Improved Understanding of Reservoir Fluid Dynamics in the North Sea Snorre Field by Combining Tracers, 4D Seismic and Production Data

Abstract

Abstract To obtain improved oil recovery (IOR) it is crucial to have a best possible description of the reservoir and the reservoir dynamics. In addition to production data, information can be obtained from 4D seismic as well as tracer monitoring. Interwell tracer testing (IWTT) has been established as a proven and efficient technology to obtain information on well-to-well communication, heterogeneities and fluid dynamics. During such tests, chemical or radioactive tracers are used to label water or gas from specific wells. The tracers are then subsequently used to trace the fluids as they move through the reservoir together with the injection phase. One of the appealing features of IWTT is that at first tracer breakthrough in a producer, immediate and unambiguous information on injector - producer communication is given. However, gas and water re-injection might complicate this simple evaluation, and later in the production history, this effect should be evaluated. Despite the appealing features of IWTT, they are still underutilised in the petroleum industry, and few of the tracer studies that are actually performed utilise the data to their full capacity - most tracer data are used in a qualitative manner [1]. To improve this situation, we combine tracer data evaluation, 4D seismic and available production data in an integrated process. The integration is demonstrated using data from the Snorre field in the North Sea. In addition to production data, extensive tracer data back to 1993 and results from three seismic surveys acquired in 1983, 1997 and 2001 were considered. Introduction The Snorre field is located in the Tampen Spur area on the Norwegian Continental Shelf and is a system of rotated fault blocks with beds dipping 4–10° towards the Northwest. The reservoir sections are truncated by the Base Cretaceous Unconformity. The reservoir sections consist of fluvial deposits of the Statfjord and Lunde Formations. The reservoir units contain thin sand layers with alternating shale in a complex fault pattern. A challenge regarding optimization of the reservoir drainage as well as oil production, is to understand how the different sand layers communicate and to what degree the faults act as barriers or not. The present work concentrates on the integration of 4D seismic and tracer methods to obtain information of fluid flow in the Upper Statfjord (US) and Lower Statfjord (LS) formations in the Central Fault Block (CFB). The outline of this fault block is indicated in Figure 1. The net/gross ratio is higher and the reservoir quality is generally better in the Upper Statfjord than the Lower Statfjord formation. The CFB is produced by water-alternating-gas (WAG) injection as drive mechanism, where the injectors are placed down-dip and the producers up-dip. The average reservoir pressure in the CFB is 300 bar and the reservoir temperature is 90 oC. Tracer data are used to understand fluid flow in the reservoir. The data gives valuable information about the dynamic behavior and well communication, but the interpretation may in some cases be complicated by re-injection of produced gas and water. Tracer studies in the Snorre field have previously been presented in several papers [2–4]. To fully utilize the data, however, integration with other types of reservoir data is important. The main objective of the seismic monitoring of Snorre is to contribute to increased oil recovery and to optimize placement of new wells. 4D analysis, together with tracers, should potentially increase the multi-disciplinary understanding of the drainage pattern in the reservoirs. The results should, in addition to all the reservoir and production data, be actively used in Target Remaining Oil (TRO) processes and well planning. In addition, the 4D data can give input to update the geological model and simulation model (history matching) and to identify possible well interventions. There is also a potential to include the data in workflows to identify lithology changes.