Preprint Article Version 1 This version is not peer-reviewed

An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique

Version 1 : Received: 9 August 2018 / Approved: 9 August 2018 / Online: 9 August 2018 (15:02:06 CEST)

How to cite: Onukwugha, C.G.; Osegi, E.N. An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique. Preprints 2018, 2018080194 (doi: 10.20944/preprints201808.0194.v1). Onukwugha, C.G.; Osegi, E.N. An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique. Preprints 2018, 2018080194 (doi: 10.20944/preprints201808.0194.v1).

Abstract

Oil and gas pipeline vandalism is a recurrent problem in oil rich zones of Nigeria and its West African neighbors and remains a challenge for multinationals to set ahead control measures to avert possible damages to operations both in infrastructure and business profit margins. In this paper, an integrative systems model comprising of a machine intelligence technique called Hierarchical Temporal Memory (HTM) and a sequence learning neural network called the Online-Sequential Extreme Learning Machine (OS-ELM) is proposed for monitoring and prediction of pipeline pressure data. The system models the continual prediction of pipeline oil/gas pressure signals useful for secure monitoring and control to avert acts of vandalism in oil and gas installations. The HTM uses a spatial pooler operated in temporal aggregated fashion and is defined as HTM-SP. The OS-ELM technique uses an explicit hierarchical training scheme so that the best cost estimates may be found after a stipulated number of trial runs. We study the performance of three OS-ELM neural activations: the sigmoid (sig), sinusoidal (sin) and radial basis function (rbf) activations. The results indicate improvement factors of 1.297, 1.297 and 1.300 of the HTM-SP over the OS-ELM sigmoid, sinusoidal and radial basis activations respectively.

Subject Areas

ANN; continual learning; machine intelligence; prediction; vandalism; security; SDR

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