Improved modelling and control of oil and gas transport facility operations using artificial intelligence
In recent years, the application of artificial intelligence (AI) based techniques to a wide range of industrial processes has become increasingly common. One reason for this development is the level of maturity of both theory of AI concepts and its implementation into application tools for commercial use. Another very important reason is the persistent drive of many industries to increase efficiencies and the realisation that this requires more effective processing of gained knowledge and information. In the oil and gas industry, due to high saturation levels of many production fields and the complex nature of processes, the need for increased efficiencies and highly effective processing of a large amount of information is particularly evident. Some organisations have recognised the opportunities offered by AI-based techniques and started exploiting them in order to improve knowledge and information handling and process efficiencies. This paper discusses the application of two AI-based techniques, fuzzy logic and artificial neural networks (ANNs), to specific problems related to the operation of oil and gas transport facilities. The background for the work, which is carried out in a co-operation between a university and a leading engineering service provider, is described firstly. This is followed by a brief summary of the fundamentals of the AI techniques considered with respect to their use for industrial purposes. Then, two case studies are presented. The first case study demonstrates the application of fuzzy logic to the control of a pump station in a pipeline system whilst the second case study shows the use of an ANN for the determination of important pipeline characteristics. Problem backgrounds, design procedures and outlines for the implementation of the used AI techniques are given. Finally, benefits of the adopted approaches are highlighted and the wider impact on both industry and research community is discussed.
modelling fuzzy logic artificial neural networks