{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/application-of-machine-learning-in-rock","title":"Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature Augmentation","arxiv_id":"1808.09856","date":"2018-08-29","proceeding":null,"authors":["Jie Chen","Yu Zeng"],"abstract":"With recent progress in algorithms and the availability of massive amounts of\ncomputation power, application of machine learning techniques is becoming a hot\ntopic in the oil and gas industry. One of the most promising aspects to apply\nmachine learning to the upstream field is the rock facies classification in\nreservoir characterization, which is crucial in determining the net pay\nthickness of reservoirs, thus a definitive factor in drilling decision making\nprocess. For complex machine learning tasks like facies classification, feature\nengineering is often critical. This paper shows the inclusion of\nphysics-motivated feature interaction in feature augmentation can further\nimprove the capability of machine learning in rock facies classification. We\ndemonstrate this approach with the SEG 2016 machine learning contest dataset\nand the top winning algorithms. The improvement is roboust and can be $\\sim5\\%$\nbetter than current existing best F-1 score, where F-1 is an evaluation metric\nused to quantify average prediction accuracy.","url_abs":"http://arxiv.org/abs/1808.09856v1","url_pdf":"http://arxiv.org/pdf/1808.09856v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"application-of-machine-learning-in-rock","repo_url":"https://github.com/jsyzeng/rock_class_xgboost","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"facies-classification","task_name":"Facies Classification"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}