A machine learning approach for calibrating seismic interval velocity in 3D velocity model

  • Le Hong Quan Russia - Vietnam Joint Venture (Vietsovpetro)
  • Tran Dong Schlumberger
  • Nguyen Van Tien Russia - Vietnam Joint Venture (Vietsovpetro)
  • Nguyen Dac The Schlumberger
Keywords: Velocity model, seismic attribute, depth uncertainty analysis, machine learning, Cuu Long basin


Velocity model technique is routinely used to convert data from the time-to-depth domain to support prospect evaluation, reservoir modelling, well engineering, and further drilling operation. In Vietnam, the conventional velocity model building workflow oversimplifies the interval velocities as only well interval velocities are populated into 2D grids for depth conversion or oversimplified calibration interval velocities by applying a single scaling factor function. This study explores the 3D velocity model workflow to obtain accurate and high-resolution interval velocities using a machine learning approach for both fields A and B in Cuu Long basin, offshore Vietnam.

To design an effective approach to depth conversion, the anisotropy factor analysis was performed to understand the differences between the seismic and well interval velocities in geological layer in the 3D structural model. The seismic interval velocity was multiplied by the anisotropy factor to achieve the scaling seismic interval velocity. The scaling seismic interval velocity, elastic attributes, geometric attributes, structural and stratigraphic attributes were used as training features (variables) for predicting interval velocity using the supervised learning algorithm in the machine learning model. Supervised learning offers an opportunity to develop an expert-knowledge-based automated system, which incorporates both domain knowledge and quantitative data mining [1]. The random forest regression algorithms were selected for predicting interval velocity after evaluating several machine learning algorithms. To provide insight into the uncertainty of final interval velocity, a depth uncertainty analysis was conducted using a blind well test for 24 wells and 7 horizons.

The comprehensive 3D velocity model using machine learning approach was built for the first time in Cuu Long basin, offshore Vietnam. The result showed the machine learning algorithm can address the disadvantages of conventional velocity calibration to create highly accurate depth representations of the subsurface including a measure of the uncertainty.


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How to Cite
Le , H. Q., Tran , D., Nguyen , V. T., & Nguyen , D. T. (2022). A machine learning approach for calibrating seismic interval velocity in 3D velocity model. Petrovietnam Journal, 10, 12 - 18. https://doi.org/10.47800/PVJ.2022.10-02