Oilfield Equipment Control Distributed Automated System

Abstract

The article proposes a neural network technique for the operational selection of optimal industrial activities. This technique is a sequential process, most of which is a separate algorithm (training of a neural network) or several algorithms (preliminary data processing). The difference between the methods described in this article is that, firstly, the use of hybrid neural network analysis technology, which uses in the simultaneous application, firstly, a multitude of neural trained neural networks with a threshold activation function in order to simultaneously offer several options solutions (this situation is typical only for the oilfield, when several types of problems arise simultaneously at the facility or there are two or more options for recommended measures for clustering, secondly, in the application of an untrained neural network for better selection. The developed methods are focused on processing oilfield data rather than any other data and, in fact, their implementation is dictated by the specifics of the information being analyzed, as well as the need for its speed processing.