Abstract

The main goal of exploration geophysics is to obtain information about the subsurface that is not directly available from surface geological observations. The results are primarily used for finding potential reservoirs that contain commercial quantities of hydrocarbons. A number of possible geophysical methods exists these days to achieve such a goal. One of them is the controlled-source electromagnetic (CSEM) method. CSEM data can provide resistivity maps of the subsurface. Because the bulk resistivity depends on the resistivity of the pore fluid, these maps may enable us to estimate the nature of the fluid content in the reservoir. The CSEM method exploits electromagnetic fields to remotely characterize the nature of the fluid content in the pores. When a dipole current source is stuck into the ground or placed in the seawater, current flows from one pole to the other through the sediments, creating an electrical field in the subsurface. If highly resistive bodies are present in the subsurface, the electrical field measured at some distance from the source will be larger in amplitude than the field in the absence of these bodies. As hydrocarbon-bearing rock is highly resistive, one may link the larger amplitude to the presence of hydrocarbon reservoirs. A logical consequence of this phenomenon is that the CSEM method may also be suited for monitoring a hydrocarbon reservoir during production. The reason is that water flooding or steam injection for oil production creates resistivity changes in the reservoir, and if those changes are large enough, we can expect differences in the CSEM response with time-lapse surveys. This consideration led us to further investigate the EM monitoring problem. We tried to answer two questions: are the time-lapse changes in the reservoir detectable, particularly in the presence of noise, and if so, could we use timelapse signals to locate where the time-lapse changes happened in the subsurface? In this thesis, we considered land CSEM and found that the resistivity change due to displacement of oil by brine can produce a small but measurable difference in the CSEM response. Interestingly, those response differences at the surface are confined to the lateral extent of resistivity changes in the subsurface, even in the presence of various kinds of repeatability noise. We found a simple and effective method to remove the repeatability noise due to the airwave. Finally, results obtained when incorporating nonlinear EM inversion into the monitoring problem suggest that this application of the CSEM method has the potential to play a significant role in the oil and gas industry.

Highlights

  • In order to assess the hydrocarbon content of potential reservoirs, an image of the subsurface is needed that indicates the chemical nature of the substances in the reservoir

  • The red dots corresponds to the estimated solution with the standard Fourier Transformation (FFT), the blue dots to the solution when with the logarithmic Fourier transformation, and the black line to the exact analytical solution

  • The computation of accurate solutions to controlled-source electromagnetic (CSEM) problems requires an accurate discretisation, a robust solver, and an accurate interpolation scheme to sample the numerical solution at receiver locations

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Summary

Introduction

In order to assess the hydrocarbon content of potential reservoirs, an image of the subsurface is needed that indicates the chemical nature of the substances in the reservoir. Several authors reported and established its position as a tool for de-risking potential hydrocarbon prospects next to seismics; see, for example, the March– April 2007 issue of Geophysics, the March 2007 issue of The Leading Edge, and the May 2009 and May 2010 issues of First Break Another potential application of CSEM is hydrocarbon reservoir monitoring during production. Water flooding or steam injection for oil production creates resistivity changes in the reservoir, and those changes potentially can be detected with time-lapse CSEM measurements. Since the resistivity structure of the earth is usually complex, a direct interpretation of time-lapse responses in terms of resistivity changes in the earth can, be difficult and biased In this case, incorporating EM inversion may provide better results

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