Analysis of strain sensor cable models and effective deployments for distributed fiber optical geotechnical monitoring system
ASME 2015 International Pipeline Geotechnical Conference (IPG2015) Bogota, Colombia
Long range, distributed fiber optic sensing systems have been an available tool for more than a decade to monitor pipeline subsidence integrity challenges. Effective deployment scenarios are an important decision to be factored into the selection of this monitoring equipment and typologies relative
to specific project needs. In an effort to analyze the effectiveness of various fiber optic deployment conditions, a controlled field experiment was conducted. Within this field experiment, a variety of distributed fiber optic sensors and point sensors were deployed in predefined positions. These
positions relative to the pipeline were selected to support a range of deployment needs including new construction or
retrofitting of existing pipelines. A 16-inch diameter by 60-meter long epoxy coated pipeline that was capable of being
pressurized to mimic operating conditions was utilized. This test pipe was installed in a typical trench setting. Conventional
point gauges were installed at key locations on the pipeline.
Fiber optic sensor cables were installed at key locations providing 14 alternative scenarios in terms of sensitivity, accuracy, and cost.
After construction of the test pipeline, real time continuous monitoring via the array of conventional and fiber
optic sensors commenced. A deep trench was excavated adjacent and parallel to the central portion of the pipeline which
began to induce subsidence in the test pipeline. Continued monitoring of the various sensors produced real time
visualization of the evolving subsidence. A comparison of the reaction of the sensors is compiled to provide an intelligent
selection criteria for integrity managers in terms of accuracy, deployment, and costs for pipeline subsidence monitoring
projects. In addition, further analysis of this sensor data should provide more insight into pipeline/soil interaction models and behaviors.