Using theoretical analysis, the single-phase gas seepage mathematical model influenced by slippage effects was established. The results show that the pressure of producing wells attenuates more violently than the well...Using theoretical analysis, the single-phase gas seepage mathematical model influenced by slippage effects was established. The results show that the pressure of producing wells attenuates more violently than the wells without slippage effects. The decay rate of reservoir pressure is more violent as the Klinkenherg factor increases. The gas prediction output gradually increases as the Klinenberg factor increases when considering gas slippage effects. Through specific examples, analyzed the law of stope pore pressure and gas output forecast changing in a hypotonic reservoir with slippage effects. The results have great theoretical significance in the study of the law of coal-bed methane migration in hypotonic reservoirs and for the exploitation of coal-bed methane.展开更多
It is a great challenge to match and predict the production performance of coalbed methane (CBM) wells in the initial production stage due to heterogeneity of coalbed, uniqueness of CBM production process, complexity ...It is a great challenge to match and predict the production performance of coalbed methane (CBM) wells in the initial production stage due to heterogeneity of coalbed, uniqueness of CBM production process, complexity of porosity-permeability variation and difficulty in obtaining some key parameters which are critical for the conventional prediction methods (type curve, material balance and numerical simulation). BP neural network, a new intelligent technique, is an effective method to deal with nonlinear, instable and complex system problems and predict the short-term change quantitatively. In this paper a BP neural model for the CBM productivity of high-rank CBM wells in Qinshui Basin was established and used to match the past gas production and predict the futural production performance. The results from two case studies showed that this model has high accuracy and good reliability in matching and predicting gas production with different types and different temporal resolutions, and the accuracy increases as the number of outliers in gas production data decreases. Therefore, the BP network can provide a reliable tool to predict the production performance of CBM wells without clear knowledge of coalbed reservoir and sufficient production data in the early development stage.展开更多
基金Supported by the Youth Program of the National Natural Science Foundation of China （51004061）
文摘Using theoretical analysis, the single-phase gas seepage mathematical model influenced by slippage effects was established. The results show that the pressure of producing wells attenuates more violently than the wells without slippage effects. The decay rate of reservoir pressure is more violent as the Klinkenherg factor increases. The gas prediction output gradually increases as the Klinenberg factor increases when considering gas slippage effects. Through specific examples, analyzed the law of stope pore pressure and gas output forecast changing in a hypotonic reservoir with slippage effects. The results have great theoretical significance in the study of the law of coal-bed methane migration in hypotonic reservoirs and for the exploitation of coal-bed methane.
基金supported by the National Basic Research Program of Chi-na ("973" Project ) (Grant No. 2009CB219600)the Major National Sci-ence and Technology Special Projects (Grant Nos. 2008ZX05034-001, 2009ZX05038-002)
文摘It is a great challenge to match and predict the production performance of coalbed methane (CBM) wells in the initial production stage due to heterogeneity of coalbed, uniqueness of CBM production process, complexity of porosity-permeability variation and difficulty in obtaining some key parameters which are critical for the conventional prediction methods (type curve, material balance and numerical simulation). BP neural network, a new intelligent technique, is an effective method to deal with nonlinear, instable and complex system problems and predict the short-term change quantitatively. In this paper a BP neural model for the CBM productivity of high-rank CBM wells in Qinshui Basin was established and used to match the past gas production and predict the futural production performance. The results from two case studies showed that this model has high accuracy and good reliability in matching and predicting gas production with different types and different temporal resolutions, and the accuracy increases as the number of outliers in gas production data decreases. Therefore, the BP network can provide a reliable tool to predict the production performance of CBM wells without clear knowledge of coalbed reservoir and sufficient production data in the early development stage.
