Reservoir fluid type identification method based on deep learning: A case study of the Chang 1 Formation in the Jiyuan oilfield of the Ordos basin, China

Abstract

With the efficient and intelligent development of computer-based big data processing, applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency. Currently, the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs, guiding subsequent production work. This study uses well logging data from the Chang 1 reservoir, partitioning the dataset based on individual wells for model training and testing. A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function. Comparative validations with five other models, including logistic regression (LR), naive Bayes (NB), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM), show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids. Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification. This model provides important references and a basis for conducting regional studies and revisiting old wells, demonstrating practical value that can be widely applied.

Keywords

Low-contrast reservoirs / Fluid types / Pore structure / Clay content / LR+NB+GBDT+RF+SVM model / Machine learning / Neural networks / Loss functions / Geophysical well logging / Oil and gas reservoir prediction

References

[1] Alizadeh B, Najjari S, Kadkhodaie-Ilkhchi A. 2012. Artificial neural network modeling and cluster analysis for organic facies and burial history estimation using well log data: A case study of the South Pars Gas Field, Persian Gulf, Iran. Computers & Geosciences, 45, 261-269. doi: 10.1016/j.cageo.2011.11.024.

[2] Andreieva V, Shvai N. 2021. Generalization of cross-entropy loss function for image classification. Mohyla Mathematical Journal, 3, 3-10. doi: 10.18523/2617-7080320203-10.

[3] Anifowose FA, Labadin J, Abdulraheem A. 2013. Prediction of petroleum reservoir properties using different versions of adaptive Neuro-Fuzzy inference system hybrid models. International Journal of Computer Information Systems and Industrial Management, Applications 5: 413-426.

[4] Bagheripour P. 2014. Committee neural network model for rock permeability prediction. Journal of Applied Geophysics, 104, 142-148. doi: 10.1016/j.jappgeo.2014.03.001.

[5] Bai Z, Tan MJ, Shi YJ, Guan XN, Wu HB, Huang YH. 2022. Log interpretation method of resistivity low-contrast oil pays in Chang 8 tight sandstone of Huanxian area, Ordos Basin by support vector machine. Scientific Reports, 12, 1046. doi: 10.1038/s41598-022-04962-0.

[6] Baziar S, Tadayoni M, Nabi-Bidhendi M, Khalili M. 2014. Prediction of permeability in a tight gas reservoir by using three soft computing approaches: A comparative study. Journal of Natural Gas Science and Engineering, 21, 718-724. doi: 10.1016/j.jngse.2014.09.037.

[7] Bhatt A, Helle HB. 1999. Porosity, Permeability and TOC Prediction from Well Logs Using a Neural Network Approach. In 61st EAGE Conference and Exhibition, Helsinki, Finland: European Association of Geoscientists & Engineers. doi:10.3997/2214-4609.201407849.

[8] Cheng C, Li PY, Chen Y, Ye Y, Gao Y, Zhang L. 2022. Research progress of reservoir logging evaluation based on machine learning. Progress in Geophysics, 37(1), 164-177 doi: 10.6038/pg2022FF0031. (in Chinese with English abstract).

[9] Das B, Chatterjee R. 2018. Well log data analysis for lithology and fluid identification in Krishna-Godavari Basin, India. Arabian Journal of Geosciences, 11(10), 231. doi: 10.1007/s12517-018-3587-2.

[10] El-M Shokir EM. 2006. A novel model for permeability prediction in uncored wells. SPE Reservoir Evaluation & Engineering, 9(3), 266-273. doi: 10.2118/87038-pa.

[11] Feng Q. 2018. Yan 9, Chang 1reservoir “four” and the logging interpretation model extension ZJ Ding bian Oilfield. Xi’an, Northwest University, Master thesis, 1-83 (in Chinese with English abstract)

[12] Helle HB, Bhatt A, Ursin B. 2002. Fluid Saturation from Well Logs Using Modular Neural Networks. In 64th EAGE Conference & Exhibition, Florence, Italy: European Association of Geoscientists & Engineers. doi:10.3997/2214-4609-pdb.5.B045.

[13] He M, Gu HM, Wan H. 2020. Log interpretation for lithology and fluid identification using deep neural network combined with MAHAKIL in a tight sandstone reservoir. Journal of Petroleum Science and Engineering, 194, 107498. doi: 10.1016/j.petrol.2020.107498.

[14] He TP, Zhou YQ, Li YH, Xie HY, Shang YH, Chen TT, Zhang ZW. 2024. Research on the microscopic pore-throat structure and reservoir quality of tight sandstone using fractal dimensions. Scientific Reports, 14, 22825. doi: 10.1038/s41598-024-74101-4.

[15] Huang ZH, Shimeld J, Williamson M, Katsube J. 1996. Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada. Geophysics, 61(2), 422-436. doi: 10.1190/1.1443970.

[16] Jaikla C, Devarakota P, Auchter N, Sidahmed M, Espejo I. 2019. FaciesNet: Machine Learning Applications for Facies Classification in Well Logs. In Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS).

[17] Jouhari H, Lei DM, Al-qaness MA, Elaziz MA, Ewees AA, Farouk O. 2019. Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times. Mathematics, 7(11), 1120. doi:10.3390/math7111120.

