Construction of geostatistical aquifer models integrating dynamic flow and tracer data using inverse technique

Natural aquifers are heterogeneous, and geostatistical methods are widely used to simulate the heterogeneity of aquifer properties. Due to limited available data, it is essential to integrate as much information as possible to reduce the uncertainty in aquifer models and flow predictions. Traditional geostatistical techniques efficiently consider static hard and soft information, such as core data and seismic data. However, dynamic flow and transport data, such as flow rates, pressure, and tracer breakthrough, are important information that are not easily considered with the traditional techniques. Integrating such dynamic data into a model requires the solution of a difficult inverse problem, since dynamic data and aquifer properties are related to each other through the non-linear flow and transport equations. A recently developed geostatistically based inverse technique, the sequential self-calibration (SSC) method, is introduced to integrate those dynamic data. The SSC method is an iterative inverse technique that is coupled with an optimization procedure. It provides for fast generation of multiple realizations of aquifer property models that jointly match pressure and tracer breakthrough data, yet display the same geostatistical characteristics. This method is flexible, computationally efficient, and robust. The main features of SSC include (1) a master point concept that reduces the number of parameters, (2) a perturbation mechanism based on kriging that accounts for the spatial correlation of the aquifer properties, (3) a fast streamline-based tracer flow simulator for integration of tracer data, and (4) a new semi-analytical streamline-based method for computing sensitivity coefficients of tracer breakthrough. Applications of the SSC method are demonstrated with a synthetic data set. Results show that tracer breakthrough data carry important information on the spatial variation of aquifer permeability in the inter-well areas. As a contrast, pressure data provide information at near well-bore areas only. Integrating pressure and breakthrough data jointly leads to significant improvement in the aquifer heterogeneity representation and a reduction in the uncertainty of aquifer model. The accuracy of flow and transport predictions can be dramatically improved by integrating dynamic data.

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