OJG  Vol.5 No.3 , March 2015
Determine Stability Wellbore Utilizing by Artificial Intelligence Systems and Estimation of Elastic Coefficients of Reservoir Rock
Abstract: Rock elastic properties such as Young’s modulus, Poisson?s ratio, plays an important role in various stages upstream of such as borehole stability, hydraulic fracturing in laboratory scale for observing mechanical properties of the reservoir rock usually using conventional cores sample that obtained from underground in reservoir condition. This method is the most common and most reliable way to get the reservoir rock properties, but it has some weaknesses. Currently, neural network techniques have replaced usual laboratory methods because they can do a similar operation faster and more accurately. To obtain the elastic coefficient, we should have compressional wave velocity (VP), shear wave (Vs) and density bulk due to high cost of (Vs) measurement and low real ability of estimation through the (Vp) and porosity. Therefore in this study, neural networks were used as a suitable method for estimating shear wave, and then elastic coefficients of reservoir rock using different relationships were predicted. Neural network used in this study was not like a black box because we used the results of multiple regression that could easily modify prediction of (Vs) through appropriate combination of data. The same information that were intended for multiple regression were used as input in neural networks, and shear wave velocity was obtained using (Vp) and well logging data in carbonate rocks. The results showed that methods applied in this carbonate reservoir was successful, so that shear wave velocity was predicted with about 92% and 95% correlation coefficient in multiple regression and neural network method, respectively.
Cite this paper: Akhundi, H. , Ghafoori, M. and Lashkaripour, G. (2015) Determine Stability Wellbore Utilizing by Artificial Intelligence Systems and Estimation of Elastic Coefficients of Reservoir Rock. Open Journal of Geology, 5, 83-91. doi: 10.4236/ojg.2015.53008.

[1]   Ameen, M.S., Smart, B.G.D., Somerville, J.M.C., Hammilton, S. and Naji, N.A. (2009) Predicting Rock Mechanical Properties of Carbonates from Wireline Logs (A Case Study: Arab-D Reservoir, Ghawar Field, Saudi Arabia). Marine and Petroleum Geology, 26, 430-444.

[2]   Saidi Nia, M. and Shadi Zade, R. (2010) The Effect of Stress Due to Drilling Operations on Well and Skin Factor, Journal of Petroleum Research, 63, 39-48.

[3]   Eskandari, H., Rezaee, M.R. and Mohammadnia, M. (2004) Application of Multiple Regression and Artificial Neural Network Techniques to Predict Shear Wave Velocity from Well Log Data for a Carbonate Reservoir, South-West Iran. CSEG Recorder, 42-48.

[4]   Habimana, J. (2002) Geomechanical Characterization of Cataclastic Rocks. International Journal of Rock Mechanics & Mining Sciences, 39, 677-693.

[5]   Wang, Z. (2000) Velocity Relationships in Granular Rocks. In: Wang, Z. and Nur, A., Eds., Seismic and Acoustic Velocities in Reservoir Rocks, 145-158.

[6]   Hasanipak, A.A. and Sharafodin, M. (2000) Analyze of Exploration Data. Tehran University Press, Tehran.

[7]   Bhatt, A. and Hell, H.B. (2002) Committee Neural Networks for Porosity and Permeability Prediction from Well Logs. Geophysical Prospecting, 50, 645-660.

[8]   Balan, B., Mohaghegh, S. and Ameri, S. (1995) State-of-Art in Permeability Determination from Well Log Data: Part 1-a Comprehensive Study, Model Development, SPE 30978.

[9]   Sohrabi, S. and Kadkhodaie, A. (2011) Estimate Stability Wellbore Based on the Elastic Coefficients Obtained from Logs. 30th Earth Science Conference, Tehran.