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| 用人工神经网络技术优化压裂用砂量 | |
| 引用本文： | 廖锐全,吴志华,徐永高,慕立俊.用人工神经网络技术优化压裂用砂量[J].石油天然气学报,2005,27(2):214-215. |
| 作者姓名： | 廖锐全 吴志华 徐永高 慕立俊 |
| 作者单位： | 1. 长江大学石油工程学院,湖北,荆州,434023 2. 长庆石油管理局第一采油技术服务处,陕西,延安,716000 3. 长庆油田分公司油气工艺技术研究院,陕西,西安,710021 |
| 基金项目： | 中国石油天然气集团公司中青年创新基金资助项目（04E707）. |
| 摘 要： | 以BP网络模型为基础,以地层渗透率、孔隙度、油层厚度、压裂砂量、砂比、施工排量和工作压力等影响压裂效果的主要参数作为输入参数,对某油藏实际的投产压裂井的数据进行训练学习,并由训练计算得到的权重系数，对油藏的压裂施工参数进行计算，得出用砂量和压后产量的关系曲线，以此为依据优化压裂用砂量。 |
| 关 键 词： | 人工神经网络 压裂 压裂用砂量 优化 |
| 文章编号： | 1000-9752（2005）02-0214-02 |
| 收稿时间： | 2005-01-10 |
| 修稿时间： | 2005年1月10日 |
Optimizing the Amount of Sand Usage in Fracture by Using Artificial Neural Networks |
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| LIAO Rui-quan,WU Zhi-hua,XU Yong-gao,MU Li-jun.Optimizing the Amount of Sand Usage in Fracture by Using Artificial Neural Networks[J].Journal of Oil and Gas Technology,2005,27(2):214-215. | |
| Authors: | LIAO Rui-quan WU Zhi-hua XU Yong-gao MU Li-jun |
| Abstract: | Based on the model of BP network, the reservoir permeability, porosity, reservoir thickness, amount of sand in fracturing, sand ratio, operational displacement and working pressure, which are major parameters influencing the fracture effect, are used as imput parameters, the parameters of production-fracturing wells of a reservoir is trained and learnt. The fracturing parameters of the reservoir are calculated by using the weighted factors obtained from calculation in training, and a correlation curve between sand producing rate and post fracturing production is derived, which is used as a basis for optimizing sand usage in fracturing. |
| Keywords: | artificial neural network amount of sand in fracturing optimization |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |
