Abstract
To monitoring the subsea pipeline in real-time, a special potentiometric sensor array and a potential prediction model are presented in this paper. Firstly, to measure the potential of seawater, a special potentiometric sensor array with Ag/AgCl all-solid-state reference electrodes is first developed in this paper. Secondly, according to the obtained distribution law of electric field intensities a prediction model of the pipeline potentials is developed. Finally, the potentiometric sensor array is applied in sink experiment and the prediction model is validated by the sink measurements. The maximum error for pipeline potential prediction model is 1.1 mV. The proposed non-contact monitoring method for subsea pipeline can predict the potential of sea pipeline in real-time, thus providing important information for further subsea pipeline maintenance.
Highlights
Marine Oil & Gas resources is a major focus of China's energy, the exploitation of which brings wide usage of submarine pipelines
Integrity of subsea pipelines is a mandatory requirement in the modern offshore Oil & Gas industry
In the offshore Oil & Gas industry, the potential of pipeline is an important indicator of pipeline integrity
Summary
Marine Oil & Gas resources is a major focus of China's energy, the exploitation of which brings wide usage of submarine pipelines. Integrity of subsea pipelines is a mandatory requirement in the modern offshore Oil & Gas industry. In the offshore Oil & Gas industry, the potential of pipeline is an important indicator of pipeline integrity. A tailored probe mounted on the ROV measures the seawater potentials in the surrounding of a subsea pipeline against a reference electrode embedded in the probe. This system cannot measure the potentials of pipeline directly. In order to overcome this disadvantage, we propose a special potentiometric sensor array which can measure seawater potential efficiently and a prediction model to monitoring pipeline potential real-time. The validity of the sensor array and the proposed prediction model is evaluated in the laboratory sink experiments
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