[1] Zhang, J.J., Applied Petroleum Geomechanics. 2019: Gulf Professional Publishing.
[2] Elkatatny, S., et al., An integrated approach for estimating static Young’s modulus using artificial intelligence tools. Neural Computing and Applications, 2019. 31(8): p. 4123-4135.
[3] Madhubabu, N., et al., Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement, 2016. 88: p. 202-213.
[4] ASTM, D7012-14 (2014) Standard test methods for compressive strength and elastic moduli of intact rock core specimens under varying states of stress and temperatures. ASTM International, West Conshohocken, 2014.
[5] Ulusay, R., The ISRM suggested methods for rock characterization, testing and monitoring: 2007-2014. 2014: Springer.
[6] Najibi, A.R., M. Ghafoori, G.R. Lashkaripour, and M.R. Asef, Empirical relations between strength and static and dynamic elastic properties of Asmari and Sarvak limestones, two main oil reservoirs in Iran. Journal of Petroleum Science and Engineering, 2015. 126: p. 78-82.
[7] Briševac, Z., P. Hrženjak, and R. Buljan, Models for estimating uniaxial compressive strength and elastic modulus. Građevinar, 2016. 68(01.): p. 19-28.
[8] Deere, D.U. and R. Miller, Engineering classification and index properties for intact rock. 1966, Illinois Univ At Urbana Dept Of Civil Engineering.
[9] Chang, C., M.D. Zoback, and A. Khaksar, Empirical relations between rock strength and physical properties in sedimentary rocks. Journal of Petroleum Science and Engineering, 2006. 51(3-4): p. 223-237.
[10] Akbar, M., et al., A snapshot of carbonate reservoir evaluation. Oilfield Review, 2000. 12(4): p. 20-21.
[11] Sayers, C.M., The elastic properties of carbonates. The Leading Edge, 2008. 27(8): p. 1020-1024.
[12] Bakhorji, A.M., Laboratory measurements of static and dynamic elastic properties in carbonate. 2010.
[13] Aboutaleb, S., M. Behnia, R. Bagherpour, and B. Bluekian, Using non-destructive tests for estimating uniaxial compressive strength and static Young’s modulus of carbonate rocks via some modeling techniques. Bulletin of Engineering Geology and the Environment, 2018. 77(4): p. 1717-1728.
[14] Çelik, S.B., Prediction of uniaxial compressive strength of carbonate rocks from nondestructive tests using multivariate regression and LS-SVM methods. Arabian Journal of Geosciences, 2019. 12(6): p. 193.
[15] Ghafoori, M., A. Rastegarnia, and G.R. Lashkaripour, Estimation of static parameters based on dynamical and physical properties in limestone rocks. Journal of African Earth Sciences, 2018. 137: p. 22-31.
[16] Tariq, Z., S. Elkatatny, M. Mahmoud, and A. Abdulraheem. A holistic approach to develop new rigorous empirical correlation for static Young's modulus. in Abu Dhabi International Petroleum Exhibition & Conference. 2016. Society of Petroleum Engineers.
[17] Brotons, V., et al., Improved correlation between the static and dynamic elastic modulus of different types of rocks. Materials and structures, 2016. 49(8): p. 3021-3037.
[18] Fjær, E., Relations between static and dynamic moduli of sedimentary rocks. Geophysical Prospecting, 2019. 67(1): p. 128-139.
[19] He, J., et al. Static and Dynamic Elastic Moduli of Bakken Formation. in International Petroleum Technology Conference. 2019. International Petroleum Technology Conference.
[20] Martínez-Martínez, J., D. Benavente, and M. García-del-Cura, Comparison of the static and dynamic elastic modulus in carbonate rocks. Bulletin of engineering geology and the environment, 2012. 71(2): p. 263-268.
[21] Missagia, R.M., L. Oliveira, I.D.A.L. Neto, and M.R. de Ceia. Evaluation of Static and Dynamic Elastic Properties in Carbonate Rocks. in 81st EAGE Conference and Exhibition 2019. 2019.
