Abstract
During oil-gas production, appropriate water injection to different production layers can effectively maintain stratum pressure and implement sustainable extraction of petroleum resources. Studying the performance of oil displacement by water is largely significant for researching the distribution of remaining-oil and adjusting the oilfield development plan. Nevertheless, the multidimensional time-varying injection-production data and 3D spatial structures of underground injection-production networks pose special challenges for effective injection-production correlation analysis. Therefore, we propose a digital-twin-driven visualization to explore and simulate the dynamic patterns of injectors and producers. First, digital twins of underground injection-production network are constructed with static 3D geospatial scenes and dynamic injection-production data, providing users with intuitive visual exploration and flexible interaction. Then, we apply the multi-step time-varying Long Short-term Memory (LSTM) model for dynamic analysis and recommendation of injection development. Furthermore, abstract information visualizations are combined with the 3D virtual environment to support the real-time monitoring and dynamic simulation of injection-production process. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system for intelligent injection-production analysis.
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References
Chelmis C (2010) Intelligent model management and visualization for smart oilfields. People. https://doi.org/10.2118/132629-MS
Chen W, Huang Z, Wu F, Zhu M, Guan H, Maciejewski R (2018) Vaud: a visual analysis approach for exploring spatio-temporal urban data. IEEE Trans Vis Comput Graph 24(9):2636–2648. https://doi.org/10.1109/TVCG.2017.2758362
Cheng H, Vyatkin V, Osipov E, Zeng P, Yu H (2020) Lstm based efast global sensitivity analysis for interwell connectivity evaluation using injection and production fluctuation data. IEEE Access 8:67289–67299. https://doi.org/10.1109/ACCESS.2020.2985230
Cheng H, Yang D, Lu C, Qin Q, Cadasse D (2022) Intelligent oil production stratified water injection technology. Wirel Commun Mob Comput. https://doi.org/10.1155/2022/3954446
Cheng H, Zhang L, Ling X, Zeng P, Yu H (2017) A pso-lssvm based petroleum production model for production-injection wells system. 2017 Chinese Automation Congress (cac), pp 5770-5775
Deli J, He L, Jiqun Z, Bin G, **aohan P, Quanbin W, Qinghai Y (2020) Data-driven optimization for fine water injection in a mature oil field. Petrol Explor Dev 47(3):674–682. https://doi.org/10.1016/S1876-3804(20)60084-2
Fan C, Matković K, Hauser H (2020) Sketch-based fast and accurate querying of time series using parameter-sharing lstm networks. IEEE Transactions on Visualization and Computer Graphics, pp 1–1. https://doi.org/10.1109/TVCG.2020.3002950
Gensler A, Henze J, Sick B, Raabe N (2016) Deep learning for solar power forecasting–an approach using autoencoder and lstm neural networks. 2016 IEEE international conference on systems, man, and cybernetics (smc), pp 002858-002865
Han D, Pan J, Pan R, Zhou D, Cao N, He J, Chen W (2022) iNet: visual analysis of irregular transition in multivariate dynamic networks. Front Comput Sci. https://doi.org/10.1007/s11704-020-0013-1
Jia W, Wang W, Zhang Z (2023) From simple digital twin to complex digital twin part ii: Multi-scenario applications of digital twin shop floor. Adv Eng Inform. https://doi.org/10.1016/j.aei.2023.101915
Löffler M, Mulzer W (2012) Triangulating the square and squaring the triangle: quadtrees and delaunay triangulations are equivalent. SIAM J Comput. https://doi.org/10.1137/110825698
Liu D, Shu L, Wang Y, Huo Z, Zhao S, **ong X (2022) Optimal injection parameters for enhancing coalbed methane recovery: a simulation study from the Shizhuang block, Qinshui basin, China. Geofluids. https://doi.org/10.1155/2022/3311827
Liu S, Wu Y, Wei E, Liu M, Liu Y (2013) Storyflow: tracking the evolution of stories. IEEE Trans Visual Comput Graph 19(12):2436–2445. https://doi.org/10.1109/TVCG.2013.196
