This paper proposes an integrated method of analytical calculation, artificial intelligence, and probabilistic analysis to cost-effectively determine geomechanical properties and in-situ stresses from borehole deformation via caliper logs. It's also demonstrated in this paper that the actual borehole size can not be simply taken as the bit size by default, and adjusted borehole size has to be used to find the reasonable borehole deformation. In the proposed method, an artificial neural network (ANN) is applied to map the relationship among in-situ stress, adjusted borehole size, geomechanical properties, and borehole displacements. The genetic algorithm (GA) searches for the set of unknown stresses and geomechanical properties that match the objective borehole deformation function. Probabilistic analysis is conducted after ANN-GA modeling to estimate the most possible ranges of the parameters. The hybrid method has been demonstrated by a field case study to estimate the adjusted borehole size, Young's modulus, and the two horizontal in-situ stresses using borehole deformation information reported from four-arm caliper logs of a vertical borehole in Liard Basin in Canada.
Acknowledgements
The authors are grateful to Dr. J.S. Bell who has provided data of Liard Basin and constructive suggestions for this work.
| [1] |
B.C. Haimson, G. Clark (Ed.), A Simple Method for Estimating in Situ Stresses as Great Depth. Field Testing and Instrumentation of Rock, STP32150S, ASTM International, West Conshohocken, PA, 1974, pp. 156-182.
|
| [2] |
B.C. Haimson, C. Fairhurst, Initiation and extension of hydraulic fractures in rock, Soc. Petrol. Eng. J. 7 (1967) 310-318.
|
| [3] |
E.M. Anderson, The dynamics of faulting, Trans. Edinb. Geol. Soc. 83 (1905) 387-402.
|
| [4] |
E.G. Kirsch, Die Theorie der Elastizität und die Bedürfnisse der Festigkeitslehre, Z. Des. Vereines Dtsch. Ingenieure 42 (1898) 797-807.
|
| [5] |
P. Peska, M.D. Zoback, Compressive and tensile failure of inclined wellbores and determination of in-situ stress and rock strength, J. Geophys. Res. 100 (B7) (1995) 12791-12811.
|
| [6] |
M.D. Zoback, D. Moos, L. Mastin, R.N. Anderson, Wellbore breakouts and in-situ stress, J. Geophys. Res. 90 (1985) 5523-5530.
|
| [7] |
W.B. Ervine, J.S. Bell, Subsurface in situ stress magnitudes from oil-well drilling records: an example from the venture area, offshore eastern Canada, Can. J. Earth Sci. 24 (1987) 1748-1759.
|
| [8] |
F.H. Cornet, B. Valette, In situ stress determination from hydraulic injection test data, J. Geophys. Res. 89 (B13) (1984) pp. 11,527-11,537.
|
| [9] |
C. Chang, M.D. Zoback, A. Khaksar, Empirical relations between rock strength and physical properties in sedimentary rocks, J. Petrol. Sci. Eng. 51 (2006) 223-237.
|
| [10] |
A. Abdulraheem, M. Ahmed, A. Vantala, T. Parvez,Prediction of rock mechanical parameters for hydrocarbon reservoirs using different artificial intelligence techniques, Paper SPE 126094 Presented at the 2009 SPE Saudi Arabia Section Technical Symposium and Exhibition. Alkhobar, Saudi Arabia, 09-11, May, 2009.
|
| [11] |
B.S. Aadnoy, Inversion technique to determine the in-situ stress field from fracturing data, J. Petrol. Sci. Eng. 4 (2) (1990) 127-141.
|
| [12] |
Y. Huang, L. Gao, Z. Yi, K. Tai, P. Kalita, P. Prapainainar, A. Garg, An application of evolutionary system identification algorithm in modelling of energy production system, Measurement 114 (2018) 122-131.
|
| [13] |
M. Ibrahim, S. Jemei, G. Wimmer, D. Hissel, Nonlinear autoregressive neural network in an energy management strategy for battery/ultra-capacitor hybrid electrical vehicles, Elec. Power Syst. Res. 136 (2016) 262-269.
|
| [14] |
Z. Sabir, M.A. Manzar, M.A.Z. Raja, M. Sheraz, A.M. Wazwaz, Neuro-heuristics for nonlinear singular Thomas-Fermi systems, Appl. Soft Comput. 65 (2018) 152-169. April 2018.
|
| [15] |
H.X. Han, S. Yin, Determination of in-situ stress and geomechanical properties from borehole deformation, Energies 11 (1) (2018) 131.
