In an article in last week’s Oil & Gas Journal, Tom Smith focused on the use of neural networks in oil and gas exploration. Because of the technology’s usefulness in classifying data and identifying patterns, it has become widely used to reduce the risk and time in the siting of oil and gas wells. All well and good, but not good enough apparently to satisfy the growing intensity of exploration.
Oil and gas apparently aren’t the only things spurting out of the oil fields. These areas are gushing data, so much data that conventional neural networks can’t process all of the information. Author Smith believes that the next step in reducing risk and wasted time in exploration will be the "unsupervised" neural network. It pushes the Known off the computer screen and replaces it with an Automated Unknown.
While the "supervised" neural network processes classified data, that is, known information, the "unsupervised" neural net can classify unclassified data and then process the patterns that result. This makes it invaluable for seismic interpretation, that is, for detecting and analyzing subtle geological variations that may be related the potential to extract usable oil or gas.
Smith predicts that unsupervised neural networks will be a "disruptive" technology in seismic interpretation. A disruptive technology is an unexpected innovation that changes the direction of progress in an industry, like digital downloads in the music industry. If he’s right, it just got a whole lot easier to strike oil.