Mechanical parameter prediction based on drilling engineering and well-logging information

Abstract While deep oil and gas resources are abundant, their complex geological environments pose significant challenges, making exploration and development both difficult and costly. The rapid and precise acquisition of subsurface structural data, along with real-time optimization of drilling plans, is crucial for enhancing the efficiency of deep oil and gas exploitation. Most drilling data are obtained from surface measurements, and while many studies concentrate on key drilling parameters such as the rate of penetration, relatively little research has been devoted to the mechanical properties of the rock. Uniaxial compressive strength (UCS) is a critical mechanical parameter for predicting sand production, analysing wellbore stability, and designing oil wells. However, UCS is traditionally determined through laboratory testing, which is both time-consuming and costly. To address these challenges, we apply rock physics modelling to estimate elastic parameters using conventional logging data, and establish a relationship between these parameters and UCS through stress–strain curves. The UCS values obtained from well logging are used to calibrate the initial UCS results derived from drilling data, which are used as the prior model for seismic inversion. This integrated method not only enhances UCS prediction accuracy under complex formation conditions but also shows better consistency between UCS estimated from drilling data and that derived from rock physics models. The calibrated parameters demonstrate robustness across different wells and geological settings. Therefore, our method provides reliable UCS prediction for identifying ‘engineering sweet spots’ in deep oil and gas reservoirs.