12 research outputs found
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