Skip to main content
SpringerLink
Log in

Cloud Computation Processing for Oilfield Block Data and Chain Drive Pumping Unit Polished Rod Motion Model

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

The oil wells are of similar physical parameters but different production parameters in an oilfield block, but selecting the equipment for every well one by one is unpractical. The measured polished rod load data was taken into account in the Fuzzy Clustering C Means algorithm to work out the typical production data, such as polished rod load on the basis of the cloud computing processing for the vast measured polished rod load data in the test wells in a block. The dynamical equation of the chain pumping unit being constructed, the load data are used to simulate the motion of the pumping unit by means of the numerical iteration algorithm. It is shown that the max acceleration does not occur at the up and down dead points in the stroke but at the position about 5–11 ∘ from the dead points. The combination of the typical production data resulting from the cloud computing processing and the numerical iteration algorithm can solve the practical problem of equipment selection and simulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

  1. Chen, F., Qi, Y., Wang, W., Ming, Y., Wang, Q., & Wu, J. (2012). Study on applicability of annular jet pump dewatering gas production in cbm wells. Oil Field Equipment, 6, 013.

    Google Scholar 

  2. Fenna, Z., Yaoguang, Q., & Chuncheng, X. (2013). Analysis of the impact of gas production channel for coalbed methane well by pulverized coal. Journal of China University of Mining and Technology, 42(3), 430–437.

    Google Scholar 

  3. Gai, K., & Li, S. (2012). Towards cloud computing: a literature review on cloud computing and its development trends. In 2012 Fourth International Conference on Multimedia Information Networking and Security (pp. 142–146). Nanjing, China.

  4. Gai, K., & Steenkamp, A. (2014). A feasibility study of Platform-as-a-Service using cloud computing for a global service organization. Journal of Information System Applied Research, 7, 28–42.

    Google Scholar 

  5. Gai, K., Du, Z., Qiu, M., & Zhao, H. (2015a). Efficiency-Aware Workload Optimizations of Heterogeneous Cloud Computing for Capacity Planning in Financial Industry. In The 2nd IEEE International Conference on CSCloud (pp. 1–6). New York, USA: IEEE.

  6. Gai, K., Qiu, M., Tao, L., & Zhu, Y. (2015b). Intrusion detection techniques for mobile cloud computing in heterogeneous 5G. Security and Communication Networks, 1–10.

  7. Gai, K., Qiu, M., Thuraisingham, B., & Tao, L. (2015c). Proactive Attribute-based Secure Data Schema for Mobile Cloud in Financial Industry. In The IEEE International Symposium on Big Data Security on Cloud; 17th IEEE International Conference on High Performance Computing and Communications (pp. 1332–1337). New York, USA.

  8. Gai, K., Qiu, L., Zhao, H., & Qiu, M. (2016a). Cost-aware multimedia data allocation for heterogeneous memory using genetic algorithm in cloud computing. IEEE Transactions on Cloud Computing, PP(99), 1.

  9. Gai, K., Qiu, M., Zhao, H., Tao, L., & Zong, Z. (2016b). Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. Journal of Network and Computer Applications, 59, 46–54.

  10. Gai, K., Qiu, L., Chen, M., Zhao, H., & Qiu, M. (2016c). SA-EAST: security-aware efficient data transmission for ITS in mobile heterogeneous cloud computing. ACM Transactions on Embedded Computing Systems, PP(99), 1.

  11. Li, J., Ming, Z., Qiu, M., Quan, G., Qin, X., & Chen, T. (2011). Resource allocation robustness in multicore embedded systems with inaccurate information. Journal of Systems Architecture, 57(9), 840–849.

    Article  Google Scholar 

  12. Li, J., Qiu, M., Niu, J., Yang, L., Zhu, Y., & Ming, Z. (2013a). Thermal-aware task scheduling in 3D chip multiprocessor with real-time constrained workloads. ACM Transactions on Embedded Computing Systems (TECS), 12(2), 24.

  13. Li, K., Gao, X., Yang, W., & Dai, Y. (2013b). Multiple fault diagnosis of down-hole conditions of suckerrod pumping wells based on freeman chain code and DCA. Petroleum Science, 10(3), 347–360.

