Slug flow represents one of the most critical and operationally challenging regimes in oil-gas-water multiphase pipelines.To advance both mechanistic understanding and predictive capability,this study integrates physi...Slug flow represents one of the most critical and operationally challenging regimes in oil-gas-water multiphase pipelines.To advance both mechanistic understanding and predictive capability,this study integrates physical analysis with data-driven modeling to elucidate the conditions governing slug formation and to enable its rapid and accurate prediction.A systematic review of existing research is first undertaken to clarify the mechanisms responsible for slug initiation.The influences of gas superficial velocity,liquid velocity,liquid viscosity,liquid surface tension,and the axial component of gravity are examined to characterize their roles in interfacial instability and flow transition.Then,the effects of temperature,total flow rate,water cut,gas-liquid ratio,and pipeline inclination angle are quantitatively assessed,revealing the dominant trends that promote or inhibit slug development.Building on this foundation,a comprehensive three-phase oil-gas-water flow model is constructed.Numerical simulations are performed for 243 operating conditions encompassing a broad range of temperatures,water cuts,gas-liquid ratios,liquid flow rates,and inclination angles.These simulated cases constitute the training dataset for nine machine learning algorithms.To evaluate generalization performance,108 additional randomly generated operating conditions are predicted,covering temperatures of 80–150◦C,water cuts of 40–90%,gas-liquid ratios of 3–30,liquid flow rates of 100–200 t/d,and inclination angles of 5–15.Comparative validation reveals marked differences in predictive accuracy.The BP neural network achieves the highest accuracy,95%,substantially outperforming XGBoost,83.3%,Random Forest and Decision Tree,81.5%,Logistic Regression and Support Vector Machine,80.6%,K-Nearest Neighbor and Naive Bayes 78.7%,and K-Means,63%.Overall,the BP neural network demonstrates superior robustness and precision in predicting previously unseen operating conditions,effectively combining the physical consistency of mechanistic modeling with the efficiency and adaptability of machine learning approaches.展开更多
Based on the conductance fluctuation signals measured from vertical upward oil-gas-water three-phase flow experiment, time frequency representation and surrogate data method were used to investigate dynamical characte...Based on the conductance fluctuation signals measured from vertical upward oil-gas-water three-phase flow experiment, time frequency representation and surrogate data method were used to investigate dynamical characteristics of oil-in-water type bubble and slug flows. The results indicate that oil-in-water type bubble flow will turn to deterministic motion with the increase of oil phase fraction f o and superficial gas velocity U sg under fixed flowrate of oil-water mixture Q mix . The dynamics of oil-in-water type slug flow becomes more complex with the increase of U sg under fixed flowrate of oil-water mixture. The change of f o leads to irregular influence on the dynamics of slug flow. These interesting findings suggest that the surrogate data method can be a faithful tool for characterizing dynamic characteristics of oil-in-water type bubble and slug flows.展开更多
Based on drilling, logging, test production and dynamic monitoring data, the control effects of low-amplitude structure on hydrocarbon accumulation and development performance of ultra-low permeability reservoirs were...Based on drilling, logging, test production and dynamic monitoring data, the control effects of low-amplitude structure on hydrocarbon accumulation and development performance of ultra-low permeability reservoirs were discussed by using the methods of dense well pattern, multi-factor geological modeling, macro and micro analysis and static and dynamic analysis. The results show that the low-amplitude structure always had a significant control and influence on the distribution and accumulation of original hydrocarbon and water and the evolution trend of water flooding performance in ultra-low permeability reservoirs, and it was not only the direction of oil and gas migration, but also a favorable place for relative accumulation of oil and gas. The controlling effect of low-amplitude structure on ultra-low permeability reservoir mainly depended on its tectonic amplitude and scale;the larger the tectonic amplitude and scale, and the higher the tectonic position of the low amplitude structure, the better the reservoir characteristic parameters, oil and gas enrichment degree and development effect, and the larger the spatial scope it controlled and influenced;water cut and oil well output always fluctuated orderly with the height of the low-amplitude structure;the dynamic response of waterflooding was closely related to the relative structural position of the injection and production wells;the injected water always advanced to the low-lying area of the structure first and then moved up to the high-lying area of the structure gradually;with the continuous expansion of the flooded area, part of the oil and gas in the low-lying part of the structure was forced to be distributed to the high part of the structure, resulting in a new oil and gas enrichment, so that the dynamic reserves of oil wells in the high part increased, and the production capacity remained stable.展开更多
We investigate the dynamic characteristics of oil-gas-water three-phase flow in terms of chaotic attractor comparison. In particular, we extract a statistic to characterize the dynamical difference in attractor probab...We investigate the dynamic characteristics of oil-gas-water three-phase flow in terms of chaotic attractor comparison. In particular, we extract a statistic to characterize the dynamical difference in attractor probability distribution. We first take time series from Logistic chaotic system with different parameters as examples to demonstrate the effectiveness of the method. Then we use this method to investigate the experimental signals from oil-gas-water three-phase flow. The results indicate that the extracted statistic is very sensitive to the change of flow parameters and can gain a quantitatively insight into the dynamic characteristics of different flow patterns.展开更多
基金funded by the Hubei Provincial Department of Education Science and Technology Plan Project(Young and Middle-aged Talent Program)(https://jyt.hubei.gov.cn/),grant number Q20241308the National Natural Science Foundation of China(https://www.nsfc.gov.cn/),grant number 52174064.
