Designing an optimal 4D streamer survey pays reservoir dividends

In this case study of a 4D survey offshore Norway, Keith Watt and Gavin Pattison of I/O company Concept Systems provide a case study of how planning and design of a 4D seismic survey ultimately rewards reservoir management. 4D started life as a research technology in the geophysicists’ domain, but the real beneficiaries of the final outcome are reservoir geoscientists. Over the last few years the reservoir engineering community has begun to understand the benefits of 4D and how the technology can be applied to optimise reservoir management programmes. Conferences such as the joint SPE/EAGE conference ‘What Do Petroleum Engineers Expect from Time Lapse Seismic, and Do Geophysicists Answer the Right Questions?’ in March this year helped to bring the two communities together to build a common understanding of the technologies. A key issue in 4D surveys is to ensure that the data is of sufficient quality to provide reliable imaging of changes in reservoir behaviour during production. For marine acquisition of 4D seismic using towed streamers, this poses a major positional challenge which Concept Systems has been addressing for several years now. The company has been involved in over 40 surveys, enough to identify that major sources of error arise from the towing of long cables and the differences in source/receiver positions between monitor and baselines surveys. To minimise these positional differences, the first priority is to measure them. In 2001 Concept began a research project in partnership with Shell Expro to develop software to measure the positional errors in 4D surveys and correlate them with the processed seismic data. The resulting ‘repeatability’ technology was used successfully in Shell’s 2002 North Sea 4D programme to QC/optimise the acquisition and to make 4D infill choices. One lesson from this early work was the significant benefit arising from anticipating the positional issues at an early stage of the process, in other words in the planning stages. The ability to measure 4D repeatability during the design/ planning phase of a 4D project, and to analyse multiple scenarios to decide on the best compromise between repeatability and acquisition costs, can significantly improve the quality of a 4D dataset. We focus here on the application of the technology and knowledge applied in the design and planning phase of a 4D project.