Engineering Geology

Engineering Geology

Optimum Bit Selection Using Data Mining Algorithms-A Case Study

Authors
Hadi Fattahi
Younes Afshari
Abstract
Introduction

Drill-bit selection is one of the most important aspects of well planning due to the bearing it can have on the overall cost of the well. Bit selection in conventional and slightly inclined wells is a very delicate and complex process. In high angle and horizontal wells it is even more difficult. Historically, drilling engineers have selected bits on the basis of what has been worked well in the area and what has been determined to have the lowest cost run from offset bit records. Often the best bit records were not available for evaluation, because the best bit may not yet have been run, may have been run by a competitor or the engineer was new to the area. As a result the bit program was generally developed by trial and error and at significant additional costs for a large number of wells. In most cases the optimum program was never reached because there was nothing to predict that a bit selection change could further reduce the cost of the well. In this study, an alternative solution approaches using the concept of the power of data mining algorithms to solve the optimum bit program for a given field is proposed.

Material and methods

It has been considered an offset well to be drilled outside the known boundaries of a known field. For this purpose, the seventh well (X-7) of the same field was used as a verification point. The data was trained using the well log and rock bit data of six wells located in the field and the real well log data of well 7 was input as unknown data. These depths are selected based on reported rock bit program. When compared to the real data, it could be observed that the models (adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules) estimates the formation hardness accurately. This minor discrepancy was also present with the company’s suggested rock bit program, which was based on the previous wells’ rock bit data.

Results and discussion

In this paper, data mining algorithms for optimum rock bit program estimation is proposed. The accuracy and efficiency of the developed data mining algorithms (adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules) that requires sonic and neutron log data input was tested for several real and synthetic cases. In the case of a development? well to be drilled outside the known boundaries of a field the model estimated rock bits with properties that consider the formation hardness correctly but slightly underestimated further rock bit details. The models also produced reasonable rock bit programs for an advance well to be drilled within the known boundaries of a field and a wildcat well drilled in a nearby field with similar rock properties to the training field. Thus it was concluded that the developed adaptive neuro fuzzy inference system is suitable as a front-end system for rock bit selection that could help engineers in decision-making analysis.

Conclusion

Optimum bit selection is one of the important issues in drilling engineering. Usually, optimum bit selection is determined by the lowest cost per foot and is a function of bit cost and performance as well as penetration rate. Conventional optimum rock bit selection program involves development of computer programs created from mathematical models along with information from previously drilled wells in the same area. Based on the data gathered on a daily basis for each well drilled, the optimum drilling program may be modified and revised as unexpected problems arose. The approaches in this study uses the power of data mining algorithms to solve the optimum bit selection problem. In order to achieve this goal, adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules were developed by training the models using real rock bit data for several wells in a carbonated field. The training of the basic models involved use of both gamma ray and sonic log data. After that the models were tested using various drilling scenarios in different lithologic units. It was observed that the adaptive neuro fuzzy inference system model has provided satisfactory results.


