An Intelligent Integration Layer for Physics–AI Collaboration in Digital Drilling: Architecture, Interface Contracts, and Numerical Demonstration

Drilling systems generate abundant predictions from sensors, physics-based models, digital twins, and artificial intelligence (AI), yet the step from prediction to engineering decision remains fragmented across modules and human expertise, and no established framework specifies how uncertain, heterogeneous predictions should be combined and checked against hard limits before an action is taken. This research article proposes, specifies, and numerically demonstrates an Intelligent Integration Layer (IIL) between model outputs and control execution. The gap is diagnosed as three interface defects: heterogeneous uncertainty that is not fused, uncertainty truncated to point estimates at module boundaries, and hard limits that are softened into penalties or checked only against nominal predictions. The IIL addresses these through three stages (Engineering Understanding, Reasoning, and Decision), explicit interface contracts, and a reproducible reasoning procedure in which candidate actions are generated around the current operating point, eliminated by a joint chance constraint, ranked, and assigned a confidence score that gates decision authority. In a synthetic drilling-ahead study, the strongest supported result is that a sample-based joint chance constraint reduced realized constraint violations from 24.6% of reasoning cycles under hard constraints on mean predictions to 1.4%. Confidence gating reduced them further, to 0.7%, but did so largely by referring 65% of cycles to a conservative simulated engineer, and its confidence score did not distinguish reliable from unreliable admissible recommendations; the gated policies are therefore reported as exploratory counterfactuals, and a policy that implements the confidence-authority rule in this reduced simulation, with engineer confirmation as the highest authority, is reported alongside them. The principal contributions are a diagnosis of the prediction-to-decision gap as a set of interface defects rather than model deficiencies; a specification of the integration layer, comprising interface contracts, a reproducible reasoning procedure, and gated levels of decision authority, whose contracts make the dimensions and time dependence of engineering feasibility explicit; and a positioning of that layer relative to chance-constrained optimization, model predictive control, safety filters, and deployed intelligent drilling systems, which clarifies what the IIL adds and what it inherits.