### Lithology Classification Based on Set-Valued Identification Method

LI Jing1, WU Lifang2,3, LÜ Wenjun1, WANG Ting4, KANG Yu1, FENG Deyong5, ZHOU Hansheng6

1. 1. Department of Automation, University of Science and Technology of China, Hefei 230027, China;
2. Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;
3. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China;
4. Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China;
5. Shengli Geophysical Research Institute, SINOPEC Group, Dongying 257022, China;
6. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
• Received:2021-03-03 Revised:2021-06-03 Online:2022-10-25 Published:2022-10-12
• Supported by:
This research was supported in part by the National Key Research and Development Project of China under Grant Nos.2018AAA0100800 and 2018YFE0106800,in part by the SINOPEC Programmes for Science and Technology Development (PE19008-8),in part by the National Natural Science Foundation of China under Grant Nos.61725304,61803370,and 61903353,in part by the Major Science and Technology Project of Anhui Province (201903a07020012),in part by the University Synergy Innovation Program of Anhui Province (GXXT-2021-010),and in part by the Fundamental Research Funds for the Central Universities (WK2100000013).

LI Jing, WU Lifang, LÜ Wenjun, WANG Ting, KANG Yu, FENG Deyong, ZHOU Hansheng. Lithology Classification Based on Set-Valued Identification Method[J]. Journal of Systems Science and Complexity, 2022, 35(5): 1637-1652.

