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Optimization of Flow Rate and Pipe Rotation Speed Considering Effective Cuttings Transport Using Data-Driven Models

Author

Listed:
  • Evren Ozbayoglu

    (Department of Petroleum Engineering, The University of Tulsa, Tulsa, OK 74104, USA)

  • Murat Ozbayoglu

    (Department of Computer Engineering, TOBB University of Economics and Technology, 06560 Ankara, Turkey)

  • Baris Guney Ozdilli

    (Department of Computer Engineering, TOBB University of Economics and Technology, 06560 Ankara, Turkey)

  • Oney Erge

    (Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA)

Abstract

Effectively transporting drilled cuttings to the surface is a vital part of the well construction process. Usually, mechanistic models are used to estimate the cuttings concentration during drilling. Based on the results from these model, operational parameters are adjusted to mitigate any nonproductive time events such as pack-off or lost circulation. However, these models do not capture the underlying complex physics completely and frequently require updating the input parameters, which is usually performed manually. To address this, in this study, a data-driven modeling approach is taken and evaluated together with widely used mechanistic models. Artificial neural networks are selected after several trials. The experimental data collected at The University of Tulsa–Drilling Research Projects (in the last 40 years) are used to train and validate the model, which includes a wide range of wellbore and pipe sizes, inclinations, rate-of-penetration values, pipe rotation speeds, flow rates, and fluid and cuttings properties. It is observed that, in many cases, the data-driven model significantly outperforms the mechanistic models, which provides a very promising direction for real-time drilling optimization and automation. After the neural network is proven to work effectively, an optimization attempt to estimate flow rate and pipe rotation speed is introduced using a genetic algorithm. The decision is made considering minimizing the required total energy for this process. This approach may be used as a design tool to identify the required flow rate and pipe rotation speed to acquire effective hole cleaning while consuming minimal energy.

Suggested Citation

  • Evren Ozbayoglu & Murat Ozbayoglu & Baris Guney Ozdilli & Oney Erge, 2021. "Optimization of Flow Rate and Pipe Rotation Speed Considering Effective Cuttings Transport Using Data-Driven Models," Energies, MDPI, vol. 14(5), pages 1-32, March.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1484-:d:513239
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    References listed on IDEAS

    as
    1. Joana Dias & Humberto Rocha & Brígida Ferreira & Maria Lopes, 2014. "A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(3), pages 431-455, September.
    2. Liu Yongwang & Liu Yu-ming & Qiu Heng-bin & Bai Yan-feng, 2017. "A New Mathematical Method for Solving Cuttings Transport Problem of Horizontal Wells: Ant Colony Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-8, August.
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