--> ABSTRACT: Integrated Fractured Reservoir Modelling Using Geomechanics and Flow Simulation, by Stephen J. Bourne, Joel J. Ita, Bettina E. Kampman-Reinhartz, Lex Rijkels, Ben J. Stephenson, and Emanuel J. M. Willemse; #90913(2000).

Datapages, Inc.Print this page

ABSTRACT: Integrated fractured reservoir modelling using geomechanics and flow simulation

Bourne, Stephen J., Joel J. Ita, Bettina E. Kampman-Reinhartz, Lex Rijkels, Ben J. Stephenson, and Emanuel J. M. Willemse , Shell International Exploration and Production B.V, Rijswijk, Netherlands

To optimise recovery in naturally fractured reservoirs, the field-scale distribution of fracture properties must be understood and quantified. This calls for a fracture modelling approach based on geomechanics, and that conforms with all geological, well test and field production data. We present a semi-deterministic method to systematically predict natural fractures, and incorporate their effect on flow simulation. First, the present-day structural reservoir geometry is used to calculate the stress distribution at the time of fracturing. This is based on geomechanical models of rock deformation such as elastic faulting. Second, the calculated stress field is used to govern the growth of fracture networks. When fractures encounter each other, bedding planes or large-scale faults their interaction is governed by rules based on outcrop studies. Third, well tests acquired in the field are simulated to validate and further constrain the extent and permeability of the predicted fracture network. Finally, the fractures are upscaled dynamically by simulating flow through the discrete fracture network per grid block, enabling field-scale multi-phase reservoir simulation. Uncertainties associated with these predictions are considerably reduced by constraining and validating the models with seismic, borehole, well test and production data. This approach is able to predict physically and geologically realistic fracture networks. Its application to both outcrops and reservoirs demonstrates a high degree of predictability in the formation and properties of natural fracture networks. In cases of limited data - where stochastical models typically fail - this method remains robust.

AAPG Search and Discovery Article #90913©2000 AAPG International Conference and Exhibition, Bali, Indonesia