Optimum Drill Bit Selection by Using Bit Images and Mathematical Investigation

Authors

1 Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia

2 Department of Chemical Engineering, Oklahoma State University, Stillwater, USA

Abstract

This study is designed to consider the two important yet often neglected factors, which are factory recommendation and bit features, in optimum bit selection. Image processing techniques have been used to consider the bit features. A mathematical equation, which is derived from a neural network model, is used for drill bit selection to obtain the bit’s maximum penetration rate that corresponds to the optimum parameters for drilling. At the end, the bit with the maximum penetration rate is chosen. The results of this study showed that bit pattern can be inserted in the calculation through a proper bit image processing technique. This is to ensure that each unique bit can be discriminated from other bits. The values of mean square error  and coefficient of determination (R2) were respectively found as  0.0037 and 0.9473, for the rate of penetration model. The image processing techniques were used to extract the bit features. The artificial neural network black box was converted to white box in order to extract a mathematical equation and visibility of the model.

Keywords


1.     Fear, M., Meany, N. and Evans, J., "An expert system for drill bit selection", in SPE/IADC Drilling Conference, Society of Petroleum Engineers. (1994).
2.     Tools, B.H., "World oil’s 2014 drill bit classifier",  (2014).
3.     Lyon, R.C., "Planning a bit selection for a deep overthrust well", in SPE Rocky Mountain Regional Meeting, Society of Petroleum Engineers. (1982).
4.     Fear, M., "How to improve rate of penetration in field operations", in SPE/IADC Drilling Conference, Society of Petroleum Engineers. (1996).
5.     Burgess, T., "Measuring the wear of milled tooth bits using mwd torque and weight-on-bit", in SPE/IADC Drilling Conference, Society of Petroleum Engineers. (1985).
6.     Xu, H., Tochikawa, T. and Hatakeyama, T., "A practical method for modeling bit performance using mud logging data", in SPE/IADC drilling conference. (1997), 127-131.
7.     Bilgesu, H., Al-Rashidi, A., Aminian, K. and Ameri, S., "An unconventional approach for drill-bit selection", in SPE Middle East Oil Show, Society of Petroleum Engineers., (2001).
8.     Bataee, M., Edalatkhah, S. and Ashena, R., "Comparison between bit optimization using artificial neural network and other methods base on log analysis applied in shadegan oil field", in International Oil and Gas Conference and Exhibition in China, Society of Petroleum Engineers., (2010).
9.     Bilgesu, H., Al-Rashidi, A., Aminian, K. and Ameri, S., "A new approach for drill-bit selection", Journal of Petroleum Technology,  Vol. 52, No. 12, (2000), 27-28.
10.   Yιlmaz, S., Demircioglu, C. and Akin, S., "Application of artificial neural networks to optimum bit selection", Computers & Geosciences,  Vol. 28, No. 2, (2002), 261-269.
11.   Hareland, G., Wu, A., Rashidi, B. and James, J., "A new drilling rate model for tricone bits and its application to predict rock compressive strength", in 44th US Rock Mechanics Symposium and 5th US-Canada Rock Mechanics Symposium, American Rock Mechanics Association., (2010).
12.   Rahman, A., Salam, A., Islam, M. and Sarker, P., "An image based approach to compute object distance", International Journal of Computational Intelligence Systems,  Vol. 1, No. 4, (2008), 304-312.
13.   Castejón, M., Alegre, E., Barreiro, J. and Hernández, L., "On-line tool wear monitoring using geometric descriptors from digital images", International Journal of Machine Tools and Manufacture,  Vol. 47, No. 12, (2007), 1847-1853.
14.   Haralick, R.M. and Shanmugam, K., "Textural features for image classification", IEEE Transactions on Systems, Man, and Cybernetics,  Vol., No. 6, (1973), 610-621.
15.   Shapiro, L. and Haralick, R., "Computer and robot vision", Reading: Addison-Wesley,  Vol. 8, (1992).
16.   Dutta, S., Datta, A., Chakladar, N.D., Pal, S., Mukhopadhyay, S. and Sen, R., "Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique", Precision Engineering,  Vol. 36, No. 3, (2012), 458-466.
17.   Clausi, D.A. and Zhao, Y., "Rapid extraction of image texture by co-occurrence using a hybrid data structure", Computers & Geosciences,  Vol. 28, No. 6, (2002), 763-774.
18.   Soh, L.-K. and Tsatsoulis, C., "Texture analysis of sar sea ice imagery using gray level co-occurrence matrices", IEEE Transactions on Geoscience and Remote Sensing,  Vol. 37, No. 2, (1999), 780-795.
19.   Gebejes, A. and Huertas, R., "Texture characterization based on grey-level co-occurrence matrix", in Proceedings in Conference of Informatics and Management Sciences. (2013).
20.   Xian, G.-m., "An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy svm", Expert Systems with Applications,  Vol. 37, No. 10, (2010), 6737-6741.
21.   Pradeep, J., Srinivasan, E. and Himavathi, S., "Neural network based recognition system integrating feature extraction and classification for english handwritten", International Journal of Engineering-Transactions B: Applications,  Vol. 25, No. 2, (2012), 99-106.
22.   Khanmohammadi, S., "Neural network sensitivity to inputs and weights and its application to functional identification of robotics manipulators", International Journal of Engineering,  Vol. 7, No. 1, 7-12.
23.   Sharifzadeh, M. and HosseinAlizadeh, R., "Artificial neural network approach for modeling of mercury adsorption from aqueous solution by sargassum bevanom algae (research note)", International Journal of Engineering-Transactions B: Applications,  Vol. 28, No. 8, (2015), 1124-1133.
24.   Hopfield, J.J., "Artificial neural networks", Circuits and Devices Magazine, IEEE,  Vol. 4, No. 5, (1988), 3-10.
25.   Reed, R.D. and Marks, R.J., "Neural smithing: Supervised learning in feedforward artificial neural networks, Mit Press,  (1998).
26.   Jamshidi, E. and Mostafavi, H., "Soft computation application to optimize drilling bit selection utilizing virtual inteligence and genetic algorithms", in IPTC 2013: International Petroleum Technology Conference., (2013).
27.   Arifovic, J. and Gencay, R., "Using genetic algorithms to select architecture of a feedforward artificial neural network", Physica A: Statistical Mechanics and its Applications,  Vol. 289, No. 3, (2001), 574-594.