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5 days ago · Before the introduction of machine learning (ML) algorithms, reservoir parameter inversion was performed using linear regression and plate associations (App, 2017). However, these methods have low practical prediction reliability, owing to the nonlinear relationship between reservoir parameters and logging data ( Bai et al., 2022 ; Li et al., 2023a , Li et al., 2023b ).
Sep 18, 2023 · This paper introduces a method of coupling several downhole parameters using machine learning algorithms. Individual models for ROP, torque on bit (TOB), MSE, and stick-slip are built using a data-driven modeling approach using the random forests algorithm.
Apr 17, 2023 · Baumgartner and van Oort 29 utilize a machine learning technique to analyze and identify downhole vibrations using high-frequency downhole measurements collected by BHA-based sensors.
May 1, 2023 · This paper has demonstrated that estimating ROP downhole using machine learning is a viable method to enable downhole position control with fast control loops. An important consideration is how the error of the estimate impacts the processes using the estimate.
Oct 19, 2020 · With high-speed telemetry and processor downhole, the system has the capability to process and analyze raw sensor signals at the bit, improving the data transmission rate, actuation delay, and response time.
- Enrique Z. Losoya, Narendra Vishnumolakala, Eduardo Gildin, Samuel Noynaert, Zenon Medina-Cetina, Je...
- 2020
Oct 23, 2024 · Given this, this article proposes a new method to improve the accuracy of dynamic drilling tool attitude measurement through machine learning, combining the in-depth analysis of the data structure ...
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Can machine learning detect downhole drilling vibrations?
How ML models can be used to predict downhole vibrations?
How can a new downhole condition identification method improve drilling efficiency?
How can machine learning improve the drilling automation process?
How do we identify downhole conditions based on historical drilling data?
How do Baumgartner & van Oort 29 use machine learning?
This section presents the proposed method for downhole condition identification, including the extraction of quali-tative trends from historical data, establishment of a knowledge base, and identification of downhole conditions based on quantitative trend rules.
