Petrophysical Multimineral Analysis Using Genetic Optimization to Solve Complex Mineral Composition in Unconventional Reservoirs

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Petrophysical multimineral analysis using genetic optimization to solve complex mineral composition in unconventional reservoirsAuthors: Reinaldo J. MichelenaKevin S. GodbeyMichael J. UlandPatricia E. RodriguesReinaldo J. MichelenaSeisPetro Geosoftware, LLCSearch for more papers by this author, Kevin S. GodbeySeisPetro Geosoftware, LLCSearch for more papers by this author, Michael J. UlandiReservoir.com, Inc.Search for more papers by this author, and Patricia E. RodriguesWhiting Petroleum CorporationSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3425780.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractPetrophysical modeling in unconventional reservoirs requires tools that take into account their complex mineral composition and lack of log information necessary to resolve this complexity in detail. We pose the estimation of properties of mineral constituents of the rock as a stochastic nonlinear optimization problem where a genetic algorithm (a type of algorithm in the artificial intelligence spectrum) replaces the time-consuming, manual trial-and-error process of adjusting properties and fitting the input logs in conventional multimineral analysis. The method requires interpretative inputs based on prior knowledge and experience, but such inputs are provided in the form of ranges instead of single property values, facilitating the work of the analyst. By testing adaptively thousands of solutions and considerably reducing the time needed to fit the input logs with a consistent set of properties, it becomes then possible to test other scenarios of input data and constituents, quantify the uncertainty and non-uniqueness of individual parameters, and shed light upon higher-level petrophysical questions such as spatial variations in kerogen maturity, water resistivity, or clay composition. We illustrate the use of the methodology to estimate fractions of constituents for the mineralogically complex Bakken Formation and to estimate variations of thermal maturity with depth in the Marcellus, shale gas Formation.Presentation Date: Wednesday, October 14, 2020Session Start Time: 8:30 AMPresentation Time: 10:35 AMLocation: 360APresentation Type: OralKeywords: petrophysics, unconventional, lithology, nonlinear, log analysisPermalink: https://doi.org/10.1190/segam2020-3425780.1FiguresReferencesRelatedDetailsCited byUsing rock-physics models to validate rock composition from multimineral log analysisLiwei Cheng, Manika Prasad, Reinaldo J. Michelena, Ali Tura, Shamima Akther, Petar Vladov Angelov, and Rao Narhari Srinivasa21 January 2022 | GEOPHYSICS, Vol. 87, No. 2Bayesian inversion for rock composition and petrophysical endpoints in multimineral analysisLiwei Cheng, Ge Jin, and Reinaldo J. Michelena1 September 2021Optimized petrophysical evaluation using nonlinear volumetric solvers in organic rich source rocksKunal Sharma, Gurkirat Singh, and Ravi Sharma1 September 2021 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Reinaldo J. Michelena, Kevin S. Godbey, Michael J. Uland, and Patricia E. Rodrigues, (2020), "Petrophysical multimineral analysis using genetic optimization to solve complex mineral composition in unconventional reservoirs," SEG Technical Program Expanded Abstracts : 2455-2463. https://doi.org/10.1190/segam2020-3425780.1 Plain-Language Summary Keywordspetrophysicsunconventionallithologynonlinearlog analysisPDF DownloadLoading ...