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Fracture density estimation is an indisputable challenge in fractured reservoir characterization. Traditional techniques of fracture characterization from core data are costly, time consuming, and difficult to use for any extrapolation to non-cored wells. The aim of this paper is to construct a model able to predict fracture density from conventional well logs calibrated to core data by using artificial neural networks (ANNs). This technique was tested in the Cambro-Ordovician clastic reservoir from Mesdar oil field (Saharan platform, Algeria). For this purpose, 170 cores (2120.14 m) from 17 unoriented wells have been studied in detail. Seven training algorithms and eight neuronal network architectures were tested. The best architecture is a four layered [6-16-3-1] network model with: a six-neuron input layer (Gamma ray, Sonic interval transit time, Caliper, Neutron porosity, Bulk density logs and core depth), two hidden layers; the first hidden layer has 16 neurons, the second one has three neurons. And a one-neuron output layer (fracture density). The results based on 8094 data points from 13 wells show the excellent prediction ability of the conjugate gradient descent (CGD) training algorithm (R-squared = 0.812).The cross plot of measured and predicted values of fracture density shows a very high coefficient of determination of 0.848. Our studies have demonstrated a good agreement between our neural network model prediction and core fracture measurements. The results are promising and can be easily extended in other similar neighboring naturally fractured reservoirs. |
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