[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128714-en":3,"doc-seo-128714-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128714,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Exploring CO2 Storage Potential in Lithuanian Deep Saline Aquifers - A Machine Learning Guided Approach","Carbon capture, utilization and storage (CCUS) is increasingly important for climate mitigation, making accurate evaluation of subsurface reservoirs essential for CO2 sequestration. Digital rock volumes derived from advanced imaging such as micro‑Xray computed tomography (MXCT) enable detailed characterization of porous and permeable geological structures. This work presents a workflow to assess CO2 storage viability in Lithuanian deep saline aquifers (Syderiai and Vaskai) using petrophysical properties estimated from digital rock volumes and demonstrates how machine learning combined with numerical modeling can support reliable subsurface CO2 storage management.","Exploring CO2 storage potential in Lithuanian deep saline aquifers using digital rock volumes: a machine learning guided approach  \nShruti Malik1, Pijus Makauskas2, Ravi Sharma3, Mayur Pal4  \n1, 2, 4Kaunas University of Technology, Department of Mathematical Modelling, Kaunas, Lithuania  \n3Department of Earth Sciences, Indian Institute of Technology Roorkee, India  \n4Corresponding author  \n[E-mail:](E-mail:1shruti.malik@ktu.lt)[1](E-mail:1shruti.malik@ktu.lt)[shruti.malik@ktu.lt](E-mail:1shruti.malik@ktu.lt), [2](2pijus.makauskas@ktu.lt)[pijus.makauskas@ktu.lt](2pijus.makauskas@ktu.lt), [3](3 ravi.sharma@es.iitr.ac.in)[ ravi.sharma@es.iitr.ac.in](3 ravi.sharma@es.iitr.ac.in), [4](4 mayur.pal@ktu.lt)[ mayur.pal@ktu.lt](4 mayur.pal@ktu.lt)  \nReceived 30 November 2023; accepted 28 December 2023; published online 31 December 2023 DOI [https://doi.org/10.21595/accus.2023.23906](https://doi.org/10.21595/accus.2023.23906)  \nCopyright © 2023 Shruti Malik, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nAbstract. The increasing significance of carbon capture, utilization and storage (CCUS) as a climate mitigation strategy has underscored the importance of accurately evaluating subsurface reservoirs for CO2 sequestration. In this context, digital rock volumes, obtained through advanced imaging techniques such as micro-Xray computed tomography (MXCT), offer intricate insights into the porous and permeable structures of geological formations. This study presents a comprehensive methodology for assessing CO2 storage viability within Lithuanian deep saline aquifers, namely Syderiai and Vaskai, by utilizing petrophysical properties estimated from digital rock volumes of samples from analogous formations. It also demonstrates the potential of integrating advanced imaging techniques, machine learning, and numerical modeling for accurate assessment and effective management of subsurface CO2 storage.  \nKeywords: carbon capture, utilization and storage (CCUS), saline aquifers, storage potential, digital rock volumes, machine learning, lattice Boltzmann method, numerical modeling.  \n1. Introduction  \nIn recent years, anthropogenic activities have led to a global increase in greenhouse gas emissions. To mitigate this, Carbon Capture, Utilization, and Storage (CCUS) has emerged as a potential solution [1] . In Lithuania's Baltic Basin, research is still in its early stages regarding the long-term fate of geological CO2 storage [2, 3] . This study focuses on the deep saline aquifers, Syderiai and Vaskai of the Baltic Basin (shown in Fig. 1) and aims to demonstrate the effective application of machine learning in extracting optimized estimates of storage and flow potential using non-destructive digital rock volumes (DRV) .  \nFig. 1. Location of Syderiai and Vaskai regions  \n44 ISSN ONLINE 2783-686X  \nEXPLORING CO2 STORAGE POTENTIAL IN LITHUANIAN DEEP SALINE AQUIFERS USING DIGITAL ROCK VOLUMES: A MACHINE LEARNING GUIDED  \nAPPROACH. SHRUTI MALIK, PIJUS MAKAUSKAS, RAVI SHARMA, MAYUR PAL  \nMachine learning algorithms offer an accurate and fast alternative to time-consuming conventional Digital rock physics method for determining optimized estimates of petrophysical properties [4-6] . The storage of captured CO2 can be done in underground geological formations such as depleted oil and gas reservoirs, deep saline aquifers, or coal seams. Amongst these, deep saline aquifers are considered as the most prospective site due to their large storage capacity and widespread geographic distribution, making it easier to find storage locations closer to the sources of CO2 emissions [3] .  \n2. Methodology  \nIn this study, digital volumes of rocks were obtained from formations analogous to Lithuanian reservoirs using Micro Xray Computed Tomography (MXCT) scanning technique. The Machine learning (M","cbCaiflA7HHGLTlB","https://ap.wps.com/l/cbCaiflA7HHGLTlB","pdf",576727,1,4,"English","en",105,"# 1. Introduction\n# 2. Methodology\n## 2.1 Digital rock volume acquisition and segmentation\n## 2.2 Machine learning for porosity and LBM for permeability\n# 3. Results\n## 3.1 Petrophysical property comparison with laboratory measurements\n## 3.2 Numerical modeling of CO2 injection and storage capacity","[{\"question\":\"What geological sites are assessed for CO2 storage in this study?\",\"answer\":\"The study evaluates Lithuanian deep saline aquifers in the Syderiai and Vaskai regions within the Baltic Basin.\"},{\"question\":\"How are petrophysical properties estimated using digital rock volumes?\",\"answer\":\"Digital rock volumes from analogous formations are obtained via MXCT scanning; machine learning estimates porosity, and Lattice Boltzmann Method simulations estimate permeability from 3D DRVs and extracted sub-volumes for REV analysis.\"},{\"question\":\"How reliable are the estimated petrophysical properties compared with laboratory data?\",\"answer\":\"Estimated porosity values closely match laboratory measurements, with error percentages around 1%–8%, while permeability errors fall roughly in the 20%–55% range.\"}]","Exploring CO2 Storage Potential in Lithuanian Deep Saline Aquifers - A Machine Learning Guided Approach | PDF",1786002819,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"exploring-co2-storage-potential-in-lithuanian-deep-saline-aquifers-a-machine-learning-guided-approach","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/exploring-co2-storage-potential-in-lithuanian-deep-saline-aquifers-a-machine-learning-guided-approach/128714/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-22","2026-08-06",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What geological sites are assessed for CO2 storage in this study?","Question",{"text":75,"@type":76},"The study evaluates Lithuanian deep saline aquifers in the Syderiai and Vaskai regions within the Baltic Basin.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are petrophysical properties estimated using digital rock volumes?",{"text":80,"@type":76},"Digital rock volumes from analogous formations are obtained via MXCT scanning; machine learning estimates porosity, and Lattice Boltzmann Method simulations estimate permeability from 3D DRVs and extracted sub-volumes for REV analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"How reliable are the estimated petrophysical properties compared with laboratory data?",{"text":84,"@type":76},"Estimated porosity values closely match laboratory measurements, with error percentages around 1%–8%, while permeability errors fall roughly in the 20%–55% range.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":29,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]