Affiliation:
1. The National Research Tomsk Polytechnic University, College of Natural Resources, School of Earth Sciences & Engineering, Tomsk, Russia
Abstract
With the development of science and technology, in the field of oilfield commissioning, the requirements for process management are more and more standardized and scientific, and the requirements for decentralized equipment status detection and maintenance are also higher and higher. It is the hope of any manager to eliminate hidden dangers and prevent them in a timely manner. This paper introduces an intelligent debugging model based on big data. Based on the big data mechanism, the model is divided into different functions according to different functions and requirements. It can ensure the authenticity of debugging data, coordinate all big data work through the big data communication mechanism, and conduct scientific management of debugging data. The model is divided into three levels: data acquisition layer, data transmission layer and control management layer. The offshore oil intelligent debugging platform software based on big data technology is built. A new intelligent debugging method for offshore oil based on big data is presented to study the warning information, fault location and equipment health status of intelligent debugging. Development for Marine oil intelligent debugging applications, for business people to provide intelligent Marine oil intelligent debugging method, provide data support for management decision-making, implementation of the lean management data in the field of intelligent debugging, improving the capacity of intelligent debugging data analysis and mining, effective use of the existing intelligent debugging automation system and other related data in the system, solved the “huge amounts of data, information, the lack of a” awkward situation, improve intelligence debugging application function, meet the demand of intelligent debugging of each department, improve the efficiency of debugging and running reliability and intelligent management.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
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