In recent years, high-frequency dielectric tools have become an increasingly accepted service for providing saturation, water salinity, and textural information. To obtain these petrophysical quantities from the measured dielectric data, a mixing law is used that relates the bulk formation complex permittivity (relative permittivity and conductivity) to the constituents of the system, including the water present in the pores. Several mixing laws have already been published in the literature. This paper is concerned with an integral part of all of them, which is the prediction of the water complex permittivity as a function of salinity, temperature, and pressure.The water complex permittivity is known to vary with salinity, temperature, pressure, and frequency of operation. We have established a dielectric water model as a function of these four quantities, and have recently validated this model with dielectric laboratory experiments on several water samples at high temperature and pressure. In this paper we fully disclose the model so that it can be used and adapted by the industry.We also demonstrate with a sensitivity analysis that using the correct water model is critical to the accuracy of the final petrophysical outputs.
We experimentally verified that material-averaging techniques can be used for numerically modeling the response of the crosswell electromagnetic (crosswell EM) system in a fractured medium. We have designed a scaled model of the crosswell EM system, and verified that the laboratory data are in good agreement with the simulated response for different cases of fractures present in a host medium. Simulations of realistic scenarios in a hydrocarbon-filled reservoir indicate that the presence of fracture clusters can significantly affect the crosswell EM system response. The magnitude of the effect primarily depends on the number of fractures and their dip relative to the transmitter and receiver wells. In particular, small clusters of fractures filled with saline connate water produce large artifacts on the response, provided that the relative dip to the wells is large enough. The presence of a fracture cluster creates a characteristic signature in the full crosswell EM tomographical data set; therefore, crosswell EM data provides information related to the main fractures that are present between the wells. The inclusion of the fracture information as an additional constraint to the inversion of the crosswell EM data is expected to provide an improved interpretation of the reservoir fluids in the interwell space.
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