Abstract
Rock mechanical properties are complex but essential for reservoir stimulation. Both experimental and empirical equation method have inevitable defects, and artificial neural network model (ANN) shows great potential in rock mechanics. This paper established a preliminary rock database from the Keshen block in Tarim Basin and improved clustering algorithm to assess the similarity between wells, creating sub-databases and realizing data sharing among similar wells. 18 parameters are used to predict ten rock mechanical properties and corresponding model inputs are optimized by correlation and sensitivity analysis separately. The results show that more accurate predictions can be obtained using optimized sample data and model inputs. The average predicted loss value using all 31 wells (221 samples) was 7.28%, while the value was reduced to 5.4% after clustering analysis using seven wells (95 samples). The average loss value can be further reduced to 4.3% using the optimized model inputs. As for rock peak stress, the predicted loss value is 2.5%, the determination coefficient (R2) is 0.983, and the root mean square error (RMSE) is 0.03. By optimizing training method, improving training sample quality, and optimizing model inputs, the problem of overfitting can be alleviated and a more reliable ANN model can be obtained.
Highlights
-
(1)
Defined homogeneity coefficient based on computed tomography and image recognition, which is important for predicting rock mechanical properties.
-
(2)
A preliminary database was established based on the rock samples from 31 wells for the training of the artificial neural network model.
-
(3)
Improved clustering algorithm and performed similarity analysis between wells, created sub-databases, and improved data quantity and quality.
-
(4)
18 parameters are used to predict 10 rock mechanical properties, and model inputs are optimized by correlation and sensitivity analysis.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The raw/processed data required to reproduce these findings will be made available on request.
References
Armaghani DJ, Mohamad ET, Momeni E et al (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9:48. https://doi.org/10.1007/s12517-015-2057-3
Armaghani DJ, Hajihassani M, Bejarbaneh BY et al (2014) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Measurement 55:487–498. https://doi.org/10.1016/j.measurement.2014.06.001
ASTM D7012-14 (2017) Test methods for compressive strength and elastic moduli of intact rock core specimens under varying states of stress and temperatures. ASTM International
ASTM D4543–19 (2019) Standard test methods for preparing rock core as cylindrical test specimens and verifying conformance to dimensional and shape tolerances. ASTM International
Baghbani A, Choudhury T, Costa S, Reiner J (2022) Application of artificial intelligence in geotechnical engineering: a state-of-the-art review. Earth-Sci Rev 228:103991. https://doi.org/10.1016/j.earscirev.2022.103991
Beiki M, Majdi A, Givshad AD (2013) Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. Int J Rock Mech Min Sci 63:159–169. https://doi.org/10.1016/j.ijrmms.2013.08.004
Ceryan N, Okkan U, Kesimal A (2013) Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks. Environ Earth Sci 68:807–819. https://doi.org/10.1007/s12665-012-1783-z
Corkum AG, Martin CD (2007) The mechanical behaviour of weak mudstone (Opalinus Clay) at low stresses. Int J Rock Mech Min Sci 44:196–209. https://doi.org/10.1016/j.ijrmms.2006.06.004
Culshaw MG (2015) Ulusay, R (ed.), 2015The ISRM suggested methods for rock characterization, testing and monitoring: 2007–2014. Bull Eng Geol Environ 74:1499–1500. https://doi.org/10.1007/s10064-015-0780-3
Dehghan S, Sattari G, Chehreh CS, Aliabadi M (2010) Prediction of uniaxial compressive strength and modulus of elasticity for travertine samples using regression and artificial neural networks. Min Sci Technol China 20:41–46. https://doi.org/10.1016/S1674-5264(09)60158-7
