Management and instant query of distributed oil and gas production dynamic data

  • Hongliang WANG ,
  • Longxin MU ,
  • Fugeng SHI ,
  • Kaiming LIU ,
  • Yurong QIAN
Expand
  • 1. PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China
    2. Xinjiang University, Urumqi 830008, China

Received date: 2019-03-07

  Revised date: 2019-06-18

  Online published: 2019-10-22

Supported by

Supported by the China National Science and Technology Major Project(2016ZX05016-006)

Abstract

The multidimensional analysis engine data management platform is constructed using big data distributed storage and parallel computing, data warehouse modeling technology, realizing the optimal management and instant query of distributed oil and gas production dynamic big data. The centralized management and quick response of the production data of more than 36×10 4 oil, gas and water wells is realized. Multidimensional analysis subject model of oil, gas and water well production is built to pretreat the relevant data. At the level of China National Petroleum Corporation (CNPC), the rapid analysis and applications such as oil and gas production tracking, early production warning of key oilfields, analysis of low production wells and long shutdown wells, classification of reservoir development laws have been realized, and the processing time has been shortened from 1 d to 5 s. The basic unit of oil and gas production analysis is refined from oilfield to single well, making the production management more detailed. The process can be traced step by step according to CNPC, oil field company, field, block and single well, and the oil and gas production performance of each unit can be mastered in real time.

Cite this article

Hongliang WANG , Longxin MU , Fugeng SHI , Kaiming LIU , Yurong QIAN . Management and instant query of distributed oil and gas production dynamic data[J]. Petroleum Exploration and Development, 2019 , 46(5) : 1014 -1021 . DOI: 10.1016/S1876-3804(19)60258-2

References

[1] PERRONS R K, JENSEN J W . Data as an asset: What the oil and gas sector can learn from other industries about “Big Data”. Energy Policy, 2015,81:117-121.
[2] SUMBAL M S, TSUI E, SEE-TO E W K . Interrelationship between big data and knowledge management: An exploratory study in the oil and gas sector. Journal of Knowledge Management, 2017,21(1):180-196.
[3] LI Jinnuo . Talking about the development trend of big data in petroleum industry. Value Engineering, 2013,32(29):172-174.
[4] LI Dawei, XIONG Huaping, SHI Guangren , et al. Preprocessing of the data tapping based on global typical oil and gas field database. Petroleum Geology and Oilfield Development in Daqing, 2016,35(1):66-70.
[5] LU Shuaishuai . Research on distributed data warehouse system for oil and gas drilling information in big data environment. Xi’an: Xi’an Petroleum University, 2018.
[6] QU Haixu . Research and application of oilfield production and operation optimization system based on big data. Daqing: Northeast Petroleum University, 2016.
[7] YANG Fei, ZHOU Jing . Development of intelligent drilling big data technology. Scientific Management, 2017(9):230-231.
[8] ZHANG Dongxiao, CHEN Yuntian, MENG Jin . Synthetic well logs generation via Recurrent Neural Networks. Petroleum Exploration and Development, 2018,45(4):598-607.
[9] HUANG Wensong, WANG Jiahua, CHEN Heping , et al. Big data paradox and modeling strategies in geological modeling based on horizontal wells data. Petroleum Exploration and Development, 2017,44(6):939-947.
[10] LI Xizhe, LIU Xiaohua, SU Yunhe , et al. Correlation between per-well average dynamic reserves and initial absolute open flow potential(AOFP) for large gas fields in China and its application. Petroleum Exploration and Development, 2018,45(6):1020-1025.
[11] KIM J S, KIM B S . Analysis of fire-accident factors using big-data analysis method for construction areas. KSCE Journal of Civil Engineering, 2017(4):1-9.
[12] WIBISONO A, JATMIKO W, WISESA H A , et al. Traffic big data prediction and visualization using fast incremental model trees-drift detection (FIMT-DD). Knowledge-Based Systems, 2016,93:33-46.
[13] ALHARTHI A, KROTOV V, BOWMAN M . Addressing barriers to big data. Business Horizons, 2017,60(3):285-292.
[14] DENG Z H, LYU S L . PrePost+: An efficient N-lists-based algorithm for mining frequent item sets via children-parent equivalence pruning. Expert Systems with Applications, 2015,42(13):5424-5432.
[15] YAN X, ZHANG J, XUN Y , et al. A parallel algorithm for mining constrained frequent patterns using MapReduce. Soft Computing, 2017,21(9):2237-2249.
[16] VO B, LE T, COENEN F , et al. Mining frequent itemsets using the N-list and subsume concepts. International Journal of Machine Learning and Cybernetics, 2016,7(2):253-265.
[17] HE Ming, CHANG Mengmeng, LIU Guoyang , et al. Log mining based on SQL-on-Hadoop query engine and its application. Journal of Intelligent Systems, 2017,12(5):717-728.
[18] LOWD D, DAVIS J . Improving Markov network structure learning using decision trees. Journal of Machine Learning Research, 2014,15(1):501-532.
[19] MCAFEE A, BRYNJOLFSSON E . Big data: The management revolution. Harv. Bus. Rev., 2012,90(10):60-66.
[20] WANG Kang, CHEN Haiguang, LI Dongjing . Performance optimization based on Hive. Journal of Shanghai Normal University (Edition of Natural Science), 2017,46(4):527-534.
[21] ZHANG Yansong, JIAO Min, ZHANG Yu , et al. Research on concurrent memory OLAP query optimization technology. Computer Research and Development, 2016,53(12):2836-2846.
[22] ZHANG Yansong, ZHANG Yu, ZHOU Xuan , et al. Research on OLAP query processing technology for asymmetric memory computing platform. Journal of East China Normal University (Edition of Natural Science), 2016(5):89-102.
[23] CAI Xukun . Design and implementation of production data aggregation and management system based on Hive and Apache Kylin. Guangzhou: South China University of Technology, 2018.
[24] CHEN L, FENG C Y . Research on the strategy for temporal information index based on HBase. Journal of Guangdong University of Technology, 2014,12(3):1-4.
[25] MALLEK H, GHOZZI F, TESTE O , et al. BigDimETL: ETL for multidimensional big data. Berlin: Springer, 2017.
Outlines

/