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Global Geology 2020, 23(2) 116-122 DOI:
10.3969/j.issn.1673-9736.2020.02.05 ISSN: 1673-9736 CN: 22-1371/P |
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| 论文 |
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Method for predicting cuttings transport using artificial neural networks in foam drilling |
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PAK Kumdol1,2, PENG Jianming2, RI Jaemyong1, CHOE Kumhyok1, HO Yinchol3 |
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1. School of Resource Exploration Engineering, Kim Chaek University of Technology, Pyongyang 999093, D. P. R. Korea;
2. College of Construction Engineering, Jilin University, Changchun 130026, China;
3. School of Information Science and Technology, Kim Chaek University of Technology, Pyongyang 999093, D. P. R. Korea |
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摘要：
Foam is used widely in underbalanced drilling for oil and gas exploration to improve well perfor-mance. Accurate prediction of the cutting transport and pressure loss in the foam drilling is an important way to prevent stuck pipe, lost circulation and to increase the rate of penetration(ROP).In foam drilling, the cuttings transport quality may be defined in terms of cuttings consistency and downhole pressure loss, which are controlled by many factors. Therefore, it is very difficult to establish the mathematical equation that reflects nonlinear relationship among various factors. The field and experimental measurements of these parameters are time consuming and costly. In this study, the authors suggest a cuttings transport mathematical modeling using BPN (back propagation network), RBFN (radial basis function network) and GRNN (general regression neural network) based on various experiment data of cuttings transport of previous researchers and compared the result with experiment data. Results of this study show that the GRNN has a correlation coefficient of 0.999 62 and an average error of 0.15 in training datasets, and a correlation coefficient of 0.998 81 and an average error of 0.612 in testing datasets, which has higher accuracy and faster training velocity than the BP network or RBFN network. GRNN can be used in many mathematical problems for accurate estimation of cuttings consistency and downhole pressure loss instead of field and experimental measurements for hydraulic design in foam drilling operation. |
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关键词：
cuttings transport
underbalanced drilling
foam
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Method for predicting cuttings transport using artificial neural networks in foam drilling |
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PAK Kumdol1,2, PENG Jianming2, RI Jaemyong1, CHOE Kumhyok1, HO Yinchol3 |
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1. School of Resource Exploration Engineering, Kim Chaek University of Technology, Pyongyang 999093, D. P. R. Korea;
2. College of Construction Engineering, Jilin University, Changchun 130026, China;
3. School of Information Science and Technology, Kim Chaek University of Technology, Pyongyang 999093, D. P. R. Korea |
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Abstract:
Foam is used widely in underbalanced drilling for oil and gas exploration to improve well perfor-mance. Accurate prediction of the cutting transport and pressure loss in the foam drilling is an important way to prevent stuck pipe, lost circulation and to increase the rate of penetration(ROP).In foam drilling, the cuttings transport quality may be defined in terms of cuttings consistency and downhole pressure loss, which are controlled by many factors. Therefore, it is very difficult to establish the mathematical equation that reflects nonlinear relationship among various factors. The field and experimental measurements of these parameters are time consuming and costly. In this study, the authors suggest a cuttings transport mathematical modeling using BPN (back propagation network), RBFN (radial basis function network) and GRNN (general regression neural network) based on various experiment data of cuttings transport of previous researchers and compared the result with experiment data. Results of this study show that the GRNN has a correlation coefficient of 0.999 62 and an average error of 0.15 in training datasets, and a correlation coefficient of 0.998 81 and an average error of 0.612 in testing datasets, which has higher accuracy and faster training velocity than the BP network or RBFN network. GRNN can be used in many mathematical problems for accurate estimation of cuttings consistency and downhole pressure loss instead of field and experimental measurements for hydraulic design in foam drilling operation. |
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Keywords:
cuttings transport
underbalanced drilling
foam
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收稿日期 2019-11-14 修回日期 2019-12-20 网络版发布日期 |
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DOI: 10.3969/j.issn.1673-9736.2020.02.05 |
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基金项目:
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通讯作者: PENG Jianming |
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作者简介: |
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作者Email: pengjm@jlu.edu.cn |
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| 参考文献： |
Akhshik S, Rajabi M. 2018. CFD-DEM modeling of cuttings transport in underbalanced drilling considering aerated mud effects and downhole conditions.Journal of Petroleum Science and Engineering,160:229-246.
Arild S. 2014. Annular frictional pressure losses during drilling:predicting the effect of drillstring rotation.Journal of Energy Resources Technology,136(3):1-5.
Cayeux E, Mesagan T, Tanripada S,et al. 2014. Real-time evaluation of hole cleaning conditions using a transient cuttings transport model.SPE Drilling & Completion,29(1):5-21.
Chen Z, Ahmed R M, Miska S Z,et al. 2007. Experimental study on cuttings transport with foam under simulated horizontal downhole conditions.SPE Drilling & Completion,22(4):304-312.
Duan M, Stefan M, Mengjiao Y,et al. 2010. Experimental study and modeling of cuttings transport using foam with drill pipe rotation.SPE Drilling & Completion,25(3):352-362.
Duan M. 2007. Study of cuttings transport using foam with drill pipe rotation under simulated downhole conditions:doctor's degree thesis. USA:University of Tulsa, 14-32.
Heydari O, Sahraei E, Skalle P. 2017. Investigating the impact of drill pipe's rotation and eccentricity on cuttings transport phenomenon in various horizontal annuluses using computational fluid dynamics (CFD).Journal of Petroleum Science and Engineering,156:801-813.
Naderi M, Khamehchi M. 2018. Cutting transport efficiency prediction using probabilistic CFD and DOE techniques. Journal of Petroleum Science and Engineering,163:58-66.
Pang B X, Wang S Y, Lu C L,et al. 2019. Investigation of cuttings transport in directional and horizontal drilling wellbores injected with pulsed drilling fluid using CFD approach.Tunnelling and Underground Space Technology,90:183-193.
Rooki R, Ardejani F D, Moradzadeh A. 2014. Hole cleaning prediction in foam drilling using artificial neural network and multiple linear regression.Geomaterials,4:47-53.
Rooki R, Rakhshkhorshid M. 2017. Cuttings transport modeling in underbalanced oil drilling operation using radial basis neural network.Egyptian Journal of Petroleum,26:541-546.
Rooki R. 2015. Estimation of pressure loss of Herschel-Bulkley drilling fluids during horizontal annulus using artificial neural network.Journal of Dispersion Science and Techno-logy,36:161-169.
Suradi S R, Mamat N S, Jaafar M Z,et al. 2015. Study of cuttings transport using stable foam based mud in inclined wellbore.Journal of Applied Sciences,15:808-814.
Yan T, Wang K L, Sun X F,et al. 2014. State-of-the-art cuttings transport with aerated liquid and foam in complex structure wells. Renewable and Sustainable Energy Reviews,37:560-568. |
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