[22] Rashidi, M., M. Hajipour, and A. Asadi. Correlation Between Static and Dynamic Elastic Modulus of Limestone Formations Using Artificial Neural Networks. in 52nd US Rock Mechanics/Geomechanics Symposium. 2018. American Rock Mechanics Association.
[23] Pourrostam, A., Estimation of Porosity and Permeability Parameters Using Well-Log and Core Data from a Gas Field in South of Iran, in Faculty of science, Department of physics. 2016, Razi University.
[24] Mehrgini, B., et al., Geomechanical characterization of a south Iran carbonate reservoir rock at ambient and reservoir temperatures. Journal of Natural Gas Science and Engineering, 2016. 34: p. 269-279.
[25] Nur, A.M. and Z. Wang, Seismic and Acoustic Velocities in Reservoir Rocks: Recent Developments Vol. 3 : Recent Developments. 2000, United States: American Association of Petroleum Geologists.
[26] Kılıç, A. and A. Teymen, Determination of mechanical properties of rocks using simple methods. Bulletin of Engineering Geology and the Environment, 2008. 67(2): p. 237.
[27] Aboutaleb, S., R. Bagherpour, M. Behnia, and M. Aghababaei, Combination of the physical and ultrasonic tests in estimating the uniaxial compressive strength and Young’s modulus of intact limestone rocks. Geotechnical and Geological Engineering, 2017. 35(6): p. 3015-3023.
[28] Eissa, E. and A. Kazi, Relation between static and dynamic Young's moduli of rocks. International Journal of Rock Mechanics and Mining & Geomechanics Abstracts, 1988. 25(6).
[29] Dinçer, İ., A. Acar, and S. Ural, Estimation of strength and deformation properties of Quaternary caliche deposits. Bulletin of Engineering Geology and the Environment, 2008. 67(3): p. 353-366.
[30] Torabi-Kaveh, M., F. Naseri, S. Saneie, and B. Sarshari, Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones. Arabian journal of Geosciences, 2015. 8(5): p. 2889-2897.
[31] Anemangely, M., A. Ramezanzadeh, and M.M. Behboud, Geomechanical parameter estimation from mechanical specific energy using artificial intelligence. Journal of Petroleum Science and Engineering, 2019. 175: p. 407-429.
[32] Asef, M. and M. Farrokhrouz, Governing parameters for approximation of carbonates UCS. Electron J Geotech Eng, 2010. 15: p. 1581-1592.
[33] Minaeian, B. and K. Ahangari, Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method. Arabian Journal of Geosciences, 2013. 6(6): p. 1925-1931.
[34] Nourani Asl, R., Estimation of uniaxial compressive strength and young’s modulus of intact rocks using the multiple statistic methods, in Department of Mining Engineering. 200, Tarbiat Modares University: Tehran, Iran.
[35] Golubev, A. and G. Rabinovich, Resultaty primeneia appartury akusticeskogo karotasa dlja predeleina proconstych svoistv gornych porod na mestorosdeniaach tverdych isjopaemych. Prikl. Geofiz. Moskva, 1976. 73: p. 109-116.
[36] Moradian, Z. and M. Behnia, Predicting the uniaxial compressive strength and static Young’s modulus of intact sedimentary rocks using the ultrasonic test. International Journal of Geomechanics, 2009. 9(1): p. 14-19.
[37] Eberhart, R. and J. Kennedy. Particle swarm optimization. in Proceedings of the IEEE international conference on neural networks. 1995. Citeseer.
[38] Anemangely, M., A. Ramezanzadeh, and B. Tokhmechi, Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour Oilfield. Journal of Natural Gas Science and Engineering, 2017. 38: p. 373-387.
[39] Anemangely, M., A. Ramezanzadeh, and B. Tokhmechi, Determination of constant coefficients of Bourgoyne and Young drilling rate model using a novel evolutionary algorithm. Journal of Mining and Environment, 2017. 8(4): p. 693-702.
[40] Anemangely, M., A. Ramezanzadeh, H. Amiri, and S.-A. Hoseinpour, Machine learning technique for the prediction of shear wave velocity using petrophysical logs. Journal of Petroleum Science and Engineering, 2019. 174: p. 306-327.
