Natural Noise Fields from 1 C/S to 100 KC/S

Maxwell, E. L.; Stone, D. L.

doi:10.21236/ad0291508

Cited by 1 publication

(1 citation statement)

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“…Here, 'F' is the product of five factors; the presence of water, the water wetting, pH, salinity or total dissolved solids, and temperature. This model was improved later by Maxwell and Campbell [18] by introducing biological parameters such as number of bacteria per area and bacteria kinetics. Other MIC modelling attempts, such as the work of Allison et al [19] and Taxén et al [20], tend to oversimplify the system and incompletely screen the MIC influencing factors.…”

Section: Summary Of Existing Modelsmentioning

confidence: 99%

Model for microbiologically influenced corrosion potential assessment for the oil and gas industry

Taleb‐Berrouane

Khan

Hawboldt

et al. 2018

Corrosion Engineering, Science and Technology

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Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. Microbiologically influenced corrosion (MIC) is inherently more complex to predict, detect and measure because, for instance, the presence of biofilm and/or bacterial products is not sufficient to indicate active microbiological corrosion. The major challenge for current MIC models is to correlate factors that influence corrosion (i.e. chemical, physical, biological and molecular variables) with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.

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Section: Summary Of Existing Modelsmentioning

confidence: 99%