Automate Short Cyclic Well Job Candidacy Using Artificial Neural Networks–Enabled Lean Six Sigma Approach: A Case Study in Oil and Gas Company

Dedi Wilantara, Nur Budi Mulyono, Doni Winarso


Artificial Neural Networks (ANNs) are a part of Artificial Intelligence (AI) that is commonly used for pattern recognition, regression and classification. This technology allows us to learn historical data and generate patterns from the precedent data. In oil and gas companies, large amounts of data are produced every day. Many accurate decisions in this type of company are made from the data. Cilon Indonesia (CI) Co. Ltd. is one of the oil and gas companies currently operating the largest oil field in Indonesia. This type of company's operation and financial profit depends on oil price, which is affected by global oil supply and demand. If oil prices fall suddenly, all oil and gas companies need to run their businesses more efficiently and effectively. There are many ways to make this kind of company run their business effectively and efficiently by implementing several strategies such as capital cost efficiency, operational cost efficiency and even laying off some employees. One of the major costs in operation in oil and gas companies is the cost for well workover. This well workover does not always produce oil gain. In fact, even it is resulting in oil gain, but not all well workover programs are economical whenever the oil price is low. This condition makes Petroleum Engineer (PE) need to select the best well workover for certain wells. Well candidates for workover are usually selected manually using data from many resources, reports and information. Well candidates are reviewed one by one, and with several criteria, the well is proposed to a certain type of well workover. This research explains how this company improves their selection of well candidates for the most economic workover called Short Cyclic Steam Stimulation (SCSS). The process improvement is done using the hybrid method: lean six sigma method and big data analytics method, which utilize ANNs to predict the oil after workover executed. The result demonstrates how this hybrid method can improve the process with a sustainable solution. Its successful improvement in PE time selects SCSS well candidates from 2 hours to 10 minutes to generate 20 wells per day. Its also improve the success rate of SCSS workover from 61% to 73%.


Artificial intelligence; artificial neural networks; big data analytics; cyclic steam injection; DMAIC; hybrid method; information technology; lean, machine learning; oil and gas company; short cyclic steam stimulation; six sigma.

Full Text:



A. P. Windarto, L. S. Dewi, and D. Hartama, “Implementation of Artificial Intelligence in Predicting the Value of Indonesian Oil and Gas Exports with BP Algorithm,†International Journal of Recent Trends in Engineering & Research (IJRTER), vol. 3, no. 10, pp. 1–12, 2017.

Ribeiro G M, Mauri G R and Lorena L A N (2011). A simple and robust simulated annealing algorithm for scheduling workover rigs on onshore oil fileds. Comput Ind Eng 60: 519-526, 2011.

Kromodihardjo, S., Kromodihardjo, E.S. Modeling of Well Service and Workover to Optimize Scheduling of Oil Well Maintenance. AMM 836, 311–316, 2016.

PT. CI, Workover Cost and Oil Gain Performance – Internal data. Riau, Indonesia, 2020.

Alvarez, J. and Han, S. Current Overview of Cyclic Steam Injection Process. J. Pet. Sci. Res. 2 (3): 116–127, 2013.

PT. CI, Short Cyclic Jobs – internal data. Riau, Indonesia, 2020.

PT. CI, Short Cyclic Steam Stimulation Well Selection – internal document. Riau, Indonesia, 2020.

ΣΕΡΣΈΜΗΣ, Αθανάσιος Κωνσταντίνου. Machine Learning Strategies to Handle Medical Data. A comparison between popular ML models using Python. Aristotle University of Thessaloniki, 2020.

Lantz B. Machine learning with R. 2nd ed. Birmingham: Packt Publishing; 2015.

Jacobs F.R., Chase R.B., Operation and Supply Chain Management. New York, USA: McGraw-Hill, 2018.

Singh, M. and Rathi, R., A structured review of Lean Six Sigma in various industrial sectors, International Journal of Lean Six Sigma, Vol. 10 No. 2, pp. 622-664, 2019.

Improta, G., Balato, G., Ricciardi, C., Russo, M.A., Santalucia, I., Triassi, M. and Cesarelli, M., Lean Six Sigma in healthcare: Fast track surgery for patients undergoing prosthetic hip replacement surgery, The TQM Journal, Vol. 31 No. 4, pp. 526-540, 2019.

Hess, J.D. and Benjamin, B.A., Applying Lean Six Sigma within the university: opportunities for process improvement and cultural change, International Journal of Lean Six Sigma, Vol. 6 No. 3, pp. 249-262, 2015.

Zhang, A., Luo, W., Shi, Y., Chia, S.T. and Sim, Z.H.X., Lean and Six Sigma in logistics: a pilot survey study in Singapore, International Journal of Operations & Production Management, Vol. 36 No. 11, pp. 1625-1643, 2016.

PT. CI, Lean Six Sigma Training – internal document. Riau, Indonesia, 2020.

Michael L.G., Rowlands D., Price M., Maxey J., The Lean Six Sigma Pocket Toolbook. New York, USA: McGraw-Hill, 2005.

Garrison R.H., Nooren E.W., Brewer P.C.. Managerial Accounting. New York, USA: McGraw-Hill, 2010.

Braglia, M., Marrazzini, L., Padellini, L. and Rinaldi, R., Managerial and Industry 4.0 solutions for fashion supply chains, Journal of Fashion Marketing and Management, Vol. ahead-of-print No. ahead-of-print, 2020.

Shearer C., The CRISP-DM model: the new blueprint for data mining, J Data Warehousing (2000); 5:13—22.

Galushkin A.I., Neural Networks Theory.Berlin, Germany: Spinger-Verlag, 2007.

Raza, J., Liyanage, J.P., Al Atat, H. and Lee, J., A comparative study of maintenance data classification based on neural networks, logistic regression and support vector machines, Journal of Quality in Maintenance Engineering, Vol. 16 No. 3, pp. 303-318, 2010.

Saputro F.P., The Improvement of Procurement Process in Oil Refinery Maintenance by Using Lean Six Sigma. Jakarta, Indonesia. 2017.

A.R. Iskandar, Adoption of Six Sigma – DMAIC Methodology to Optimize Service Level at PT. Ultra Prima Corrugator. Bandung, Indonesia. 2019.

Alkunsol, W.H., Sharabati, A.-A.A., AlSalhi, N.A. and El-Tamimi, H.S. Lean Six Sigma effect on Jordanian pharmaceutical industry’s performance, International Journal of Lean Six Sigma, Vol. 10 No. 1, pp. 23-43, 2019.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development