Estimation of mechanical properties of rock using artificial intelligence

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Laura Viviana Galvis
César Augusto Ochoa
Henry Arguello Fuentes
Jenny Mabel Carvajal Jiménez
Zuly Himelda Calderón Carrillo


artificial intelligence, artificial neural network, genetic algorithm, petrophysical properties, mechanical properties.


This paper discusses how two artificial intelligence techniques were combined, neural networks and genetic algorithms for the development of a computational tool used for the estimation of mechanical properties such as tensile strength, uniaxial compressive strength and triaxial compressive strength in sandstones, from petrophysical properties using data from tests of Rock Mechanics Laboratory of the Colombian Petroleum Institute - Ecopetrol SA as training data, to improve the design of non-destructive testing with some degree of confidence and resulting in cost reduction.

PACS: 91.60.Ba, 91.60.Dc

MSC: 82C32


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[1] A Baykasolu, T Dereli, and S Tani. Prediction of cement strength using soft computing techniques. Cement and concrete research, 34(11):2083–2090, 2004.

[2] G. W. Ellis, C. Yao, R. Zhao, and D. Penumadu. Stress-strain modeling of sands using artificial neural networks. 121(5):429–435, 1995.

[3] J. H. Garrett Jr. J. Ghaboussi and X.Wu. Knowledge based modeling of material behavior with neural networks. 117(1):132–153, 1991.

[4] John R. Koza. Genetic programming: on the programming of computers by means of natural selection. page 819, Jan 1992.

[5] F Meulenkamp and M Alvarez. Application of neural networks for the prediction of the unconfined compressive strength (ucs) from equotip hardness. International Journal of rock mechanics and mining sciences, 36:29–39, Feb 1999.

[6] F Meulenkamp. Improving the prediction of the ucs, by equotip readings using statistical and neural network models. Memoirs of the Centre for Engineering Geology in the Netherlands, 162:127, 1997.

[7] VK Singh, D Singh, and TN Singh. Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. International Journal of Rock Mechanics and Mining Sciences, 38(2):269–284, 2001.

[8] A Baykasoglu, H Gullu, H Canakci, and L Ozbakir. Prediction of compressive and tensile strength of limestone via genetic programming. Expert Systems with Applications, 35(1-2):111–123, 2008.

[9] Hanifi C¸ anakcı, Adil Baykaso˘glu, and Hamza G¨ull¨u. Prediction of compressive and tensile strength of gaziantep basalts via neural networks and gene expression programming. Neural Comput & Applic, 18(8):1031–1041, Nov 2009.

[10] Johann Gasteiger and Jure Zupan. Neural networks in chemistry. Angewandte Chemie International Edition in English, 32(4):503–527, 1993.

[11] Constantin von Altrock. Fuzzy logic and neurofuzzy applications explained. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1995.

[12] T. Dereli and A. Baykaso˘glu. The use of artificial intelligence techniques in design and manufacturing: a review. Polytech, 3:27–60, 2000.

[13] S.K Sinha and F Karray. Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm. Neural Networks, IEEE Transactions on, 13(2):393–401, 2002.

[14] I.S Bajwa andM.A Choudhary. A study for prediction of minerals in rock images using back propagation neural networks. Advances in Space Technologies, 2006 International Conference on, pages 185–189.

[15] Yuan-Kai Wang and Kuo-Chin Fan. Applying genetic algorithms on pattern recognition: an analysis and survey. Pattern Recognition, 1996., Proceedings of the 13th International Conference on, 2:740–744 vol.2, 1996.

[16] U. Maulik. Medical image segmentation using genetic algorithms. Information Technology in Biomedicine, IEEE Transactions on, 13(2):166–173, 2009.

[17] K. Miroslav, H. Rafael, and C.J. Oscar. Evaluacion hidrogeológica de pozos a través de registros geofísicos, Fundamentos. Unam, 2005.

[18] R. Corzo Rueda y C. Rincón Pabón. Proyecto de grado, Medición y evaluación de la magnitud y dirección de los esfuerzos in-situ en campo. Universidad Industrial de Santander, Colombia, 2004.

[19] M. Lara Encabo. Proyecto de grado, Framework para redes neuronales en Java. Universidad Pontificia Comillas ICAI, España, 2006.