4D Seismic Imaging for Reservoir Management

4D seismic analysis for reservoir monitoring and surveillance is an exciting topic with a long and successful track record of applications in industry and academia. Leading edge solutions have been developed over several decades to tackle the majority of 4D interpretational challenges and deliver over 80% of value to reservoir management. In recent years, as we push to deliver more details to the asset team, 4D imaging has once again become the centre of attention. This is particularly the case in the pre-salt reservoirs of areas like the Campos Basin, but also structurally complex fields in the Gulf of Mexico and North Sea.

In these recent developments we strive to apply image-consistent, pre-stack methodologies that deliver more stable, accurate and higher resolution detection of the subsurface changes. One particular emphasis on this approach is the control of amplitudes, which are not necessarily preserved in standard migration schemes. After appraisal of existing 4D imaging solutions, the initial aim of this project is to develop an understanding of the errors that arise during migration of 4D seismic data, and to determine using sensitivity studies how these might contribute to uncertainties in the interpretation. This will be achieved by exploring a series of synthetic datasets generated by modelling several field-based scenarios from our portfolio of available data. Next, the results of this study will be used to develop a pre-stack method that provides a practical solution for imaging physically consistent subsurface changes. The scheme will be applied to field data supplied by our sponsorship group.

You will join the ETLP research team which has nineteen years of experience in quantitative 4D seismic interpretation and is funded by a number of oil, energy and service companies. For more information on our activities please visit our website: www.pet.hw.ac.uk/research/etlp. You will work on this PhD project supervised by both Prof Colin MacBeth and Dr Hamed Amini.