Optimizing Deep Water Pipeline Routes In Areas Of Geologic Complexity An Example From The Gulf Of Mexico

Meyer, V. (ed.), Frontiers in Offshore Geotechnics III: proceedings of the Third International Symposium on Frontiers in Offshore Geotechnics (ISFOG 2015), Oslo, Norway, 10-12 June 2015, CRC Press, Boca Raton, pp. 957-962.

10 Jun 2015
Haneberg, W.C., Devine, C.A.,, Feregrino, D.N.V., and Calderon, M.O.
DOI: 10.1201/b18442-139

Abstract
Deep-water pipeline route selection is guided by primary and secondary constraints ranging from the mechanical properties of the pipe to the presence of seafloor geohazards. Using the example of a 33 km long proposed pipeline in the Gulf of Mexico, we show how largely qualitative categorical geohazard maps can be combined with quantitative information about seafloor geometry to produce composite geocost maps and optimal pipeline route estimates in the early stages of projects. This stands in contrast to the alternative of evaluating potential geohazards only after a route has been selected and surveyed, at which point it may be difficult to select alternates. Seafloor conditions encountered between the two pipeline termini in this project included a variety of static and dynamic, geological and geometric hazards such as zones of tectonic uplift, landslides, and areas of rough seafloor that may give rise to pipeline problems such as spanning, burial, or loading. Of particular concern was a geologically young anticline with pronounced seafloor expression and evidence of chronic slope instability along its steeply-dipping western limb. Because the component maps used to create the composite geocost surface each contain their own inherent subjectivities, uncertainties, and limits of resolution, we used a resampling based Monte Carlo approach to generate a cloud of 100 equally probable optimized routes for each of two scenarios. Areas in which the equally probable routes were tightly clustered indicate low sensitivity to input variations and well-defined optimal route corridors. Areas characterized by significant dispersion of routes, however, indicate high sensitivity to input variations, the absence of a single well-defined corridor at the resolution of the available data, and a need for additional detailed information. These results were used to define corridors of interest in which more detailed surveys could be undertaken to further refine the route options.

 

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