Training-while-drilling approach to inclination prediction in directional drilling utilizing recurrent neural networks

Tunkiel, Andrzej T.; Sui, Dan; Wiktorski, Tomasz

doi:10.1016/j.petrol.2020.108128

Cited by 13 publications

(2 citation statements)

References 17 publications

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“…Compared with the traditional design model, the time cost is reduced. Well trajectory design is a parameter matrix optimization problem, and the optimization objectives are usually borehole length, string torque, target hit, and oil and gas production [15].…”

Section: Intelligent Prediction Of Well Trajectorymentioning

confidence: 99%

Application of Artificial Intelligence in Drilling and Completion

Yang,

Shang,

et al. 2024

Advances in Oil and Gas Well Engineering

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In this chapter, we will delve into the applications of Artificial Intelligence (AI) in drilling and completion engineering within the oil and gas industry. The scope of this chapter will include the fundamentals of machine learning and deep learning, the essential algorithms, and the workflow of AI in drilling and completion engineering, from data collection to implementation and optimization. Furthermore, we will discuss various AI application areas, such as drilling parameter optimization, downhole environment detection, intelligent completion design, and more. Lastly, we will address the challenges and prospects of AI in drilling and completion engineering, examining issues related to data quality, model accuracy, reliability, and future development trends. This comprehensive exploration aims to provide readers with a solid understanding of the potential and limitations of AI in the drilling and completion engineering domain.

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Section: Intelligent Prediction Of Well Trajectorymentioning

confidence: 99%

Application of Artificial Intelligence in Drilling and Completion

Yang,

Shang,

et al. 2024

Advances in Oil and Gas Well Engineering

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“…(3) Trajectory control. Horizontal wells require directional operations due to the need for a well incline and azimuth adjustment [26]. The positive displacement motor (PDM) is a commonly used tool for trajectory control.…”

Section: Corrected Wobmentioning

confidence: 99%

Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells

Zhao,

Yin,

2023

Applied Sciences

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The prediction and optimization of the rate of penetration (ROP) for horizontal wells are more complicated than for vertical wells, but most of the current ROP prediction studies are for vertical wells, which cannot be adapted to the complex drilling characteristics of horizontal wells. To this end, this paper proposes a data knowledge dual-driven horizontal well ROP prediction method. Firstly, the drilling characteristics of horizontal wells are analyzed, showing that the horizontal wells ROP prediction model cannot be modeled using surface measurement data; secondly, based on the analysis of horizontal well drilling characteristics, a physical model-based horizontal well ROP modeling data pre-processing method is proposed by introducing the drag and torque model. Finally, a data knowledge dual-driven horizontal well ROP prediction method is proposed in conjunction with data-driven algorithms. The proposed horizontal well ROP prediction method is applied to the A1~A4 wells in the Sichuan area. Compared with the conventional data-driven ROP prediction method, the prediction accuracy of this method is improved by 30%. The proposed method can provide a basis for the intelligent optimization and management of ROP during the drilling of horizontal wells.

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