The escript cookbook: Release - 3.2.1 (r3613)

escript is a python based environment that has been developed to solve complex mathematical models, particularly coupled, non-linear and time-dependent partial differential equations. The intention of this cookbook is to introduce new users to escript and provide a set of examples which demonstrate the major concepts and can be adapted to new problems. Although most of the examples in this cookbook are focused on the disciplines of geophysics and geology, they provide a solid introduction to escript and its capabilities

The escript cookbook: Release - 3.2 (r3422)

escript is a python based environment that has been developed to solve complex mathematical models, particularly coupled, non-linear and time-dependent partial differential equations. The intention of this cookbook is to introduce new users to escript and provide a set of examples which demonstrate the major concepts and can be adapted to new problems. Although most of the examples in this cookbook are focused on the disciplines of geophysics and geology, they provide a solid introduction to escript and its capabilities

An assessment of the assisted seismic history matching workflow, practical innovations and solutions

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