Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Petrophysical analysis is an industry-standard practice for reservoir evaluation as it provides critical inputs for characterizing subsurface formations and estimating resource potential. Khadro/Ranikot Formation sands are proliferous producers in the Central Indus Basin, Pakistan. The demarcate potential in intercalated sand shale layers that are thin and heterogeneous makes it a challenging reservoir. Conventional petrophysical interpretation is laborious and does not produce up-to-mark results due to reservoir complexity, data limitations, and associated uncertainties. Hence, an emerging and delicate machine-learning (ML) approach has been comprehensively applied to analyze the potential and robustly interpret well log data while addressing the associated challenges. This case study entails a thorough evaluation of well log quality, assessing several algorithms such as least-squares support vector machines (one-class SVM), Random Forest Regressor (RFR), Extra Tree Regressor (ETR), Gradient Boosting Regressor (GBR), Decision Tree Classifier (DTC), etc. to compare their efficacy and reliability. One-class SVM helps to reduce outliers with great certainty, while the missing logs sonic (DT) and density (RHOB) are precisely predicted via GBR and ETR with 0.66 and 0.88 R 2, respectively. Hence, providing reliable and optimized quality logs suitable for ML-based petrophysics. ML worked on these augmented logs by dividing the data into 60% training and 40% testing. The ETR outperformed the rest of the models with a correlation of 0.99 and 0.91 among conventional and ML results. Likewise, RFR performed exceptionally well for water saturation modeling, expressing the highest 0.93 correlation. Finally, DTC modeled reservoir facies with the best 91% accuracy and 0.935 F1 measures at the blind well. Excellent calibration of >85% is met with the estimates obtained by the predictive model compared to conventional methods. This comprehensive approach offers cost-effective and robust workarounds for modern formation evaluation with minimal uncertainty and resource-efficient multiwell interpretation within complex reservoirs and sets the stage for further research in the machine-learning ecosystem.

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