1887

Abstract

Summary

Hydraulic fracturing is one of the most beneficial operations targeted to enhance oil production from unconventional reservoirs, and, certainly, the essential criterion of its success is properly planned hydraulic fracturing design. To make it optimal, the specialist should analyze plenty of appropriate data sources and decide which of them have the greatest impact on the outcome. It seems that machine learning algorithms are effective solution to the problem as they help finding hidden correlations between input and output variables (cumulative oil production, in this case) and highlight those which exert influence mostly. It is worth noting that one of the most valuable aspect of such approach is an opportunity to process vast amount of various data, which is directly relevant to the analysis from the engineer’s point of view. The goal of the research is to find the most robust algorithm able to forecast the target variable and define key hydraulic fracturing design parameters.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202053133
2020-11-16
2024-03-28
Loading full text...

Full text loading...

References

  1. Bakshi, A., Uniacke, E., Korjani, M. and Ershaghi, I.
    [2017] A Novel Adaptive Non-Linear Regression Method to Predict Shale Oil Well Performance Based on Well Completions and Fracturing Data. SPE185695-MS.
    [Google Scholar]
  2. Bowie, B.
    [2018] Machine Learning Applied to Optimize Duvernay Well Performance. SPE 189823-MS.
    [Google Scholar]
  3. DomingosP.
    [2012] A few useful things to know about machine learning. Communications of the ACM. 55(10), 78–87.
    [Google Scholar]
  4. FriedmanJ., HastieT. and TibshiraniR.
    [2001] Elements of Statistical Learning: Prediction, Inference and Data Mining. Springer.
    [Google Scholar]
  5. Lolon, E., Hamidieh, K., Weijers, L., Mayerhofer, M., Melcher, H. and Oduba, O.
    [2016] Evaluating the Relationship between Well Parameters and Production Using Multivariate Statistical Models: A Middle Bakken and Three Forks Case History. SPE 179171-MS.
    [Google Scholar]
  6. Nejad, A. M., Sheludko, S., Shelley, R. F., Hodgson, T. and McFall, P. R.
    [2015] A Case History: Evaluating Well Completions in the Eagle Ford Shale Using a Data-Driven Approach. SPE 173336-MS.
    [Google Scholar]
  7. Schuetter, J., MishraS., Zhong, M. and LaFollette, R. F.
    [2015] Data Analytics for Production Optimization in Unconventional Reservoirs. SPE 178653-MS/URTeC:2167005.
    [Google Scholar]
  8. Shelley, R. F., & Grieser, W. V.
    [1999] Artificial Neural Network Enhanced Completions Improve Well Economics. SPE 52959.
    [Google Scholar]
  9. Shelley, R. F., Saugier, L. D., Al-Tailji, W., Guliyev, N., Shah, K. and Godwin, J.
    [2012] Data-Driven Modeling Improves the Understanding of Hydraulic Fracture Stimulated Horizontal Eagle Ford Completions // SPE 152121.
    [Google Scholar]
  10. Wang, S. and Chen, S.
    [2016] A Comprehensive Evaluation of Well Completion and Production Performance in Bakken Shale Using Data-Driven Approaches. SPE 181803-MS.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202053133
Loading
/content/papers/10.3997/2214-4609.202053133
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error