In hydrocarbon reservoir monitoring, assisted seismic history matching (ASHM) remains
a large and intractable problem. Despite advances in optimisation algorithms, quantification of uncertainty, data quality, data processing, computational resources and general
subsurface knowledge, practical implementations of assisted/automated seismic history
matching (ASHM) remain boutique and inflexible. Consideration of recent research on
ASHM problems highlights a single-minded focus on algorithmic solutions, that ignore
the broader perspective of ASHM as a multidisciplinary framework for improving subsurface models. This thesis expands the consideration of ASHM beyond the optimisation,
to propose a novel three-phase approach. ASHM is posed as a larger workflow that includes acquiring, evaluating and establishing an ASHM model (Phase 1), history matching
(Phase 2) and model evaluation and improvement (Phase 3). By taking a big picture perspective with respect to ASHM, additional value and patterns to workflows emerge that
will improve the adoption of ASHM within the subsurface industry, by offering pragmatic
and targeted guidance to development, evaluation and improvement around subsurface
models via ASHM
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