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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.

We modeled time-domain EM measurements of induction currents for marine and land applications with a frequency-domain code. An analysis of the computational complexity of a number of numerical methods shows that frequency-domain modeling followed by a Fourier transform is an attractive choice if a sufficiently powerful solver is available. A recently developed, robust multigrid solver meets this requirement. An interpolation criterion determined the automatic selection of frequencies. The skin depth controlled the construction of the computational grid at each frequency. Tests of the method against exact solutions for some simple problems and a realistic marine example demonstrate that a limited number of frequencies suffice to provide time-domain solutions after piecewise-cubic Hermite interpolation and a fast Fourier transform.

In the application of controlled source electromagnetics for reservoir monitoring on land, repeatability errors in the source will mask the time-lapse signal due to hydrocarbon production when recording surface data close to the source. We demonstrate that at larger distances, the airwave will still provide sufficient illumination of the target. The primary airwave diffuses downward into the earth and then is scattered back to the surface. The time-lapse difference of its recorded signal reveals the outline on the surface of the resistivity changes in a hydrocarbon reservoir under production. However, repeatability errors in the primary airwave can destroy the signal-to-noise ratio of the time-lapse data. We present a simple and effective method to remove the primary airwave from the data, which we call partial airwave removal. For a homogeneous half space and a delta-function type of source, the surface expression of the airwave does not depend on frequency. For this reason, the primary airwave can be subtracted from the data using recordings at two frequencies, one low enough with a skin depth of the order of the reservoir depth that is sensitive to the reservoir, the other high enough to only sense the near surface. The method does not affect secondary airwave components created by signals that have propagated through the earth and returned to the surface. We show that the method provides a direct indicator of production-related time-lapse changes in the reservoir. We illustrate this for several models, including a general 3D heterogeneous model and one with strong surface topography, for situations where survey repeatability errors are large.

In the application of controlled source electromagnetics for reservoir monitoring on land, the timelapse signal measured with a surface-to-surface acquisition can reveal the lateral extent on the surface of resistivity changes at depth in a hydrocarbon reservoir under production. However, a direct interpretation of the time-lapse signal may generally be difficult and biased. We investigated if non-linear inversion can use time-lapse responses to characterize the subsurface resistivity changes. We examined two different strategies, using a full non-linear inversion algorithm as the interpretation tool: inverting the reference and monitor data independently or in sequence. In the second case, the inversion result of the reference data set serves as an initial guess for the inversion of the monitor data set. Numerical examples show that independent inversion of the data sets can provide an estimate of the depth and lateral extent of the resistivity changes. The second strategy of sequential inversion produces less satisfactory results. We illustrate the independent inversion approach for an example with large survey repeatability errors are large and another one with a complex overburden.