1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. Third EAGE Digitalization Conference and Exhibition
  5. Conference paper

Abstract

Summary

Optimal well controls to maximize the net present value (NPV) are usually obtained by coupling the numerical reservoir simulator with optimization algorithms. This approach requires significant number of simulations that are computationally expensive. Proxy models have a high capability to identify complex dynamic reservoir behavior in short time.

This study proposes a methodology by developing smart proxy models (SPMs) using Artificial Neural Network (ANN) for a synthetic field model to predict field production profiles. The method then integrates the established proxy models with Genetic Algorithm (GA) to solve the well control optimization problem. From SPM-GA coupling, the optimum well control parameters, namely bottom hole pressures of the injectors and producers are investigated to maximize NPV.

The developed SPMs produce outputs within seconds, while the numerical simulator takes an average time of 30 minutes for the case study. SPM-GA coupling works well for well control optimization by finding BHP configuration that gives an increase of over 30% in NPV, and requires fewer simulations compared to the traditional approach. The results show that the established proxy models are robust and efficient tools for mimicking the numerical simulator performance in well control optimization. Significant reduction in computational time and resources is observed.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202332027
2023-03-20
2024-04-28

  • From This Site

    /content/papers/10.3997/2214-4609.202332027
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Awotunde, A., Sibaweihi, N. [2018] A multi-level clustering method for field development optimization under uncertainty, in EAGE-TNO Workshop on OLYMPUS Field Development.
     [Google Scholar]
  2. Chaturvedi, A. [2021] Well Control Optimization by Coupling Smart Proxy Models with Genetic Algorithm. Master’s thesis, Norwegian University of Science and Technology.
     [Google Scholar]
  3. Fonseca, R.M., Rossa, E.D., Emerick, A.A., Hanea, R.G., Jansen, J.D. [2018] Overview of the Olympus Field Development Optimization Challenge, in EAGE.
     [Google Scholar]
  4. Isebor, O.J., Durlofsky, L.J., Echeverría Ciaurri, D. [2014] A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls.Computational Geosciences, 18, 463–482.
     [Google Scholar]
  5. Jansen, J.D., Durlofsky, L.J. [2017] Use of reduced-order models in well control optimization.Optimization and Engineering, 18, 105–132.
     [Google Scholar]
  6. Jin, Z.L., Liu, Y., Durlofsky, L.J. [2020] Deep-learning-based surrogate model for reservoir simulation with time-varying well controls.J. Pet. Sci. Eng., 192, 107273.
     [Google Scholar]
  7. Ng, C.S.W., Jahanbani, G. A. [2022] Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron.Appl. Comput. Geosci., 16, 100103.
     [Google Scholar]
  8. Ng, C.S.W., Jahanbani, G. A. [2023] Optimizing initiation time of waterflooding under geological uncertainties with Value of Information: Application of simulation-regression approach.J. Pet. Sci. Eng., 220, 111166.
     [Google Scholar]
  9. Ng, C.S.W., Jahanbani, G. A., Nait Amar, M. [2021] Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization.Journal of Petroleum Exploration and Production Technology, 11, 3103–3127.
     [Google Scholar]
  10. Ng, C.S.W., Nait Amar, M., Jahanbani, G. A., Imsland, L.S. [2023] A Survey on the Application of Machine Learning and Metaheuristic Algorithms for Intelligent Proxy Modeling in Reservoir Simulation.Comput. Chem. Eng., 170, 108107.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202332027
Loading
Well Control Optimization Using Smart Proxy Models
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202332027
/content/papers/10.3997/2214-4609.202332027
/content/papers/10.3997/2214-4609.202332027
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

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