Inference of Hydraulically Fractured Reservoir Properties from Production Data Using the Indicator-Based Probability Perturbation Assisted History-Matching Method

Hydraulically fractured horizontal wells are widely adopted for the development of tight or shale gas reservoirs. The presence of highly heterogeneous, multi-scale, fracture systems often renders any detailed characterization of the fracture properties challenging. Discrete fracture network (DFN) model offers a viable alternative for explicit representation of multiple fractures in the domain, where the comprising fracture properties are defined in accordance to specific probability distributions. However, even with the successful modelling of a DFN, the relationship between a set of fracture parameters and the corresponding production performance is highly nonlinear, implying that a robust history-matching workflow capable of updating the pertinent DFN model parameters is required for calibrating stochastic reservoir models to both geologic and dynamic production data. This paper proposes an integrated approach for the history matching of hydraulically fractured reservoirs. First, multiple realizations of the DFN model are constructed conditioning to available geological information such as seismic data and well logs, which are useful for inferring the prior probability distributions of relevant fracture parameters. Next, the models are upscaled into equivalent continuum dual-permeability models and subjected to numerical multiphase flow simulation. The predicted production performance is compared with the actual recorded responses. Finally, the DFN-model parameters are adjusted following an indicator-based probability perturbation method. Although the probability perturbation technique has been applied to update facies distributions in the past, its application in modeling DFN distributions is limited. To account for the non-Gaussian nature of the DFN parameters, an indicator formulation is proposed. The algorithm aims at minimizing the objective function, while reducing the uncertainties in the unknown fracture parameters. The method is applied to estimate the posterior probability distributions of the transmissivity of the primary fracture (T-pf), transmissivity of the secondary induced fracture (T-sf) and global fracture intensity (P-32G(sf)) in a multifractured shale gas well in the Horn River Basin. An initial realization of the DFN model is sampled from the prior probability distributions using the Monte Carlo simulation. These probability distributions are updated to match the production history, and multiple equi-probable realizations of the DFN models are sampled from the updated (posterior) distributions accordingly. The final sampled realizations of the DFN model are consistent with both static geological information and dynamic production history.