Abstract
Neural networks have been widely applied in system dynamics modeling. One particular type of networks, hybrid neural networks, combines a neural network model with a physical model, which can increase rate of convergence in training. However, most existing hybrid neural network methods require an explicit physical model constructed, which sometimes might not be feasible in practice or could weaken the capability of capturing complex and hidden physical phenomena. In this paper, we propose a novel approach to construct a hybrid neural network. The new method incorporates the physical information to the structure of network construction, but does not need an explicit physical model constructed. The method is then applied to modeling of bit-rock interaction in the down-hole drilling system as a case study, to demonstrate its effectiveness in modeling complex process and efficiency of convergence in training.
Issue Section:
Research Papers
Keywords:
neural network bit–rock interaction
References
1.
Abiodun
,
O. I.
,
Jantan
,
A.
,
Omolara
,
A. E.
,
Dada
,
K. V.
,
Mohamed
,
N. A.
, and
Arshad
,
H.
, 2018
, “
State-of-the-Art in Artificial Neural Network Applications: A Survey
,” Heliyon
,
4
(11
), p. e00938
.10.1016/j.heliyon.2018.e009382.
Garcia
,
H. E.
, and
Vilim
,
R. B.
, 1998
, “
Combining Physical Modeling, Neural Processing, and Likelihood Testing for Online Process Monitoring
,” SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics
, Vol.
1
,
IEEE
, San Diego, CA, Oct. 11–14, pp. 806–810.10.1109/ICSMC.1998.7255133.
Forssell
,
U.
, and
Lindskog
,
P.
, 1997
, “
Combining Semi-Physical and Neural Network Modeling: An Example Ofits Usefulness
,” IFAC Proc. Vol.
,
30
(11
), pp. 767
–770
.10.1016/S1474-6670(17)42938-74.
Piron
,
E.
,
Latrille
,
E.
, and
Rene
,
F.
, 1997
, “
Application of Artificial Neural Networks for Crossflow Microfiltration Modelling: ‘Black-Box’ and Semi-Physical Approaches
,” Comput. Chem. Eng.
,
21
(9
), pp. 1021
–1030
.10.1016/S0098-1354(96)00332-85.
Qi
,
H.
,
Zhou
,
X.-G.
,
Liu
,
L.-H.
, and
Yuan
,
W.-K.
, 1999
, “
A Hybrid Neural Network-First Principles Model for Fixed-Bed Reactor
,” Chem. Eng. Sci.
,
54
(13–14
), pp. 2521
–2526
.10.1016/S0009-2509(98)00523-56.
Wang
,
S.
,
Wang
,
F.
,
Devabhaktuni
,
V. K.
, and
Zhang
,
Q.-J.
, 1999
, “
A Hybrid Neural and Circuit-Based Model Structure for Microwave Modeling
,” 1999 29th European Microwave Conference
,
IEEE, Munich, Germany
, Oct. 5–7, pp. 174
–177
.10.1109/EUMA.1999.3383017.
Dolara
,
A.
,
Grimaccia
,
F.
,
Leva
,
S.
,
Mussetta
,
M.
, and
Ogliari
,
E.
, 2015
, “
A Physical Hybrid Artificial Neural Network for Short Term Forecasting of pv Plant Power Output
,” Energies
,
8
(2
), pp. 1138
–1153
.10.3390/en80211388.
Cao
,
M.
,
Wang
,
K.
,
Fujii
,
Y.
, and
Tobler
,
W.
, 2004
, “
A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model
,” ASME J. Dyn. Sys., Meas., Control
,
126
(1
), pp. 144
–153
.10.1115/1.16499809.
Roubos
,
J.
,
Krabben
,
P.
,
Setnes
,
M.
,
Babuska
,
R.
,
Heijnen
,
J.
, and
Verbruggen
,
H.
, 1999
, “
Hybrid Model Development for Fed-Batch Bioprocesses; Combining Physical Equations With the Metabolic Network and Black-Box Kinetics
,” 6th Workshop on Fuzzy Systems
,
Brunel University, Uxbridge, London, UK, Sept. 8–9.https://www.researchgate.net/publication/2460680_Hybrid_Model_Development_for_Fed-Batch_Bioprocesses_Combining_Physical_Equations_With_the_Metabolic_Network_and_Black-Box_Kinetics10.