  14. Li, Y., Gai, K., Ming, Z., Zhao, H., & Qiu, M. (2016). Intercrossed access control for secure financial services on multimedia big data in cloud systems. ACM Transactions on Multimedia Computing Communications and Applications, PP(99), 1.

    Google Scholar 

  15. Niu, J., Gao, Y., Qiu, M., & Ming, Z. (2012). Selecting proper wireless network interfaces for user experience enhancement with guaranteed probability. Journal of Parallel and Distributed Computing, 72(12), 1565–1575.

    Article  Google Scholar 

  16. Qiu, M., Ming, Z., Li, J., Liu, S., Wang, B., & Lu, Z. (2012). Three-phase time-aware energy minimization with DVFS and unrolling for chip multiprocessors. Journal of System Architecture, 58(10), 439–445.

    Article  Google Scholar 

  17. Qiu, M., Zhong, M., Li, J., Gai, K., & Zong, Z. (2015). Phase-change memory optimization for green cloud with genetic algorithm. IEEE Transactions on Computers, 64(12), 3528– 3540.

    Article  MathSciNet  MATH  Google Scholar 

  18. Qiu, M., Gai, K., Thuraisingham, B., Tao, L., & Zhao, H. (2016). Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry. Future Generation Computer Systems, PP, 1.

    Google Scholar 

  19. Quan, Z., Quan, L., & Zhang, J. (2014). Review of energy efficient direct pump controlled cylinder electro-hydraulic technology. RSER, 35, 336–346.

    Google Scholar 

  20. Shen, R., QI, M., Liu, J., & Tang, F. (2013). Key technology research of vrml simulation about semisubmersible drilling platform. Oil Field Equipment, 6, 003.

    Google Scholar 

  21. Wu, G., Zhang, H., Qiu, M., Ming, Z., Li, J., & Qin, X. (2013). A decentralized approach for mining event correlations in distributed system monitoring. Journal of parallel and Distributed Computing, 73(3), 330–340.

    Article  MATH  Google Scholar 

  22. Xu, F., Zhao, Z., & Zhou, Y. (2014). Numerical analysis method for outline shape design of rear horse head of dual horse head pumping unit. Oil Field Equipment, 6, 006.

    Google Scholar 

  23. Yang, D., Gao, X., & Dai, Y. (2010). Dynamic simulation system of variable parameter flexible linkage mechanism of dual horse head pump unit. Jixie Gongcheng Xuebao, 46(9), 59– 65.

    Article  Google Scholar 

  24. Yin, H., & Gai, K. (2015). An Empirical Study on Preprocessing High-dimensional Class-imbalanced Data for Classification. In The IEEE International Symposium on Big Data Security on Cloud (pp. 1314–1319). New York, USA.

Download references

Acknowledgments

This work was supported by grants from National Major Special Project of Oil and Gas “Study and Promotion of the Self-Adaptive Control Technology of Drainage Based on Shaft Flow Field” (2016ZX05042003-001); National Major Special Project of Oil and Gas “Key Equipment Development of Integrated Development of Three Kind of Unconventional gas in One Well” (2016ZX05066004-002); NSFC (51174224); “Fundamental Research Funds for the Central Universities” (16CX02004A)

Author information

Authors and Affiliations

  1. The Petroleum University of China (East China), Qingdao, Shandong, 266555, China

    Haihui Zhao, Yaoguang Qi, Hongwei Du, Ningning Wang & Guofu Zhang

  2. Energy Equipment and Engineering Research Institute of Lanshi Group Ltd, Lanzhou, China

    Wenbao Liu & Hailong Lu

Authors
  1. Haihui Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaoguang Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongwei Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ningning Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guofu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wenbao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hailong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haihui Zhao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, H., Qi, Y., Du, H. et al. Cloud Computation Processing for Oilfield Block Data and Chain Drive Pumping Unit Polished Rod Motion Model. J Sign Process Syst 89, 41–50 (2017). https://doi.org/10.1007/s11265-016-1175-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-016-1175-9

Keywords

Search

Navigation