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

Evren Ozbayoglu, Murat Ozbayoglu, Baris Guney Ozdilli and Oney Erge
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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

Energies, 2021, vol. 14, issue 5, 1-32

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.

Keywords: cuttings transport; artificial neural networks; optimization; hole cleaning; machine learning; data driven (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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