Physics-Informed Deep Learning Prediction of Completion Offsets for Automated Cased-Hole Petrophysical Analysis

A deep learning data processing pipeline is developed which automates spectral yield adjustments in neutroninduced gamma-ray spectroscopy for cased-hole formation evaluation logging in Oil and Gas wells. Traditional workflows depend on a manual-intensive iterative exercise led by subject matter experts to find cased-hole completion (casing and cement) offsets thereby isolating formation-specific yields. Using a novel mixed-data transfer learning technique, models are trained on combined laboratory and field datasets, incorporating spectral yields, formation properties, geometric parameters (e.g., cement/casing areas), and engineered feature combinations. Using the cement fraction method, calcium contributions from cement were systematically added to the laboratory dataset, extending its applicability to carbonate formations. Conventional transfer learning from laboratory data trained model to field data proved ineffective due to catastrophic forgetting; instead, training on a mixed dataset from the outset achieved robust performance over diverse controlled and field datasets by consistently achieving desired accuracies of 0.01 for cement capture (silicon and calcium) offsets, 0.03 for casing capture (iron) offset and normalized value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0}$</tex> for inelastic calcium offset. This work advances automated formation evaluation front by embedding first-principles and domain knowledge with scalable deep learning solutions, enhancing efficiency and reproducibility in subsurface compositional analysis.