantitative normality tests reject normality of the CO₂ data and the seasonal models proposed are nonlinear. A simple reaction kinetics example demonstrates that nonlinearity in the detrending model can lead to non-Gaussian posterior distributions. Therefore Bayesian methods estimation will be necessary. Here, nonlinear least squares is used to reduce computational effort. A Bayesian method of model selection called the deviance information criterion (DIC) is introduced as a way to avoid overfitting. DIC is used to choose between the proposed models and it is determined that a model using a straight line to represent emissions driven growth, the solar radiation model and a 6-month harmonic term does the best job of explaining the data. Improving the model is shown to have two important consequences: reduced variability in the residuals and reduced autocorrelation.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Chemical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Haidari, Arman S

Advisor dc:contributor.advisor

• Gregory J. McRae.

• Chemical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/65758

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/65758