计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 605-608.
• 综合、交叉与应用 • 上一篇
刘胜娃1, 孙俊明2, 高翔2, 王敏2
LIU Sheng-wa1, SUN Jun-ming2, GAO Xiang2, WANG Min2
摘要： 长庆钻井公司的各部门在过去的十几年里积累了海量的各类钻井数据,随着企业云平台的建设完成和投入运营,目前已经利用数据集成技术将各部门的数据进行了汇总和统一规范管理。挖掘这些积累的宝贵数据可以为科学地制定钻井方案提供参考。机械钻速的准确预测对于科学配置钻井资源、降低钻井成本有重要作用。文中介绍了一种基于人工神经网络技术的定向井机械钻速预测模型实现方法,由钻井领域专家确定网络的输入和输出,由数据工程师设计网络拓扑结构和网络训练。实验结果表明,在数据量较充足、数据质量较高的条件下,采用神经网络构建的预测模型的预测准确度较高,完全能够满足用户的需求。
中图分类号:
| [1]ZHANG N N,WANG Q,WANG J J,et al.Characteristics of oil and gas discoveries in recent 20 years and future exploration in the world.China Petroleum Exploration,2018,23(1):44-53.[2]HU W R,BAO J W.Development trends of oil industry and China’s countermeasures.Journal of China University of Petroleum(Edition of Natural Science) ,2018,42(4):1-10. [3]Yi C,Li G S,SHEN Z H.Experimental study on drilling rate improvement by self-resonating cavitating jet[J]. Natural Gas Industry,2006,26(5):52-54. [4]Yaralia O.Assessment of relationships between drilling rate index and mechanical properties of rocks.Tunnelling & Underground Space Technology Incorporating Trenchless Technology Research,2013,33(33):46-53. [5]LI C S.Study of Method for Predict Rate of Penetration Based of Multiple Regression Analysis.Science Technology & Engineering,2013,13(7):1740-1744. [6]Njobuenwu D O,Wobo C A.Effect of drilled solids on drilling rate and performance.Journal of Petroleum Science & Engineering,2007,55(3):271-276. [7]Rashidi B,Hareland G ,Wu Z .Performance,simulation and field application modeling of rollercone bits.Journal of Petroleum Science and Engineering,2015,133:507-517. [8]BI X L,YAN T,TAO L J.Research on optimizing bit by neural network in Qingshen Oilfield.Journal of Harbin Engineering University,2006,27(z1):111-114. [9]LI J,LI K,WANG B.An Evaluation Model of Drilling Safety Based on Combined Rough Set and Neural Network.Journal of Southwest Petroleum University,2017,39(5):120-128. [10]ZHONG J B ,SHI Y J ,ZHANG H T,et al.Construction and Application of Log Data Transmission and Cooperative Evaluation System Based on Network.Well Logging Technology,2016,40(3):356-363. [11]GUAN Z C,SHENG Y N,XU Y Q,et al.Dynamic risk assessment method of drilling based on PSO optimized BP neural network.Journal of Safety Science & Technology,2017,13(8):5-11. [12]GUO Z M,ZHOU A Y.Research on Data Quality and Data Cleaning:a Surve.Journal of Software,2002,13(11):2076-2082. [13]PIAO J S,YIN K,FAN L M.Neural Network Method in the Penetration Rate Prediction of Percussive Drilling.Journal of Jilin University,2009,39(5):882-886. [14]Reich Y ,Barai S V .Evaluating machine learning models for engineering problems.Artificial Intelligence in Engineering,1999,13(3):257-272. [15]ZHU Q S,ZHOU D D,HUANG W.Application Research of Preprocess in BP Neural Network Sample Data .World Sci-Tech R & D ,2012,34(4):624-626. [16]Hinton G,Vinyals O,Dean J.Distilling the Knowledge in a Neural Network.Computer Science,2015,14(7):38-39. [17]WANG R B,XU H Y,LI B,et al.Research on Method of Determining Hidden Layer Nodes in BP Neural Network.Computer Technology and Development,2018,28(4):31-35. |
| [1] | 周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085 |
| [2] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
| [3] | 宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053 |
| [4] | 何强, 尹震宇, 黄敏, 王兴伟, 王源田, 崔硕, 赵勇. 基于大数据的进化网络影响力分析研究综述 Survey of Influence Analysis of Evolutionary Network Based on Big Data 计算机科学, 2022, 49(8): 1-11. https://doi.org/10.11896/jsjkx.210700240 |
| [5] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
| [6] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
| [7] | 王润安, 邹兆年. 基于物理操作级模型的查询执行时间预测方法 Query Performance Prediction Based on Physical Operation-level Models 计算机科学, 2022, 49(8): 49-55. https://doi.org/10.11896/jsjkx.210700074 |
| [8] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
| [9] | 陈晶, 吴玲玲. 多源异构环境下的车联网大数据混合属性特征检测方法 Mixed Attribute Feature Detection Method of Internet of Vehicles Big Datain Multi-source Heterogeneous Environment 计算机科学, 2022, 49(8): 108-112. https://doi.org/10.11896/jsjkx.220300273 |
| [10] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
| [11] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
| [12] | 闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042 |
| [13] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
| [14] | 彭双, 伍江江, 陈浩, 杜春, 李军. 基于注意力神经网络的对地观测卫星星上自主任务规划方法 Satellite Onboard Observation Task Planning Based on Attention Neural Network 计算机科学, 2022, 49(7): 242-247. https://doi.org/10.11896/jsjkx.210500093 |
| [15] | 费星瑞, 谢逸. 基于HMM-NN的用户点击流识别 Click Streams Recognition for Web Users Based on HMM-NN 计算机科学, 2022, 49(7): 340-349. https://doi.org/10.11896/jsjkx.210600127 |
|
||
首页