Knowledge Mining for Oilfield Development Index Prediction Model Using Deep Learning

doi:10.11885/j.issn.1674-5086.2020.05.11.02

Abstract

Abstract: The changing characteristics of oilfield development index are regarded as the important basis of oilfield development planning, oilfield exploitation evaluation, oilfield development scheme design and adjustment, decision management problems of oilfield development risk prediction and early warning, etc. For one of the unsolved bottleneck problem of building intelligent oilfield, i.e. the problem on knowledge mining of selecting prediction method and model of oilfield development indexes intelligent prediction system, based on the massive data of oilfield development, this paper uses the convolutional neural network and cyclic neural network of deep learning to extract the characteristics and knowledge reflecting the development dynamic of oilfield. On this basis, combining the model base and knowledge base of oilfield development index prediction, a knowledge mining method to select the optimal prediction model of oilfield development index is proposed through the input information and dynamic characteristics index of oilfield development, the model base and the knowledge base of oilfield development index prediction by using the joint extraction method of entity and relationship of deep learning. The simulation example of conceptual design shows that the proposed knowledge mining process may realize autonomous obtaining an appropriate prediction model of oilfield development index as long as inputting relevant information of oilfield development.

Key words: oilfield development index, prediction model, knowledge mining, deep learning, entities and relationship

CLC Number:

TE33

ZHONG Yihua, WANG Shuning, LUO Lan, YANG Jinlian, YUE Yongpeng. Knowledge Mining for Oilfield Development Index Prediction Model Using Deep Learning[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 63-74.

