Their results demonstrated that the observed and neuro-fuzzy calculated fracture density may be reconciled well (correlation coefficient of 98%). ( 2011) presented a model that uses an adaptive neuro-fuzzy inference system to estimate fracture density using conventional good logs. Using artificial neural networks and conventional well logs calibrated to core data, Zazoun ( 2013) implemented a model that can forecast fracture density (ANNs). Their investigation demonstrated a significant relationship between fracture density and the energy of calipers, sonic (DT), density (RHOB) and lithology (PEF) logs in each well. In their study, the energy of the petrophysical logs was calculated in the fractured zones and linear and nonlinear regressions were established between them. ( 2010) utilized the power of petrophysical logs and a novel technique for estimating fracture density in fractured zones. Numerous studies on the characterization of naturally fractured reservoirs have been conducted recently. In this study, an artificial intelligence approach was used to derive image log-derived fracture parameters from petrophysical logs quickly with reliable accuracy. Using image logs in a well is economically expensive and is acquired in a few wells of a hydrocarbon field. Image logs can identify fractures in the excellent way with a high resolution. In addition, by interpreting them, other geological features such as stratification, stylolite, faults and anhydrite nodes can be identified. Image logs of the formation provide essential information about fractures, such as their dip and azimuth, fracture spacing, fracture density and aperture. This method is cost-effective and is currently in use. However, they are not accurate enough due to their low resolution. One of the methods is to use petrophysical logs such as neutron, density and sonic. Different methods have been proposed to identify small-scale fractures around the well (e.g. Large-scale fractures are like significant faults seen at seismic sections. Natural fractures in reservoirs range from large to small-scale fractures. In general, fractures play a significant part in the production of fractured reservoirs (Kadkhodaie et al. Proper knowledge of fractures is essential in oil production and development plans. Natural fractures are the most significant factors determining the hydraulic behavior of oil and gas reservoirs. The results of this study can successfully be used as an aid in a more successful reservoir dynamic modeling and production data analysis. The RF algorithm showed higher stability and robustness in predicting fracture intensity with a correlation coefficient of 93%. The findings of this study demonstrate that the measured and FFNN calculated fracture intensity is in excellent agreement with image log results showing a correlation coefficient of 92%. The models' performance was also improved by increasing the number of neurons in the hidden layers from 8 to 35. The CFNN model outperformed the FFNN model with lower neurons. According to the findings of this research, the FFNN model showed a higher KGE and WI, and a higher correlation coefficient ( R 2) compared to the CFNN model. Conventional good logs and full-bore micro-resistivity imaging data were available from three drilled wells of the Mozduran reservoir, Khangiran gas field. The model performance was assessed using statistical measures including the root mean squared error (RMSE), coefficient of determination ( R 2), mean absolute error (MAE), Kling Gupta efficiency (KGE) and Willmott’s index (WI). In this study, the feed-forward neural networks (FFNN), cascade feed forward neural networks (CFFN) and random forests (RF) were used to determine fracture density from petrophysical logs. Fractured zones can be detected by using seismic data, petrophysical logs, well tests, drilling mud loss history and core description. Modeling fractured reservoirs requires an understanding of fracture characteristics. Natural fractures play an essential role in the characterization and modeling of hydrocarbon reservoirs.
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