Digital Twin Ready Predictive Risk Management of Subsurface Injection Nodes Using Mechanics Informed Cross Fault Machine Learning

Predictive monitoring of injection operations requires evidence that warning rules transfer across fault conditions and loading rates. We develop a seven-variable representation of mechanical, hydraulic, and acoustic measurements and retrospectively evaluate source-calibrated Watch, Mitigate, and Hold indicators over 60, 30, and 10 s horizons. A separate Mahalanobis gate measures similarity to the source states. The original cohort comprises 18 pressure-stage events from two rough specimens and one smooth specimen; an independent archive adds six slow-creep stage peaks from a fourth specimen. A pump-based reconstruction reproduces every original event and model input. In the original rough-to-smooth transfer, Watch and Mitigate intercepted 6/6 events, with median warning time s of 16.5 and 3.5 s. In reverse transfer, both indicators intercepted 12/12 events but produced 22.9 and 42.2 s/event of pre-horizon alert activity. Deleting one smooth-source event changed support coverage in the final 30 s to 19.7–88.6%. On the independent specimen, the unchanged rough-source Watch indicator intercepted 5/6 peaks, whereas Mitigate and Hold intercepted none. The smooth-source Mitigate indicator intercepted 6/6 peaks but was active before its horizon for 72.8 s/event. Despite these contrasting outcomes, the source-domain gates included 99.7–100.0% of external states. The contributions are reproducible event construction, source-only policy evaluation, calibration sensitivity analysis, and an independent laboratory transfer test. The results show that high empirical support does not establish transferable warning thresholds. They motivate further advisory evaluation with separate prediction and support outputs. Four specimens, retrospective processing, and the absence of field validation do not establish operational readiness.