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Year 2026
 
Vol 67, Issue 4, [08 - 2026] - Full papers

Dynamic Young’s modulus prediction from drilling parameters: A case study of CT oil field, offshore Vietnam

DOI:10.46326/JMES.2026.67(4).06

  • Affiliations:

    IPR research Group, Hanoi University of Mining and Geology, Hanoi, Vietnam

  • *Corresponding:
    This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Received: 13th-Nov-2025
  • Revised: 20th-Feb-2026
  • Accepted: 23rd-Apr-2026
  • Online: 1st-Aug-2026
Pages: 60 - 70
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Abstract:

Real-time prediction of dynamic Young modulus (Edyn) during drilling is crucial for optimizing operational efficiency and mitigating geohazards, particularly in complex geological settings such as offshore Vietnam. While conventional methods like core analysis and wireline logging are accurate, they are often costly, time-consuming, and disruptive to operations. This study investigates the feasibility of using machine learning (ML) models to predict Edyn from real-time drilling parameters, applying to the CT oil field, offshore Vietnam. Six standard drilling parameters were utilized as model inputs: weight on bit, torque, rotary speed, rate of penetration, mud flow rate, and standpipe pressure. The target variable, Edyn was computed from compressional (Vp) and shear (Vs) wave velocities, measured by dipole sonic tools, which served as the ground truth. Three algorithms-Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF)-were trained and cross-validated using 1061 data points from Wells A and B. Evaluation based on Correlation Coefficient (R) and Root Mean Square Error (RMSE) showed that the ANN model yielded superior predictive accuracy compared to both SVM and RF during training and testing phases. On a blind test (Well C), the optimized ANN achieved high correlation (R = 0.912) and low error (RMSE = 0.134 GPa), confirming its robustness for real-time operational support. Although this study focuses on Edyn as a primary geomechanical indicator, these real-time estimates serve as a critical first step toward comprehensive geomechanical assessment. It provides a reliable proxy for rock stiffness trends; thus, the static modulus could be potentially derived through established empirical correlations in future stages of the research. This methodology also provides a feasible, data-driven workflow for estimating rock strength continuously (meter-by-meter) while drilling.

How to Cite
Vu, D.Hong, Nguyen, H.Tien and Vu, T.Thiet 2026. Dynamic Young’s modulus prediction from drilling parameters: A case study of CT oil field, offshore Vietnam. Journal of Mining and Earth Sciences. 67, 4 (Aug, 2026), 60-70. DOI:https://doi.org/10.46326/JMES.2026.67(4).06.
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