Document Type : Research Paper


1 Ph.D. Candidate, Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Professor, Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Professor, Industrial Management, Islamic Azad University, Science and Research Branch, Tehran, Iran.


Iran is one of the largest oil and gas producers in the world. Intelligent manufacturing approaches can lead to better performance and lower costs of the well drilling process. One of the most critical issues during the drilling operation is the wellbore stability. Instability of wellbore can occur at different stages of a well life and inflict heavy financial and time damage on companies. A controllable factor can prevent these damages by selecting a proper drilling mud weight. This research presents a drilling mud weight estimator for Iranian wells using deep-learning techniques. Our Iranian data set only contains 900 samples, but efficient deep-learning models usually need large amounts of data to obtain acceptable performance. Therefore, the samples of two data sets related to the United Kingdom and Norway fields are also used to extend our data set. Our final data set has contained more than half-million samples that have been compiled from 132 wells of three fields. Our presented mud weight estimator is an artificial neural network with 5 hidden layers and 256 nodes in each layer that can estimate the mud weight for new wells and depths with the mean absolute error (MAE) of smaller than ±0.039 pound per gallon (ppg). In this research, the presented model is challenged in real-world conditions, and the results show that our model can be reliable and efficient in the real world.


  • Collecting an extensive data set with various features of wells obtained by merging the data of the United Kingdom, Norway, and Iran fields;
  • Presenting an accurate mud weight estimator using a deep-learning model;
  • Challenging the presented model in real-world conditions and providing solutions to improving its performance.