[41] Anemangely, M., A. Ramezanzadeh, and M. Mohammadi Behboud, Geomechanical parameter estimation from mechanical specific energy using artificial intelligence. Journal of Petroleum Science and Engineering, 2019. 175: p. 407-429.
[42] Anemangely, M., et al., Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network. Journal of Geophysics and Engineering, 201: p. 1146-1159.
[43] Anemangely, M., et al., Development of a new rock drillability index for oil and gas reservoir rocks using punch penetration test. Journal of Petroleum Science and Engineering, 2018. 166: p. 131-145.
[44] Sabah, M., et al., A machine learning approach to predict drilling rate using petrophysical and mud logging data. Earth Science Informatics, 2019: p. 1-21.
[45] Pedersen, M.E.H. and A.J. Chipperfield, Simplifying Particle Swarm Optimization. Applied Soft Computing, 2010. 10(2): p. 618-628.
[46] Coello, C.A.C., G.B. Lamont, and D.A. Van Veldhuizen, Evolutionary algorithms for solving multi-objective problems. Vol. 5. 2007: Springer.
[47] Maity, R., Statistical methods in hydrology and hydroclimatology. 2018: Springer.
[48] Brotons, V., R. Tomás, S. Ivorra, and A. Grediaga, Relationship between static and dynamic elastic modulus of calcarenite heated at different temperatures: the San Julián’s stone. Bulletin of Engineering Geology and the Environment, 2014. 73(3): p. 791-799.
[49] Christaras, B., F. Auger, and E. Mosse, Determination of the moduli of elasticity of rocks. Comparison of the ultrasonic velocity and mechanical resonance frequency methods with direct static methods. Materials and Structures, 1994. 27(4): p. 222-228.
[50] Ameen, M.S., et al., Predicting rock mechanical properties of carbonates from wireline logs (A case study: Arab-D reservoir, Ghawar field, Saudi Arabia). Marine and Petroleum Geology, 2009. 26(4): p. 430-444.
[51] Lacy, L.L. Dynamic rock mechanics testing for optimized fracture designs. in SPE annual technical conference and exhibition. 1997. Society of Petroleum Engineers.
[52] KIANPOUR, M., M. SAYARI, and A. OROMIEHIE, A FUZZY MODEL TO PREDICT THE UNIAXIAL COMPRESSIVE STRENGTH AND THE MODULUS OF ELASTICITY OF SHEMSHAK FORMATION SHALES. Geol Mag, 2012(83): p. 103–110.
[53] Yilmaz, I. and A. Yuksek, An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock mechanics and rock engineering, 2008. 41(5): p. 781.
[54] Yasar, E. and Y. Erdogan, Correlating sound velocity with the density, compressive strength and Young's modulus of carbonate rocks. International Journal of Rock Mechanics and Mining Sciences, 2004. 41(5): p. 871-875.
[55] Horsrud, P., Estimating mechanical properties of shale from empirical correlations. SPE Drilling & Completion, 2001. 16(02): p. 68-73.
[56] Rzhevsky, V. and G. Novik, The Physics of rocks. 320 str. Mir Publischers. Moskva, 1971.
[57] Lashkaripour, G.R., Predicting mechanical properties of mudrock from index parameters. Bulletin of Engineering Geology and the Environment, 2002. 61(1): p. 73-77.
[58] Lashkaripour GR, D.M., Prediction mechanical properties of mud rock from index parameters, in Conference on Probabilistic Methods in Geotechnical Engineering. 1993: Australia,. p. 195–200.
[59] Vernik, L., M. Bruno, and C. Bovberg. Empirical relations between compressive strength and porosity of siliciclastic rocks. in International journal of rock mechanics and mining sciences & geomechanics abstracts. 1993. Elsevier.
[60] Dehghan, S., G. Sattari, S.C. Chelgani, and M. Aliabadi, Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Mining Science and Technology (China), 2010. 20(1): p. 41-46.
[61] Yilmaz, I., Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. Int. J. Rock Mech. Min. Sci., 2009. 46: p. 803-810.
[62] Garagon, M. and T. Çan, Predicting the strength anisotropy in uniaxial compression of some laminated sandstones using multivariate regression analysis. Materials and structures, 2010. 43(4): p. 509-517.
