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The development of resource plays is moving on from drilling on a regular grid; but it is not sufficient to 'simply' identify a sweetspot in a resource play. It is also necessary to understand connectivity and compartmentalisation, this may be achieved through the development of a realistic fractured reservoir model. Seismic data volumes, inversion studies and rock physics provide a wealth of information covering reservoir intervals. Co-rendering of the data volumes leads to a more easily interpretable image of the subsurface and a better understanding of the reservoir. This paper discusses the construction of a realistic DFN model that was built using inputs derived from a 3D multi-component seismic dataset, seismic attributes, anisotropy information, seismic inversion results and well data. As model incorporates the seismic data deterministically the DFN can be used to guide well placement and planning, predict interwell connectivity and can be used to forward model completions in terms of fracture generation or reactivation and micro-seismic event generation.

The drilling and completion of wells in resource plays has tended to be based on drilling a pre-determined number of wells with uniformly spaced laterals drilled on pre-defined azimuths. The development of resource plays is now incorporating geophysics. Workflows have been developed that make 3D seismic data relevant to unconventional resource optimisation and to improve the economics of exploration and development. This can be achieved by constructing models, incorporating data sets derived from the seismic volumes and wireline data and then forward modelling and subsequently optimising well placement and hydraulic fracture design. The workflow described here starts with a 3D surface-seismic multi-component data set and goes through to hydraulic fracture simulation and potential well optimization achieved through the construction of a discrete fracture network (DFN) model. The data sets are derived from azimuthal velocity analysis and anisotropic parameter calculations; seismic attribute analysis, interpretation and inversion provide a rich data source to develop a model, driven by deterministic data. Three theoretical wells were included in the model, the location of one indicated that it was drilled into the intersection of two faults which could have been avoided. The other two were forward modelled, simulating hydraulic fracturing; this showed that in areas of high fracture density there was a larger number of stimulated fractures and predicted micro-seismicity, suggesting the reactivation of pre-existing fault and fractures. In areas with lower fracture densities, additional fractures were generated suggesting that hydraulic fracturing would result in the generation of new tensile fractures. The modelling provided an enhanced understanding of the reservoir, providing greater insight than would be obtained from co-rendering. Importantly, the model tested different well positions, stage spacings and drilling azimuths. In this case study the locations and azimuths of the laterals were changed with differences in the stage-to-stage stimulated volume of between 3 and 30%. This was particularly the case for the central stages of the modelled wells and this has clear financial implications in the development of the resource in terms of drilling costs, completions and production.

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