Difference between revisions of "Machine learning"

From SEG Wiki
Jump to: navigation, search
Line 1: Line 1:
 
'''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.<ref>https://en.wikipedia.org/wiki/Machine_learning</ref>
 
'''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.<ref>https://en.wikipedia.org/wiki/Machine_learning</ref>
  
ML algorithms are being used in the Oil and Gas sector for various applications:
+
ML algorithms are being used in the Oil and Gas sector for various applications such as:
 
* [[Facies classification using machine learning|facies classification]]
 
* [[Facies classification using machine learning|facies classification]]
 
* [[quantitative interpretation]]
 
* [[quantitative interpretation]]
 +
* geobody interpretation
 
* [[Microseismic|micro-seismic]] event detection
 
* [[Microseismic|micro-seismic]] event detection
 +
* velocity picking
 +
* image analysis of rock thin sections
 +
* seismic processing such as [[Coherent linear noise|ground-roll noise]] attenuation
 
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 [https://www.aapg.org/ AAPG], [https://seg.org SEG] & [https://www.spe.org/en/ SPE] decided to organize the first conference fully dedicated to the topic: "[https://energyindata.org/Default.aspx?TabId=37&language=en-US Energy in Data]"
 
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 [https://www.aapg.org/ AAPG], [https://seg.org SEG] & [https://www.spe.org/en/ SPE] decided to organize the first conference fully dedicated to the topic: "[https://energyindata.org/Default.aspx?TabId=37&language=en-US Energy in Data]"
 
== References ==
 
== References ==
 
<references />
 
<references />

Revision as of 21:49, 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