The usefulness of electromagnetic (EM) methods for mineral exploration is severely restricted by the presence of a conductive overburden. Approximately 80% of the Australian continent is covered by regolith that contains some of the most conductive clays on Earth. As a result, frequency-domain methods are only effective for near surface investigations and time-domain methods, that are capable of deeper exploration, require the measurement of very small, late-time signals. Both methods suffer from the fact that the currents in the conductive Earth layers contribute a large portion of the total measured signal that may mask the signal from a conductive target. In the search for non-layered structures, this form of geological noise is the greatest impediment to the success of EM surveys in conductive terrains. Over the years a range of data acquisition and processing techniques have been used in an effort to enhance the response of the non-layered target and thereby increase the likelihood of its detection.The combined use of a variety of survey configurations to assist exploration and interpretation is not new and is practiced regularly. The active nature of EM exploration means that the measured response is determined to a large degree by the way in which the Earth is energised. Geological structures produce different responses to different stimuli. In this work, two new methods of data combination are used to transform the measured data into a residual quantity that enhances the signature of non-layered geological structures. Based on the concept of data redundancy and tested using the results of numerical modelling, the new combinations greatly increase the signal to noise ratio for targets located in a conductive environment by reducing the layered Earth contribution. The data combinations have application to frequency-domain and time-domain EM surveys and simple ++ / interpretive rules can be applied to the residuals to extract geological parameters useful in exploration. The new methods make use of inductive loop sources and can therefore also be applied to airborne surveys.Airborne surveys present special difficulties due to the data acquisition procedures commonly used. Flight-line related artefacts such as herringbones detract from the appearance of maps and make boundary definition more difficult. A new procedure, based on the Radon transform, is used to remove herringbones from airborne EM maps and locate the conductive boundaries correctly, making interpretation more reliable and easier. In addition, selective filtering of the Radon transform data enables the enhancement or attenuation of specific linear features shown in the map to emphasise features of interest. Comparison of the Radon transform procedures with the more conventional Fourier transform methods shaves the Radon transform processing to be more versatile and less prone to distortion of the features in a map.The procedures developed in this work are applied to field data with good results.

While geophysical reservoir characterization has been an area of research for the last three decades, geophysical reservoir monitoring, time-lapse studies, have recently become an important geophysical application. Generally speaking, the main target is to detect, estimate, and discriminate the changes in subsurface rock properties due to production. This research develops various sensitivity and feasibility analyses to investigate the effects of production-induced time-lapse changes on geophysical measurements including seismic and controlled-source electromagnetic (CSEM) data. For doing so, a realistic reservoir model is numerically simulated based on a prograding near-shore sandstone reservoir. To account for the spatial distribution of petrophysical properties, an effective porosity model is first simulated by Gaussian geostatistics. Dispersed clay and dual water models are then efficiently combined with other well-known theoretical and experimental petrophysical correlations to consistently simulate reservoir model parameters. Next, the constructed reservoir model is subjected to numerical simulation of multi-phase fluid flow to replicate a waterflooding scenario of a black oil reservoir and to predict the spatial distributions of fluid pressure and saturation. A modified Archie’s equation for shaly sandstones is utilized to simulate rock resistivity. Finally, a geologically consistent stress-sensitive rock physics model, combined with the modified Gassmann theory for shaly sandstones, is utilized to simulate seismic elastic parameters. As a result, the comprehensive petro-electro-elastic model developed in this dissertation can be efficiently utilized in sensitivity and feasibility analyses of seismic/CSEM data with respect to petrophysical properties and, ultimately, applied to reservoir characterization and monitoring research. Using the resistivity models, a base and two monitor time-lapse CSEM surveys are simulated via accurate numerical algorithms. 2.5D CSEM modeling demonstrates that a detectable time-lapse signal after 5 years and a strong time-lapse signal after 10 years of waterflooding are attainable with the careful application of currently available CSEM technology. To simulate seismic waves, I employ different seismic modeling algorithms, one-dimensional (1D) acoustic and elastic ray tracing, 1D full elastic reflectivity, 2D split-step Fourier plane-wave (SFPW), and 2D stagger grid explicit finite difference (FD). My analyses demonstrate that acoustic modeling of an elastic medium is a good approximation up to ray parameter (p) equal to 0.2 sec/km. However, at p=0.3 sec/km, differences between elastic and acoustic wave propagation is the more dominant effect compared to internal multiples. Here, converted waves are also generated with significant amplitudes compared to primaries and internal multiples. I also show that time-lapse modeling of the reservoir using SFPW approach is very fast compared to FD, 100 times faster for my case here. It is capable of handling higher frequencies than FD. It provides an accurate image of the waterflooding process comparable to FD. Consequently, it is a powerful alternative for time-lapse seismic modeling. I conclude that both seismic and CSEM data have adequate but different sensitivities to changes in reservoir properties and therefore have the potential to quantitatively map production-induced time-lapse changes. / text