By analyzing the characteristics and the production mechanism of rock burstthat goes with abnormal gas emission in deep coal seams,the essential method of eliminatingabnormal gas emission by eliminating the occurrence...By analyzing the characteristics and the production mechanism of rock burstthat goes with abnormal gas emission in deep coal seams,the essential method of eliminatingabnormal gas emission by eliminating the occurrence of rock burst or depressingthe magnitude of rock burst was considered.The No.237 working face was selected asthe typical working face contacting gas in deep mining;aimed at this working face,a systemof rock burst prediction and control for coal seam contacting gas in deep mining wasestablished.This system includes three parts:① regional prediction of rock burst hazardbefore mining,② local prediction of rock burst hazard during mining,and ③ rock burstcontrol.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 National Natural Science Foundation(Instrument)of China(50427401)the National High Technology Research and Development Program of China(2006AA06Z119)+1 种基金the National Key Technology R&D Program in 11th Five Years Plan of China(2007BA29B01)the New Century Excellent Talents in University(NCET-06-0477)
文摘By analyzing the characteristics and the production mechanism of rock burstthat goes with abnormal gas emission in deep coal seams,the essential method of eliminatingabnormal gas emission by eliminating the occurrence of rock burst or depressingthe magnitude of rock burst was considered.The No.237 working face was selected asthe typical working face contacting gas in deep mining;aimed at this working face,a systemof rock burst prediction and control for coal seam contacting gas in deep mining wasestablished.This system includes three parts:① regional prediction of rock burst hazardbefore mining,② local prediction of rock burst hazard during mining,and ③ rock burstcontrol.
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.