Mayani MG, Baybolov T, Rommetveit R, Ødegaard SI, Koryabkin V, Lakhtionov S (2020) Optimizing drilling wells and increasing the operation efficiency using digital twin technology. Iadc/spe international drilling conference and exhibition
Mota R, Ferreira N, Silva J, Horga M, Lage M, Ceferino L, Miranda F (2022) A comparison of spatiotemporal visualizations for 3d urban analytics. IEEE Transactions on Visualization and Computer Graphics, pp 1–11. https://doi.org/10.1109/TVCG.2022.3209474
Peng Y, Fan X, Chen R, Yu Z, Liu S, Chen Y, Zhou F (2023) Visual abstraction of dynamic network via improved multi-class blue noise sampling. Front Comput Sci. https://doi.org/10.1007/s11704-021-0609-0
Radwan AE, Nabawy BS, Kassem AA, Hussein WS (2021) Implementation of rock ty** on waterflooding process during secondary recovery in oil reservoirs: a case study, El Morgan oil field, Gulf of Suez, Egypt. Nat Resour Res 30(2):1667–1696. https://doi.org/10.1007/s11053-020-09806-0
Shen F, Ren SS, Zhang XY, Luo HW, Feng CM (2021) A digital twin-based approach for optimization and prediction of oil and gas production. Math Prob Eng. https://doi.org/10.1155/2021/3062841
Silva LMR, Soares CG (2021) Oilfield development system optimization under reservoir production uncertainty. Ocean Eng. https://doi.org/10.1016/j.oceaneng.2021.108758
Sun S, Zhang T (2020) A 6m digital twin for modeling and simulation in subsurface reservoirs. Adv Geo-Energy Res 4(4):349–351
Tao F, Zhan H, Liu A, Nee AYC (2019) Digital twin in industry: state-of-the-art. IEEE Trans Ind Inf 15(4):2405–2415. https://doi.org/10.1109/TII.2018.2873186
Wang X, Chen W, **a J, Chen Z, Xu D, Wu X, Schreck T (2020) Conceptexplorer: visual analysis of concept Driftsin multi-source time-series data. IEEE Conf Vis Anal Sci Technol. https://doi.org/10.1109/VAST50239.2020.00006
Wei L, Pu D, Huang M, Miao Q (2020) Applications of digital twins to offshore oil/gas exploitation: from visualization to evaluation. IFAC-Pap OnLine 53(5):738–743
**e C, Chen W, Huang X, Hu Y, Barlowe S, Yang J (2014) Vaet: a visual analytics approach for e-transactions time-series. IEEE Trans Visual Comput Graph 20(12):1743–1752. https://doi.org/10.1109/TVCG.2014.2346913
**e Y, Ming K, Liu C, Sheng L, Yang L, Dang B (2022) Joint electronic and hydraulic control system for separate layer water injection. In: 2022 4th international conference on intelligent control, measurement and signal processing (icmsp), pp 554-557
Xu J, Guo T (2021) Application and research on digital twin in electronic cam servo motion control system. Int J Adv Manuf Technol 112(3–4):1145–1158. https://doi.org/10.1007/s00170-020-06553-7
Yang W, Liu M, Wang Z, Liu S (2024) Foundation models meet visualizations: Challenges and opportunities. Computational Visual Media. https://arxiv.org/abs/2310.05771
Yoon D, Lallier LF, Lanteaume LC (2017) Inferring stratigraphie forward models quantitative information to asses stratigraphic well correlation uncertainty. Eage conference exhibition, Vol 2017, pp 1-5
Yousefi SH, Rashidi F, Sharifi M, Soroush M, Ghahfarokhi AJ (2021) Interwell connectivity identification in immiscible gas-oil systems using statistical method and modified capacitance-resistance model: a comparative study. J Petrol Sci Eng. https://doi.org/10.1016/j.petrol.2020.108175
Yu Y, Zeng X, Xue X, Ma J (2022) Lstm-based intrusion detection system for vanets: a time series classification approach to false message detection. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2022.3190432
Zhang K, Chen H, Dai H-N, Liu H, Lin Z (2021) Spovis: decision support system for site selection of sports facilities in digital twinning cities. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2021.3089330
Zhang L, Xu C, Zhang K, Yao C, Yang Y, Yao J (2020) Production optimization for alternated separate -layer water injection in complex fault reservoirs. J Petrol Sci Eng. https://doi.org/10.1016/j.petrol.2020.107409
Zhang M, Tao F, Nee AYC (2021) Digital twin enhanced dynamic jobshop scheduling. J Manuf Syst 58:146–156. https://doi.org/10.1016/j.jmsy.2020.04.008
