A Bayesian belief network (BBN) for combining evidence from multiple CO2 leak detection technologies

A Bayesian belief network (BBN) methodology is developed for integrating CO 2 leak detection inferences from multiple monitoring technologies at a geologic sequestration site. The methodology is demonstrated using two monitoring methods: near‐surface soil CO 2 flux measurement and near‐surface perfluoromethylcyclohexane (PMCH) tracer monitoring, from the Zero Emission Research and Technology (ZERT) release test in 2007. Statistical models are fitted to natural background soil CO 2 flux and background PMCH tracer concentrations to determine critical levels for leak inference. Leakage‐induced increments of soil CO 2 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 CO 2 flux and PMCH tracer concentration are compared. Given the test condition, the greater sensitivity of the PMCH tracer allows it to detect smaller leaks, while detection by the soil CO 2 flux monitors implies that a larger leak is most likely present. © 2012 Society of Chemical Industry and John Wiley & Sons, Ltd

[1]  Susan L. Brantley,et al.  Measured carbon dioxide emissions from Oldoinyo Lengai and the skewed distribution of passive volcanic fluxes , 1995 .

[2]  Mark E. Borsuk,et al.  Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian probability network , 2006 .

[3]  Mitchell J. Small,et al.  Probabilistic design of a near-surface CO2 leak detection system. , 2011, Environmental science & technology.

[4]  Richard Hammack,et al.  Near-surface monitoring for the ZERT shallow CO2 injection project , 2009 .

[5]  Curtis M. Oldenburg,et al.  On Leakage and Seepage from Geologic Carbon Sequestration Sites: Unsaturated Zone Attenuation , 2003 .

[6]  Sakari Kuikka,et al.  Learning Bayesian decision analysis by doing: lessons from environmental and natural resources management , 1999 .

[7]  Mitchell J. Small,et al.  Integrating Location Models with Bayesian Analysis to Inform Decision Making , 2010 .

[8]  Garret Veloski,et al.  Atmospheric tracer monitoring and surface plume development at the ZERT pilot test in Bozeman, Montana, USA , 2010 .

[9]  Andrea Castelletti,et al.  Bayesian networks in water resource modelling and management , 2007, Environ. Model. Softw..

[10]  Anthony J. Jakeman,et al.  A Bayesian network approach for assessing the sustainability of coastal lakes in New South Wales, Australia , 2007, Environ. Model. Softw..

[11]  Gareth Johnson,et al.  The use of stable isotope measurements for monitoring and verification of CO2 storage , 2009 .

[12]  Rick Chalaturnyk,et al.  IEA GHG Weyburn CO2 monitoring and storage project , 2005 .

[13]  Ray Leuning,et al.  Atmospheric monitoring and verification technologies for CO2 geosequestration , 2008 .

[14]  Yongtae Park,et al.  Large engineering project risk management using a Bayesian belief network , 2009, Expert Syst. Appl..

[15]  Chul-Un Ro,et al.  Determination of Atmospheric Perfluorocarbon Background Concentrations of fL/L Range at the Western Coastal Area of Korea , 2002 .

[16]  Ton Wildenborg,et al.  Large-scale CO2 injection demos for the development of monitoring and verification technology and guidelines (CO2 ReMoVe) , 2009 .

[17]  Mitchell J. Small,et al.  Expert System Methodology for Evaluating Reductive Dechlorination at TCE Sites , 1999 .

[18]  Jens Birkholzer,et al.  A shallow subsurface controlled release facility in Bozeman, Montana, USA, for testing near surface CO2 detection techniques and transport models , 2010 .

[19]  Mark E. Borsuk,et al.  A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis , 2004 .

[20]  F. Girardi,et al.  The field campaigns of the European Tracer Experiment (ETEX): overview and results , 1998 .

[21]  B. Marcot,et al.  Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement , 2001 .

[22]  Grant S. Bromhal,et al.  The use of tracers to assess leakage from the sequestration of CO2 in a depleted oil reservoir, New Mexico, USA , 2007 .

[23]  Mitchell J. Small,et al.  Bayesian hierarchical models for soil CO2 flux and leak detection at geologic sequestration sites , 2011 .

[24]  Thomas H. Wilson,et al.  Atmospheric and soil-gas monitoring for surface leakage at the San Juan Basin CO2 pilot test site at Pump Canyon New Mexico, using perfluorocarbon tracers, CO2 soil-gas flux and soil-gas hydrocarbons , 2013 .

[25]  Sally M. Benson,et al.  Measuring permanence of CO2 storage in saline formations: the Frio experiment , 2005 .

[26]  Marina Pantazidou,et al.  Site-specific updating and aggregation of Bayesian belief network models for multiple experts. , 2004, Risk analysis : an official publication of the Society for Risk Analysis.

[27]  Curtis M. Oldenburg,et al.  Coupled Vadose Zone and Atmospheric Surface-Layer Transport of Carbon Dioxide from Geologic Carbon Sequestration Sites , 2004 .

[28]  Curtis M. Oldenburg,et al.  Modeling Gas Transport in the Shallow Subsurface During the ZERT CO2 Release Test , 2010 .

[29]  Danny C. Lee,et al.  Population Viability Assessment of Salmonids by Using Probabilistic Networks , 1997 .