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Estimating corrosion growth rates for underground pipelines is a challenging problem. There are confounding variables with complex interaction effects that may result in unexpected outcomes. For instance, the relationship between soil conditions and AC interference is highly non-linear and challenging to model. This work expands upon prior work using a suite of machine learning tools to estimate corrosion rates. However, instead of estimating a single corrosion growth rate for a single girth weld address (GWA), this work estimates a distribution of potential corrosion growth rates. Modeling distributions provide a more effective risk-measurement framework, especially concerning high volatility or areas of severe tail risk.
This work relies heavily on machine learning and geospatial tools - particularly artificial neural networks and gradient boosted trees to estimate the corrosion rates and non-linear processes. Building upon prior work using data from a North American Operator, the models in this paper use additional variables from recent research in AC interference and microbiologically influenced corrosion to construct a higher accuracy and distribution-based model of pipeline corrosion risk.
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Estimating corrosion growth rate for underground pipelines is a non-linear multivariate problem. There are many potential confounding variables such as soil parameters cathodic protection AC/DC interference seasonal / climate conditions and proximity to unique geographic features such as wetlands or polluted environments. The work presented provides an approach for estimating underground corrosion growth rates using a dataset of observations from a North American pipeline operator. Extensive geospatial data is utilized that has been obtained from public and private sources and extrapolated using Inverse distance weighted (IDW) interpolation. This work presents a model using IDW to estimate parameters involving soil interference geography and climate factors for any location in North America.Using this data this work then presents several different machine learning approaches including Generalized Linear Models eXtreme Boosted Trees and Neural Networks. All three provide an accurate estimation for corrosion growth rates for an underground asset at any latitude and longitude pair in North America. Each method comes with potential benefits and pitfalls specifically; trade-offs between model accuracy and transparency. This work presents a framework for comparing geo-spatial and machine learning estimates. Findings and a framework are provided for owners to assess how to think machine learning on their own assets.
Decorative finishing materials provide a wide variance in coverage rates. Like straight painting with the production rate adjustments for spraying versus rolling, the application method significantly impacts the material coverage rate. Aside from the paint itself, decorative finishing can incorporate a number of the following products: Custom Modellos (one-time masking patterns), leaf or foil, stencils, and other media for embellishment (glass) which need to be appended to the pricing.
The following paper discusses models and procedures for estimating the corrosion-related metal loss and loss patterns on carbon steel exposed in a marine environment. This includes immersion and atmospheric exposure and the impact of coatings.
On an increasingly frequent basis, pipeline operators are using risk-based decision making to prioritize cross-company expenditures. Due to the long-term mitigation benefits of Cathodic Protection (CP), when planning external corrosion mitigation activities, pipeline operators typically prioritize mitigation of deeper anomalies for integrity expenditures due to their higher Probability of Failure (PoF). However, anomalies that are not receiving adequate CP or those experiencing electrical interference may remain unaddressed using this rationale. This paper presents both a qualitative and semi-quantitative approach to support the quantification of the risk reduction benefits gained from external corrosion prevention on pipelines. This can help in the efficient prioritization of both pro-active and re-active integrity repair activities. Supporting examples are also discussed to help explain the intended use of the methodology and the interpretation of the results.
Cleaning, coating, and the nondestructive testing (NDT) of corrosion-susceptible surfaces requires extensive manual labor, often at heights that can create dangerous occupational environments. Drones, also known as uncrewed/unmanned aerial vehicles or systems (UAVs, UASs), can be leveraged to perform some of these tasks, including cleaning and coating, while keeping workers safely on the ground.
In conjunction with the water dew point line, a simplified ammonium chloride corrosion chart has been developed to evaluate ammonium chloride corrosion potential at different temperatures with different water partial pressures.
Protective organic coatings are the primary form of corrosion control for steel structures exposed in a marine environment. For more than fifty years, testing of coatings suitable for various service environments has relied substantially on exposure of coated steel panels of different configurations followed by evaluation via visual inspection. Exposure may include accelerated testing or natural environmental exposure in immersion or atmospheric conditions.
Industry constantly seeks improved methods to evaluate protective coatings. In immersion service, protective coatings act to reduce electrochemical activity at the metal/coating interface. Tracking this activity via the use of segmented panel testing appears to offer additional insight into coating performance that may aid in coating design and predicting longer-term performance.
Destructive physical inspection for corrosion under protective coatings post-exposure suggests that significant metal loss may be occurring in the absence of a visual indication and that actual corrosion/material loss does not correlate with the visual inspection data.
This work evaluated the effectiveness of the dewatering process after hydrotesting and examined the internal corrosion threat posed by residual water trapped in crevices - and water pushed into a dead leg – of a pipeline.