The shear velocity is one of the most critical parameters in determining the mechanical rock elastic properties, which serve as inputs for different studies such as wellbore stability, mechanical earth modeling, hydraulic fracturing, and reservoir characterization. However, the sonic log is not acquired in every drilled well. We analyzed the log data of more than 35000 wells in the Williston Basin, and we found that only very few wells had sonic logs. For this reason, several studies attempted to correlate the shear velocity (or slowness) to other easily accessible properties; these will be presented in the literature review, with their pros and cons.

The focus of this paper is to apply machine learning algorithms to synthesize the shear slowness log. Our models are trained and tested with log data from 27 wells drilled in the Bakken petroleum system, Williston Basin. Logging data include Gamma Ray, Deep Resistivity, Density, Neutron Porosity, and Shear Slowness. Five different algorithms were developed and tested against blind data including Xtreme Gradient Booster, Random Forest Regressor, Linear Regression, Ada Boost Regression, and Bayesian Ridge Regression. Overall, the R2-score varied from 0.55 to 0.92, with the XGBoost outperforming the other algorithms.


With more than a century of oil and gas production, companies have generated a tremendous amount of data with every drilled well and field operation. These data banks are either unused or poorly analyzed. While the proper technical analysis of this could improve the performance of the oil and gas industries significantly.

In the past, the focus of researchers and engineers was to develop efficient tools and methods to acquire necessary data. With the advances in computational power, companies realized that they could take advantage of the data more efficiently. The application of artificial intelligence (AI) and specifically machine learning (ML) along with big data analytics have known an enormous surge.

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