Leveraging Analog Wells to Fine-Tune Ai Foundation Models for Well Log Prediction

Abstract When analyzing the eligibility of unconventional reservoirs for CO2 Capture and Storage, it is crucial to have access to key wellbore log data. However, such data is not always acquired or may be of poor quality due to a challenging measurement environment. To facilitate reservoir interpretation and to account for varying geological contexts, our goal is to automatically predict and reconstruct well logs at scale using state-of-the-art machine learning models specialized to each well's geological environment. We introduce a novel AI fine-tuning approach for well log prediction which automatically finds the optimal analog wells for each well of interest. Foundation AI regression models are trained on public datasets and fine-tuned on the analog wells. For log reconstruction, we also introduce a novel segmentation-based algorithm that automatically identifies the inconsistent well intervals where the log prediction differs significantly compared to the existing log. In such intervals, we keep between the measured and the predicted logs, the one that is the most consistent with its local neighboring well log data intervals. When fine-tuning a Foundation AI model by using the wells automatically selected by our new method rather than using all available wells in a study, we could decrease by 24.5% the prediction error, as measured on a public wellbore dataset from Wyoming for the bulk density measurement. On the same study, when comparing with the approach of using a frozen foundation model, we achieve with our method a prediction error that is 39.4% smaller. These experiments show a significant increase in well log prediction quality when using our novel AI analogs-based method compared to classical log prediction methods. We also experiment with different types of AI regression models, including XGBoost and transformer-based models adapted for time-series imputation tasks. We find that transformers-based models achieve the best performance among all compared models. In particular, the self-attention imputation for time-series model (SAITS) achieves a reconstruction error of the bulk density measurement that is 20% lower compared to XGBoost on a test set of 170 wells from the Netherlands. This model benchmarking allows us to further increase the quality of our well log prediction and reconstruction, which in turns facilitates petrophysical interpretation for wellbore interpreters. Our novel AI-powered log prediction method solves the well log covariate shift issue found in the literature by automatically finding the optimal analog wells to use for fine-tuning, thus leading to better quality predicted logs. We also introduce a new method to automatically detect and reconstruct inconsistent measurements intervals. This method is designed and implemented to perform log prediction and reconstruction automatically at scale, thus facilitating the petrophysical interpretation task.