Improving reservoir management

Existing methods (seismic and electromagnetic) suffer from serious limitations in their ability to provide continuous, low cost data that can be interpreted to determine water location, volume, and movement throughout a reservoir. Seismic surveys typically require a surface generated signal for deep reservoir phase definition which is costly and in some cases difficult or impossible to obtain. Also, time based (4D) seismic must image phases (to contrast gas versus water acoustic velocity) and does not proportionately see varying volumes. Conventional EM has sensitivity to conductivity changes within the reservoir but only provides information where there are conductivity contrasts such as saline formations, but not depleted reservoirs. Development and commercialization of a robust miniaturised gravity sensor would address these limitations and provide a step change in subsurface verification and monitoring capability. Gravity has inherent advantages to seismic and EM. The gravity signal is always present and does not require a source to generate a response. Also, by detecting differentials in mass or density, gravity offers complementary and independent measures to seismic (which measures acoustic velocity) and EM (which measures differential resistivity). The combination of 4D seismic plus gravity will result in reduced error and improved seismic interpretations. Unlike EM, gravity is deep reading - able to penetrate up to 2000'. Gravity, and in particular MEMS technology, is lower cost (only a few pounds per sensor). MEMS technology also offers other distinct advantages for wellbore deployment, including ease of miniaturizaton and packaging and flexibility on orientation. Because of these attributes, the MEMS gravity sensor is the optimum technology to underpin a system of periodic or low cost permanent sensor arrays in wellbores. This project aims to pilot the development of a new MEMS gravity tool for reservoir imaging.

Following protection of intellectual property generated through the project, the results will be disseminated to the academic community through leading international conferences and journals.

The sensor aspects of the technology will deal with issues underlying the interaction of noise and non-linear processes in microelectromechanical devices, and approaches to improving long-term device stability by compensating for bias drift and temperature effects. Issues underlying robustness of the device to high temperature environments will be addressed. Additionally, elements related to sensor calibration and data conditioning will be pursued within the University environment. These aspects will be disseminated through publication.

There will be wide dissemination of the projects' results to researchers in oil companies familiar with surface gravity surveys who will be interested in evaluating the detailed performance of the sensors in the Cornwall test. As the tool is developed, the academics will also explore making elements of the tool available to other user communities e.g. researchers engaged in the field of CO2 storage monitoring and researchers engaged in earthquake seismology.

Academics in the microsystems (MEMS) area will be immediate academic beneficiaries though user communities including those in broader geophysics and environmental monitoring fields will also benefit from the results.