Abstract

Time-lapse interferometric synthetic aperture radar (InSAR) remote sensing methods of surface deformation have proven their use in desert environments. The data are acquired frequently without the need to send personnel or equipment into the field. The quality and accuracy of the data is very high. The spatial resolution of the data is excellent and matches that of surface seismic data. These characteristics make the data well suited for a variety of time-lapse monitoring tasks. In this study, we describe the accuracy of the InSAR technique relative to other measurements such as the global positioning system and precise leveling (acquired at known stable locations). We illustrate two case studies of differing natures. In one case, gas production leads to reservoir compaction, which is tracked as surface subsidence with time using frequent InSAR data. The results are used to map zones of increased deformation and identify areas with localized changes. These insights are being used to influence decisions on new wells and well interventions, to provide support for management of facility integrity, and to advise building code and material selection that can withstand the expected rate of deformation. The second case of a shallow steam flood illustrates the use of InSAR data to identify areas of surface uplift following thermal expansion of the reservoir. These data are also used to support the monitoring of the steam chamber growth and confinement in the reservoir. The information from InSAR will become more valuable for reservoir management when the steam chamber matures and conventional downhole data acquisition consequently becomes challenging. In summary, oil and gas fields located in arid environments lend themselves well to remote sensing using the InSAR technique because (1) they are sizeable (from tens to hundreds of square kilometers); (2) they are free from vegetation, snow cover, and most atmospheric distortions, although cloud and pollution can affect the data quality; and (3) they benefit from highly repeatable long-term regular monitoring.

You do not currently have access to this article.