Advertisement

Clean Technologies and Environmental Policy

, Volume 21, Issue 8, pp 1655–1664 | Cite as

Prediction of CO2 storage site integrity with rough set-based machine learning

  • Kathleen B. Aviso
  • Jose Isagani B. Janairo
  • Michael Angelo B. Promentilla
  • Raymond R. TanEmail author
Original Paper

Abstract

CO2 capture and storage (CCS) and negative emissions technologies (NETs) are considered to be essential carbon management strategies to safely stabilize climate. CCS entails capture of CO2 from combustion products from industrial plants and subsequent storage of this CO2 in geological formations or reservoirs. Some NETs, such as bioenergy with CCS and direct air capture, also require such CO2 sinks. For these technologies to work, it is essential to identify and use only secure geological reservoirs with minimal risk of leakage over a timescale of multiple centuries. Prediction of storage integrity is thus a difficult but critical task. Natural analogues or naturally occurring deposits of CO2, can provide some information on which geological features (e.g., depth, temperature, and pressure) are predictive of secure or insecure storage. In this work, a rough set-based machine learning (RSML) technique is used to analyze data from more than 70 secure and insecure natural CO2 reservoirs. RSML is then used to generate empirical rule-based predictive models for selection of suitable CO2 storage sites. These models are compared with previously published site selection rules that were based on expert knowledge.

Graphic abstract

Keywords

CO2 storage CO2 removal (CDR) Artificial intelligence (AI) Classification 

Notes

Acknowledgements

This work was supported via the Philippine Higher Education Research Network (PHERNet) Sustainability Studies Program Granted to De La Salle University by the Commission on Higher Education of the Republic of the Philippines.

