Modelling Hydraulically Fractured Tight Gas Reservoirs with an Artificial Intelligence (AI)-Based Simulator, Deep Net Simulator (DNS)

Hydraulic fracturing in tight gas reservoirs increases the connectivity of the well to further areal regions, thus boosting the production as well as the net-present-value of the asset. This type of reservoir typically exhibits considerable uncertainty in rock and fracture properties, which coupled with significant heterogeneity makes history matching, uncertainty quantification, and optimisation time-consuming tasks. Therefore, engineers are always looking for processes to reduce simulation time. Artificial intelligence enables machine-learning to learn from data. This allows for time-consuming fluid flow equations to be explicitly formulated while keeping the accuracy found through the implicit approach. This is achieved through the use of deep learning. In this research, a fully standalone simulator is developed for a range of hydraulically fractured tight gas reservoirs in a 2-dimensional space. Considering the low value of metrics (RMSE<65 psi, MAPE < 0.99%, and R2 ≈ 1) for training, validation and test sets, the results confirmed that the developed model, Deep Net Simulator (DNS), is accurate and reliable when compared with numerical models. Furthermore, DNS shows remarkable reliability when comparing the results of 140 unseen complete reservoir models over a 4-year period against a numerical simulator. The average value of MAPE for all 140 cases is 10.55%.