Due to strong nonlinearities in the governing diffusivity equation for flow in porous media, numerically assisted rate-transient analysis (RTA) techniques have been suggested for the analysis of multiphase production data from multifractured horizontal wells (MFHWs). However, these methods are based on some limiting assumptions that cannot be generalized for three-phase flow or when relative permeability is unknown. In this study, a new RTA-assisted history-matching technique is proposed to simultaneously match production data and diagnostic plots during the calibration process.

In the proposed method, the objective function is modified to include the derivative of the integral of rate-normalized pressure for the primary phases. As such, in the history-matching process using compositional numerical simulation, the flow regimes are also matched, which can increase the reliability of the calibrated numerical model. This approach is applied to a challenging data set of production data from an MFHW completed in a Canadian shale reservoir hosting a near-critical gas condensate fluid.

The results demonstrate that when the modified objective function is used, the history-matching scheme will reject models that cannot reproduce the flow regimes even if the production data are visually matched. Another benefit of this modified history-matching workflow is that, unlike other numerically assisted RTA techniques, it is not limited to any specific conceptual model or reservoir geometry. Further, interactions between parameters are accounted for during the calibration process. Including the derivative terms in the objective function can ensure a better history-matched model with improved forecast quality. However, comparing the convergence rates of the history-matching with the standard and modified objective functions indicates that adding the derivative terms comes with an additional computational cost requiring more iterations and a slower convergence rate.

In this study, a modified objective function is introduced for the first time to enhance the numerical history-matching process to ensure the resulting calibrated model can also reproduce the observed transient flow regimes. This approach is easy to implement and is not limited to a specific model geometry or any input-output relationship.

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