Ding QL, Ju F, Mao XB et al (2016) Experimental investigation of the mechanical behavior in unloading conditions of sandstone after high-temperature treatment. Rock Mech Rock Eng 49:2641–2653. https://doi.org/10.1007/s00603-016-0944-x
Golovin SV, Baykin AN (2018) Influence of pore pressure on the development of a hydraulic fracture in poroelastic medium. Int J Rock Mech Min Sci 108:198–208. https://doi.org/10.1016/j.ijrmms.2018.04.055
Guha RD, Singh TN (2020) Predicting deformational properties of Indian coal: Soft computing and regression analysis approach. Measurement 149:106975. https://doi.org/10.1016/j.measurement.2019.106975
Han B, Zdravkovic L, Kontoe S (2016) Numerical and analytical investigation of compressional wave propagation in saturated soils. Comput Geotech 75:93–102. https://doi.org/10.1016/j.compgeo.2016.01.019
Huang D, Gu D, Yang C et al (2016) Investigation on mechanical behaviors of sandstone with two preexisting flaws under triaxial compression. Rock Mech Rock Eng 49:375–399. https://doi.org/10.1007/s00603-015-0757-3
Jamshidi A, Nikudel MR, Khamehchiyan M et al (2016) A correlation between P-wave velocity and Schmidt hardness with mechanical properties of travertine building stones. Arab J Geosci 9:568. https://doi.org/10.1007/s12517-016-2542-3
Jamshidi A, Zamanian H, Sahamieh RZ (2018) The effect of density and porosity on the correlation between uniaxial compressive strength and p-wave velocity. Rock Mech Rock Eng 51:1279–1286. https://doi.org/10.1007/s00603-017-1379-8
Kahraman S (2001) Evaluation of simple methods for assessing the uniaxial compressive strength of rock. Int J Rock Mech Min Sci 38:981–994. https://doi.org/10.1016/S1365-1609(01)00039-9
Kainthola A, Singh PK, Verma D et al (2015) Prediction of strength parameters of Himalayan rocks: a statistical and ANFIS approach. Geotech Geol Eng 33:1255–1278. https://doi.org/10.1007/s10706-015-9899-z
Kaunda R (2014) New artificial neural networks for true triaxial stress state analysis and demonstration of intermediate principal stress effects on intact rock strength. J Rock Mech Geotech Eng 6:338–347. https://doi.org/10.1016/j.jrmge.2014.04.008
Lawal AI, Kwon S (2021) Application of artificial intelligence to rock mechanics: an overview. J Rock Mech Geotech Eng 13:248–266. https://doi.org/10.1016/j.jrmge.2020.05.010
Li C, Zhou J, Dias D, Gui Y (2022) A kernel extreme learning machine-grey wolf optimizer (KELM-GWO) model to predict uniaxial compressive strength of rock. Appl Sci 12:8468. https://doi.org/10.3390/app12178468
Liang WG, Xu SG, Zhao YS (2006) Experimental study of temperature effects on physical and mechanical characteristics of salt rock. Rock Mech Rock Eng 39:469–482. https://doi.org/10.1007/s00603-005-0067-2
Lin H, Zhou FJ, Tian YK, et al (2021) Prediction of rock constitutive relation under high temperature and high stress by recurrent neural network. OnePetro
Ma X, Wang G, Hu D et al (2020) Mechanical properties of granite under real-time high temperature and three-dimensional stress. Int J Rock Mech Min Sci 136:104521. https://doi.org/10.1016/j.ijrmms.2020.104521
Mahmoodzadeh A, Mohammadi M, Ibrahim HH et al (2021) Artificial intelligence forecasting models of uniaxial compressive strength. Transp Geotech. 27:100499. https://doi.org/10.1016/j.trgeo.2020.100499
Manouchehrian A, Sharifzadeh M, Moghadam RH (2012) Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics. Int J Min Sci Technol 22:229–236. https://doi.org/10.1016/j.ijmst.2011.08.013
Meng Q, Qian W, Liu J et al (2020) Analysis of triaxial compression deformation and strength characteristics of limestone after high temperature. Arab J Geosci 13:153. https://doi.org/10.1007/s12517-020-5151-0
Meulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39. https://doi.org/10.1016/S0148-9062(98)00173-9
