Faults can severely compartmentalize pressures and fluids in producing reservoirs, and it is therefore important to take these effects into account when modelling field production characteristics. The Brent Group fields, northern North Sea, contain a complex arrangement of fault juxtapositions of a well-layered sand-shale reservoir stratigraphy, and fault zones containing a variety of fluid flow-retarding fault rock products. It has been our experience that these fault juxtapositions impact the ‘plumbing’ of the faulted layering system in the reservoirs and the models that are built to mimic them – and are, in fact, a first-order sensitivity on compartmentalization of pressures and fluid flow during production simulation. It is important, therefore, to capture and validate the geological feasibility of fault- horizon geometries, from the seismic interpretation through to the static geocellular model, by model building in conjunction with the interpretation. It is then equally important to preserve this geometrical information during geocellular transfer to the simulation model, where it is critical input data used for calculation of fault zone properties and fault transmissibility multipliers, used to mimic the flow-retarding effects of faults. Application of these multipliers to geometrically weak models tends to produce ambiguous or otherwise potentially misleading simulation results. We have systematically modelled transmissibility multipliers from the upscaled cellular structure and property grids of geometrically robust models – with reference to data on clay content and permeability of fault rocks present within drill core from the particular reservoir under study, or from similar nearby reservoirs within the same stratigraphy. Where these transmissibility multipliers have been incorporated into the production simulation models, the resulting history matches are far better and quicker than had been achieved previously. The results are particularly enhanced where the fault rock data are drawn from rocks that have experienced a similar burial–strain history to the reservoir under study.

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Probabilistic modelling is one of the most frequently used methods in reservoir simulation to manage uncertainties and assess their impact on reservoir behavior/cumulative production. However, depending on the extent of the uncertainty, 100s of scenarios can be generated leaving engineers unable to meaningfully analyze this data. To remedy this an unsupervised machine learning based workflow was developed to identify unique scenarios which was then paired with an integrated dashboard to enable rapid and deep analysis. A case study was done using data from a Shell operated gas field in the North Sea. Data was first mined from 480 history matched scenarios using python; out of which 20 unique clusters were identified through K-Means clustering of pressure and saturation changes with time in each gridblock. This meant that the team had to look only at 20 scenarios instead of 480 to understand the effect of different inputs on pressure and saturation response. For enhanced analysis, an integrated visualisation dashboard was created to visualize pressure and saturation changes, production profiles and connect them back to input parameters The new methodology enabled the team to integrate different aspects of reservoir modelling from static to dynamic to surface constraints on a single dashboard, making it possible to find patterns in large volumes of data which was previously not possible. For example, a cluster was identified which had high water movement; upon inspection of input parameters it was seen that late life recovery was significantly different in this cluster as compared to others. Being able to visualize different properties of multiple scenarios simultaneously at both group and grid level is a very powerful tool that not only generates insights but significantly reduces analysis time and helps in quality checking property modelling and grid behavior. The developed workflow is quite generic in nature, capable of working with various simulators and can be extended to assessing history match quality in Assisted History Matching (AHM) and multi-scenario modelling. Key parameters impacting different scenarios were identified and the team observed 10x reduction in time and significant reduction in manpower requirements through the new approach

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe Brent Slumps of the Brent Field were formed by crestal collapse of the main Brent reservoir during the late Jurassic. Production from the Brent Slumps has been dominated by two main factors: varying levels of connectivity with the West Flank of the field through cross-fault juxtaposition and preferential production via prolific high-permeability zones. Remaining Brent Slumps undeveloped reserves represent a significant fraction of the field total.In order to improve the understanding of historical production performance and to address the remaining hydrocarbon volumes in place, a new realistically faulted 3-D simulation model has been constructed for the Slumps. The model contains over 140 listric and antithetic faults and utilises Shell's proprietary reservoir simulator (MoReS).The model basis is a very fine cellular 3-D static model that contains a detailed description of both reservoir structure and facies-controlled permeability architecture. Simulation models are extracted at various scales, ranging from coarse (quick look at material balance and pressures), to very fine (investigation of flushing patterns), using upgridding and upscaling algorithms that guarantee consistency of volumetrics, connectivity and well data.The simulation model is defined in terms of geological entities, i.e., individual faults, fault blocks, horizons and zones. This enables a high-level and systematic treatment of fault transmissibility and allows for monitoring of, e.g., material balance/pressures per fault block and flow across individual fault surfaces. This greatly facilitates sharing of simulation results within the multi-disciplinary development team.The integrated static/dynamic model is now used to help identify and rank the portfolio of remaining development targets, in addition to underpinning the planning of complex, multi-target horizontal wells. Many infill targets are relatively small and lie close to the economic threshold. The improved technical integrity provided by the new model has been critical in providing sufficient confidence to support an infill drilling programme.After outlining the particular development challenges for the Brent Slumps, the paper sketches the static model (seismic, analogues, structure, rock properties), explains the philosophy behind the integrated static/dynamic model and illustrates its utilisation (and successes) with a number of examples.