Olausson
,
P.
,
Hägsta Hl
,
D.
,
Arriagada
,
J.
,
Dahlquist
,
E.
, and
Assadi
,
M.
, 2003
, “
Hybrid Model of an Evaporative Gas Turbine Power Plant Utilizing Physical Models and Artificial Neural Networks
,” ASME
Paper No. GT2003-38116. 10.1115/GT2003-3811611.
Krama
,
A.
,
Gharib
,
M.
,
Refaat
,
S. S.
, and
Palazzolo
,
A.
, 2021
, “
Design and Hardware in-the-Loop Validation of an Effective Super-Twisting Controller for Stick-Slip Suppression in Drill-String Systems
,” ASME J. Dyn. Syst., Meas., Control
,
143
(11
), p. 111008. 10.1115/1.405185312.
Balanov
,
A.
,
Janson
,
N.
,
McClintock
,
P. V.
,
Tucker
,
R.
, and
Wang
,
C.
, 2003
, “
Bifurcation Analysis of a Neutral Delay Differential Equation Modelling the Torsional Motion of a Driven Drill-String
,” Chaos, Solitons Fractals
,
15
(2
), pp. 381
–394
.10.1016/S0960-0779(02)00105-413.
Khulief
,
Y.
,
Al-Sulaiman
,
F.
, and
Bashmal
,
S.
, 2007
, “
Vibration Analysis of Drillstrings With Self-Excited Stick–Slip Oscillations
,” J. Sound Vib.
,
299
(3
), pp. 540
–558
.10.1016/j.jsv.2006.06.06514.
Richard
,
T.
,
Germay
,
C.
, and
Detournay
,
E.
, 2007
, “
A Simplified Model to Explore the Root Cause of Stick–Slip Vibrations in Drilling Systems With Drag Bits
,” J. Sound Vib.
,
305
(3
), pp. 432
–456
.10.1016/j.jsv.2007.04.01515.
Besselink
,
B.
,
Vromen
,
T.
,
Kremers
,
N.
, and
Van De Wouw
,
N.
, 2016
, “
Analysis and Control of Stick-Slip Oscillations in Drilling Systems
,” IEEE Trans. Control Systems Technol.
,
24
(5
), pp. 1582
–1593
.10.1109/TCST.2015.250289816.
Germay
,
C.
,
Denoël
,
V.
, and
Detournay
,
E.
, 2009
, “
Multiple Mode Analysis of the Self-Excited Vibrations of Rotary Drilling Systems
,” J. Sound Vib.
,
325
(1–2
), pp. 362
–381
.10.1016/j.jsv.2009.03.01717.
Ritto
,
T. G.
,
Soize
,
C.
, and
Sampaio
,
R.
, 2009
, “
Non-Linear Dynamics of a Drill-String With Uncertain Model of the Bit–Rock Interaction
,” Int. J. Non-Linear Mech.
,
44
(8
), pp. 865
–876
.10.1016/j.ijnonlinmec.2009.06.00318.
Tian
,
D.
, and
Song
,
X.
, 2019
, “
Control of a Downhole Drilling System Using Integral Barrier Lyapunov Functionals
,” American Control Conference (ACC
), Philadelphia, PA, July 10–12, pp. 1349
–1354
.10.23919/ACC.2019.881537019.
Cho
,
K.
,
Van Merriënboer
,
B.
,
Gulcehre
,
C.
,
Bahdanau
,
D.
,
Bougares
,
F.
,
Schwenk
,
H.
, and
Bengio
,
Y.
, 2014
, “
Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation
,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar. Association for Computational Linguistics, Oct. 25–29, pp. 1724
–1734
.20.
Chung
,
J.
,
Gulcehre
,
C.
,
Cho
,
K.
, and
Bengio
,
Y.
, 2014
, “
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
,” CoRR, vol. abs/1412.3555, 2014.21.
Wang
,
Y.
,
Sun
,
Z.
, and
Stelson
,
K. A.
, 2011
, “
Modeling, Control, and Experimental Validation of a Transient Hydrostatic Dynamometer
,” IEEE Trans. Control Syst. Technol.
,
19
(6
), pp. 1578
–1586
.10.1109/TCST.2010.2082542Copyright © 2023 by ASME
You do not currently have access to this content.