Mishra DA, Basu A (2013) Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system. Eng Geol 160:54–68. https://doi.org/10.1016/j.enggeo.2013.04.004
Mishra DA, Srigyan M, Basu A, Rokade PJ (2015) Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests. Int J Rock Mech Min Sci 80:418–424. https://doi.org/10.1016/j.ijrmms.2015.10.012
Mohamad ET, Armaghani DJ, Momeni E et al (2018) Rock strength estimation: a PSO-based BP approach. Neural Comput Appl 30:1635–1646. https://doi.org/10.1007/s00521-016-2728-3
Momeni E, Armaghani DJ, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63. https://doi.org/10.1016/j.measurement.2014.09.075
Monjezi M, Khoshalan HA, Razifard M (2012) A Neuro-genetic network for predicting uniaxial compressive strength of rocks. Geotech Geol Eng 30:1053–1062. https://doi.org/10.1007/s10706-012-9510-9
Moussas VC, Diamantis K (2021) Predicting uniaxial compressive strength of serpentinites through physical, dynamic and mechanical properties using neural networks. J Rock Mech Geotech Eng 13:167–175. https://doi.org/10.1016/j.jrmge.2020.10.001
Peng L, Song EX (2012) Compressional wave velocity and its physical nature in saturated soils with extreme permeability values. Rock Soil Mech 33:1979–1985
Rabbani E, Sharif F, Salooki MK, Moradzadeh A (2012) Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties. Int J Rock Mech Min Sci 56:100–111. https://doi.org/10.1016/j.ijrmms.2012.07.033
Raterman KT, Liu Y, Warren L (2019) Analysis of a drained rock volume: an eagle ford example. In: Unconventional Resources Technology Conference, Denver, Colorado, 22?24 July 2019. Unconventional Resources Technology Conference (URTeC); Society of Exploration Geophysicists, pp 4106–4125
Rezaei M, Majdi A, Monjezi M (2014) An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining. Neural Comput Appl 24:233–241. https://doi.org/10.1007/s00521-012-1221-x
Saldaña M, González J, Pérez-Rey I et al (2020) Applying statistical analysis and machine learning for modeling the UCS from P-wave velocity, density and porosity on dry travertine. Appl Sci 10:4565. https://doi.org/10.3390/app10134565
Sarkar K, Tiwary A, Singh TN (2010) Estimation of strength parameters of rock using artificial neural networks. Bull Eng Geol Environ 69:599–606. https://doi.org/10.1007/s10064-010-0301-3
Sarkar K, Vishal V, Singh TN (2012) An empirical correlation of index Geomechanical parameters with the compressional wave velocity. Geotech Geol Eng 30:469–479. https://doi.org/10.1007/s10706-011-9481-2
Sharma LK, Vishal V, Singh TN (2017) Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties. Measurement 102:158–169. https://doi.org/10.1016/j.measurement.2017.01.043
Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12:40–45. https://doi.org/10.1016/j.asoc.2011.09.010
Singh R, Umrao RK, Ahmad M et al (2017) Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement 99:108–119. https://doi.org/10.1016/j.measurement.2016.12.023
Singh R, Vishal V, Singh TN, Ranjith PG (2013) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23:499–506. https://doi.org/10.1007/s00521-012-0944-z
Skentou AD, Bardhan A, Mamou A et al (2023) Closed-form equation for estimating unconfined compressive strength of granite from three non-destructive tests using soft computing models. Rock Mech Rock Eng 56:487–514. https://doi.org/10.1007/s00603-022-03046-9
Still EK, Schreiber DK, Wang J, et al (2021a) Three-dimensional alpha shapes. https://doi.org/10.48550/ARXIV.MATH/9410208
Still EK, Schreiber DK, Wang J, Hosemann P (2021b) Alpha shape analysis (ASA) framework for post- clustering property determination in atom probe tomographic data. Microsc Microanal 27:297–317. https://doi.org/10.1017/S1431927620024939
Torabi-Kaveh M, Naseri F, Saneie S, Sarshari B (2015) Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones. Arab J Geosci 8:2889–2897. https://doi.org/10.1007/s12517-014-1331-0
Wang J (2020) Intelligent prediction of the deep rock mechanics under coupling effects of temperature and pressure. Master's thesis, Xiangtan University
