Difference between revisions of "Machine learning"

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* geobody interpretation
 
* geobody interpretation
 
* [[Microseismic|micro-seismic]] event detection
 
* [[Microseismic|micro-seismic]] event detection
* velocity picking
+
* [[Velocity analysis|velocity picking]]
 
* image analysis of rock thin sections
 
* image analysis of rock thin sections
 
* seismic processing such as [[Coherent linear noise|ground-roll noise]] attenuation
 
* seismic processing such as [[Coherent linear noise|ground-roll noise]] attenuation

Revision as of 21:52, 24 May 2019

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1]

ML algorithms are being used in the Oil and Gas sector for various applications such as:

Although ML algorithms appeared decades ago, SEG members started publishing about them in the mid-90s. In 2019, in response to "the digital transformation of Oil and Gas" the AAPG, SEG & SPE decided to organize the first conference fully dedicated to the topic: "Energy in Data"

References