Machine Learning: Using Optimized KNN (K-Nearest Neighbors) to Predict the Facies Classifications

PreviousNext No AccessProceedings of the 13th SEGJ International Symposium, Tokyo, Japan, 12–14 November 2018Machine Learning: Using Optimized KNN (K-Nearest Neighbors) to Predict the Facies ClassificationsAuthors: Hadyan PratamaHadyan PratamaPertamina UniversitySearch for more papers by this authorhttps://doi.org/10.1190/SEGJ2018-139.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Machine learning is getting more popularity nowadays since it has potential to improve the quality of our life in many fields through data learning and optimization. In a short explanation, machine learning can be seen as non-linear equation optimization that try to predict one output from several input parameters although it may not have any logical relationships. In the world of geoscience work, one challenging problem is lithology estimation from several logs input and/or seismic data. Until today, predicting lithology from seismic data is common and popularly known as seismic inversion or color inversion, where seismic data has been converted from impedance to lithology to help interpreter pick horizons. This study is focusing on an effort to estimate facies or lithology by only looking some well logs data input using a method named K-Nearest Neighbor method as one of many machine learning methods. I have tried to pick a unique K value with best errors for predicting facies classification. The value has been tested at training data with satisfactory results that is better than the results from tradidtional KKN. In the summary, although in this study KNN method successfully estimated the lithology, reasonable and practical results should be considered with several things such as regional geological model, a continuous improved model with newer data once acquired, coring data validation, and etc. Future work that try to expand the capability between seismic data and lithology facies would be interesting to pursue. Keywords: machine learning, k-nearest neighbor, euclidean distance, facies classificationPermalink: https://doi.org/10.1190/SEGJ2018-139.1FiguresReferencesRelatedDetailsCited byAdaptive boosting of random forest algorithm for automatic petrophysical interpretation of well logs5 September 2022 | Acta Geodaetica et Geophysica, Vol. 1997A K-Nearest Neighbors Algorithm in Python for Visualizing the 3D Stratigraphic Architecture of the Llobregat River Delta in NE Spain19 July 2022 | Journal of Marine Science and Engineering, Vol. 10, No. 7Automatic Lithology Identification by Applying LSTM to Logging Data: A Case Study in X Tight Rock ReservoirsIEEE Geoscience and Remote Sensing Letters, Vol. 18, No. 8 Proceedings of the 13th SEGJ International Symposium, Tokyo, Japan, 12–14 November 2018ISSN (online):2159-6832Copyright: 2019 Pages: 588 publication data© 2018 Published in electronic format with permission by the Society of Exploration Geophysicists of JapanPublisher:Society of Exploration GeophysicistsSociety of Exploration Geophysicists of Japan HistoryPublished Online: 29 Apr 2019 CITATION INFORMATION Hadyan Pratama, (2019), "Machine Learning: Using Optimized KNN (K-Nearest Neighbors) to Predict the Facies Classifications," SEG Global Meeting Abstracts : 538-541. https://doi.org/10.1190/SEGJ2018-139.1 Plain-Language Summary Keywordsmachine learningk-nearest neighboreuclidean distancefacies classificationPDF DownloadLoading ...