Reservoir Inverse Modelling and Uncertainty Quantification
Reservoir management relies on a proper reservoir characterization. This characterization is accomplished in practice by solving inverse problems based on measurements. Since the number of measurements is smaller than the number of unknown parameters, inverse problems are in general ill-conditioned. Thus, multiple solutions that match the measure data within a certain level of accuracy (model uncertainty) can be found. At Stanford Smart Fields we dedicate some effort developing inverse modeling methodologies, together with means to quantify model uncertainty and to describe how it propagates along the reservoir management closed loop. Many of these methodologies consider measurements of disparate nature (e.g., production and time-lapse seismic data) in order to reduce model uncertainty.
- History Matching Using Integrated Data With an Application to a North Sea Field
- Uncertainties in Rock Pore Compressibility and Effects on Seismic History Matching
- Application of Particle Swarm Methods for Joint Inversion of Production and Time-Lapse Seismic Data
- Time-lapse seismic imaging by linearized inversion