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References

[1] 钟仪华. 特高含水期油田开发规划动态预测方法研究[D]. 成都:西南石油大学, 2008. ZHONG Yihua. The study of dynamic prediction methods for oilfield development programming in extra-high watercut stage[D]. Chengdu:Southwest Petroleum University, 2008.
[2] ZHONG Yihua, LI Lina, WANG Yu. The study of model selection method on intelligent prediction system and its applications[C]. International Conference on Computational & Information Sciences, 2011. doi:10.1109/ICCIS. 2011.301
[3] ZHONG Yihua, ZHU Mingxia, ZHANG Zhiyin. An intelligent prediction model for oilfield production based on fuzzy expert system[J]. Advances in Intelligent and Soft Computing, 2012, 147:475-484. doi:10.1007/978-3-642-28592-9_50
[4] 张志银. 油田开发指标系统的智能预测研究[D]. 成都:西南石油大学, 2012. ZHANG Zhiyin. The research of intelligent prediction on oilfield development index system[D]. Chengdu:Southwest Petroleum University, 2012.
[5] 廖昱芯. 基于智能预测系统的知识挖掘及应用研究[D]. 成都:西南石油大学, 2013. LIAO Yuxin. The research of knowledge mining based on intelligent prediction system and its application[D]. Chengdu:Southwest Petroleum University, 2013.
[6] 李丽娜. 基于智能预测的模糊预测控制方法及应用研究[D]. 成都:西南石油大学, 2013. LI Lina. The research of fuzzy predictive control method based on intelligent prediction and its application[D]. Chengdu:Southwest Petroleum University, 2013.
[7] ZHONG Yihua, WANG Dan, YUE Yongpeng, et al. Intelligent prediction system of oilfield development index[C]. Proceedings of the Asia-Pacific Computer Science and Application Conference, 2014:229-233. doi:10.1201/b18508-40
[8] 王丹. 基于Agent的油田开发指标的智能预测系统研究[D]. 成都:西南石油大学, 2015. WANG Dan. The research on intelligent prediction system of oilfield development index based on Agent[D]. Chengdu:Southwest Petroleum University, 2015.
[9] ZHONG Yihua, LÜ Xiaodie, BAO Min, et al. Design for intelligent prediction system of oilfield development index based on pattern recognition[J]. Filomat, 2018, 32(5):1757-1764. doi:10.2298/FIL1805757Z
[10] YANG Qingjun, ZHANG Shulin, FEI Qi. Integrating neural network and numerical simulation for production performance prediction of low permeability reservoir[J]. Petroleum Science and Technology, 2005, 23(5-6):579-590. doi:10.1081/LFT-200032851
[11] AI-JASMI A K, GOEL H K, NASR H, et al. Short-term production prediction in real time using intelligent techniques[C]. SPE 164813-MS, 2013.
[12] ZHONG Yihua, ZHAO Lei, LIU Zhibin, et al. Using a support vector machine method to predict the development indices of very high water cut oilfields[J]. Petroleum Science, 2010, 7(3):379-384. doi:10.1007/s12182-010-0081-1
[13] 朱秀莉,李龙,李盼池. 基于T-S推理网络的油田开发指标预测方法[J]. 计算机应用研究, 2011, 28(8):2991-2993. doi:10.3969/j.issn.1001-3695.2011.08.052 ZHU Xiuli, LI Long, LI Panchi. Forecasting methods of oil field development indexes based on T-S reasoning networks[J]. Application Research of Computers, 2011, 28(8):2991-2993. doi:10.3969/j.issn.1001-3695.2011.08.052
[14] 张巧翠. 基于模糊计算的油田开发指标预测模型及应用研究[D]. 大庆:东北石油大学, 2011. ZHANG Qiaocui. Research on model and application of oilfield development index prediction based on fuzzy computation[D]. Daqing:Northeast Petroleum University, 2011.
[15] 张宇航. 基于油藏建模成果的油田开发指标预测及状态评估方法[D]. 大庆:东北石油大学, 2016. ZHANG Yuhang. The method of oilfield development index prediction and condition assessment based on the reservoir modeling results[D]. Daqing:Northeast Petroleum University, 2016.
[16] SRIDEVI S, RAJARAM S, SWADHIKAR C. An intelligent prediction system for time series data using periodic pattern mining in temporal databases[C]. Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia, 2010:163-171. doi:10.1145/1963564.1963592
[17] ROSLI N S, IBRAHIM R, ISMAIL I. Intelligent prediction system for gas metering system using particle swarm optimization in training neural network[J]. Procedia Computer Science, 2017, 105:165-169. doi:10.1016/j.procs. 2017.01.197
[18] HAMED Z S, JAMES P K, S A. Developing an intelligent expert system for stream flow prediction, integrated in a dynamic decision support system for managing multiple reservoirs:A case study[J]. Expert Systems with Applications, 2017, 83:145-163. doi:10.1016/j.eswa.2017.04.039
[19] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521:436-444. doi:10.1038/nature14539
[20] 谷建伟,周梅,李志涛,等. 基于数据挖掘的长短期记忆网络模型油井产量预测方法[J]. 特种油气藏,2019,26(2):77-81. doi:10.3969/j.issn.1006-6535.2019.02.013 GU Jianwei, ZHOU Mei, LI Zhitao, et al. Oil well production forecast with long-short term memory network model based on data mining[J]. Special Oil and Gas Reservoir, 2019, 26(2):77-81. doi:10.3969/j.issn.1006-6535.2019.02.013
[21] 邹晶. 基于深度学习的实体与关系联合提取方法研究[D]. 成都:电子科技大学, 2019. ZOU Jing. Research on entity and relationship joint extraction method based on deep learning[D]. Chengdu:University of Electronic Science and Technology of China, 2019.
[22] 李元,冯成成. 基于一维卷积神经网络深度学习的工业过程故障检测[J]. 测控技术, 2019, 38(9):36-40. doi:10.19708/j.ckjs.2019.09.007 LI Yuan, FENG Chengcheng. Fault detection of industrial process based on deep learning of one-dimensional convolutional neural network[J]. Measurement & Control Technology, 2019, 38(9):36-40. doi:10.19708/j.ckjs.2019.09.007
[23] 林锦发,程良伦,应稼田. 基于一维卷积神经网络的多特征社交网络流行度预测研究[J]. 现代计算机, 2019, 643(7):3-9. doi:10.3969/j.issn.1007-1423.2019.07.001 LIN Jinfa, CHENG Lianglun, YING Jiatian. Research on popularity prediction of multi-feature social networks based on one-dimensional convolutional neural networks[J]. Modern Computer, 2019, 643(7):3-9. doi:10.3969/j.issn.1007-1423.2019.07.001
[24] 李道全,王雪,于波,等. 基于一维卷积神经网络的网络流量分类方法[J]. 计算机工程与应用, 2020, 56(3):94-99. doi:10.3778/j.issn.1002-8331.1905-0139 LI Daoquan, WANG Xue, YU Bo, et al. Network traffic classification method based on one-dimensional convolutional neural network[J]. Computer Engineering and Applications, 2020, 56(3):94-99. doi:10.3778/j.issn.1002-8331.1905-0139
[25] 宋辉,陈伟,李谋杰,等. 基于卷积门控循环单元网络的储层参数预测方法[J]. 油气地质与采收率, 2019, 26(5):73-78. doi:10.13673/j.cnki.cn37-1359/te.2019.05.009 SONG Hui, CHEN Wei, LI Moujie, et al. A method to predict reservoir parameters based on convolutional neural network-gated recurrent unit(CNN-GRU)[J]. Petroleum Geology and Recovery Efficiency, 2019, 26(5):73-78. doi:10.13673/j.cnki.cn37-1359/te.2019.05.009
[26] 李大中,颖宇,王超. 基于卷积循环神经网络深度学习的短期风速预测[J]. 电力科学与工程, 2019, 35(8):1-6. doi:10.3969/j.ISSN.1672-0792.2019.08.001 LI Dazhong, YING Yu, WANG Chao. Short-term wind speed prediction based on convolutional cyclic neural network and deep learning[J]. Electric Power Science and Engineering, 2019, 35(8):1-6. doi:10.3969/j.ISSN.1672-0792.2019.08.001
[27] 李东东,王浩,杨帆,等. 基于一维卷积神经网络和Soft-Max分类器的风电机组行星齿轮箱故障检测[J]. 电机与控制应用, 2018, 45(6):80-87, 108. LI Dongdong, WANG Hao, YANG Fan, et al. Fault detection of wind turbine planetary gear box using 1D convolutional neural networks and soft-max classifier[J]. Electric Machines & Control Application, 2018, 45(6):80-87, 108.