Main Subjects

Aadnoy, B. S., and Belayneh, M., Elastoplastic Fracturing Model for Wellbore Stability Using Non-Penetrating Fluids, Journal of Petroleum Science and Engineering, Vol. 45, No. 3-4, p. 179–192, 2004.
Aadnoy, B. S., and Chenevert, M. E., Stability of Highly Inclined Boreholes (Includes Associated Papers 18596 and 18736), SPE Drilling Engineering, Vol. 2, No. 04, p. 364–374, 1987.
Abbas, A. K., Almohammed, H. H., Alqatran, G., Mohammed, H. Q., and Mohammed, A., Determination of Safe Operating Mud Weight Window from Well Logging Data Using Machine Learning Algorithms, Offshore Technology Conference Asia, Offshore Technology Conference, 2020.
Ahmed S, A., Elkatatny, S., Ali, A. Z., Abdulraheem, A., and Mahmoud, M., Artificial Neural Network ANN Approach to Predict Fracture Pressure, SPE Middle East Oil and Gas Show and Conference, Onepetro, 2019.
Aird, P., Deepwater Drilling, Gulf Professional Publishing, Elsevier, Vol. 441, p. 475, 2019.
Alsiyabi, K., AL-Aamri, M., and Siddiqui, N. A., Effective Geomechanics Approach for Wellbore Stability Analysis, SPE Middle East Oil and Gas Show and Conference, Onepetro, 2019.
Bakhtiarizadeh, S., Asadi, I. S., and Sadat, A. Y., Solving Bore Hole Instability Problem of Underbalanced Drilling in Several Wells of an Iranian Oil Field, Middle East Drilling Technology Conference, and Exhibition, Onepetro, 2009.
Bandura, L., Halpert, A. D., and Zhang, Z., Machine Learning in The Interpreter’s Toolbox: Unsupervised, Supervised, and Deep-learning Applications, 2018 SEG International Exposition and Annual Meeting, Onepetro, p. 4633–4637, 2018.
Bataee, M., Irawan, S., and Kamyab, M., Artificial Neural Network Model for Prediction of Drilling Rate of Penetration and Optimization of Parameters, Journal of The Japan Petroleum Institute, Vol. 57, No. 2, p. 65–70, 2014.
Behnoud Far, P., and Hosseini, P., Estimation of Lost Circulation Amount Occurs During Under Balanced Drilling Using Drilling Data and Neural Network, Egyptian Journal of Petroleum, Vol. 26, No. 3, p. 627–634, 2017.
Cheatham, J. B., Wellbore Stability, Journal of Petroleum Technology, Vol. 36, No. 06, p. 889–896, 1984.
Chollet, F., Deep Learning with Python, Simon and Schuster, 2017.
Chollet, F., Keras, Https://Github.Com/Fchollet/Keras/, 2015.
De Felice, F., Petrillo, A., and Zomparelli, F., Prospective Design of Smart Manufacturing: an Italian Pilot Case Study, Manufacturing Letters, Vol. 15, p. 81–85, 2018.
Deng, S., Fan, H., Tian, D., Liu, Y., Zhou, Y., Wen, Z., and Ren, W., Calculation and Application of Safe Mud Density Window in Deepwater Shallow Layers, Offshore Technology Conference, One petro, 2016.
Domingues, I., Amorim, J. P., Abreu, P. H., Duarte, H., and Santos, J., Evaluation of Oversampling Data Balancing Techniques in The Context of Ordinal Classification. 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, p. 1–8, 2018.
Equinor-Company, Volve Dataset, Https://Www.Equinor.Com/, 2021.
García, S., Luengo, J., And Herrera, F., Data Preprocessing in Data Mining, Cham, Switzerland, Springer International Publishing., Vol. 72, 2015.
Han, Y., and Meng, F. F., Selecting Safe Mud Weight Window for Wellbore in Shale While Drilling Using Numerical Simulation, IADC/SPE Drilling Conference and Exhibition, Onepetro, 2014.
Hareland, G., and Dehkordi, K. K., Wellbore Stability Analysis in UBD Wells of Iranian Fields, SPE Middle East Oil and Gas Show and Conference, One petro, 2007.
Kamgue Lenwoue, A. R., Deng, J., Liu, W., Li, H., Palencia Yrausquin, E., and Matamba, G., Wellbore Stability Analysis Using an Elastoplastic Mogi–Coulomb Model, 53rd US Rock Mechanics/Geomechanics Symposium, One petro, 2019.
Khatibi, S., Farzay, O., and Aghajanpour, A., A Method to Find Optimum Mud Weight in Zones with No Safe Mud Weight Windows, 52nd US Rock Mechanics/Geomechanics Symposium, Onepetro, 2018.
Kim, P., MATLAB Deep Learning. With Machine Learning, Neural Networks and Artificial Intelligence, Vol. 130, No. 21, 2017.
Kim, S., Kang, I., And Kwak, N., Semantic Sentence Matching with Densely-connected Recurrent and Co-Attentive Information, Proceedings of The AAAI Conference on Artificial Intelligence, Vol. 33, No. 01, P. 6586–6593, 2019.
