The Lecture will be held on Wednesday, , ההרצאה תתקיים ביום רביעי

27 June 2012, at 3:00 pm ,15:00 27 ביוני 2012 , בשעה

Room 206, Wolfson Mechanical Engineering Building, , חדר 206 , בניין וולפסון להנדסה מכנית

Tel-Aviv University אוניברסיטת תל אביב

A Bayesian Belief Network (BBN) methodology is developed for integrating CO2 leak

detection inferences from multiple monitoring technologies at a geologic sequestration site.

The methodology is demonstrated using two monitoring methods, near-surface soil CO2 flux

measurement and near-surface perfluoromethylcyclohexane (PMCH) tracer monitoring.

Statistical models are fitted to natural background soil CO2 flux and background PMCH

tracer concentrations to determine critical levels for leak inference. Leakage-induced

increments of soil CO2 flux and PMCH tracer concentrations are computed through

TOUGH2 simulations for different leakage rates and subsurface permeabilities. The

background characterizations and the simulation results are subsequently used to determine

the conditional probabilities of leak detection in the BBN model. The BBN model is

illustrated for use in evaluating the performance of alternative monitoring networks in a

network design phase, and for combining inferences from multiple observations in the

operational phase of a site. The detection capabilities of combined networks with different

monitoring densities for soil CO2 flux and PMCH tracer concentration are compared.

Implications for policy are discussed, including the assurance of carbon credits earned

through capture and storage.