Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 202550th U.S. Rock Mechanics/Geomechanics Symposium

Predicting Drill Bit Footage Method Based on PSO-WNN

View Full Paper
Ask AI
Bookmark
Share

Authors

TPTao PanXSXianzhi SongMLMuchen Liu

Discussion

Loading...

Member takes

Overview

Hybrid framework integrates particle swarm optimization and wavelet neural networks to enhance prediction accuracy in drilling operations, indicating improved resource allocation.

Key Points

  • The proposed model achieves a determination coefficient (R2) of 0.9619, showcasing high prediction accuracy.
  • Utilizing advanced data preprocessing techniques significantly enhances the model's predictive capabilities over conventional methods.
  • Field deployment in Xinjiang Oilfield confirmed an 83.75% prediction accuracy across eight monitored wells, indicating operational viability.
  • Comparison with benchmark methods revealed a 23.6% improvement in accuracy, highlighting the model's effectiveness.

Cite This Study

Pan et al. (2025) studied this question.

synapsesocial.com/papers/68c1b60d54b1d3bfb60eb4e6https://doi.org/10.56952/arma-2025-0177
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning-Based Drill Bit Wear Prediction for Enhanced Drilling Performance2024
  2. 2Intelligent prediction methods for rock mechanical parameters based on logging while drilling responses2026
  3. 3Prediction of plugging formulation based on <scp>PSO‐BP</scp> optimization neural network2024 · 1 citations
  4. 4Prediction model of lost circulation based on drilling parameters with PSO-BP neural network2026 · 2 citations
  5. 5Real-Time Artificial Intelligence-Enhanced Machine Learning Technique for Accurate Drilling Parameter Prediction and Optimization2024 · 4 citations