Keywords
 Bit selection
Adaptive neuro fuzzy inference system
K-nearest neighbors
Decision tree
Bayesian classification theory
Association rules
1. Bilgesu H., Al-Rashidi A., Aminian K., Ameri S., "An Unconventional Approach for Drill-Bit Selection", In: SPE Middle East Oil Show, Society of Petroleum Engineers, (2001). ## 2. Perrin V., Mensa-Wilmot G., Alexander W., "Drilling index-a new approach to bit performance evaluation", In: SPE/IADC drilling conference, Society of Petroleum Engineers, (1997). ## 3. Suba S., Christopher T., "A study on milestones of association rule mining algorithms in large databases", International Journal of Computer Applications, 47 (3) (2012) (0975-888). ## 4. Uboldi V., Civolani L., Zausa F., "Rock strength measurements on cuttings as input data for optimizing drill bit selection. In: SPE Annual Technical Conference and Exhibition", Society of Petroleum Engineers (1999). ## 5. Clegg J. M., Barton S. P., "Improved Optimisation of Bit Selection Using Mathematically Modelled Bit Performance Indices. In: IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition", Society of Petroleum Engineers (2006). ## 6. Thomas J., "Case History: PC Analysis of Bit Records Enhances Drilling Operations in Southern Alabama. In: SPE/IADC Drilling Conference", Society of Petroleum Engineers (1989). ## 7. Adam T., Millheim K., Chenevert M., Young F., "Applied drilling engineering", SPE Textbook Series, Dallas, TX 2 (1991). ## 8. Bourgoyne Jr. A., Millheim K., Chenevert M., Young Jr. F., "Applied Drilling Engineering", SPE Textbook Series Vol. 2, Second Printing, Society of Petroleum Engineers (1991) 152-155. ## 9. Teale R., "The concept of specific energy in rock drilling", In Int J Rock Mech Min Sci, Vol 1. Elsevier (1965) 57-73. ## 10. Mason K. L., "Three-cone bit selection with sonic logs", SPE Drilling Engineering 2 (02) (1987) 135-142## 11. Dumans C. F. F., "Maidla EE PDC bit selection method through the analysis of past bit performances. In: SPE Latin America Petroleum Engineering Conference", Society of Petroleum Engineers (1990). ## 12. Falcao J., Maidla E., Dumans C., Dezen J., "PDC bit selection through cost prediction estimates using crossplots and sonic log data. In: SPE/IADC Drilling Conference", Society of Petroleum Engineers (1993). ## 13. McAdams S. C., Gustafson W., Cooper N., "Higher Rotary Speeds Applied To Insert Bits Reduce Drilling Costs. In: SPE Annual Technical Conference and Exhibition",Society of Petroleum Engineers (1979). ## 14. Xu H., Tochikawa T., Hatakeyama T., "A Method for Bit Selection by Modelling ROP and Bit-Life. In: Annual Technical Meeting", Petroleum Society of Canada (1997). ## 15. Rabia H., Farrelly M., Barr M., "A new approach to drill bit selection. In: European Petroleum Conference", Society of Petroleum Engineers (1986). ## 16. Rabia H., "Oilwell drilling engineering: principles and practice", Graham & Trotman, Limited (1985). ## 17. Abbas R., "Using an optimum bit selection method for determined wells in Northern Iraq", Leeds University (2007). ## 18. Borah M. J., "Study of specific energy method of bit selection on the basis of drill bit-depth data in upper Assam oil fields", Int J Mech Eng Tech 6 (10) (2015)103-114. ## 19. Farrelly M., Rabia H., "Bit performance and selection: a novel approach. In: SPE/IADC Drilling Conference", Society of Petroleum Engineers (1987). ## 20. Javani D., Hashempour A., "The Significance of using Geological and Geomechanical Data in Selection of Optimal Bits", In: 13th ISRM International Congress of Rock Mechanics, International Society for Rock Mechanics (2015). ## 21. Fattahi H., "Prediction of slope stability state for circular failure: A hybrid support vector machine with harmony search algorithm", Int J Optim Civil Eng 5 (1) (2015) 103-115. ## 22. Fattahi H., "Prediction of earthquake induced displacements of slopes using hybrid support vector regression with particle