Novel Probabilistic-based Framework for Improved History Matching of Shale Gas Reservoirs
Creators: Nwabia, Francis N
Publisher: name: University of Alberta Library
Year: 2022

<p>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. The 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 with 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.<br>
This thesis will develop an integrated approach for the history matching of hydraulically fractured reservoirs. First, multiple realizations of the DFN model are constructed with conditioning data based on available geological information such as seismic data, well logs, and rate transient analysis (RTA) interpretations, which are useful for inferring the prior probability distributions of relevant fracture parameters. A pilot point scheme and sequential indicator simulation are employed to update the distributions of fracture intensities which represent the abundance of secondary fractures (NFs) in the entire reservoir volume. Next, the model realizations are upscaled into an equivalent continuum dual-porosity dual-permeability model 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.<br>
An indicator formulation is proposed to account for the non-Gaussian nature of the DFN parameters. The algorithm aims at minimizing the objective function while reducing the uncertainties in the unknown fracture parameters.<br>
The novel probabilistic-based framework is applied to estimate the posterior probability distributions of transmissivity of the primary fracture (Tpf), transmissivity of the secondary induced fracture (Tsf) and secondary fracture intensity (Psf32L), secondary fracture aperture (re), length and height (L and H), 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 realizations of the DFN models are sampled from the updated (posterior) distributions accordingly. The key novelty in the developed probabilistic approach is that it accounts for the highly nonlinear relationships between fracture model parameters and the corresponding flow responses, and it yields an ensemble of DFN realizations calibrated to both static and dynamic data, as well as the related upscaled flow-simulation models. The results demonstrate the utility of the developed approach for estimating secondary fracture parameters, which are not inferable from other static information alone.</p>