Improve the accuracy of underwater cathodic protection surveys
To reduce risk and increase inspection speeds—while lowering costs—for underwater pipeline surveys, new inspection methods must be considered.
To reduce risk and increase inspection speeds—while lowering costs—for underwater pipeline surveys, new inspection methods must be considered. Current methods are slow, capital intensive, and tend to produce a low value-to-cost ratio.
In fact, pipeline surveys rarely identify issues, but are nevertheless required, either through regulations or company guidelines. Therefore, they are a sunk cost that needs to be reduced, and autonomous underwater vehicles (AUVs) may provide the means.
Oceaneering is developing a method for inspecting subsea pipelines that uses AUVs combined with analytical techniques and machine learning. The aim is to improve data quality and reduce costs compared to the current practice of using a remotely operated vehicle (ROV) to perform pipeline and cathodic-protection (CP) surveys.
The overall goal is to reduce offshore time, which is the largest cost factor, while still maintaining the data quality of ROV surveys.
Yesterday and today
Traditionally, long pipelines are inspected with an ROV. The ROV is operated by a pilot on the service vessel, an expensive proposition in and of itself. The benefits of this method are instant feedback, the ability to reroute (or sidestep) for spans; inspection flexibility; and direct CP measurements. The challenges associated with this method are pace, weather, and poor track records for performing CP surveys, as well as ill-equipped ROVs.
In this scenario, the ROV sits on top of the pipeline in order to walk it, taking remote electrode measurements and pipeline footage and data. Besides extended use of the service vessel, the ROV relies on cameras to perform visual inspections, along with direct stabs that make physical contact with the pipeline to measure CP voltage. The process is time consuming. The ROV collects limited data and is hampered by the pipeline’s protective coating.
Recently introduced ROV-based systems are said to increase accuracy and speed. However, results presented are often best-case scenarios, leaving clients frustrated with actual outcomes.
The alternative approach under discussion employs an AUV equipped with lasers, high-definition imaging, and field-gradient sensors. The AUV automatically follows the pipeline, gathering data from a distance above the seabed of about 1 meter to 2 meters, or 3 ft. to 7 ft. Analytical methods (including visual processing, shape filling, trending, and mechanistic calculations) are used to evaluate pipeline integrity and identify areas that are prone to failure.
Automation of the survey increases speed and removes minimizes personnel, thereby reducing errors and costs.
By combining AUV inspections with high-definition imaging technologies, it is possible to take pipeline scans and images and place them into an algorithm that will identify various aspects of the pipeline.
As noted, with current methods this process is slow, because of the man-hours involved and because someone must manually review the footage and mark down features. Current anode inspection involves a subjective grading system (of 1 to 4), where the operator views the anode and assigns a grade in 25% increments, which may not be accurate.
Alternatively, the incorporation of machine learning into the AUV-driven process means that, via the algorithm, the machine is taught to do the processing needed, identifying anodes, free spans, and scars along the pipeline. “Teaching” involves feeding the machine with hundreds of hours of correctly identified pipeline features (such as anodes, scars, and free spans).
Additionally, a point cloud system determines anode degradation and pipeline condition. With multiple surveys over several years, it’s possible to see how the anode degrades over time, as well as display more quantitative data. For pipeline conditions, scars, free spans, and lateral movements can be tracked over the life of the pipeline, thereby identifying for engineers those trends that could lead to issues. Using point cloud data, degradation can be quantified more accurately, for greater survey confidence.
ROVs versus AUVs
Three distinct cost categories impact the two different survey mechanisms and methods. These are the preparation, execution, and reporting of the work.
Preparation includes any work involving mobilization of equipment and personnel. Here there is minimal difference in cost between ROVs and AUVs.
Execution of the work is where AUVs can deliver the most savings compared to ROVs. Using an AUV is a faster process because AUVs move at higher speeds compared to ROVs, without reducing the data quality. An AUV can reduce offshore time by 40%.
Use of the AUV does entail an increase in time needed for onshore analysis and reporting. The anticipated increase for work reporting of about 50% is due to increased engineering hours to quality check the automated data. However, overall, AUV use is still cheaper on a per-hour basis because the largest cost component per project is the boat rental time.
On average, Oceaneering estimates a total survey savings of 30% to 40% using the AUV and incorporating tools such as 3D imaging and high-definition algorithms (see bar graph). Thus, AUV-based inspections can be performed in one-third the time and at a lower cost compared to ROV inspections.
In addition, regularly scheduled surveys performed using AUVs enable engineers to overlay multiple data sets to evaluate CP anode degradation rates and pipeline integrity over the pipeline’s planned lifetime. This new approach offers a more accurate and cost-effective alternative to long-term evaluation and prediction of a CP system performance and overall pipeline condition based on ROV surveys.
Due to the current state of the oil and gas industry, clients are asking for ways to improve pipeline surveys, from both a technical and financial standpoint.
A client recently reached out to Oceaneering to inspect one of its pipelines in the Gulf of Mexico. The client had used an ROV for previous surveys. These surveys had produced little useful data, and the client was now interested in AUVs, but it was fortunate that the pipeline had existing data to compare to that of the new technology being implemented.
Over the coming years, the previous ROV surveys will be compared to the ongoing AUV surveys to determine if automation and machine learning are providing the same quality data as the ROV surveys.
The key performance indicator will be AUV data as accurate, if not more accurate, than the ROV-provided data. A faster AUV survey is a requirement that is currently being met.
The key to using AUV technology coupled with 3D high-definition imaging is getting rid of siloed information. Pipelines operate over decades and information can be lost easily. Additionally, technologies have made some data incomplete or invalid. By overlaying what may appear to be non-related data sets, engineers are able to see patterns that could not be seen previously, and this enables better predictions, such as for the loss of CP on a pipeline. However, AUV technology development is not without its challenges. For one, the computing technology involved continues to evolve.
Adam Haavisto is an integrity engineer – offshore asset integrity, Oceaneering
Original content can be found at Oil and Gas Engineering.