文摘Slug flow represents one of the most critical and operationally challenging regimes in oil-gas-water multiphase pipelines.To advance both mechanistic understanding and predictive capability,this study integrates physical analysis with data-driven modeling to elucidate the conditions governing slug formation and to enable its rapid and accurate prediction.A systematic review of existing research is first undertaken to clarify the mechanisms responsible for slug initiation.The influences of gas superficial velocity,liquid velocity,liquid viscosity,liquid surface tension,and the axial component of gravity are examined to characterize their roles in interfacial instability and flow transition.Then,the effects of temperature,total flow rate,water cut,gas-liquid ratio,and pipeline inclination angle are quantitatively assessed,revealing the dominant trends that promote or inhibit slug development.Building on this foundation,a comprehensive three-phase oil-gas-water flow model is constructed.Numerical simulations are performed for 243 operating conditions encompassing a broad range of temperatures,water cuts,gas-liquid ratios,liquid flow rates,and inclination angles.These simulated cases constitute the training dataset for nine machine learning algorithms.To evaluate generalization performance,108 additional randomly generated operating conditions are predicted,covering temperatures of 80–150◦C,water cuts of 40–90%,gas-liquid ratios of 3–30,liquid flow rates of 100–200 t/d,and inclination angles of 5–15.Comparative validation reveals marked differences in predictive accuracy.The BP neural network achieves the highest accuracy,95%,substantially outperforming XGBoost,83.3%,Random Forest and Decision Tree,81.5%,Logistic Regression and Support Vector Machine,80.6%,K-Nearest Neighbor and Naive Bayes 78.7%,and K-Means,63%.Overall,the BP neural network demonstrates superior robustness and precision in predicting previously unseen operating conditions,effectively combining the physical consistency of mechanistic modeling with the efficiency and adaptability of machine learning approaches.
基金Supported by the National Natural Science Foundation of China (50974095, 41174109)Gao Zhongke (高忠科) was also supported by the National Natural Science Foundation of China (61104148)+2 种基金the National Science and Technology Major Projects (2011ZX05020-006)Specialized Research Fund for the Doctoral Program of Higher Education of China(20110032120088)the Independent Innovation Foundation of Tianjin University
文摘Based on the conductance fluctuation signals measured from vertical upward oil-gas-water three-phase flow experiment, time frequency representation and surrogate data method were used to investigate dynamical characteristics of oil-in-water type bubble and slug flows. The results indicate that oil-in-water type bubble flow will turn to deterministic motion with the increase of oil phase fraction f o and superficial gas velocity U sg under fixed flowrate of oil-water mixture Q mix . The dynamics of oil-in-water type slug flow becomes more complex with the increase of U sg under fixed flowrate of oil-water mixture. The change of f o leads to irregular influence on the dynamics of slug flow. These interesting findings suggest that the surrogate data method can be a faithful tool for characterizing dynamic characteristics of oil-in-water type bubble and slug flows.
基金Supported by Open Fund(PLC20190203)of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Chengdu University of Technology)the Natural Science Foundation of Shaanxi Province,China(2006Z07,2010JM5003)Youth Science and Technology Innovation Fund Project of Xi’an Petroleum University(2012BS010)
文摘Based on drilling, logging, test production and dynamic monitoring data, the control effects of low-amplitude structure on hydrocarbon accumulation and development performance of ultra-low permeability reservoirs were discussed by using the methods of dense well pattern, multi-factor geological modeling, macro and micro analysis and static and dynamic analysis. The results show that the low-amplitude structure always had a significant control and influence on the distribution and accumulation of original hydrocarbon and water and the evolution trend of water flooding performance in ultra-low permeability reservoirs, and it was not only the direction of oil and gas migration, but also a favorable place for relative accumulation of oil and gas. The controlling effect of low-amplitude structure on ultra-low permeability reservoir mainly depended on its tectonic amplitude and scale;the larger the tectonic amplitude and scale, and the higher the tectonic position of the low amplitude structure, the better the reservoir characteristic parameters, oil and gas enrichment degree and development effect, and the larger the spatial scope it controlled and influenced;water cut and oil well output always fluctuated orderly with the height of the low-amplitude structure;the dynamic response of waterflooding was closely related to the relative structural position of the injection and production wells;the injected water always advanced to the low-lying area of the structure first and then moved up to the high-lying area of the structure gradually;with the continuous expansion of the flooded area, part of the oil and gas in the low-lying part of the structure was forced to be distributed to the high part of the structure, resulting in a new oil and gas enrichment, so that the dynamic reserves of oil wells in the high part increased, and the production capacity remained stable.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.41174109 and 61104148)the National Science and Technology Major Project of the Ministry of Science and Technology of China(Grant No.2011ZX05020-006)the Tianjin City High School Science and Technology Fund Planning Project,China(Grant No.20130718)
文摘We investigate the dynamic characteristics of oil-gas-water three-phase flow in terms of chaotic attractor comparison. In particular, we extract a statistic to characterize the dynamical difference in attractor probability distribution. We first take time series from Logistic chaotic system with different parameters as examples to demonstrate the effectiveness of the method. Then we use this method to investigate the experimental signals from oil-gas-water three-phase flow. The results indicate that the extracted statistic is very sensitive to the change of flow parameters and can gain a quantitatively insight into the dynamic characteristics of different flow patterns.
