Abstract
Modeling the rate of penetration (ROP) plays a fundamental role in drilling optimization since the achievement of an optimum ROP can drastically reduce the overall cost of drilling activities. Evolved Extreme learning machine (ELM) with the evolutionary algorithms and multi-layer perceptron with Levenberg-Marquardt training algorithm (MLP-LMA) were proposed in this study to predict ROP. This paper focused mainly on two aspects. The first one was the investigation of the whale optimization algorithm (WOA) to optimize the weights and biases between input and hidden layers of ELM to enhance its prediction accuracy. The other was to adopt a prediction methodology that seeks to update the predictive model at each formation in order to reduce the dimension of input data and mitigate the effect of non real-time data such as the formation properties on the bit speed prediction. The prediction models were trained and tested using 3561 data points gathered from an Algerian field. The statistical and graphical evaluation criteria show that the ELM-WOA exhibited higher accuracy and generalization performance compared with the ELM-PSO and MLP-LMA. Furthermore, ELM-WOA was compared with two well-known ROP correlations in the literature, and the comparison results reveal that the proposed ELM-WOA model is superior to the pre-existing correlations. The findings of this study can help for the achievement of an optimum ROP and the reduction of the non-productive time. In addition, the outputs of this study can be used as an objective function during the real-time optimization of the drilling operation.
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Abbreviations
- AI:
-
artificial intelligence
- ANN:
-
artificial neural network
- BBO:
-
biogeography-based optimizer
- EIA:
-
Energy Information Agency
- ELM:
-
extreme learning machine
- GA:
-
genetic algorithm
- ICA:
-
imperialist competitive algorithm
- K:
-
drillability constant
- LMA:
-
Levenberg-Marquardt algorithm
- MLP:
-
Multi-layer perceptron
- MSE:
-
mean square error
- PSO:
-
particle swarm optimization
- Q:
-
flow rate
- R2 :
-
coefficient of determination
- RMSE:
-
root means square error
- ROP:
-
rate of penetration
- RPM:
-
revolution per minute
- RTOM:
-
real time drilling operations monitoring
- SPP:
-
stand pipe pressure
- SVM:
-
support vector machine
- T:
-
drilling torque
- UCS:
-
unconfined compressive strength
- WAG:
-
water alternating gas
- WOA:
-
whale optimization algorithm
- WOB:
-
weight on bit
References
Abbas AK, Rushdi S, Alsaba M (2018) Modeling rate of penetration for deviated wells using artificial neural network. In: Abu Dhabi Int Petrol Exhib Conf (ADIPEC), 12-15 November, Abu Dhabi, UAE. https://doi.org/10.2118/192875-MS
Al-AbdulJabbar A, Elkatatny S, Mahmoud M, Abdulraheem A (2018) Predicting rate of penetration using artificial intelligence techniques. In: SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, 23–26 April, Dammam, Saudi Arabia. https://doi.org/10.2118/192343-MS
Ameli F, Hemmati-Sarapardeh A, Schaffie M, Husein MM, Shamshirband S (2018) Modeling interfacial tension in N2/n-alkane systems using corresponding state theory: application to gas injection processes. Fuel 222:779–791
Amirian E, Dejam M, Chen Z (2018) Performance forecasting for polymer flooding in heavy oil reservoirs. Fuel 216:83–100
Appl FC, Rowley DS (1968) Analysis of the cutting action of a single diamond. Soc Pet Eng J 8:269–280
Asadi MB, Dejam M, Zendehboudi S (2020) Semi-analytical solution for productivity evaluation of a multi-fractured horizontal well in a bounded dual-porosity reservoir. J Hydrol 581:124288
Ashrafi SB, Anemangely M, Sabah M, Ameri MJ (2019) Application of hybrid artificial neural networks for predicting rate of penetration (ROP): a case study from Marun oil field. J Pet Sci Eng 175:604–623
