Quantum hardware remains highly susceptible to noise, making compilation a critical step for obtaining reliable execution on existing devices. Today's quantum compilers approximate hardware noise using calibration-derived costs over gates, qubits, and coupling edges, and use the costs to guide qubit mapping, routing, and scheduling. However, this abstraction is incomplete: semantically equivalent circuits with identical compiler-predicted hardware noise can still behave very differently on real devices. Execution context can contribute to this gap: short ordered gate sequences on specific qubit neighborhoods can be associated with hardware-model mismatch. Identifying these backend-dependent abnormal patterns can guide gate-ordering rewrites in the circuit family and layout where they were observed. To find these patterns, we propose QRisk, a framework that isolates recurring abnormal gate sequences from quantum hardware executions. QRisk starts from a circuit whose hardware output is much worse than predicted by the calibration-based noise model. It then reduces the circuit at the segment level, identifying groups of segments whose removal lowers the discrepancy ratio or whose retention within the tested circuit context preserves an elevated ratio. The resulting circuit fragments are treated as candidate abnormal patterns. On three IBM Heron r2 backends (quantum devices) across 30 qubit layouts, QRisk finds 25 recurring abnormal gate patterns. Reordering commuting gates to break the two highest-recurrence patterns in their Grover discovery circuits reduces mean hardware-model mismatch by 24% on ibm_fez and 45% on ibm_marrakesh.

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