Interpreting biostratigraphical data using fuzzy logic: The identificationof regional mudstones within the Fleming field, UK North Sea

The Fleming gas-condensate field, located on the eastern flank of the Centr al Graben, UK North Sea, is an elongate stratigraphic pinch-out whose reser voir is composed of stacked turbidite sandstones of the lower Palaeogene Ma ureen Formation. The sandstones have a sheet-like geometry with each sandst one lobe being partially offset into the swales of the preceding lobes. In such depositional environments, the understanding of lateral and vertical s andstone 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 compo sition of agglutinated foraminiferal populations, that could be used to der ive a qualitative measure of the level of pelagic influence within mudstone s interbedded with turbidite sandstones. It was considered that mudstones w ith a high pelagic influence are likely to be more laterally extensive than those with a low pelagic index. A fuzzy logic workflow was constructed usi ng this model and was applied to the Fleming field in order to identify lat erally persistent mudstones. This approach was combined with high-resolutio n correlation of bioevents using graphic correlation. A detailed layering s cheme for the Fleming field was defined and this predicted the presence of afield-wide mudstone. Initial attempts at history matching during reservoir simulation using a si mple six-layer lithostratigraphical scheme were not successful. A revised l ayering scheme defined by biofacies modelling and graphic correlation was u sed to produce a 13-layer model; this was later simplified by combination w ith 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 distributio n and the dynamic behaviour of the field, and also improved history matchin g against production data. The biostratigraphical model applied using a fuz zy logic approach is authenticated by the reservoir simulation (fluid flow) and pre- and post-maintenance well pressure tests of well 22/5b-A3 which s howed that the perforated interval in that well is isolated from the perfor ated intervals in the other producing wells. While history matching during reservoir simulation is important, the predictive capability of the fuzzy l ogic model proved to be critical to our understanding of the field.