Zhang T, Li Y, Cai J, Meng Q, Sun S, Li C (2020) A digital twin for unconventional reservoirs: a multiscale modeling and algorithm to investigate complex mechanisms. Geofluids. https://doi.org/10.1155/2020/8876153
Zhang WE, Chang R, Zhu M, Zuo J (2022) Time series visualization and forecasting from Australian building and construction statistics. Appl Sci 12:2420. https://doi.org/10.3390/app12052420
Zhao Y, Ge L, **e H, Bai G, Zhang Z, Wei Q, Zhou F (2023) Astf: visual abstractions of time-varying patterns in radio signals. IEEE Trans Visual Comput Graph 29(1):214–224. https://doi.org/10.1109/TVCG.2022.3209469
Zhao Y, Luo F, Chen M, Wang Y, **a J, Zhou F, Chen W (2019) Evaluating multi-dimensional visualizations for understanding fuzzy clusters. IEEE Trans Vis Comput Graph 25(1):12–21. https://doi.org/10.1109/TVCG.2018.2865020
Zhao Y, Luo X, Lin X, Wang H, Kui X, Zhou F, Chen W (2020) Visual analytics for electromagnetic situation awareness in radio monitoring and management. IEEE Trans Vis Comput Graph 26(1):590–600. https://doi.org/10.1109/TVCG.2019.2934655
Zhao Y, Lv S, Fan Y, Yuan J, Jiang H, Zhou F (2023) Malicious webshell family dataset for webshell multi-classification research. Vis Inform. https://doi.org/10.1016/j.visinf.2023.06.008
Zheng L, Pei X, Liu H, Chen D, Yu J (2015) Electronic control separate layer flooding-a case study in Jilin oil field. Spe/iatmi Asia Pacific oil & gas conference and exhibition
Zhou Y, Lei S, Du X, Ju S, Li W (2021) Injection-production optimization of carbonate reservoir based on an inter-well connectivity model. Energy Explor Exploit 39(5):1666–1684. https://doi.org/10.1177/0144598721994653
Zhou Z, Meng L, Tang C, Zhao Y, Guo Z, Hu M, Chen W (2019) Visual abstraction of large scale geospatial origin-destination movement data. IEEE Trans Vis Comput Graph 25(1):43–53. https://doi.org/10.1109/TVCG.2018.2864503
Zhou Z, Shi C, Shen X, Cai L, Wang H, Liu Y, Chen W (2021) Context-aware sampling of large networks via graph representation learning. IEEE Trans Vis Comput Graph 27(2):1709–1719. https://doi.org/10.1109/TVCG.2020.3030440
Zhou Z, Sun L, Wang H, Yu W, Liu Y, Zhang X, Chen W (2023) IMGC: interactive multiple graph clustering with constrained Laplacian rank. IEEE Trans Human-Mach Syst 53(2):427–437. https://doi.org/10.1109/THMS.2022.3227181
Zhou Z, Ye L, Cai L, Wang L, Wang Y, Wang Y, Wang Y (2024) Conceptthread: visualizing threaded concepts in MOOC videos. IEEE Trans Vis Comput Graph. https://doi.org/10.1109/TVCG.2024.3361001
Zhou Z, Zhang X, Yang Z, Chen Y, Liu Y, Wen J, Chen W (2020) Visual abstraction of geographical point data with spatial autocorrelations. 2020 IEEE conference on visual analytics science and technology (vast), pp 60-71
Zhou Z, Zheng F, Wen J, Chen Y, Li X, Liu Y, Chen W (2023) A userdriven sampling model for large-scale geographical point data visualization via convolutional neural networks. IEEE Trans Human-Mach Syst 53(5):885–894. https://doi.org/10.1109/THMS.2023.3296692
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (62277013,62177040,U22A2032), National Key R &D Program of China (Grant No.2022YFE0137800), National Statistical Science Research Project (2022LY099), Zhejiang Provincial Natural Science Foundation of China (Grant No.LTGG24F020006), Zhejiang Provincial Science and Technology Program in China (No.2021C03137), Zhejiang Provincial Science and Technology Plan Project (2023C01120), Public Welfare Plan Research, Project of Zhejiang Provincial Science and Technology Department (LTGG23H260003, LGF22F020034), and Open Project Program of the State Key Laboratory of CAD &CG (Grant No.A2301), Zhejiang University.
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Liu, Y., **ao, Z., Lu, K. et al. Iptwins: visual analysis of injection-production correlations using digital twins. J Vis 27, 485–502 (2024). https://doi.org/10.1007/s12650-024-00971-5
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DOI: https://doi.org/10.1007/s12650-024-00971-5