Machine-learning for sedimentary facies prediction

US11814931B2 · kind B2 · utility

Assignee

• SCHLUMBERGER TECHNOLOGY CORPORATION · US

Inventors

• Nadege Bize-Forest · Clamart, FR
• Laura Lima Angelo dos Santos · Rio de Janeiro, BR
• Lucas Lima de Carvalho · Cachan, FR
• Victoria Baines · Rio de Janeiro, BR
• Austin Boyd · Ridgefield, US
• Peter Schlicht · Wolfsburg, DE
• Josselin Kherroubi · Clamart, FR

Key dates

• Filing date: May 31, 2019
• Grant date: Nov 14, 2023
• Expiry date: Feb 7, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G01V2200/16
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Using machine learning for sedimentary facies prediction by using one or more logs acquired in a borehole. This includes performing a petrophysical clustering of borehole depths wherein the depths of the borehole are gathered into clusters based on similarities in the one or more logs. Also performed is a log inclusion optimization, including a selection of one or more parameters of the petrophysical clustering, wherein the one or more parameters include a number and/or type of considered logs among the one or more logs and/or a clustering method. Also performed is a classification of the clusters into core depositional facies using one or more predetermined rules.