Change of the A Priori Stochastic Structure in the Conditional Simulation of Transmissivity Fields

Abstract

The development of methods for the stochastic simulation of transmissivity (T) fields has progressed, allowing simulations that are conditional not only to T measurements but to piezometric head and solute concentration data. Some methods are even able to honour secondary data and travel time information. However, most of these methods require an a priori definition of the stochastic structure of T fields that is inferred only from T measurements. Thus, the additional conditioning data, that implicitly integrate information not captured by T data, might lead to changes in the a priori model. Different simulation methods will allow different degrees of structure adaptation to the whole set of data. This paper illustrates the application of a new stochastic simulation method, the Gradual Conditioning (GC) method, to two different sets of data, both non-multiGaussian, one based on a 2D synthetic aquifer and another on a 3D real case (MADE site). We have studied how additional data change the a priori model. Results show how the GC method honours the a priori model in the synthetic case, showing fluctuations around it for the different simulated fields. However, in the 3D real case study, it is shown how the a priori structure is slightly modified not following just fluctuations but possibly the effect of the additional information on T, implicit in piezometric and concentration data. Thus, we consider that implementing inversion methods able to yield a posteriori structures that incorporate more data might be of great importance in real cases.