Li, W., Classifying Geological Structure Elements from Seismic Images Using Deep Learning, 2018 SEG International Exposition and Annual Meeting, One petro, p. 4643–4648, 2018.
Li, Y., Sun, R., and Horne, R., Deep Learning for Well Data History Analysis, SPE Annual Technical Conference and Exhibition, Onepetro,‏ 2019.
Liu, Z., Zhou, F., Feng, X., Yang, Z., and Fan, F., Calculation of Drilling Mud Density Window and Open-hole Wellbore Extension Limit for Extended Reach Wells, 52nd US Rock Mechanics/Geomechanics Symposium, Onepetro, 2018.
Mclellan, P. J., Assessing the Risk of Wellbore Instability in Horizontal and Inclined Wells, Journal of Canadian Petroleum Technology, Vol. 35, No. 05, 1996.
Meyers, R. A., Handbook of Petroleum Refining Processes, McGraw-Hill Education, 2016.
NDR, UK National Data Repository, Https://Ndr.Ogauthority.Co.Uk/, 2021.
Norwegian-petroleum, Volve Field, Https://Www.Norskpetroleum.No/En/Facts/Field/Volve/, 2021.
Oil And Gas Authority (OGA), National Data Repository (NDR), Https://Www.Ogauthority.Co.Uk/, 2021.
Park, R. G., and Park, G., Foundations of Structural Geology, Glasgow and London: Blackie, p. 148, 1989.
Pars Drilling Fluids (PDF), Http://Www.Parsdrill.Com/, 2021.
Pattillo, P. D., Elements of Oil and Gas Well Tubular Design, Gulf Professional Publishing, 2018.
Peng, S., and Zhang, J., Engineering Geology for Underground Rocks, Springer Science and Business Media, 2007.
Pereira, L. G., Gandelman, R. A., Clemente, R. G., Teixeira, P. H. S., And Teixeira, G. T., Development of Software to Predict Mud Weight for Pre-salt Drilling Zones Using Machine Learning, OTC Brazil, One petro, 2013.
Phan, D.T., Liu, C., Altammar, M.J., Han, Y., And Abousleiman, Y.N., Application of Artificial Intelligence to Predict Time-dependent Safe Mud Weight Windows for Inclined Wellbores, International Petroleum Technology Conference, One petro, 2020.
Raschka, S., and Mirjalili, V., Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-learn and TensorFlow, Second Edition, 2017.
Sebtosheikh, M. A., Motafakkerfard, R., Riahi, M. A., and Moradi, S., Separating Well Log Data to Train Support Vector Machines for Lithology Prediction in A Heterogeneous Carbonate Reservoir. Iranian Journal of Oil and Gas Science and Technology, Vol. 4, No. 2, p. 1–14, 2015.
Shadizadeh, S. R., Karimi, F., and Zoveidavianpoor, M., Drilling Stuck Pipe Prediction in Iranian Oil Fields: An Artificial Neural Network Approach, Iran J. Chem. Eng., Vol. 7, No. 4, P. 29–41, 2010.
Shen, X., Williams, K., and Standifird, W., Enhanced 1D Prediction of The Mud Weight Window for A Subsalt Well, Deepwater GOM, SPE Deepwater Drilling and Completions Conference, Onepetro, 2014.
Silva, C., Rabe, C., And Fontoura, S., Geomechanical Model and Wellbore Stability Analysis of Brazil’s Pre-Salt Carbonates, A Case Study in Block BMS-8, OTC Brazil, Onepetro, 2017.
Tewari, S., Assessment of Data-driven Ensemble Methods for Conserving Wellbore Stability in Deviated Wells, SPE Annual Technical Conference, and Exhibition, Onepetro, 2019.
Tuptuk, N., and Hailes, S., Security of Smart Manufacturing Systems, Journal of Manufacturing Systems, Vol. 47, p. 93–106, 2018.
Yavari, H., Sabah, M., Khosravanian, R., and Wood, D., Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling Rate, Iranian Journal of Oil and Gas Science and Technology, Vol. 7, No. 3, p. 73–100, 2018.
Zahiri, J., Abdideh, M., And Ghaleh Golab, E., Determination of Safe Mud Weight Window Based on Well Logging Data Using Artificial Intelligence, Geosystem Engineering, Vol. 22, No. 4, p. 193–205, 2019.
Zeynali, M. E., Mechanical and Physico-chemical Aspects of Wellbore Stability During Drilling Operations, Journal of Petroleum Science and Engineering, Vol. 82, p. 120–124, 2012.
Zhou, H., Niu, X., Fan, H., And Wang, G., Effective Calculation Model of Drilling Fluids Density and ESD For HTHP Well While Drilling, IADC/SPE Asia Pacific Drilling Technology Conference, Onepetro, 2016.
Zoback, M. D., Barton, C. A., Brudy, M., Castillo, D. A., Finkbeiner, T., Grollimund, B. R., Moos, D. B., Peska, P., Ward, D. J., and Wiprut, D. J., Determination of Stress Orientation and Magnitude in Deep Wells, International Journal of Rock Mechanics and Mining Sciences, Vol. 40, No. 7–8, P. 1049–1076, 2003.