Research on identification model of element logging shale formation based on IPSO-SVM

Abstract

In the process of shale gas drilling, geo-steering plays an important role in shale gas drilling. This paper analyzes the constituent elements of shale formation, and selects the most suitable constituent elements of shale formation. A particle swarm optimization algorithm based on improved inertia weight and acceleration factor is proposed to optimize the parameters of support vector machine. The lithology identification model of shale formation is established based on IPSO-SVM. According to the experimental analysis based on the field historical data, the recognition rate of IPSO-SVM is increased by 17.79%, 10.17% and 8.05%, respectively, compared with SVM, GA-PSO and PSO-SVM. In terms of running time, the running time of IPSO-SVM is 13.76s and 9.5s shorter than that of GA-PSO, PSO-SVM, respectively. By comparing the experimental results of different models, IPSO-SVM has the advantages of strong robustness, strong reliability, high accuracy and fast convergence speed. It provides a theoretical basis for precise geo-steering and finding the optimal shale layer.

Keywords

Element logging / Particle swarm optimization / Geo-steering / SVM / Shale

Declaration of competing interests

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

References

[1] Jingling Xu, et al., Research on stratigraphic division and sand bed correlation based on 'three instantaneous' attribute spectra of logging data, Energy Explor. Exploit. 28 (6) (2010) 467-481.

[2] Zhanhong Liu, Orbital cycles analysis and its genesis significance for the sequence hierarchy: a case study of Carboniferous Karashayi Formation, Central Tarim basin, J. Earth Sci. 23 (4) (2012) 516-528.

[3] Jingling Xu, et al., Research on stratigraphic division and sand bed correlation based on 'three instantaneous' attribute spectra of logging data, Energy Explor. Exploit. 28 (6) (2010) 467-481.

[4] X. Zhang, et al., A study of the distribution of formation drillability, Liq. Fuel. Technol. 29 (2) (2011) 149-159.

[5] Hai Ma, Formation drillability prediction based on multi-source information fusion, Journal of Petroleum ence & Engineering 78 (2) (2011) 438-446.

[6] Andrei A. Bunaciu, E.G. UdriTioiu H.Y. Aboul-Enein, X-ray diffraction: instrumentation and applications, Crit. Rev. Anal. Chem. 45 (4) (2015) 289-299.

[7] I.M. Al-Alfy, et al., Gamma ray spectrometry logs as a hydrocarbon indicator for clastic reservoir rocks in Egypt, Appl. Radiat. Isot. 73 (2013) 90-95.

[8] S.M. Salehi, B. Honarvar, Automatic identification of formation iithology from well log data: a machine learning approach, Journal of Petroleum Science Research 3 (2) (2014) 73.

[9] Schmitt Paula, et al., Electrofacies modelling and lithological classification of coals and mud-bearing fine-grained siliciclastic rocks based on neural networks, Earth Sci. Res. 2 (2012) 1.

[10] A.A. Silva, et al., Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information, J. Appl. Geophys. 117 (2015) 118-125.

[11] H. Luo, et al., A lithology identification method for continental shale oil reservoir based on BP neural network, J. Geophys. Eng. 15 (3) (2018) 895-908.

[12] Jean-Yves Chatellier, Erik Quartero, Arnaud De Coninck, The Use of X-Ray Fluorescence for Stratigraphy and Structural Analysis in the Utica and Lorraine Shale, molgat, marianne, 2010.

[13] H. Liang, J. Zou, Z. Li, M. Junaid, Y. Lu, Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm, FuturGenerComput Syst 95 (2019) 454-466.

[14] Singh, Ajay, Application of data mining for quick root-cause identification and automated production diagnostic of gas wells with plunger lift, SPE Prod. Oper. 32 (3) (2017) 279-293.

[15] Juan Zhao H.Y. Xiong, Based on SVM classification of connection pools algorithm, Adv. Mater. Res. 945-949 (2014) 2435-2438.

[16] Chun Hua Qian, H.Q. Qiang S.R. Gong, An image classification algorithm based on SVM, Appl. Mech. Mater. 738-739 (2015) 542-545.

[17] H. Wei, Y. Zhang, An effective routing of optimization of SVM algorithm in electrocardiogram classification, J. Comput. Inf. Syst. 10 (14) (2014) 6075-6082.

[18] Haibo Liang J. Zou, Rock image segmentation of improved semi-supervised SVMeFCM algorithm based on chaos, Circ. Syst. Signal Process. 39 (2) (2020) 571-585.

[19] James Kennedy, Particle Swarm Optimization, 2011.

[20] Zhi Hui Zhan, J. Zhang, Adaptive Particle Swarm Optimization, 2008.

[21] Ahmed A.A. Esmin R.A. Coelho S. Matwin, A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data, Artif. Intell. Rev. 44 (1) (2015) 23-45.

[22] Shuangyin Liu, et al., Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization, Comput. Electron. Agric. 95 (2013) 82-91.

[23] Xinhua Xue, Evaluation of concrete compressive strengthbased on an improved PSO-LSSVM model, Comput. Concr. 21 (5) (2018) 505-511.

[24] Jiawei Zhang, et al., Support vector machine modeling using particle swarm optimization approach for the retrieval of atmospheric ammonia concentrations, Environ. Model. Assess. 21 (4) (2016) 531-546.

[25] Xiaokai, et al., Classification of spot-welded joint strength using ultrasonic signal time-frequency features and PSO-SVM method, Ultrasonics 91 (2019) 161-169.

[26] Shuixia Chen, Jian-qiang Wang, Hong-yu Zhang, A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting, Technol. Forecast. Soc. Change 146 (2019) 41-54.