Domain-Informed Structured Detection of As-Drilled Trajectory Transitions for Post-Well Conformance Assessment

Post-well conformance assessment requires identifying the realized locations where an as-drilled trajectory changes between its principal inclination regimes; planned breakpoints are targets, not observations. We formulate this task as sparse event localization using domain-informed multiscale inclination features, planned-trajectory deviations, event-specific LightGBM models, a well-level Drop-presence model, and exact Build–Hold or Build–Hold–Drop decoding. The operational scope is restricted to completed, conventional single-cycle trajectories containing exactly one Build, exactly one Hold, and at most one Drop; build-only, repeated-transition, seven-section, and online trajectories are excluded. We evaluated 63 field wells, including 25 with Drop, by fixed five-fold well-level cross-validation. At the prespecified 60 m tolerance, the detector achieved a macro-F1 of 0.806 (95% confidence interval, 0.737–0.869), versus 0.616 for a derivative rule, 0.567 for change-point dynamic programming, and 0.615 for segmented regression. Its paired advantage over the strongest classical comparator was 0.190 (0.111–0.268). Build, Hold, and Drop F1 values were 0.984, 0.857, and 0.577. Drop-presence ranking was strong (average precision, 0.944), but only 15 of 22 emitted true Drops were localized within 60 m. All seven localization failures were late and spanned 115.9–347.5 m; they were associated with lower ranks of the labeled station score and weaker early inclination decline. On 47 matched wells, planned anchors achieved 0.281, versus 0.809 for the detector; sensitivity analysis over 324 anchor-rule configurations did not close this gap. Exact decoding guaranteed valid event order but produced the same predictions as greedy selection; thus, its demonstrated contribution on this dataset is output validity rather than localization-accuracy improvement. Validation was limited to 63 wells from a single competition-supplied field dataset; external multi-field generalization remains untested.