Aug 2026· SPE Nigeria Annual International Conference and Exhibition· 0 citations· 20 references
TL;DR
This study investigates an artificial intelligence (AI) based approach for real-time prediction of Young's modulus and UCS using drilling and logging data and reveals that neutron porosity, formation bulk density and Gamma Ray are the three most influential predictors.
Abstract
Rock geomechanical properties are vital parameters used for field development studies. Young's modulus (E) and uniaxial compressive strength (UCS) are two of the most fundamental variables used to characterize rock strength and formation deformation behavior. The conventional laboratory measurements of these parameters are costly, time-consuming and often not available during drilling operations. Consequently, real-time estimation of Static young modulus and UCS are crucial for drilling optimization, wellbore stability analysis and fracture containment assessment. This study investigates an artificial intelligence (AI) based approach for real-time prediction of Young's modulus and UCS using drilling and logging data. Ensemble supervised machine learning models were assessed to find the most accurate predictive framework. The Machine Learning models were developed using offset field dataset from five wells, comprising approximately 5,600 depth-indexed samples. The input variables includes rate of penetration (ROP), weight on bit (WOB), torque (T), Equivalent circulation density (ECD) and Gamma Ray (GR) and acoustic log responses of density and neutron. Four machine learning algorithms were investigated and compared, including Extreme gradient boosting (XGBoost), Random forest (RF), Extreme trees (ET) and Categorical Boosting (CB). The datasets were preprocessed, normalized and divided into training and testing subsets using 80:20 split respectively. During the model development, hyperparameter tuning was performed to optimize model performance and two independent wells not used during model development were used for blind testing to evaluate the generalization capability of the models. The results showed that all algorithms achieved a coefficient of determination (R2 >0.90) on the test datasets with ET and CB provided the best overall predictive performance. The optimized models achieved coefficients of determination (R2) of 0.88 for UCS and 0.875 for Young's modulus, with corresponding root mean square errors of 1484 psi and 3.59 GPa, respectively. Features importance analysis revealed that neutron porosity (NPHI), formation bulk density (RHOB) and Gamma Ray are the three most influential predictors. The proposed framework enables continuous, depth-based estimation of rock mechanical properties in real time, supporting improved geomechanical modeling and drilling optimization. The approach shows strong potential for integration into real-time drilling advisory systems for autonomous drilling workflows
The proposed framework provides accurate, robust and interpretable prediction of rock mechanical properties, demonstrating its potential for geotechnical characterization and transportation infrastructure applications.
Aman Jangir, Biswajit Acharya· Transportation Infrastructur...· 0 citations
A machine learning framework that predicts rock cohesion and angle of internal friction from easily measurable physical properties, enabling rapid and cost-effective estimation without the need for complex laboratory testing is developed.
Accurate rate of penetration (ROP) prediction in heterogeneous formations remains a key challenge for drilling optimization, as existing empirical models rely on fixed-structure coefficients unable to adapt to rapid lithological transitions. This study presents a novel exponential-form ROP model integrating surface drilling parameters (weight on bit (W), rotary speed (N), torque (T), and standpipe pressure (SPP)), log-derived geomechanical properties (dynamic combined compressibility modulus for carbonates; total porosity for sandstones), and three physically motivated energy parameters: rotational mechanical power per unit bit area (Prot), axial crushing energy (AE/AEs), and hydraulic cleaning efficiency (Hce). Bit wear is quantified through a modified Hareland and Hoberock wear function requiring no laboratory measurements. Parameter selection used combined Pearson and Spearman correlation analysis across 16 candidate variables from a raw dataset of 9375 depth readings for Well A and 4443 for Well B (at 0.25 m intervals). The model was developed using nonlinear least squares regression (Levenberg–Marquardt algorithm) in MATLAB. Validated on two vertical wells penetrating mixed carbonate and clastic sequences in a Middle Eastern offshore field and benchmarked against four classical formulations, the model achieves R2 = 0.6568–0.6766 across full heterogeneous sections, improving on the best benchmark by margins of 0.36–0.46. Under lithology-specific calibration, R2 advances to 0.8239–0.9139, with MAPE reducing to 5.71%. The model is limited to two vertical wells in a single field; further field validation is recommended before broader deployment.
Ahmed S. Alhalboosi, Musaed N. J. AlAwad, M. Khamis· Applied Sciences· 0 citations
The results showed that all models successfully captured the relationship between the input parameters and uniaxial compressive strength, although their predictive capabilities differed considerably, and KStar produced the most accurate predictions.
