Drilling Rig Operation Mode Recognition by an Artificial Neuronet

• Main Authors: Abu-Abed Fares, Borisov Nikolay
• Format: Article
• Language: English
• Published: EDP Sciences 2017-01-01
• Series: E3S Web of Conferences
• Online Access: https://doi.org/10.1051/e3sconf/20172101019

The article proposes a way to develop a drilling rig operation mode classifier specialized to recognize pre-emergency situations appearable in commercial oil-and-gas well drilling. The classifier is based on the theory of image recognition and artificial neuronet taught on real geological and technological information obtained while drilling. To teach the neuronet, a modified backpropagation algorithm that can teach to reach the global extremum of a target function has been proposed. The target function was a relative recognition error to minimize in the teaching. Two approaches to form the drilling rig pre-emergency situation classifier based on a taught neuronet have been considered. The first one involves forming an output classifier of N different signals, each of which corresponds to a single recognizable situation and, and can be formed on the basis of the analysis of M indications, that is using a uniform indication vocabulary for all recognized situations. The second way implements a universal classifier comprising N specialized ones, each of which can recognize a single pre-emergency situation and having a single output.