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This tutorial re-visits DSIVC or “data space inversion with variable control”. DSIVC is a numerically cheap way to undertake optimization under uncertainty. The present tutorial continues a previous tutorial on the same subject by presenting a new (and somewhat more efficient) methodology for achieving similar optimization outcomes. The tutorial also examines circumstances that may challenge […]

This comprehensive tutorial demonstrates fast, efficient calibration of a complex 3D model. It complements a previous tutorial on a similar subject. However it shows how calibration results can be better, and can be achieved more easily, with recent software advances. Pilot points are used in an innovative way. They support hydraulic property characterization of a

Optimization under uncertainty is notoriously numerically intensive. However its numerical burden can be reduced if data space inversion (DSI) is used to construct a surrogate statistical model that can be used in place of the numerical model. This tutorial explores how new ideas from the petroleum industry can be explored using programs from the PEST

Ensemble space inversion (ENSI) enables efficient, regularisation-constrained calibration of complex, highly-parameterised models. This tutorial demonstrates how linear analysis can be undertaken in partnership with the ENSI calibration. This provides estimates of parameter and predictive uncertainty at minimal numerical cost. This tutorial is a continuation of another GMDSI tutorial. These tutorials can be done independently or

Ensemble space inversion (ENSI) is implemented through the PEST_HP suite (version 18). Using ENSI you can calibrate a complex model quickly. The calibration subspace is comprised of random parameter realisations as well as individual parameters. Realisations can be different for different parameter types. Regularisation seeks a minimum error variance solution within the confines of the working