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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.
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 ).
Jan 14, 2019 · In this paper, three different machine‐learning techniques were applied to flow‐rate/pressure interpretation. We formulated the machine‐learning techniques into a linear regression (LR) on parameters that connect the nonlinear flow‐rate features with pressure targets.
- Chuan Tian, Roland N. Horne
- 2019
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
Dec 1, 2022 · Drilling a well is a dynamic process to reach the target formations. During drilling, many surface and downhole parameters are routinely measured and recorded, such as ROP, torque, RPM, weight-on-bit (WOB), etc. These parameters help drillers monitor the drilling, and then make real-time decisions to optimize drilling or detect anomalies.
Oct 23, 2024 · An integrated machine learning (ML) workflow was successfully implemented to estimate the in situ stresses in subsurface rocks A constitutive relationship between ultrasonic wave velocities and s... Abstract The optimum performance of various subsurface operations such as stimulation treatments, wellbore drilling, horizontal well placement, underground mining, and tunneling rely on accurate es...
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Jun 17, 2018 · Three feature-based machine learning techniques were explored to interpret PDG data, namely, the linear approach, the kernel method, and kernel ridge regression.