Lithology classification using well logs plays a key role in reservoir exploration.This paper studies the problem of lithology identification based on the set-valued method (SV),which uses the SV model to establish the relation between logging data and lithologic types at a certain depth point.In particular,the system model is built on the assumption that the noise between logging data and lithologic types is normally distributed,and then the system parameters are estimated by SV method based on the existing identification criteria.The logging data of Shengli Oilfield in Jiyang Depression are used to verify the effectiveness of SV method.The results indicate that the SV model classifies lithology more accurately than the Logistic Regression model (LR) and more stably than uninterpretable models on imbalanced dataset.Specifically,the Macro-F1 of the SV models (i.e.,SV (3),SV (5),and SV (7)) are higher than 85%,where the sandstone samples account for only 22%.In addition,the SV (7) lithology identification system achieves the best stability,which is of great practical significance to reservoir exploration.
 [1] Delfiner P, Peyret O, and Serra O, Automatic determination of lithology from well logs, SPE Formation Evaluation, 1987, 2(3):303-310.[2] Honarkhah M and Caers J, Direct pattern-based simulation of non-stationary geostatistical models, Mathematical Geosciences, 2012, 44(6):651-672.[3] Zych M, Stachura G, Hanus R, et al., Application of artificial neural networks in identification of geological formations on the basis of well logging data-A comparison of computational environments'efficiency, International Workshop on Modeling Social Media, Lyon France, 2018.[4] Chang J, Li J, Kang Y, et al., Unsupervised domain adaptation using maximum mean discrepancy optimization for lithology identification, Geophysics, 2020, 86(2):1-84.[5] Li Z, Kang Y, Lü W, et al., Interpretable semisupervised classification method under multiple smoothness assumptions with application to lithology identification, IEEE Geoscience and Remote Sensing Letters, 2021, 18(3):386-390.[6] Chang J, Kang Y, Li Z, et al., Cross-domain lithology identification using active learning and source reweighting, IEEE Geoscience and Remote Sensing Letters, 2020, DOI:10.1109/LGRS. 2020.3041960.[7] Zhang G, Wang Z, and Chen Y, Deep learning for seismic lithology prediction, Geophysical Journal International, 2018, 215(2):1368-1387.[8] Zhu L, Zhang C, Zhang C, et al., Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning, Applied Soft Computing, 2019, 83(105):596-619.[9] Ahmadi M A and Chen Z, Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs, Petroleum, 2019, 5(3):271-284.[10] Zhu L, Zhang C, Zhang C, et al., A new and reliable dual model-and data-driven toc prediction concept:A toc logging evaluation method using multiple overlapping methods integrated with semi-supervised deep learning, Journal of Petroleum Science and Engineering, 2020, 188(106):944-960.[11] Sebtosheikh M A, Motafakkerfard R, Riahi M A, et al., Support vector machine method, a new technique for lithology prediction in an iranian heterogeneous carbonate reservoir using petrophysical well logs, Carbonates and Evaporites, 2015, 30(1):59-68.[12] Deng C, Pan H, Fang S, et al., Support vector machine as an alternative method for lithology classification of crystalline rocks, Journal of Geophysics and Engineering, 2017, 14(2):341-349.[13] Li Z, Kang Y, Feng D, et al., Semi-supervised learning for lithology identification using laplacian support vector machine, Journal of Petroleum Science and Engineering, 2020, 195(107):510-523.[14] Ma Y Z, Gomez E, and Luneau B, Integration of seismic and well-log data using statistical and neural network methods, The Leading Edge, 2017, 36(4):324-329.[15] Liu H, Wu Y, Cao Y, et al., Well logging based lithology identification model establishment under data drift:A transfer learning method, Sensors, 2020, 20(13):3643-3660.[16] Bestagini P, Lipari V, and Tubaro S, A machine learning approach to facies classification using well logs, SEG Technical Program Expanded Abstracts, 2017, 2137-2142, DOI:10.1190/segam2017-17729805.1.[17] Dev V A and Eden M R, Evaluating the boosting approach to machine learning for formation lithology classification, Computer Aided Chemical Engineering, 2018, 44:1465-1470.[18] Ao Y, Li H, Zhu L, et al., Logging lithology discrimination in the prototype similarity space with random forest, IEEE Geoscience and Remote Sensing Letters, 2018, 16(5):687-691.[19] Sun J, Li Q, Chen M, et al., Optimization of models for a rapid identification of lithology while drilling:A win-win strategy based on machine learning, Journal of Petroleum Science and Engineering, 2019, 176:321-341.[20] Zhu L, Li H, Yang Z, et al., Intelligent logging lithological interpretation with convolution neural networks, Petrophysics, 2018, 59(6):799-810.[21] Imamverdiyev Y and Sukhostat L, Lithological facies classification using deep convolutional neural network, Journal of Petroleum Science and Engineering, 2019, 174:216-228.[22] Zhu L, Zhang C, Zhang Z, et al., High-precision calculation of gas saturation in organic shale pores using an intelligent fusion algorithm and a multi-mineral model, Advances in Geo-Energy Research, 2020, 4(2):135-151.[23] Xie Y, Zhu C, Zhou W, et al., Evaluation of machine learning methods for formation lithology identification:A comparison of tuning processes and model performances, Journal of Petroleum Science and Engineering, 2018, 160:182-193.[24] Le Y W, Zhang J F, and Yin G G, System identification using binary sensors, IEEE Transactions on Automatic Control, 2003, 48(11):1892-1907.[25] Guo J, Zhang J F, and Zhao Y, Adaptive tracking of a class of first-order systems with binaryvalued observations and fixed thresholds, Journal of Systems Science&Complexity, 2012, 25(6):1041-1051.[26] Kang G, Bi W, Zhao Y, et al., A new system identification approach to identify genetic variants in sequencing studies for a binary phenotype, Human Heredity, 2014, 78(2):104-116.[27] Wang L Y, Yin G G, Zhang J F, et al., System Identification with Quantized Observations, Basel, Switzerland:Birkhäuser Press, 2010.[28] Zhao Y, Zhang J F, and Guo J, System identification and adaptive control of set-valued systems, Journal of Systems Science and Mathematical Sciences, 2012, 32(10):1257-1265.[29] Chen T, Zhao Y, and Jung L L, Impulse response estimation with binary measurements:A regularized fir model approach, IFAC Proceedings Volumes, 2012, 45(16):113-118.[30] Bi W, Kang G, Zhao Y, et al., SVSI:Fast and powerful set-valued system identification approach to identifying rare variants in sequencing studies for ordered categorical traits, Annals of Human Genetics, 2015, 79(4):294-309.[31] Zhao Y, Bi W, and Wang T, Iterative parameter estimate with batched binary-valued observations, Science China Information Sciences, 2016, 59(5):1-18.[32] Bi W, Zhao Y, Liu C, et al., Set-valued analysis for genome-wide association studies of complex diseases, Proceedings of the 32nd Chinese Control Conference (CCC), Xi'an, China, 2013.[33] Wang T, Bi W, Zhao Y, et al., Radar target recognition algorithm based on rcs observation sequence-set-valued identification method, Journal of Systems Science&Complexity, 2016, 29(3):573-588.[34] Wang X, Hu M, Zhao Y, et al., Credit scoring based on the set-valued identification method, Journal of Systems Science&Complexity, 2020, 33(5):1297-1309.[35] Dev V A and Eden M R, Formation lithology classification using scalable gradient boosted decision trees, Computers and Chemical Engineering, 2019, 128(2):392-404.
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