Ayoub M, Shien G, Diab D, Ahmed Q (2017) Modeling of drilling rate of penetration using adaptive neuro-fuzzy inference system. Int J Appl Eng Res 12:12880–12891
Bahiuddin I, Mazlan SA, Shapiai MI, Imaduddin F (2017) Study of extreme learning machine activation functions for magnetorheological fluid modelling in medical devices application. In: 2017 International Conference on Robotics, Automation and Sciences (ICORAS), Melaka, 2017, pp. 1-5. https://doi.org/10.1109/ICORAS.2017.8308053
Barzegar R, Moghaddam AA, Adamowski J, Fijani E (2017) Comparison of machine learning models for predicting fluoride contamination in groundwater. Stoch Environ Res Risk Assess 31:2705–2718
Bingham G (1965) A new approach to interpreting rock drillability. Tech Man Repr, Oil Gas J 1965:93 P
Bourgoyne AT Jr, Young FS Jr (1974) A multiple regression approach to optimal drilling and abnormal pressure detection. Soc Pet Eng J 14:371–384
Bourgoyne Jr AT, Millheim KK, Chenevert ME, Young Jr FS (1991) Applied drilling engineering. United States
Chen Y, Kloft M, Yang Y, Li C, Li L (2018) Mixed kernel based extreme learning machine for electric load forecasting. Neurocomputing 312:90–106
Chiou J-S, Tsai S-H, Liu M-T (2012) A PSO-based adaptive fuzzy PID-controllers. Simul Model Pract Theory 26:49–59
Crow DJG, Anderson K, Hawkes AD, Brandon N (2018) Impact of drilling costs on the US gas industry: prospects for automation. Energies 11:2241
Cui M, Sun M, Zhang J et al (2014) Maximizing drilling performance with real-time surveillance system based on parameters optimization algorithm. Adv Pet Explor Dev 8:15–24
da Costa NL, Llobodanin LAG, de Lima MD, Castro IA, Barbosa R (2018) Geographical recognition of Syrah wines by combining feature selection with extreme learning machine. Measurement 120:92–99
Dejam M, Hassanzadeh H, Chen Z (2018) Semi-analytical solution for pressure transient analysis of a hydraulically fractured vertical well in a bounded dual-porosity reservoir. J Hydrol 565:289–301
Diker A, Avci D, Avci E, Gedikpinar M (2019) A new technique for ECG signal classification genetic algorithm wavelet kernel extreme learning machine. Optik (Stuttg) 180:46–55
Duda RO, Hart PE, Stork DG (2012) Pattern classification. John Wiley & Sons
Eckel JR (1968) Microbit studies of the effect of fluid properties and hydraulics on drilling rate, II. In: Fall meeting of the Society of Petroleum Engineers of AIME Houston, Texas, 29 September–2 October. https://doi.org/10.2118/2244-MS
Elkatatny S (2017) Real-time prediction of rheological parameters of KCl water-based drilling fluid using artificial neural networks. Arab J Sci Eng 42:1655–1665. https://doi.org/10.1007/s13369-016-2409-7
Elkatatny S, Abdulraheem A, Mahmoud M, et al (2018a) Prediction of rate of penetration of deep and tight formation using support vector machine. In: SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, 23–26 April, Dammam, Saudi Arabia. https://doi.org/10.2118/192316-MS
Elkatatny S, Mousa T, Mahmoud M (2018b) A new approach to determine the rheology parameters for water-based drilling fluid using artificial neural network. Soc Pet Eng - SPE Kingdom Saudi Arab Annu Tech Symp Exhib 2018, SATS 2018. https://doi.org/10.2118/192190-ms
Elkatatny S, Al-AbdulJabbar A, Abdelgawad K (2020) A new model for predicting rate of penetration using an artificial neural network. Sensors 20:2058
Eren T, Ozbayoglu ME (2011) Real-time drilling rate of penetration performance monitoring. In: Offshore mediterranean conference and exhibition, 23-25 March, Ravenna, Italy
Estes JC, Randall B V (1977) Practical application of optimized drilling operations. In: IADC Drilling Technology Conf., 16-18 March, New Orleans
Galle EM, Woods HB (1963) Best constant weight and rotary speed for rotary rock bits. In: Drilling and production practice. American Petroleum Institute, 1 January, New York.