|
| [16] |
S. Zhang, S. Yin, Determination of horizontal in-situ stresses and natural fracture properties from wellbore deformation, Int. J. Oil Gas Coal Technol. 7 (1) (2014) 1-28.
|
| [17] |
S. Zhang, S. Yin, Determination of in situ stresses and elastic parameters from hydraulic fracturing tests by geomechanics modeling and soft computing, J. Petrol. Sci. Eng. 124 (2014) 484-492.
|
| [18] |
S. Zhang, S. Yin, Determination of earth stresses using inverse analysis based on coupled numerical modeling and soft computing, Int. J. Comput. Appl. Technol. 52 (1) (2015) 18-28.
|
| [19] |
J.S. Bell, In Situ Stress Orientations and Magnitudes in the Liard Basin of Western Canada, Geological Survey of Canada, 2015, p. 410, https://doi.org/10.4095/295742 Open File 7049.
|
| [20] |
D.N. Dewhurst, A. Hennig, Geomechanical properties related to top seal leakage in the Carnarvon Basin, Northwest Shelf, Australia, Petrol. Geosci. 9 (2003) 255-263.
|
| [21] |
M.O. Eshkalak, S.D. Mohaghegh, S. Esmaili, Geomechanical properties of unconventional shale reservoirs, J. Petrol. Eng. 2014 (2014) 10. Article ID 961641 https://doi.org/10.1155/2014/961641.
|
| [22] |
Q. Gao, J. Tao, J. Hu, X. Yu, Laboratory study on the mechanical behaviors of an anisotropic shale rock, J. Rock Mech. Geotech. Eng. 7 (2015) 213-219.
|
| [23] |
Md A. Islam, P. Skalle, An experimental investigation of shale mechanical properties through drained and undrained test mechanisms, Rock Mech. Rock Eng. 46 (2013) 1391-1413.
|
| [24] |
M. Josh, L. Esteban, C.D. Piane, J. Sarout, D.N. Dewhurst, M.B. Clennell, Laboratory characterisation of shale properties, J. Petrol. Sci. Eng.88-89 (2012) 107-124.
|
| [25] |
A. Garg, B. Panda, K. Shankhwar, Investigation of the joint length of weldment of environmental-friendly magnetic pulse welding process, Int. J. Adv. Manuf. Technol. 87 (2016) 2415-2426.
|
| [26] |
A. Garg, V. Vijayaraghavan, J. Zhang, J.S.L. Lam, Robust model design for evaluation of power characteristics of the cleaner energy system, Renew. Energy 112 (2017) 302-313.
|
| [27] |
A. Garg, K. Shankhwar, D. Jiang, V. Vijayaraghavan, B.N. Panda, S.S. Panda, An evolutionary framework in modelling of multi-output characteristics of the bone drilling process, Neural Comput. Appl. 29 (11) (2018) 1233-1241.
|
| [28] |
W.S. McCulloch, W.H. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys. 5 (1943) 115-133.
|
| [29] |
S. Mohaghegh, Virtual-intelligence applications in petroleum engineering Part 1-artificial neural networks, J. Petrol. Technol. 52 (2000) 64-72.
|
| [30] |
S. Zhang, S. Yin, A hybrid ANN-GA method for analysis of geotechnical parameters, 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, 2016, https://doi.org/10.1109/FSKD.2016.7603141.
|
| [31] |
H. Aguir, H. BelHadjSalah, R. Hambli, Parameter identification of an elasto-plastic behavior using artificial neural networks-genetic algorithm method, Mater. Des. 32 (2011) 48-53.
|
| [32] |
J.H. Holland,Adaptation in Natural and Artificial Systems, first ed.ed., University of Michigan Press, 1975.
|
| [33] |
D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989.
|
| [34] |
J.H. Holland, Adaptation in Natural and Artificial Systems, second ed., MIT Press, Cambridge, MA, 1992.
|
| [35] |
M. Dashti, A.M. Stuart, The Bayesian Approach to Inverse Problems, Cornell University, 2015 arXiv: 1302.6989v4 [math.PR].
|
| [36] |
R. Arnold, J. Townend, A Bayesian approach to estimation tectonic stress from seismological data, Geophys. J. Int. 170 (2007) 1336-1356.
|
| [37] |
H. EI-Ramly, N.R. Morgenstern, D.M. Cruden, Probabilistic slope stability analysis for practice, Can. Geotech. J. 39 (2002) 665-683.
|
| [38] |
R.E. Walpole, R.H. Mayers, S.L. Mayers, K. Ye, Probability & Statistic for Engineers & Scientists, seventh ed., Prentice-Hall, Inc, New Jersey, USA, 2002.
|
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