Application of artificial neural networks in fracture characterization and modeling technology

Fracture characterization and modeling technology can characterize the fractures of naturally fractured reservoirs. In this work, a novel application of Artificial Neural Networks (ANNs) will be introduced which can be used to improve this technology. The new technique by using the feed-forward ANN with backpropagation learning rule can predict the fractures dip inclination degree of the third well using the data from the other two wells nearby. The result obtained showed that the ANNs model can simulate the relationship between fractures dips in these three wells which the multiple R of training and test sets for the ANN model are 0.95099 and 0.912197, respectively.

[1]  Kamel Baddari,et al.  Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an artificial neural network in Hassi Messaoud oil field, Algeria , 2014 .

[2]  R. Nasimi,et al.  Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling , 2011 .

[3]  Amir Kavousi,et al.  3D fracture modeling in Parsi oil field using artificial intelligence tools , 2010 .

[4]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[5]  Tayfun Babadagli,et al.  Estimation of equivalent fracture network permeability using fractal and statistical network properties , 2012 .

[6]  Abbas Seifi,et al.  Assisted history matching using artificial neural network based global optimization method – Applications to Brugge field and a fractured Iranian reservoir , 2014 .

[7]  Z. Movahed.  Enhanced Reservoir Description in Carbonate and Clastic Reservoirs , 2007 .

[8]  Radzuan Junin,et al.  Formation evaluation in dezful embayment of Iran using oil-based-mud imaging techniques , 2014 .

[9]  M. Alizadeh,et al.  Fault Interpretation Using Image Logs , 2014 .

[10]  Radzuan Junin,et al.  Evaluate the borehole condition to reduce drilling risk and avoid potential well bore damages by using image logs , 2014 .

[11]  Linsong Cheng,et al.  A new fracture prediction method by combining genetic algorithm with neural network in low-permeability reservoirs , 2014 .

[12]  Fred Kofi Boadu,et al.  Predicting oil saturation from velocities using petrophysical models and artificial neural networks , 2001 .

[13]  Ibrahim Sami Nashawi,et al.  Estimating the fracture gradient coefficient using neural networks for a field in the Middle East , 2005 .

[14]  M. Adibifard,et al.  Artificial Neural Network (ANN) to estimate reservoir parameters in Naturally Fractured Reservoirs using well test data , 2014 .

[15]  T. Aïfa,et al.  Magnetic susceptibility and its relation with fractures and petrophysical parameters in the tight sand oil reservoir of Hamra quartzites, southwest of the Hassi Messaoud oil field, Algeria , 2014 .

[16]  H. Memarian,et al.  Comparison of Asmari, Pabdeh and Gurpi formation's fractures, derived from image log , 2009 .