{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:29:21Z","timestamp":1753889361728,"version":"3.41.2"},"reference-count":11,"publisher":"SPE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper introduces description and results of creating a complex method for automatic interpretation of well-logging data and the further construction of the first approximation of the geological model. That procedure is aimed at mass re-interpretation of a large number of wells in terrigenous deposits of Tula and Bobrikovian horizons of Tatarstan. It was solved with the use of machine learning methods and artificial neural networks. We also proposed an improved solution to the problem of determining the reference value for normalization of neutron logging in the absence of reference horizon data. The priority task of well logs depth matching is solved by use of correlation coefficient, logistic regression and the idea that the expert has a different preference to different depth intervals in the investigated horizon. The next issue is the stratigraphic division which also was solved by logistic regression training as the most common tool in allocating one object from others and alternately defining each boundary of deposits. Solution for the problem of the reference maximum value of neutron logging in the absence of a reference Famennian stage was established by revealing the algorithmic relationship between the boundary values of the porosity for the reservoirs of the underlying carbonate reservoirs and the calculation of the porosity from the normalized neutron log. Further question of determining the lithology is solved with the use of two-layer perceptron and an expanded use of well log values, that is, a two-meter interval of curve values was chosen for interpreting each point in depth, which allowed to take into account the change zones of rocks and include shape of the curves in addition to their quantitative indications. Calculation of porosity and the oil saturation coefficient includes already existing dependencies for the processed objects of Tatarstan, therefore the accuracy of the calculation depends only on the quality of the determination of the reference values of radioactive and neutron logs, which was successfully carried out. The final goal in obtaining such a large volume of results of automated calculations for wells is the construction of a geological model, where a detailed correlation, grid construction and interpolation of petrophysical parameters on the grid are carried out.<\/jats:p>","DOI":"10.2118\/191595-18rptc-ms","type":"proceedings-article","created":{"date-parts":[[2018,10,9]],"date-time":"2018-10-09T20:59:26Z","timestamp":1539118766000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Approach to Open Hole Interpretation and Static Modelling Applied to a Giant Field"],"prefix":"10.2118","author":[{"given":"Amir","family":"Ismagilov","sequence":"additional","affiliation":[{"name":"Kazan Federal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladislav","family":"Sudakov","sequence":"additional","affiliation":[{"name":"Kazan Federal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danis","family":"Nurgaliev","sequence":"additional","affiliation":[{"name":"Kazan Federal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timur","family":"Murtazin","sequence":"additional","affiliation":[{"name":"Kazan Federal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergey","family":"Usmanov","sequence":"additional","affiliation":[{"name":"Kazan Federal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natalya","family":"Nugumanova","sequence":"additional","affiliation":[{"name":"Kazan Federal University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"861","published-online":{"date-parts":[[2018,10,15]]},"reference":[{"volume-title":"Deep Learning using Rectified Linear Units (ReLU)","year":"2018","author":"","key":"2025012317292165500_R1"},{"key":"2025012317292165500_R2","first-page":"45","author":"Dementev","year":"1974","journal-title":"Primenenie informacionnih mer v neftepromislovoi geologii"},{"key":"2025012317292165500_R3","first-page":"27s.","author":"Demyanov","year":"1998","journal-title":"Sovmestnoe stohasticheskoe modelirovanie prostranstvennoi variabelnosti i neopredelennosti"},{"key":"2025012317292165500_R4","first-page":"319","author":"Devis","year":"1990","journal-title":"Statisticheskii analiz v geologii"},{"key":"2025012317292165500_R5","first-page":"37","author":"Dressler","year":"2007","journal-title":"Interpolation methods for construction of surfaces"},{"key":"2025012317292165500_R6","first-page":"15","volume":"192","author":"Grishkevich","year":"1984","journal-title":"Izlojenie zadachi korrelyacii bolshogo chisla skvajin v terminah teorii raschlenennih algoritmov. Metodi matematicheskogo modelirovaniya pri reshenii prikladnih zadach neftyanoi geologii"},{"key":"2025012317292165500_R7","first-page":"22","volume":"31","author":"Jekovskii","year":"1963","journal-title":"Novii metod stratigraficheskoi korrelyacii. \u00abNeftepromislovoe delo\u00bb"},{"key":"2025012317292165500_R8","first-page":"624","volume":"3","author":"Lapkovskii","year":"2015","journal-title":"Korrelyaciya razrezov skvajin kak mnogomernaya optimizacionnaya zadacha. Geologiya i geofizika"},{"key":"2025012317292165500_R9","first-page":"72","author":"Putilov","year":"2011","journal-title":"Trehmernoe geologicheskoe modelirovanie pri razrabotke neftyanih i gazovih mestorojdenii"},{"key":"2025012317292165500_R10","first-page":"11","author":"Validov","year":"2017","journal-title":"Development of the Approach for Automatic Well Logging Interpretation for Big Number of Wells with the Use of Machine Learning"},{"key":"2025012317292165500_R11","first-page":"183","volume-title":"Metodicheskoe rukovodstvo po opredeleniyu parametrov kollektorov geofizicheskimi metodami","author":"Zinnatullin","year":"1993"}],"event":{"name":"SPE Russian Petroleum Technology Conference","start":{"date-parts":[[2018,10,15]]},"location":"Moscow, Russia","end":{"date-parts":[[2018,10,17]]},"acronym":"18RPTC"},"container-title":["SPE Russian Petroleum Technology Conference"],"original-title":[],"link":[{"URL":"https:\/\/onepetro.org\/SPERPTC\/proceedings-pdf\/doi\/10.2118\/191595-18RPTC-MS\/2266733\/spe-191595-18rptc-ms.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/onepetro.org\/SPERPTC\/proceedings-pdf\/doi\/10.2118\/191595-18RPTC-MS\/2266733\/spe-191595-18rptc-ms.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T17:29:29Z","timestamp":1737653369000},"score":1,"resource":{"primary":{"URL":"https:\/\/onepetro.org\/SPERPTC\/proceedings\/18RPTC\/18RPTC\/D013S002R007\/236589"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,15]]},"references-count":11,"URL":"https:\/\/doi.org\/10.2118\/191595-18rptc-ms","relation":{},"subject":[],"published":{"date-parts":[[2018,10,15]]},"article-number":"D013S002R007"}}