[63] Bradford, I., J. Fuller, P. Thompson, and T. Walsgrove. Benefits of assessing the solids production risk in a North Sea reservoir using elastoplastic modelling. in SPE/ISRM Rock Mechanics in Petroleum Engineering. 1998. Society of Petroleum Engineers.
[64] Soroush, H., Evaluation of moisture effect on mechanical properties and determination of effective quantitative indexes, in Department of Mining and Metals. 1998, Amirkabir University of Technology: Tehran, Iran.
[65] Karakul, H. and R. Ulusay, Empirical correlations for predicting strength properties of rocks from P-wave velocity under different degrees of saturation. Rock mechanics and rock engineering, 2013. 46(5): p. 981-999.
[66] Selçuk, L. and A. Nar, Prediction of uniaxial compressive strength of intact rocks using ultrasonic pulse velocity and rebound-hammer number. Quarterly Journal of Engineering Geology and Hydrogeology, 2016. 49(1): p. 67-75.
[67] Altindag, R., Correlation between P-wave velocity and some mechanical properties for sedimentary rocks. Journal of the Southern African Institute of Mining and Metallurgy, 2012. 112(3): . 229-237.
[68] Beiki, M., A. Majdi, and A.D. Givshad, Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. International Journal of Rock Mechanics and Mining Sciences, 2013(63): p. 159-169.
[69] Kahraman, S., Evaluation of simple methods for assessing the uniaxial compressive strength of rock. International Journal of Rock Mechanics and Mining Sciences, 2001. 38(7): p. 981-994.
[70] Mohamad, E.T., D.J. Armaghani, E. Momeni, and S.V.A.N.K. Abad, Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bulletin of Engineering Geology and the Environment, 2015. 74(3): p. 745-757.
[71] Çobanoğlu, İ. and S.B. Çelik, Estimation of uniaxial compressive strength from point load strength, Schmidt hardness and P-wave velocity. Bulletin of Engineering Geology and the Environment, 2008. 67(4): p. 491-498.
[72] Ghazvinian, A., V. Rasouli, and R. Nourani Asl. Application of multiple statistic methods in estimation of uniaxial compressive strength of intact rocks using indirect tests. in Proceedings of 3rd Rock Mech Congress, Tehran, Iran. 2007.
[73] Azimian, A., Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests. Acta Geotechnica, 2017. 12(2): p. 321-333.
[74] Hakan, E. and D. Kanik, Multicriteria decision-making analysis based methodology for predicting carbonate rocks' uniaxial compressive strength. Earth Sciences Research Journal, 2012. 16(1): p. 65-74.
[75] Khandelwal, M., Correlating P-wave velocity with the physico-mechanical properties of different rocks. Pure and Applied Geophysics, 2013. 170(4): p. 507-514.
[76] Militzer, H., Einige Beiträge der Geophysik zur primärdatenerfassung im Bergbau. 1973.
[77] Kurtuluş, C., F. Sertçelik, and I. Sertçelik, Correlating physico-mechanical properties of intact rocks with P-wave velocity. Acta Geodaetica et Geophysica, 2016. 51(3): p. 571-582.
[78] Freyburg, E., Der Untere und mittlere Buntsandstein SW-Thuringen in seinen gesteinstechnicschen Eigenschaften. Deustche Gesellschaft Geologische Wissenschaften. A; Berlin, 1972. 176: p. 911-919.
[79] Sharma, P. and T. Singh, A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength. Bulletin of Engineering Geology and the Environment, 2008. 67(1): p. 17-22.
[80] Shalabi, F.I., E.J. Cording, and O.H. Al-Hattamleh, Estimation of rock engineering properties using hardness tests. Engineering Geology, 2007. 90 p. 138-147.
[81] Moos, D., P. Peska, T. Finkbeiner, and M. Zoback, Comprehensive wellbore stability analysis utilizing quantitative risk assessment. Journal of Petroleum Science and Engineering, 2003. 38(3-4): p. 97-109.
[82] Ceryan, N., U. Okkan, and A. Kesimal, Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks. Environmental earth sciences, 2013. 68(3): p. 807-819.
