In order to understand the monitoring efficiency status of the well-water-level observation network in China after the completion of the 10 th "Five-year Plan " digital network project,and to provide a basis...In order to understand the monitoring efficiency status of the well-water-level observation network in China after the completion of the 10 th "Five-year Plan " digital network project,and to provide a basis for the future network optimization and equipment updating, the monitoring efficiency of the well-water-level observation network was evaluated. On the whole,61. 8% observing stations have good monitoring effectiveness,the observation environment of 73. 5% of observing stations meets the monitoring requirements of well-water-level. The operation status of the network is as a whole getting better,operation rates of 75% observing instruments are above 95%. Most well water levels can monitor crustal stress changes and seismic activities. However,some observation stations,due to inherent deficiency in wells,environmental disturbance,instrument aging,and low-level operation and maintenance,need to improve the monitoring efficiency level by taking measures such as observation environment improvement,equipment updating,and management training. About 6. 5% of the stations need to stop observation due to the unqualified observational environment.展开更多
The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi-...The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example, the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.展开更多
In view of the difficulty of automatic adjustment, the recovery lag and the major accident potential of the mine ventilation system, an experimental model of the pipe net was established according to the typical one m...In view of the difficulty of automatic adjustment, the recovery lag and the major accident potential of the mine ventilation system, an experimental model of the pipe net was established according to the typical one mine and one working face ventilation system of Daliuta coal mine. Using the best uniform approximation method of Chebyshev interpolation to fit the fan performance curve, we experimentally determined fan characteristics with different frequencies and establish the data base for the curves. Based on ventilation network monitoring theory, we designed a monitoring system for ventilation network parameter monitoring and fan operating frequency automatic control. Using the absolute methane emission quantity to predict the air quantity requirement of branch and fan frequency, we established a f-ω regulation model based on fan frequency and absolute methane emission quantity. After analysing methane emission and distribution characteristics, using CO_2 to simulate the methane emission characteristics from a working face, we verified the correctness and rationality of the f-ω regulation model. The fan operation frequency is adjusted by the method of air adjustment change with methane emission quantity and the curve searching method after determining air quantity requirements. The results show that the air quantity in a branch strictly changes according to the f-ω regulation model, in the airincreasing dilution by fan frequency regulation, the CO_2 concentration is limited to the set threshold value. The paper verifies the practicability of a frequency regulation system and the feasibility of the frequency adjustment scheme and provides guidance for the construction of automatic frequency conversion control system in coal mine ventilation networks.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funded by the National Key Basic Research Program of China(Grant No.2013CB733205)
文摘In order to understand the monitoring efficiency status of the well-water-level observation network in China after the completion of the 10 th "Five-year Plan " digital network project,and to provide a basis for the future network optimization and equipment updating, the monitoring efficiency of the well-water-level observation network was evaluated. On the whole,61. 8% observing stations have good monitoring effectiveness,the observation environment of 73. 5% of observing stations meets the monitoring requirements of well-water-level. The operation status of the network is as a whole getting better,operation rates of 75% observing instruments are above 95%. Most well water levels can monitor crustal stress changes and seismic activities. However,some observation stations,due to inherent deficiency in wells,environmental disturbance,instrument aging,and low-level operation and maintenance,need to improve the monitoring efficiency level by taking measures such as observation environment improvement,equipment updating,and management training. About 6. 5% of the stations need to stop observation due to the unqualified observational environment.
文摘The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example, the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.
基金support from the National Key Research and Development Plan (No.2016YFC0801800)the National Natural Science Foundation of China (No.51404263)+2 种基金the National Natural Science Foundation of Jiangsu (No.BK20130203)the Project Funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutionsthe Fundamental Research Funds for the Central Universities (Nos.2014XT02 and 2014ZDPY03)
文摘In view of the difficulty of automatic adjustment, the recovery lag and the major accident potential of the mine ventilation system, an experimental model of the pipe net was established according to the typical one mine and one working face ventilation system of Daliuta coal mine. Using the best uniform approximation method of Chebyshev interpolation to fit the fan performance curve, we experimentally determined fan characteristics with different frequencies and establish the data base for the curves. Based on ventilation network monitoring theory, we designed a monitoring system for ventilation network parameter monitoring and fan operating frequency automatic control. Using the absolute methane emission quantity to predict the air quantity requirement of branch and fan frequency, we established a f-ω regulation model based on fan frequency and absolute methane emission quantity. After analysing methane emission and distribution characteristics, using CO_2 to simulate the methane emission characteristics from a working face, we verified the correctness and rationality of the f-ω regulation model. The fan operation frequency is adjusted by the method of air adjustment change with methane emission quantity and the curve searching method after determining air quantity requirements. The results show that the air quantity in a branch strictly changes according to the f-ω regulation model, in the airincreasing dilution by fan frequency regulation, the CO_2 concentration is limited to the set threshold value. The paper verifies the practicability of a frequency regulation system and the feasibility of the frequency adjustment scheme and provides guidance for the construction of automatic frequency conversion control system in coal mine ventilation networks.
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.
