فهرست نشریات علمی دسترسی آزاد ایران

Accurate determination of the natural gas compressibility factor is crucial for reservoir simulation and material balance computations in petroleum engineering. The data-driven AI techniques, like artificial neural networks, fuzzy systems, and neuro-fuzzy systems, are gaining momentum in estimating fluid properties. An adaptive neuro-fuzzy inference system (ANFIS) is applied here to develop a model to estimate the compressibility factor of two natural gas types. The Takagi-Sugeno fuzzy inference system serves as the foundation for constructing the ANFIS model, where the triangular membership functions are applied. The training data consists of 80% of the available data selected randomly, and the remaining 20% is applied in testing. This developed model is of high accuracy in estimating the compressibility factors of natural gas types, with an average absolute relative deviation of 0.05% and a maximum absolute relative deviation of 0.55% difference between the estimated and experimental value data. Comparing the findings here with the correlations indicates that the ANFIS model in terms of accuracy outperforms its counterparts in this realm.