Optimal Survey Design for Offshore Deformation Monitoring

¹ WX Geo Services LLC, Houston, Texas, USA.
² Faculty of Geoformation & Real Estate (FGHT), UTM, Jalan Ilmu, 81310 Skudai, Johor Bahru, Johor, Malaysia.

^* Corresponding author: martin.rayson@geomaticsolutions.com
^† Email: safaruddin@graduate.utm.my
^‡ Email: razalimahmud@utm.my

Abstract

Monitoring deformation experienced by facilities engaged in offshore hydrocarbon extraction is important to understanding any structural integrity changes a production and development asset may suffer. Facilities can be platform or seabed based depending upon water depth and environment in which the assets are situated. There have been some well document case studies showing different survey techniques being used to undertake such monitoring campaigns in a variety of different settings, and reference is made to these publications. This research takes a step back to examine the problem from a Geomatics perspective. This is achieved by firstly, developing mathematical model(s) showing all the deformation parameters a facility will experience and secondly, determining an optimal survey design that will measure the deformation parameters to required quality measure tolerance. This includes consideration of coordinate system usage and the actual survey observation campaign. The optimal observation scheme is described by the selection of where survey control stations will be located, and the observations made between the stations to measure the deformation parameters of the model. Next, consideration is given to the error budget the observations must operate within to develop the stochastic model incorporate into the survey design. The dynamic deformation model must include the rates of change a facility is expected to experience within each of the directional vectors of the model (e.g. pitch, roll, yaw and heave) and the statistical tolerance to which they are required. The results of the survey design will compute aposteriori matrices from which the classical quality measures are derived. The computational process enables multiple scenarios to be examined to help determine optimal observation schemes for individual facilities in different offshore environments. Finally, current technologies are examined to determine which are best suited to measure the rates of change built into the deformation model to the quality tolerances specified. The final observation scheme will recommend an optimal combination of technologies to best determine subsidence rates for different offshore environments.