Real-time estimation of rock geomechanical properties plays a vital role in evaluating wellbore stability, optimizing drilling operations, completion design and maintaining formation integrity, thereby enhancing overall drilling efficiency. Conventional empirical correlations often lack the accuracy and real-time predictive capability required for key properties such as young's modulus. Furthermore, these correlations rely on input parameters such as compressional and shear wave travel times and bulk density that are typically measured approximately 90 ft behind the drill bit during drilling operations. This study presents a robust model for real-time prediction of a computed static Young's modulus while drilling, using near-bit drilling parameters and gamma ray measurements.
A total of 6,279 datasets from five wells in the Volve Field were collected for this study. The database consists of logging-while-drilling measurements and drilling parameters. The model predictors include standpipe pressure, rate of penetration, weight on bit, surface torque, gamma ray, true formation resistivity and equivalent circulating density. The input features were selected based on their proximity to the drill bit and their strong influence on the target variables. As an initial step, exploratory and preprocessing data analysis was performed to investigate the relationships between the input features and the target variables, and to remove outliers and abnormal data. Data from five wells were used for model development (randomly split into 80% for training and 20% for testing), while one additional well was reserved as blind validation sets. For model development, both supervised regression machine learning algorithms (Extreme Gradient Boosting and Random Forest) and deep learning algorithms (multilayer perceptron neural networks) were trained and optimized for real-time prediction of young's modulus.
The results show that the XGB, ET and RF models achieved high accuracy (R2 = 0.99 – 1.00) on the training dataset but experienced a notable decrease to approximately 0.91 on the validation dataset. The multilayer perceptron (MLP) achieved a consistent accuracy of 0.87 on both the training and validation datasets. Comparative analysis revealed that XGB, ET and RF exhibited mild overfitting, indicated by approximately 10% accuracy drop between training and validation. On the blind test, the optimized MLP model demonstrated superior generalization performance compared to RF and XGB. Model evaluation confirmed that the MLP accurately captured depth-dependent variations of young's modulus in offset wells, with maximum mean absolute percentage errors (MAPE) of 4.32%.
This study presents a robust and reliable machine learning framework for real-time prediction of young's modulus, reducing dependence on core testing and enabling dynamic decision-making during drilling operations. The proposed method offers a scalable approach for intelligent formation characterization and can be seamlessly integrated into real-time drilling advisory systems. Its implementation supports confident drilling and mitigates uncertainties related to wellbore instability, ultimately reducing non-productive time and overall operational costs.
R. Elgaddafi, B. M. Bitayib, K. Amadi et al.· SPE Nigeria Annual Internati...· 0 citations
Accurate and scalable estimation of rock shear strength parameters is essential for remote-sensing-supported geological hazard assessment, slope stability evaluation, and engineering geological mapping. However, determining cohesion and internal friction angle requires multiple triaxial tests under different confining pressures, which are time-consuming, costly, and difficult to apply widely. To support remote-sensing-oriented geoscience and civil engineering applications, this study develops a hybrid machine learning framework for estimating cohesion and internal friction angle from geophysical and mechanical indicators. A cross-source database was compiled from published rock records collected from the Jinchuan mining area in China and the Luhri area in India. After completeness screening and unit harmonization, 213 mixed-lithology cases were retained for modeling, with P-wave velocity, density, uniaxial compressive strength, and tensile strength used as input variables. An Equilibrium Optimizer was coupled with a multilayer perceptron to optimize the network weights and biases, and model performance was evaluated using five-fold cross-validation, independent testing, repeated runs, and comparisons with conventional MLP and several typical machine learning models. The proposed EO–MLP model achieved high prediction accuracy, with test-set coefficient of determination values of 0.946 for internal friction angle and 0.983 for cohesion and corresponding RMSE values of 1.072 and 0.671, respectively. Robust scaler normalization produced the best performance among the three tested normalization strategies. SHapley Additive exPlanations analysis indicated that density was the dominant predictor of cohesion, whereas uniaxial compressive strength and P-wave velocity made the largest contributions to internal friction angle prediction. The proposed framework provides an indirect data-driven tool for estimating shear strength parameters and can complement engineering-geological investigation and rock engineering design.
Baohua Liu, Ze Xiang, Hang Lin· Applied Sciences· 0 citations
