Carbon capture and storage(CCS) in geologic formations has been recognized as a promising option for reducing carbon dioxide(CO) emissions from large stationary sources.However,the pressure buildup inside the storage ...Carbon capture and storage(CCS) in geologic formations has been recognized as a promising option for reducing carbon dioxide(CO) emissions from large stationary sources.However,the pressure buildup inside the storage formation can potentially induce slip along preexisting faults,which could lead to felt seismic ground motion and also provide pathways for brine/COleakage into shallow drinking water aquifers.To assess the geomechanical stability of faults,it is of crucial importance to know the in situ state of stress.In situ stress measurements can provide some information on the stresses acting on faults but with considerable uncertainties.In this paper,we investigate how such uncertainties,as defined by the variation of stress measurements obtained within the study area,could influence the assessment of the geomechanical stability of faults and the characteristics of potential injection-induced seismic events.Our modeling study is based on a hypothetical industrial-scale carbon sequestration project assumed to be located in the Southern San Joaquin Basin in California,USA.We assess the stability on the major(25 km long) fault that bounds the sequestration site and is subjected to significant reservoir pressure changes as a result of 50 years of COinjection.We present a series of geomechanical simulations in which the resolved stresses on the fault were varied over ranges of values corresponding to various stress measurements performed around the study area.The simulation results are analyzed by a statistical approach.Our main results are that the variations in resolved stresses as defined by the range of stress measurements had a negligible effect on the prediction of the seismic risk(maximum magnitude),but an important effect on the timing,the seismicity rate(number of seismic events) and the location of seismic activity.展开更多
Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and qua...Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an epidemic.Methods We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing.We demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs.Results The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model misspecification.Applying the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of 2.14.Conclusions The proposed estimation framework can be used to identify geographic variation in IFRs across settings.展开更多
基金funded by the Assistant Secretary for Fossil Energy,National Energy Technology Laboratory,National Risk Assessment Partnership of the U.S.Department of Energy under Contract No.DEAC02-05CH11231Swiss National Science Foundation Ambizione Energy grant(PZENP2_160555)
文摘Carbon capture and storage(CCS) in geologic formations has been recognized as a promising option for reducing carbon dioxide(CO) emissions from large stationary sources.However,the pressure buildup inside the storage formation can potentially induce slip along preexisting faults,which could lead to felt seismic ground motion and also provide pathways for brine/COleakage into shallow drinking water aquifers.To assess the geomechanical stability of faults,it is of crucial importance to know the in situ state of stress.In situ stress measurements can provide some information on the stresses acting on faults but with considerable uncertainties.In this paper,we investigate how such uncertainties,as defined by the variation of stress measurements obtained within the study area,could influence the assessment of the geomechanical stability of faults and the characteristics of potential injection-induced seismic events.Our modeling study is based on a hypothetical industrial-scale carbon sequestration project assumed to be located in the Southern San Joaquin Basin in California,USA.We assess the stability on the major(25 km long) fault that bounds the sequestration site and is subjected to significant reservoir pressure changes as a result of 50 years of COinjection.We present a series of geomechanical simulations in which the resolved stresses on the fault were varied over ranges of values corresponding to various stress measurements performed around the study area.The simulation results are analyzed by a statistical approach.Our main results are that the variations in resolved stresses as defined by the range of stress measurements had a negligible effect on the prediction of the seismic risk(maximum magnitude),but an important effect on the timing,the seismicity rate(number of seismic events) and the location of seismic activity.
基金K.A.and J.L.were supported by a grant from the Benioff Center for Microbiome MedicineThis research used resources of the Oak Ridge Leadership Computing Facility,which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725+5 种基金This manuscript has been coauthored by UT-Battelle,LLC under contract no.DE-AC05-00OR22725 with the U.S.Department of EnergyThe United States Government retains and the publisher,by accepting the article for publication,acknowledges that the United States Government retains a nonexclusive,paid-up,irrevocable,world-wide license to publish or reproduce the published form of this manuscript,or allow others to do so,for United States Government purposesThe Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan(http://energy.gov/downloads/doe-public-access-plan,last accessed September 16,2020)Work at Oak Ridge and Lawrence Berkeley National Laboratories was supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory,a consortium of DOE national laboratories focused on response to COVID-19,with funding provided by the Coronavirus CARES Actwas facilitated by previous breakthroughs obtained through the Laboratory Directed Research and Development Programs of the Lawrence Berkeley and Oak Ridge National Laboratories.M.P.J.was supported by a grant from the Laboratory Directed Research and Development(LDRD)Program of Lawrence Berkeley National Laboratory under U.S.Department of Energy Contract No.DE-AC02-05CH11231Oak Ridge National Laboratory would also like to acknowledge funding from the U.S.National Science Foundation(EF-2133763).
文摘Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an epidemic.Methods We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing.We demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs.Results The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model misspecification.Applying the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of 2.14.Conclusions The proposed estimation framework can be used to identify geographic variation in IFRs across settings.
