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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.
Dec 1, 2022 · This paper presents a comprehensive review of machine learning studies for drilling applications in the following categories: (1) drilling fluids; (2) drilling hydraulics; (3) drilling dynamics; (4) drilling problems; and (5) miscellaneous drilling applications.
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.
The extracted patterns were demonstrated to be useful in three ways: one was deconvolution by predicting the pressure corresponding to a constant rate history; the other two were to predict...
Dec 1, 2023 · Accurate prediction of downhole torque is a key technique to improve the rate of penetration and achieve safe drilling in the horizontal section. However, it is currently difficult to directly measure downhole torque due to the limitations of downhole measuring tools.
Jan 1, 2022 · Results show that the LSTM models trained using the combination of only the engineering parameters (ROP, WOB, WHO, SPP, and Torque) and the mud tank parameters (volume) do not have the capability to effectively evaluate gas kick size quantitatively in the downhole environment.
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Oct 23, 2024 · Machine learning techniques are very promising in identifying the structural relationships existing between the inputs and target variables, these techniques were recently successfully applied to ...