References

  1. Aviso KB, Tan RR, Culaba AB (2008) Application of rough sets for environmental decision support in industry. Clean Technol Environ Policy 10:53–66CrossRefGoogle Scholar
  2. Breiman L (1984) Classification and regression trees, 1st edn. Routledge, New YorkGoogle Scholar
  3. Bruhn T, Naims H, Olfe-Kräutlein B (2016) Separating the debate on CO2 utilisation from carbon capture and storage. Environ Sci Policy 60:38–43CrossRefGoogle Scholar
  4. CASSEM (2011) CO2 aquifer storage site evaluation and monitoring. Heriot-Watt University, EdinburghGoogle Scholar
  5. Celia MA, Nordbotten JM, Bachu S, Dobossy M, Court B (2009) Risk of leakage versus depth of injection in geological storage. Energy Procedia 1:2573–2580CrossRefGoogle Scholar
  6. Chadwick RA, Arts R, Bernstone C, May F, Thibeau S, Zweigel P (2008) Best practice for the storage of CO2 in saline aquifers. British Geological Survey Occasional Publication, NottinghamGoogle Scholar
  7. Cortes C, Vapnik V, Saitta L (1995) Support-vector networks. Mach Learn 20:273–297Google Scholar
  8. Duan X (2018) Application of artificial intelligence in evaluation and management of SEC oil and gas reserves. Chem Eng Trans 71:925–930Google Scholar
  9. Haszeldine RS, Flude S, Johnson G, Scott V (2018) Negative emissions technologies and carbon capture and storage to achieve the Paris Agreement commitments. Philos Trans R Soc A: Math Phys Eng Sci.  https://doi.org/10.1098/rsta.2016.0447 CrossRefGoogle Scholar
  10. Herzog HJ (2011) Scaling up carbon dioxide capture and storage: from megatons to gigatons. Energy Econ 33:597–604CrossRefGoogle Scholar
  11. IEA GHG (2009) CCS site characterisation criteria. IEA Greenhouse Gas R&D Programme, ReadingGoogle Scholar
  12. IPCC (2018) Summary for policymakers. In: Masson-Delmotte V, Zhai P, Pörtner HO, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T (eds) Global warming of 1.5 °C. An IPCC special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. World Meteorological Organization, GenevaGoogle Scholar
  13. Kemper J (2015) Biomass and carbon dioxide capture and storage: a review. Int J Greenh Gas Control 40:401–430CrossRefGoogle Scholar
  14. Klemeš JJ, Varbanov PS, Walmsley TG, Jia X (2018) New directions in the implementation of pinch methodology (PM). Renew Sustain Energy Rev 98:439–468CrossRefGoogle Scholar
  15. Mahajan P, Kandwal R, Vijay R (2012) Rough set approach in machine learning: a review. Int J Comput Appl 56:1–13Google Scholar
  16. Makridakis S, Spiliotis E, Assimakopoulos V (2018) Statistical and machine learning forecasting methods: concerns and ways forward. PLoS ONE 13:1–26CrossRefGoogle Scholar
  17. McCulloch WS, Pitts W (1990) A logical calculus of the ideas immanent in nervous activity. Bull Math Biol 52:99–115CrossRefGoogle Scholar
  18. Middleton RS, Yaw S (2018) The cost of getting CCS wrong: uncertainty, infrastructure design, and stranded CO2. Int J Greenh Gas Control 70:1–11CrossRefGoogle Scholar
  19. Minx JC, Lamb WF, Callaghan MW, Fuss S, Hilaire J, Creutzig F, Amann T, Beringer T, De Oliveira Garcia W, Hartmann J, Khanna T, Lenzi D, Luderer G, Nemet GF, Rogelj J, Smith P, Vicente Vicente JL, Wilcox J, Del Mar Zamora Dominguez M (2018) Negative emissions—part 1: research landscape and synthesis. Environ Res Lett 13:063001CrossRefGoogle Scholar
  20. Miocic JM, Gilfillan MSV, Roberts JJ, Edlmann K, McDermott CI, Haszeldine RS (2016) Controls on CO2 storage security in natural reservoirs and implications for CO2 storage site selection. Int J Greenh Gas Control 51:118–125CrossRefGoogle Scholar
  21. Mukherjee R (2017) Selection of sustainable process and essential indicators for decision making using machine learning algorithms. Process Integr Optim Sustain 1:153–163CrossRefGoogle Scholar
  22. Pawlak Z (1982) Rough sets. Int J Inf Comput Sci 11:341–356CrossRefGoogle Scholar
  23. Pawlak Z (1999) Rough classification. Int J Hum Comput Stud 51:369–383CrossRefGoogle Scholar
  24. Pawlak Z (2002) Rough sets, decision algorithms and Bayes’ theorem. Eur J Oper Res 136:181–189CrossRefGoogle Scholar
  25. Pawlak Z, Slowinski R (1994) Decision analysis using rough sets. Int Trans Oper Res 1:107–114CrossRefGoogle Scholar
  26. Phelps JJC, Blackford JC, Holt JT, Polton JA (2015) Modelling large-scale CO2 leakages in the North Sea. Int J Greenh Gas Control 38:210–220CrossRefGoogle Scholar
  27. Ponton JW, Klemeš JJ (1993) Alternatives to neural networks for inferential measurement. Comput Chem Eng 17:991–1000CrossRefGoogle Scholar
  28. ProSoft (1999) User’s guide ROSE 2 rough set data explorer. idss.cs.put.poznan.pl/site/fileadmin/projects-images/rose_manual.pdf. Accessed 18 March 2019
  29. Roberts JJ, Wood RA, Haszeldine RS (2011) Assessing the health risks of natural CO2 seeps in Italy. Proc Natl Acad Sci USA 108:16545–16548CrossRefGoogle Scholar
  30. Sanz-Perez ES, Murdock CR, Didas SA, Jones CW (2016) Direct capture of CO2 from ambient air. Chem Rev 116:11840–11876CrossRefGoogle Scholar
  31. Sigurdsson H (1988) Gas bursts from cameroon crater lakes: a new natural hazard. Disasters 12:131–146CrossRefGoogle Scholar
  32. Tan RR (2005) Rule-based life cycle impact assessment using modified rough set induction methodology. Environ Model Softw 20:509–513CrossRefGoogle Scholar
  33. Tapia JFD, Tan RR (2015) Optimal revamp of multi-region carbon capture and storage (CCS) systems by two-step linear optimization. Energy Syst 6:269–289CrossRefGoogle Scholar
  34. Tapia JFD, Promentilla MAB, Tseng M-L, Tan RR (2017) Screening of carbon dioxide utilization options using hybrid Analytic hierarchy process-data envelopment Analysis method. J Clean Prod 165:1361–1370CrossRefGoogle Scholar
  35. Thengane SK, Tan RR, Foo DCY, Bandyopadhyay S (2019) A pinch-based approach for targeting carbon capture, utilization, and storage systems. Ind Eng Chem Res 58:3188–3198CrossRefGoogle Scholar
  36. Upham P, Roberts T (2011) Public perceptions of CCS: emergent themes in pan-European focus groups and implications for communications. Int J Greenh Gas Control 5:1359–1367CrossRefGoogle Scholar
  37. Yang L, Liu S, Tsoka S, Papageorgiou LG (2015) Sample re-weighting hyper box classifier for multi-class data classification. Comput Ind Eng 85:44–56CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Chemical Engineering DepartmentDe La Salle UniversityManilaPhilippines
  2. 2.Biology DepartmentDe La Salle UniversityManilaPhilippines

Personalised recommendations