INTERPRETING BIOSTRATIGRAPHICAL DATA USING FUZZY LOGIC:
THE IDENTIFICATION OF REGIONAL MUDSTONES WITHIN THE FLEMING FIELD, UK NORTH SEA
M. I. Wakefield*+, R. J. Cook*, H. Jackson* and P. Thompson*
*BG Group, 100 Thames Valley Park Drive, Reading RG6 1PT.
+Author for correspondence: e-mail email@example.com
The Fleming gas-condensate field, located on the eastern flank of the Central Graben, UK North Sea, is an elongate stratigraphic pinch-out whose reservoir is composed of stacked turbidite sandstones of the lower Palaeogene Maureen Formation. The sandstones have a sheet-like geometry with each sandstone lobe being partially offset into the swales of the preceding lobes. In such depositional environments, the understanding of lateral and vertical sandstone connectivity, a major uncertainty in reservoir modelling and well planning and production strategies, depends upon the choice of depositional model that is applied and the lateral continuity of pelagic mudstones.
Previously published work defined a model, based on variations in the composition of agglutinated foraminiferal populations, that could be used to derive a qualitative measure of the level of pelagic influence within mudstones interbedded with turbidite sandstones. It was considered that mudstones with a high pelagic influence are likely to be more laterally extensive than those with a low pelagic index. A fuzzy logic workflow was constructed using this model and was applied to the Fleming field in order to identify laterally persistent mudstones. This approach was combined with high-resolution correlation of bioevents using graphic correlation. A detailed layering scheme for the Fleming field was defined and this predicted the presence of a field-wide mudstone.
Initial attempts at history matching during reservoir simulation using a simple six-layer lithostratigraphical scheme were not successful. A revised layering scheme defined by biofacies modelling and graphic correlation was used to produce a 13-layer model; this was later simplified by combination with the six-layer model to produce a ten-layer model. This layering scheme is shown to provide a better understanding of both net-to-gross distribution and the dynamic behaviour of the field, and also improved history matching against production data. The biostratigraphical model applied using a fuzzy logic approach is authenticated by the reservoir simulation (fluid flow) and pre- and post-maintenance well pressure tests of well 22/5b-A3 which showed that the perforated interval in that well is isolated from the perforated intervals in the other producing wells. While history matching during reservoir simulation is important, the predictive capability of the fuzzy logic model proved to be critical to our understanding of the field.