{"id":3775,"date":"2022-09-08T10:34:00","date_gmt":"2022-09-08T02:34:00","guid":{"rendered":"http:\/\/www.oktcz.com\/?p=3775"},"modified":"2022-09-16T10:37:07","modified_gmt":"2022-09-16T02:37:07","slug":"nabors-well-data-labs-engage-in-project-to-build-machine-learning-model-to-optimize-well-planning-performance","status":"publish","type":"post","link":"https:\/\/www.oktcz.com\/en\/zuixinjishu-en\/nabors-well-data-labs-engage-in-project-to-build-machine-learning-model-to-optimize-well-planning-performance.html","title":{"rendered":"Nabors, Well Data Labs engage in project to build machine learning model to optimize well planning, performance"},"content":{"rendered":"\n
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Machine learning techniques are nowadays often used to analyze historical well data, allowing drilling contractors and operators to optimize performance while drilling new wells and predict outcomes in real time. In this interview with DC from the 2022 IADC Advanced Rig Technology Conference in Austin, Texas, on 31 August, Allan Nandlal, Product Manager at Well Data Labs, and Malini Manocha, Director of RigCLOUD at Nabors Industries, discuss the results of a collaborative project in which the two companies developed a machine learning model to help predict and optimize well planning and real-time operations. The two speak about the methodology used to build the model, as well as the results they saw from testing.\uff082022-09-08\uff09<\/p>\n","protected":false},"excerpt":{"rendered":"

Machine learning techniques are nowadays often used to analyze historical well data, allowing drilling contractors and operators to optimize performance while drilling new wells and predict outcomes in real time. In this interview with DC from the 2022 IADC Advanced Rig Technology Conference in Austin, Texas, on 31 August, Allan Nandlal, Product Manager at Well …<\/p>\n

Nabors, Well Data Labs engage in project to build machine learning model to optimize well planning, performance<\/span> \u66f4\u591a \u00bb<\/a><\/p>\n","protected":false},"":2,"featured_media":1515,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":""},"categories":[57],"tags":[],"_links":{"self":[{"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/posts\/3775"}],"collection":[{"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/types\/post"}],"":[{"embeddable":true,"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/comments?post=3775"}],"version-history":[{"count":1,"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/posts\/3775\/revisions"}],"predecessor-version":[{"id":3776,"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/posts\/3775\/revisions\/3776"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/media\/1515"}],"wp:attachment":[{"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/media?parent=3775"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/categories?post=3775"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.oktcz.com\/en\/wp-json\/wp\/v2\/tags?post=3775"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}