Gharbi RBC, Mansoori GA (2005) An introduction to artificial intelligence applications in petroleum exploration and production. J Pet Sci Eng 49:93–96
Ghoneim A, Muhammad G, Hossain MS (2020) Cervical cancer classification using convolutional neural networks and extreme learning machines. Futur Gener Comput Syst 102:643–649
Haleem A, Javaid M, Khan IH (2019) Current status and applications of artificial intelligence (AI) in medical field: an overview. Curr Med Res Pract 9:231–237
Han F, Yao H-F, Ling Q-H (2013) An improved evolutionary extreme learning machine based on particle swarm optimization. Neurocomputing 116:87–93
Hareland G, Hoberock LL (1993) Use of drilling parameters to predict in-situ stress bounds. In: SPE/IADC Drilling Conference, 22-25 February. Society of Petroleum Engineers, Amsterdam. https://doi.org/10.2118/25727-MS
Hareland G, Rampersad PR (1994) Drag-bit model including wear. In: SPE Latin America/Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers, Buenos Aires, Argentina. https://doi.org/10.2118/26957-MS
Haykin SS (2009) Neural networks and learning machines/Simon Haykin. Prentice Hall, New York
Hegde C, Gray KE (2017) Use of machine learning and data analytics to increase drilling efficiency for nearby wells. J Nat Gas Sci Eng 40:327–335
Hegde C, Wallace S, Gray K (2015) Using trees, bagging, and random forests to predict rate of penetration during drilling. In: SPE Middle East Intelligent Oil and Gas Conference and Exhibition. Society of Petroleum Engineers, 15-16 September, Abu Dhabi, UAE. https://doi.org/10.2118/176792-MS
Hegde C, Daigle H, Millwater H, Gray K (2017) Analysis of rate of penetration (ROP) prediction in drilling using physics-based and data-driven models. J Pet Sci Eng 159:295–306. https://doi.org/10.1016/j.petrol.2017.09.020
Hegde C, Soares C, Gray K (2018) Rate of penetration (ROP) modeling using hybrid models: deterministic and machine learning. In: Unconventional Resources Technology Conference. Society of Exploration Geophysicists, American Association of Petroleum …, Houston, pp 3220–3238
Heidari AA, Faris H, Aljarah I, Mirjalili S (2019) An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Comput 23:7941–7958
Hemmati-Sarapardeh A, Dabir B, Ahmadi M, Mohammadi AH, Husein MM (2019) Modelling asphaltene precipitation titration data: a committee of machines and a group method of data handling. Can J Chem Eng 97:431–441
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366
Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks (IEEE cat. No. 04CH37541). IEEE, pp 985–990
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501
Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B 42:513–529
Jarek K, Mazurek G (2019) Marketing and artificial intelligence. Cent Eur Bus Rev 8:46–55
Jiang M, Pan Z, Li N (2017) Multi-label text categorization using L21-norm minimization extreme learning machine. Neurocomputing 261:4–10
Kang X, Zhao Y, Li J (2018) Predicting refractive index of ionic liquids based on the extreme learning machine (ELM) intelligence algorithm. J Mol Liq 250:44–49
Kang X, Lv Z, Chen Z, Zhao Y (2020) Prediction of ammonia absorption in ionic liquids based on extreme learning machine modelling and a novel molecular descriptor SEP. Environ Res. https://doi.org/10.1016/j.envres.2020.109951
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol 4, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Kyllingstad Å, Thoresen KE (2018) Improving surface WOB accuracy. In: IADC/SPE Drilling Conference and Exhibition. Society of Petroleum Engineers, 6-8 March, Fort Worth, Texas, USA. https://doi.org/10.2118/189601-MS
Lashkarbolooki M, Hezave AZ, Ayatollahi S (2012) Artificial neural network as an applicable tool to predict the binary heat capacity of mixtures containing ionic liquids. Fluid Phase Equilib 324:102–107
Li L-L, Sun J, Tseng M-L, Li Z-G (2019) Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation. Expert Syst Appl 127:58–67
