Probabilistic Integration of Geomechanical and Geostatistical Inferences for Mapping Natural Fracture Networks
Geomechanical modeling of the fracturing process accounts for the physical factors that inform the propagation and termination of the fractures. However, the resultant models may not honor the fracture statistics derived from auxiliary sources such as outcrop images. Stochastic algorithms, on the ot...
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| Veröffentlicht in: | Mathematical geosciences 2023-07, Vol.55 (5), p.645-671 |
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| creator | Chandna, Akshat Srinivasan, Sanjay |
| description | Geomechanical modeling of the fracturing process accounts for the physical factors that inform the propagation and termination of the fractures. However, the resultant models may not honor the fracture statistics derived from auxiliary sources such as outcrop images. Stochastic algorithms, on the other hand, generate natural fracture maps based purely on statistical inferences from outcrop images excluding the effects of any physical processes guiding the propagation and termination of fractures. This paper, therefore, focuses on presenting a methodology for combining information from geomechanical and stochastic approaches necessary to obtain a fracture modeling approach that is geologically realistic as well as consistent with the geomechanical conditions for fracture propagation. As a prerequisite for this integration approach, a multi-point statistics-based stochastic simulation algorithm is implemented that yields the probability of fracture propagation along various paths. The application and effectiveness of this probability integration paradigm are demonstrated on a synthetic fracture set. |
| doi_str_mv | 10.1007/s11004-022-10041-x |
| format | Article |
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However, the resultant models may not honor the fracture statistics derived from auxiliary sources such as outcrop images. Stochastic algorithms, on the other hand, generate natural fracture maps based purely on statistical inferences from outcrop images excluding the effects of any physical processes guiding the propagation and termination of fractures. This paper, therefore, focuses on presenting a methodology for combining information from geomechanical and stochastic approaches necessary to obtain a fracture modeling approach that is geologically realistic as well as consistent with the geomechanical conditions for fracture propagation. As a prerequisite for this integration approach, a multi-point statistics-based stochastic simulation algorithm is implemented that yields the probability of fracture propagation along various paths. 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However, the resultant models may not honor the fracture statistics derived from auxiliary sources such as outcrop images. Stochastic algorithms, on the other hand, generate natural fracture maps based purely on statistical inferences from outcrop images excluding the effects of any physical processes guiding the propagation and termination of fractures. This paper, therefore, focuses on presenting a methodology for combining information from geomechanical and stochastic approaches necessary to obtain a fracture modeling approach that is geologically realistic as well as consistent with the geomechanical conditions for fracture propagation. As a prerequisite for this integration approach, a multi-point statistics-based stochastic simulation algorithm is implemented that yields the probability of fracture propagation along various paths. The application and effectiveness of this probability integration paradigm are demonstrated on a synthetic fracture set.</description><subject>Algorithms</subject><subject>Chemistry and Earth Sciences</subject><subject>Computer Science</subject><subject>Crack propagation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Fracture mechanics</subject><subject>Geomechanics</subject><subject>Geostatistics</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Integration</subject><subject>Modelling</subject><subject>Outcrops</subject><subject>Physical factors</subject><subject>Physics</subject><subject>Probability theory</subject><subject>Special Issue</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Statistics for Engineering</subject><subject>Stochasticity</subject><issn>1874-8961</issn><issn>1874-8953</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9UMtOwzAQtBBIlMIPcLLEOeBH7CRHVNFSqRQOcLYcd1NSWjvYrih_j0MQ3DjNaOex0iB0Sck1JaS4CTRBnhHGsp7Q7HCERrQs8qysBD_-5ZKeorMQNoRIygUdIfvkXa3rdtuG2Bo8txHWXsfWWewaPAO3A_OqbWv0Fmu76i8hJr13p9PcNuDBGgi4cR4_6K5r7Rovddz7JE-9NokBXkL8cP4tnKOTRm8DXPzgGL1M754n99nicTaf3C4yzXMWM8YEFFUuGlKVstCJlIRKWTU1L2oAYUQSSS2gbsSqMoSbfAWS1VJoakAUfIyuht7Ou_c9hKg2bu9teqlYyYpKckar5GKDy3gXgodGdb7daf-pKFH9rmrYVaVd1feu6pBCfAiFZLZr8H_V_6S-AIIIfSY</recordid><startdate>20230701</startdate><enddate>20230701</enddate><creator>Chandna, Akshat</creator><creator>Srinivasan, Sanjay</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-6876-0490</orcidid></search><sort><creationdate>20230701</creationdate><title>Probabilistic Integration of Geomechanical and Geostatistical Inferences for Mapping Natural Fracture Networks</title><author>Chandna, Akshat ; Srinivasan, Sanjay</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-225e7945f09867a45f801669fb37bee5c59450b5ebf5d9c03c4de62b65a1ce573</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Chemistry and Earth Sciences</topic><topic>Computer Science</topic><topic>Crack propagation</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Fracture mechanics</topic><topic>Geomechanics</topic><topic>Geostatistics</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Hydrogeology</topic><topic>Integration</topic><topic>Modelling</topic><topic>Outcrops</topic><topic>Physical factors</topic><topic>Physics</topic><topic>Probability theory</topic><topic>Special Issue</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Statistics for Engineering</topic><topic>Stochasticity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chandna, Akshat</creatorcontrib><creatorcontrib>Srinivasan, Sanjay</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Mathematical geosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chandna, Akshat</au><au>Srinivasan, Sanjay</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Probabilistic Integration of Geomechanical and Geostatistical Inferences for Mapping Natural Fracture Networks</atitle><jtitle>Mathematical geosciences</jtitle><stitle>Math Geosci</stitle><date>2023-07-01</date><risdate>2023</risdate><volume>55</volume><issue>5</issue><spage>645</spage><epage>671</epage><pages>645-671</pages><issn>1874-8961</issn><eissn>1874-8953</eissn><abstract>Geomechanical modeling of the fracturing process accounts for the physical factors that inform the propagation and termination of the fractures. 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| subjects | Algorithms Chemistry and Earth Sciences Computer Science Crack propagation Earth and Environmental Science Earth Sciences Fracture mechanics Geomechanics Geostatistics Geotechnical Engineering & Applied Earth Sciences Hydrogeology Integration Modelling Outcrops Physical factors Physics Probability theory Special Issue Statistical analysis Statistical methods Statistics Statistics for Engineering Stochasticity |
| title | Probabilistic Integration of Geomechanical and Geostatistical Inferences for Mapping Natural Fracture Networks |
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