Risk Reduction in the Derivation of While-Drilling Wellbore Geomechanical Properties by Utilization of Real-Time Surface Logging and Gamma Ray Data Through the Application of Machine-Learning Techniques to Pre-Existing Data Sets
Authors:   Kuwait Oil Company, Dashti Jalal , Al-Jenaie Jarrah ,   Kuwait Oil Company, Al-Ajmi Bader ,   Kuwait Oil Company, Matar Othman ,   Kuwait Oil Company, Al-Qattan Faisal ,   Kuwait Oil Company, Colombo Ivo ,   Geolog Technologies, Russo Eliana Rosa ,   Geolog, Shoeibi Ahmad ,   Geolog International, Moazami Majid ,   Geolog International
Published: November 2024

The understanding of geomechanical parameters is fundamental in addressing several issues related to drilling and reservoir characterization, such as borehole stability, hydraulic fracturing design, and reservoir simulation. The geomechanical parameters are usually calculated from sonic and density well logs. However, these logs may not always be available, especially in highly deviated and horizontal wells, for either technical or economic reasons. Conversely, surface logging is present during drilling operations most of the time, especially for safety reasons, and the drilling parameters collected are rarely used in the geomechanical characterization of the subsurface. The aim of this work is the development of a fast and reliable method for geomechanical parameters evaluation using surface logging data (weight on bit (WOB), rate of penetration (ROP), torque, rotation per minute (RPM), standpipe pressure) and well-log data (gamma ray (GR) log, sonic log, bulk density log). Moreover, a flexible workflow has been developed to derive the GR data from X-ray fluorescence (XRF) analysis of cuttings via the computation of a chemical gamma ray (CGR) in case of failure of the downhole tools. The presented methodology embeds different machine-learning algorithms, such as random forest, XGBoost, and support vector regression, to derive Young’s modulus, uniaxial compressive strength, Poisson’s ratio, bulk modulus, shear modulus, and seismic velocities Vp and Vs, hereinafter simply named geomechanical parameters. The machine-learning models are trained and tested on data coming from 11 wells drilled in the same geological units but in different fields in Kuwait. During the train and validation phase, the drilling parameters are combined with GR, sonic, and bulk density logs of 10 wells to compute the geomechanical parameters using the empirical relationships available in the literature. The model is then tested against data coming from an unseen well (blind test) to predict the geomechanical parameters using only the drilling data and GR log. The results demonstrate that the model trained with drilling parameters and well-log data from wells drilled in different fields is able to predict the geomechanical properties of a new well in the same geological units. These outcomes highlight the potential value of this procedure and highlight several implications for reservoir characterization, e.g., enabling detection of zones of potential borehole instability and computing the proper mud-weight window, and also in the completion phase, providing valuable geomechanical information, e.g., identification of potential fracture intervals and estimation of in-situ stress. This approach also represents a cost-effective method to obtain geomechanical parameters reducing the requirement of sonic and density logs in newly drilled wells. Once the model has been trained, it is possible to predict the geomechanical parameters in newly drilled wells using only surface logging data and GR log, obtaining the results in real time, quasi-real time, and post-drilling phase. In addition, the GR data can be either from downhole tools or derived from XRF analysis of cuttings, allowing this methodology to also be a viable and supportive backup where downhole logs are present and a reliable alternative when they are not.