Mahdaviara M, Menad NA, Ghazanfari MH, Hemmati-Sarapardeh A (2020) Modeling relative permeability of gas condensate reservoirs: advanced computational frameworks. J Pet Sci Eng 189:106929
Maurer WC (1962) The“ perfect-cleaning” theory of rotary drilling. J Pet Technol 14:1–270
Nait Amar M, Noureddine Z (2019) An efficient methodology for multi-objective optimization of water alternating CO2 EOR process. J Taiwan Inst Chem Eng 99:154–165. https://doi.org/10.1016/j.jtice.2019.03.016
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Nait Amar M, Zeraibi N, Redouane K (2018a) Optimization of WAG process using dynamic proxy, genetic algorithm and ant colony optimization. Arab J Sci Eng 43:6399–6412. https://doi.org/10.1007/s13369-018-3173-7
Nait Amar M, Zeraibi N, Redouane K (2018b) Bottom hole pressure estimation using hybridization neural networks and grey wolves optimization. Petroleum 4:419–429. https://doi.org/10.1016/j.petlm.2018.03.013
Peterson JL (1976) Diamond drilling model verified in field and laboratory tests. J Pet Technol 28:213–222
Reddy GT, Reddy MPK, Lakshmanna K, Kaluri R, Rajput DS, Srivastava G, Baker T (2020) Analysis of dimensionality reduction techniques on big data. IEEE Access 8:54776–54788
Rostami A, Hemmati-Sarapardeh A, Shamshirband S (2018) Rigorous prognostication of natural gas viscosity: smart modeling and comparative study. Fuel 222:766–778
Rousseeuw PJ, Leroy AM (2005) Robust regression and outlier detection. John Wiley & Sons, New York
Sengupta S, Basak S, Peters RA (2019a) And recent developments with hybridization perspectivesParticle swarm optimization: a survey of historical. Mach Learn Knowl Extr 1:157–191
Sengupta S, Basak S, Peters RA (2019b) Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives. Mach Learn Knowl Extr 1:157–191
Shi Y, Eberhart R (1998) Modified particle swarm optimizer. In: Proceedings of the IEEE Conference on Evolutionary Computation proceedings, 1998. IEEE world congress on computational intelligence, pp 69–73. https://doi.org/10.1109/ICEC.1998.699146
Soares C, Gray K (2019) Real-time predictive capabilities of analytical and machine learning rate of penetration (ROP) models. J Pet Sci Eng 172:934–959
Wang D, Wei S, Luo H, Yue C, Grunder O (2017) A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine. Sci Total Environ 580:719–733
Wardlaw HWR (1972) Optimization of rotary drilling parameters. Dissertation, University of Texas at Austin
Warren TM (1981) Drilling model for soft-formation bits. J Pet Technol 33:963–970. https://doi.org/10.2118/8438-PA
Warren TM (1987) Penetration rate performance of roller cone bits. SPE Drill Eng 2:9–18
Watkins WA, Schevill WE (1979) Aerial observation of feeding behavior in four baleen whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaeangliae, and Balaenoptera physalus. J Mammal 60:155–163
Yan Y, Borhani TN, Clough PT (2020) Machine learning applications in chemical engineering. Mach Learn Chem 17:340
Zhu P, Kang X, Zhao Y, Latif U, Zhang H (2019) Predicting the toxicity of ionic liquids toward acetylcholinesterase enzymes using novel QSAR models. Int J Mol Sci 20:2186
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Appendix 1. Statistical formulas
Appendix 1. Statistical formulas
The mathematical formula for the statistical indexes considered in this study are shown below:
In the aforementioned expressions, N represents the number of data points, ROPreal is the measured rate of penetration value, ROPpredicted is the calculated ROP value by the developed models and \( \overline{ROP} \) is the average of the measured rate of penetration values.
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Youcefi, M.R., Hadjadj, A., Bentriou, A. et al. Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm. Earth Sci Inform 13, 1351–1368 (2020). https://doi.org/10.1007/s12145-020-00524-y
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DOI: https://doi.org/10.1007/s12145-020-00524-y