Fractures play an important role in well placement by influencing the well productivity and dominating the fluid flow underground. Though seismic data is often used to identify fracture swarms, the conductivities of fractures can be hard to evaluate, and data quality of seismic surveys typically decreases as the reservoir becomes deeper. In terms of inferring complex fracture patterns, dynamic production data integration can play a vital role. This paper presents a hierarchical multi-scale history matching approach that combines evolutionary algorithm and streamline method to calibrate fracture permeabilities in a HPHT tight gas reservoir using dual porosity models. The reservoir is located in the Tarim basin, China, and has a depth of more than 7500 m with high pressure (18000 psi) and high temperature (340 °F). The fracture properties of the dual porosity model are initially derived from seismic attributes and further calibrated with dynamic data using the proposed multi-scale history matching. The calibrated fracture model can detect the fracture swarm locations underground. The streamlines generated from the history matched model in conjunction with reservoir properties are used to define a ‘depletion capacity map’ which is then used for optimal infill well placement.
Most of the previous streamline-based field applications are limited to incompressible or slightly compressible flow. In this paper streamline-based analytical sensitivities are extended to highly compressible flow. To our knowledge, this is the first-time streamlines have been used to facilitate history matching and optimal well placement for gas reservoirs.
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