| 基于人工神经网络的钻压优化模型的建模方法 |
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| 引用本文： | 史玉升.基于人工神经网络的钻压优化模型的建模方法[J].地球科学,1999,24(4):432-436. |
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| 作者姓名： | 史玉升 |
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| 作者单位： | 华中理工大学机械学院武汉 |
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| 基金项目： | 华中理工大学校科研和教改项目;; |
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| 摘 要： | 提出卫种新的钻压优化模型建模方法－－神经网络法，它可以克服传统方法需要建立数学模型的缺陷，满足自动送钻对实时性的要求。给出了利用反向传播神经网络（ＢＰ网络）建立钻压优化模型的方法，计算机仿真研究表明：利用这种新方法建立的模型不但能够满足自动送钻实时优化钻压的要求，而且也可以用于离线的钻压参数优选。
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| 关 键 词： | 自动送钻 钻压优化模型 神经网络 实时 |
MODELING METHOD FOR WEIGHT-ON-BIT OPTIMIZING MODEL BASED ON ARTIFICIAL NEURAL NETWORK |
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| Shi Yusheng.MODELING METHOD FOR WEIGHT-ON-BIT OPTIMIZING MODEL BASED ON ARTIFICIAL NEURAL NETWORK[J].Earth Science-Journal of China University of Geosciences,1999,24(4):432-436. |
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| Authors: | Shi Yusheng |
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| Abstract: | The
research into the groundwater seepage field and karst environment changes in a large
water-filled impounding reservoir in the carbonate rock region is both an engineering
problem and a theoretical problem of karst environment. The numerical simulation is
adopted in this paper with the Gaobazhou and Geheyan reservoirs as examples to simulate
the groundwater flow network features of the plane and profile of the groundwater seepage
field and also to simulate the spatial change pattern in the groundwater seepage flow
velocity before and after the water is filled in the impounding reservoir. On this basis,
the theoretical model of the DBL (diffusion boundary layer) is applied to the analysis of
the karst environment evolution trend at the different positions of the research region.
The research results show that a large increase in the water level in the reservoir, and
the construction of the seepage-proof curtain has resulted in the decrease in the karst
intensity at the front of the curtain, but in the increase in the karst intensity at the
back of the curtain where the calcite precipitation occurs. This phenomenon has been
proved true in the water-filled Geheyan reservoir. |
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| Keywords: | automatic bit feeding optimizing model for weight_on_bit (WOB) neural network real time |
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