swarm optimization", Int J Optim Civil Eng 5 (3) (2015) 267-282. ## 23. Bilgesu H., Al-Rashidi A., Aminian K., Ameri S., "A new approach for drill bit selection", In SPE Eastern Regional Meeting, Society of Petroleum Engineers (2000). ## 24. Yιlmaz S., Demircioglu C., Akin S., "Application of artificial neural networks to optimum bit selection", Comput Geosci 28 (2) (2002) 261-269. ## 25. Bataee M., Kamyab M., Ashena R., "Investigation of various ROP models and optimization of drilling parameters for PDC and Roller-Cone bits in Shadegan Oil field. In: International Oil and Gas Conference and Exhibition in China", Society of Petroleum Engineers (2010). ## 26. Bataee M., Mohseni S., "Application of Artificial Intelligent Systems in ROP Optimization: a Case Study", In SPE middle east unconventional gas conference and exhibition, Society of Petroleum Engineers (2011). ## 27. Edalatkhah S., Rasoul R, Hashemi A., "Bit selection optimization using artificial intelligence systems", Petrol Sci Technol 28 (18) (2010) 1946-1956. ## 28. Momeni M., Ridha S., Hosseini S., Liu X., Atashnezhad A., Ghaheri S., "Optimum Drill Bit Selection by Using Bit Images and Mathematical Investigation", Int J Eng Trans 30 (11) (2017) 1807-1813. ## 29. Momeni M., Hosseini S. J., Ridha S., Laruccia M. B., Liu X., "An optimum drill bit selection technique using artificial neural networks and genetic algorithms to increase the rate of penetration", J Eng Sci Tech 13 (2) (2018) 361-372. ## 30. Rokach L., Maimon O., "Decision trees. In: Data mining and knowledge discovery handbook", Springer (2005) 165-192. ## 31. Breiman L., Friedman J., Olshen R., Stone C., "Classification and Regression Trees. Belmont", CA: Wadsworh. Inc (1984). ## 32. Chicago I. "Clementine 12 User Manual", (2007). ## 33. Hanson R., Stutz J, Cheeseman P., "Bayesian classification theory", (1991). ## 34. Friedman N., Geiger D., Goldszmidt M., "Bayesian network classifiers", Machine learning 29 (2-3) (1997) 131-163. ## 35. Hand D. J., "Construction and assessment of classification rules", Vol. 15. Wiley Chichester (1997). ## 36. Cohen W. W., "Fast effective rule induction. In: Machine Learning Proceedings", Elsevier, (1995) 115-123. ## 37. Wu X., Kumar V., Quinlan J. R., Ghosh J., Yang Q., Motoda H., McLachlan G. J., Ng A., Liu B., Philip S. Y., "Top 10 algorithms in data mining. Knowledge and information systems 14 (1) (2008) 1-37. ## 38. Cleary J. G., Trigg L. E., "K*: An instance-based learner using an entropic distance measure", In Machine Learning Proceedings, Elsevier (1995). 108-114. ## 39. Lin C.-T., Lee C. S. G., "Neural-network-based fuzzy logic control and decision system", IEEE T Comput 40 (12) (1991) 1320-1336. ## 40. Karimpouli S., Fattahi H., "Estimation of P-and S-wave impedances using Bayesian inversion and adaptive neuro-fuzzy inference system from a carbonate reservoir in Iran", Neural Comput Appl 29 (11) (2018) 1059-1072. ## 41. Jang J.-S., "ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man", and cybernetics 23 (3) (1993) 665-685. ## 42. Fattahi H., "Indirect estimation of deformation modulus of an in situ rock mass: an ANFIS model based on grid partitioning", fuzzy c-means clustering and subtractive clustering. J Geosci, 1-10. ## 43. Fattahi H., Agah A., Soleimanpourmoghadam N., "Multi-Output Adaptive Neuro-Fuzzy Inference System for Prediction of Dissolved Metal Levels in Acid Rock Drainage: a Case Study", J AI Data Mining 6 (1) (2018) 121-132. ## 44. Fattahi H., "Prediction of slope stability using adaptive neuro-fuzzy inference system based on clustering methods", J Min Environ 8 (2) (2017) 163-177. ## 45. Drake J. T., "Communications phase synchronization using the adaptive network fuzzy inference system (anfis)", New Mexico State University (2000). ## 46. Kohavi R., "Glossary of terms. Special issue on applications of machine learning and the knowledge discovery process", 30 (271) (1998) 127-132. ##