Why is it called ack?

The internet’s Transmission Control Protocol (TCP) relies on ACKnowledgements for reliability. Because of ACKs, “no transmission errors will affect the correct delivery of data” on the internet^[1].

acK for pipelines is a set of tools and practices for inexpensive, high-temporal-resolution pipeline state monitoring. With acK, we ensure that the pipeline can safely transport its cargo at all points in time. Like the Internet’s TCP, this assurance relies on continuous integrity checks. acK performs these checks with frequent low-resolution scans. It combines these scans with rapid data fusion algorithms and then uses machine learning to compare the pipe’s dynamic state with its most recent baseline analysis.

The Concept

The pinpoint weather forecasts of the Weather Underground inspired acK. Before the Weather Underground changed things, weather forecasters relied on expensive and super-precise weather stations usually located at airports. These stations are super accurate but have a low spatial resolution. With this model, the further one was from the airport, the less representative the forecast.

The same happens with pipelines. Like old-style weather forecasts, modern integrity practices rely on expensive inspection tools and subsequent analyses that are precise for a point in time. Like the weather forecast, the further we are from this point in time the less representative the analysis. The temporal resolution is poor. The consequence is that short-cycle events like river scouring, tampering, and aggressive corrosion growth can compromise pipeline integrity between inspections and catch an operator unaware.

In 1991, as part of his Ph.D. dissertation, Jeff Masters improved the spatial resolution of his weather forecasts by incorporating data from low-resolution personal weather stations, the kind installed on the roofs or backyards of random hobbyists. While these stations generate low-quality data, the large number of them and their wide geographic distribution allowed Masters to produce hyper-local weather forecasts with tremendous spatial resolution.

A similar improvement in temporal resolution is possible for pipelines. A new generation of low-cost inspection tools, like smart balls and integrated cleaning and inspecting pigs, can be run frequently thereby giving regular looks at the pipe. According to the law of large numbers, all this data, in the aggregate, will converge on the truth. The result is a high-time-resolution situational awareness of the pipe condition.

Further, acK makes a new low-cost maintenance paradigm possible. Its aggregate pipeline health signal has reasonable accuracy and good spatial and temporal resolution. It also offers a low-cost, do-it-yourself-ish way to responsibly monitor unregulated and smaller assets, like flow and process lines, which may be burdensome to manage using other strategies.