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Equation (6) is gained by using reservoir parameters of Block Five of Bonan in Shengli oilfield.
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One method to answer this question is by using reservoir simulation; however, this will require a reservoir simulator representing the shale gas storage and flow behaviours.
Another successful application of GA in identifying the heterogeneous reservoir properties by matching the tracer breakthrough profiles using six reservoir parameters was demonstrated by Guerreiro et al. (1998).
By definition, an emulator uses reservoir properties as input parameters in a statistical model constructed from simulator outputs.
It is clear that a large proportion of this reservoir is not replicative competent however no absolute measurement of this reservoir component is available and we have therefore tried to improve our understanding of the reservoir by using several virological parameters.
By using the real reservoir data and fracturing parameters, we simulated the volumetric fracturing in a tight oil formation of a certain oilfield, and examined the impact of the horizontal stress difference, linear density of natural fractures, number and spacing of perforation clusters on fracture patterns.
We tested this hypothesis by using a "wave reservoir" model, which calculates reservoir pressure on the basis of the pressure and flow velocity waveforms.
History matching has long been used to estimate reservoir parameters from dynamic production history data.
Artificial Neural Network (ANN) is used to estimate reservoir parameters from well test data.
For hydraulically fractured horizontal wells, transient well testing is commonly used to determine reservoir parameters and to estimate well productivity.
It is a process which describes different reservoir parameters using well field data.
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