[1] Hapnes, M. (2014). Drilling in salt formation and rate of penetration modeling, Petroleum Geoscience and Engineering, Supervisor: John-Morten Godhavn, Norwegian University of Science and Technology (NTNU).
[2] Bourgoyne, A.T., Millheim, K.K., Chenevert, M.E., & Young, F.S. (2003). Applied drilling engineering, Ninth Edition, SPE, Richardson, 2, p. 232.
[3] Al-Betairi E.A., Moussa M., & Al-Otaibi, S. (2005). Multiple regression approach to optimize drilling operations in the Arabian Gulf area. SPE Drilling Engineering, 3 (1), 83-88.
[4] Yilmaz, S., Demircioglu, C., & Akin, S. (2002). Application of artificial neural networks to optimum bit selection. Computers and Geosciences, 28, 261–269.
[5] Edalatkhah, S., Rasoul, R., & Hashemi, A. (2012). Bit selection optimization using artificial intelligence systems. Petroleum Science and Technology, 28 (18), 1946-1956.
[6] نوروزی بزمین آبادی، س.؛ رمضان زاده، ا.؛ جلالی، س.م. ا.؛ تخمچی، ب. (1393). مدلسازی نرخ نفوذ حفاری با استفاده از شبکه عصبی مصنوعی، مطالعه موردی در یکی از چاه های میدان نفتی آزادگان، کنفرانس ملی علوم معدنی، ساری.
[7] مشعشعی، س. ح.؛ ابراهیم آبادی. آ.؛ امامزاده. ا.؛ (1397)، ارائه ابزار گرافیکی به منظور پیشبینی نرخ نفوذ حفاری با استفاده از شبکههای هوشمند، مجله علمی - پژوهشی پژوهش نفت، 28 (101)، 112 – 125.
[8] Moraveji, M.K., & Naderi., M. (2016). Drilling rate of penetration prediction and optimization using response surface methodology and bat algorithm. Journal of Natural Gas Science and Engineering, 31 (7), 829-841.
[9] Amer, M.M., Dahab, A.S., & Hashem El-Sayed, A.A. (2017). An ROP predictive model in Nile Delta area using artificial neural networks, SPE Kingdom of Saudi Arabia Annul Technical Symposium and Exhibition, SPE, 12 (5), 124-135.
[10] Khaksar Manshad, A., Rostami, H., Toreifi, H., & Mohammadi, A.H. (2017). Optimization of drilling penetration rate in oil fields using artificial intelligence technique, Nova Science Publishers, Inc., Chapter 13, 18 (11), 12-21.
[11] Priyangga, H.Y., & Ruliandi, D. (2018). Application of pattern recognition and classification using artificial neural network in geothermal operation. Forty-Third Workshop on Geothermal Reservoir Engineering, Stanford University, Stanford, California.
[12] Astrini, Y., Advarel, P., & Dorman P. (2019). Rate of penetration prediction using artificial neural network to predict ROP for nearby well in a geothermal Field, 44th workshop on geothermal reservoir engineering, Standford university, Standford, California SGP-TR-214, pp. 1-5.
[13] Tokhmechi, B. (2019). New approaches in 3D geomechanical earth modeling. Journal of Petroleum Geomechanics, 3 (1), 45-64.
[14] Burgoyne, D.J., & Young, T.C., (1984). Applying a genetic neuro-model reference adaptive controller in drilling optimization. World Oil Magazine, 228.
[15] Anemangely, A., Ramezanzadeh, A., & Tokhmechi B. (2017). Determination of constant coefficients of Bourgoyne and Young drilling rate model using a novel evolutionary algorithm. Journal of Mining and Environment, 8 (4), 693-702.
[16] اصانلو، م.؛ (1376). روشهای حفاری. تهران: نشر صدا.
[17] Warren, T.M. (1987). Penetration rate Performance of roller cone bits. SPE Drilling Engineering, 9-18.
[18] Conningham, R.A. (1978). An empirical approach for relating drilling parameters. Journal of Petroleum Technology, 30 (7), 987-991.
[19] کیا، س.م. (1395). شبکههای عصبی در متلب. تهران: خدمات نشر کیان رایانه سبز، خلیج فارس (زاینس).
[20] امیری بختیار، ح.؛ سجادی، ف.؛ مرادی، ن. (١٣٨۶). تطابق نموداری در بیو استراتیگرافی سازند آسماری در میدان نفتی اهواز. مجله علوم دانشگاه تهران، 33 (1)، 101-106.
[21] Beck, F.E., Powell, J.W., & Zamora, M. (1995). The effect of rheology on rate of penetration. In SPE/IADC Drilling Conference, Amsterdam.
[22] Kummen H.T., & Wold, A.A. (2015). The effect of cuttings on annular pressure loss, an analysis of field data in the North Sea. Norwegian University of Science and Technology.
[23] Eberhart, R.C., & Kennedy, J.M. )1995). A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39-43.
[24] Mohaghegh, S. (2000). Virtual-intelligence applications in petroleum engineering: part 1- artificial neural network. Journal of Petroleum science and Engineering, 52 (9), 64-73.
[25] حبیبپور گتابی، ک.؛ صفری، ر. (1390). راهنمای جامع کاربرد SPSS در تحقیقات پیمایشی، تهران: انتشارات متفکران.