Wang ZL, Shi H, Wang JG (2018) Mechanical behavior and damage constitutive model of granite under coupling of temperature and dynamic loading. Rock Mech Rock Eng 51:3045–3059. https://doi.org/10.1007/s00603-018-1523-0
Xie H, Li C, He Z et al (2021) Experimental study on rock mechanical behavior retaining the in situ geological conditions at different depths. Int J Rock Mech Min Sci 138:104548. https://doi.org/10.1016/j.ijrmms.2020.104548
Xie S, Han Z, Chen Y et al (2022) Constitutive modeling of rock materials considering the void compaction characteristics. Arch Civ Mech Eng 22:60. https://doi.org/10.1007/s43452-022-00378-9
Xing Y, Zhang G, Li S (2020) Thermoplastic constitutive modeling of shale based on temperature-dependent Drucker-Prager plasticity. Int J Rock Mech Min Sci 130:104305. https://doi.org/10.1016/j.ijrmms.2020.104305
Yagiz S (2011) P-wave velocity test for assessment of geotechnical properties of some rock materials. Bull Mater Sci 34:947–953. https://doi.org/10.1007/s12034-011-0220-3
Yan B, Guo YC, Zhu QF, Hu P (2019) Prediction of trixial compressive strength of sandstone based on PSO-BP neural network. J China Three Gorges Univ (natural Science). 41:51–54
Yesiloglu-Gultekin N, Gokceoglu C, Sezer EA (2013) Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. Int J Rock Mech Min Sci 62:113–122. https://doi.org/10.1016/j.ijrmms.2013.05.005
Yilmaz I, Yuksek G (2009) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. Int J Rock Mech Min Sci 46:803–810. https://doi.org/10.1016/j.ijrmms.2008.09.002
Zhang H, Li CC (2019) Effects of confining stress on the post-peak behaviour and fracture angle of fauske marble and iddefjord granite. Rock Mech Rock Eng 52:1377–1385. https://doi.org/10.1007/s00603-018-1695-7
Zhang H, Wu S, Zhang Z (2022) Prediction of uniaxial compressive strength of rock via genetic algorithm—selective ensemble learning. Nat Resour Res 31:1721–1737. https://doi.org/10.1007/s11053-022-10065-4
Zhang K, Zhou H, Shao J (2013) An experimental investigation and an elastoplastic constitutive model for a porous rock. Rock Mech Rock Eng 46:1499–1511. https://doi.org/10.1007/s00603-012-0364-5
Zhang P, Mishra B, Heasley KA (2015) Experimental investigation on the influence of high pressure and high temperature on the mechanical properties of deep reservoir rocks. Rock Mech Rock Eng 48:2197–2211. https://doi.org/10.1007/s00603-015-0718-x
Zhang Q, Song JR (1992) Predicting mechanical behaviors of rockor rock engineering by neural network. Chin J Rock Mech Eng 01:35–43
Zhao H, Shi C, Zhao M, Li X (2017) Statistical damage constitutive model for rocks considering residual strength. Int J Geomech 17:04016033. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000680
Zheng H, Pu C, Wang Y, Sun C (2020) Experimental and numerical investigation on influence of pore-pressure distribution on multi-fractures propagation in tight sandstone. Eng Fract Mech 230:106993. https://doi.org/10.1016/j.engfracmech.2020.106993
Zhou F, Lai Y, Song R (2013) Propagation of plane wave in non-homogeneously saturated soils. Sci China Technol Sci 56:430–440. https://doi.org/10.1007/s11431-012-5106-0
Acknowledgements
The authors would like to acknowledge the data support from the Tarim oilfield and the financial support from National Natural Science Foundation of China (No. 52174045).
Author information
Authors and Affiliations
Contributions
This study was supported and supervised by FZ. All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YT, HL, LH and XT. The first draft of the manuscript was written by YT and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tian, Y., Zhou, F., Hu, L. et al. Optimization of Rock Mechanical Properties Prediction Model Based on Block Database. Rock Mech Rock Eng 56, 5955–5978 (2023). https://doi.org/10.1007/s00603-023-03378-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00603-023-03378-0