[18] Karimpouli S, Fathianpour N, Roohi J. 2010. A new approach to improve neural networks' algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN). Journal of Petroleum Science and Engineering, 73(3-4), 227-232. doi: 10.1016/j.petrol.2010.07.003.

[19] Lin TY, Goyal P, Girshick R, He KM, Dollár P. 2020. Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 318-327. doi: 10.1109/TPAMI.2018.2858826.

[20] Li X, Shi YJ, Wang L, Hu S. 2013. Logging identification and evaluation technique of tight sandstone gas reservoirs: Taking Sulige gas field as an example. Natural Gas Geoscience, 24(1), 62-68 doi: 10.11764/j.issn.1672-1926.2013.01.62. (in Chinese with English abstract).

[21] Luo SL, Hu GM, Li LX, Wang JW, Li XT. 2015. Genetic analysis and well-log evaluation of the productivity simulation for unconventional gas reservoirs of tight sanstone: A case from B gas reservoirs in A sag. Progress in Geophysics, 30(6), 2714-2722 doi: 10.6038/pg20150632. (in Chinese with English abstract).

[22] Maiti S, Krishna Tiwari R, Kümpel HJ. 2007. Neural network modelling and classification of lithofacies using well log data: A case study from KTB borehole site. Geophysical Journal International, 169(2), 733-746. doi: 10.1111/j.1365-246x.2007.03342.x.

[23] Mohaghegh S, Arefi R, Bilgesu I, Ameri S, Rose D. 1995. Design and Development of An Artificial Neural Network for Estimation of Formation Permeability. SPE Computer Applications 7( 6): 151-54. doi:10.2118/28237-PA.

[24] Mohaghegh S, Arefi R, Ameri S, Aminiand K, Nutter R. 1996. Petroleum reservoir characterization with the aid of artificial neural networks. Journal of Petroleum Science and Engineering, 16(4), 263-274. doi: 10.1016/s0920-4105(96)00028-9.

[25] Nair V, Hinton GE. 2010. Rectified Linear Units Improve Restricted Boltzmann Machines Vinod Nair. Proceedings of ICML, 27: 807-814.

[26] Nooruddin HA, Hossain ME. 2011. Modified Kozeny-Carmen correlation for enhanced hydraulic flow unit characterization. Journal of Petroleum Science and Engineering, 80(1), 107-115. doi: 10.1016/j.petrol.2011.11.003.

[27] Pan Y, Wang J, Wu Y, Peng HH, Yang H, Chen C. 2022. Reconstructed quality improvement with a stochastic gradient descent optimization algorithm for a spherical hologram. Applied Optics, 61(17), 5341-5349. doi: 10.1364/AO.462161.

[28] Peng Z, Yang HJ, Pan HP, Ji Y. 2016. Identification of low resistivity oil and gas reservoirs with multiple linear regression model. 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). August 13-15, 2016, Changsha, China. IEEE, 529-533. doi: 10.1109/FSKD.2016.7603229.

[29] Rider MH. 2018. The Geological Interpretation of Well Logs. Cambridge, Cambridge University Press, 207-220.

[30] Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. 2014. Dropout: Prevent NN from overfitting. Journal of Machine Learning Research, 15, 1929-1958. doi: 10.5555/2627435.2670313.

[31] Tahmasebi P, Hezarkhani A. 2012. A fast and independent architecture of artificial neural network for permeability prediction. Journal of Petroleum Science and Engineering, 86-87, 118-126. doi: 10.1016/j.petrol.2012.03.019.

[32] Tan MJ, Bai Y, Zhang HT, Li GR, Wei XP, Wang AD. 2020. Fluid typing in tight sandstone from wireline logs using classification committee machine. Fuel, 271, 117601. doi: 10.1016/j.fuel.2020.117601.

[33] Wang DX. 2016. Study on the rock physics model of gas reservoirs in tight sandstone. Chinese Journal of Geophysics, 59, 4603-22. doi:10.6038/cjg20161222.

[34] Wu XM, Shi YZ, Fomel S, Liang LM. 2018. Convolutional Neural Networks for Fault Interpretation in Seismic Images. In SEG Technical Program Expanded Abstracts 2018, Anaheim, California: Society of Exploration Geophysicists, 1946-50. doi:10.1190/segam2018-2995341.1.

[35] Yang WB, Xia KW, Fan SR. 2023. Oil logging reservoir recognition based on TCN and SA-BiLSTM deep learning method. Engineering Applications of Artificial Intelligence, 121, 105950. doi:10.1016/j.engappai.2023.105950.

[36] Zhang GY. 2019. Recognition and Prediction Methods of High-quality Reservoirs of Tight Sandstones in Shaximiao Formation in Chuanxi Depression. Beijing, China University of Petroleum, Ph. D thesis, 1-151 (in Chinese with English abstract).

[37] Zhou XM, Li YW, Song XD, Jin LX, Wang XX. 2023. Thin reservoir identification based on logging interpretation by using the support vector machine method. Energies, 16(4), 1638. doi:10.3390/en16041638.

[38] Zhou XQ, Zhang ZS, Zhang CM. 2021. Bi-LSTM deep neural network reservoir classification model based on the innovative input of logging curve response sequences. IEEE Access, 9, 19902-15. doi:10.1109/ACCESS.2021.3053289.