一、林木资产(Timber Capital)的理论与方法投入和产出的分析是测定和评价生产力强弱的基本方法和手段,对林业的木材生产力的测定和评价亦不例外。问题在于如何确定和量化表征木材生产力的投入指标和产出指标。以P.J.Ince 博士和 John F...一、林木资产(Timber Capital)的理论与方法投入和产出的分析是测定和评价生产力强弱的基本方法和手段,对林业的木材生产力的测定和评价亦不例外。问题在于如何确定和量化表征木材生产力的投入指标和产出指标。以P.J.Ince 博士和 John Fedkiw 为代表的美国林业经济专家提出了以单位面积的生长量指标来衡量其产出水平,以林木资产和有效供材林地面积指标来衡量其投入水平。一般对于单位面积的生长量和有效供材林地面积两项指标表征的木材生产力大小是可以理解和接受的。关于林木资产指标,研究结果指出木材生产力所表达的意义在于它体现了森林的木材生产数量和可供生产木材的森林资源数量之间的相互的关系。换言之。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 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.
