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.
ML algorithms are being used in the Oil and Gas sector for various applications such as:
- facies classification
- quantitative interpretation
- geobody interpretation
- micro-seismic event detection
- velocity picking
- image analysis of rock thin sections
- seismic processing such as 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 AAPG, SEG & SPE decided to organize the first conference fully dedicated to the topic: "Energy in Data"