Real-time prediction of acoustic velocities while drilling vertical complex lithology using ai technique

Mechanical rock properties are often determined using sonic log data—compressional velocity (V_p) and shear velocity (V_s). However, a sonic well log is not always acquired due to deteriorated hole condition (i.e., hole washout), sonic tool failures, especially in high-pressure, high-temperature (HPHT) wells, and relatively high cost. This paper introduces two data-driven models, namely artificial neural network (ANN) and random forest (RF), to estimate K and F across different formations that are characterized by deep burial depth and strong heterogeneity. Two types of actual field data were used to develop the models: (i) drilling surface parameters, which include flow rate, standpipe pressure, rotary speed, and surface torque, and (ii) acoustic velocities V_p and V_s, which were acquired by a conventional sonic log. Well-1 and Well-2 with data points of 6,846 were used to develop the models, while Well-3 with 1,016 data points was used to evaluate the capability of the developed models to generalize on an unseen data set with different statistical behavior. Furthermore, Well-3 was used to compare the accuracy of the developed models with the earliest published correlations in estimating the V_p. The results showed that the RF outperformed the optimized ANN in estimating V_p and V_s in Well-3. The RF predicted the V_p with a low average absolute percentage error (AAPE) of 0.9% and correlation of coefficient (R) of 0.87, while the AAPE and R were 6.7 % and 0.45 in the case of ANN. Similarly, the RF estimated the F with an AAPE of 1.1% and R of 0.85, whereas the ANN predicted the F with an AAPE of 9.5% and R of 0.40. Furthermore, the RF was the most accurate in determining F in Well-3 compared to the earliest published correlations.