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Sequential indicators will be calculated where changes in prescribing behaviours are measured only for patients not meeting guideline-driven indicators in the preceding 6-12-month period (e.g. proportion of CHF patients using an ACE inhibitor or angiotensin II-receptor antagonist at 6 months, out of those not receiving this treatment at baseline).
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Sequential indicator simulation can better predict complex geological phenomena.
In this study, we use sequential indicator simulation to establish the reservoir architecture model.
Unconditional sequential indicator simulation (SIS) is employed to implement the spatial bootstrap and quantify the prior proportion uncertainty.
Geophysical and lithologic logs are used to identify hydrogeologic categories and to condition stochastic simulations using Sequential Indicator Simulation (SIS).
Thus, sequential indicator simulation is combined with artificial interaction to modify the architecture model of the reservoir.
With the rock types of each well known, a 3D rock-type model will be established through sequential indicator simulation.
Porosity model was generated using Gaussian random function simulation while the sequential indicator simulation was applied for the facies modeling.
Fluvial sediments with multi-scale channels are difficult to model using classical two-point statistical methods, e.g. sequential indicator simulation (SIS) or object-based modelling (ObjM).
Monte Carlo realizations of the three-dimensional geo-material distributions are generated through the Sequential Indicator and the Truncated Plurigaussian Simulation methods.
After the rock types of all wells are predicted, a 3D rock-type model is established by the sequential indicator simulation as shown in Fig. 11a.
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