Guozhong Gao

The practical application of 2D inversion to marine controlled-source electromagnetic data

An algorithm is presented for the inversion of marine controlled-source electromagnetic (CSEM) data that uses a 2D finite difference (FD) forward driver. This code is demonstrated by inverting a CSEM data set collected at Hydrate Ridge, Oregon, consisting of 25 seafloor sites recording a 5-Hz transmission frequency. The sites are located across a bathymetric high, with variations in water depth of ≈300 m along the 16-km profile. To model this complex seafloor bathymetry accurately, the FD grid was designed by careful benchmarking using a different 2D finite element (FE) forward code. A comparison of the FE and FD forward model solutions verifies that no features in the inversion are due to inaccuracies of the FD grid. The inversion includes the local seawater conductivity–depth profile as recorded by the transmitter’s conductivity–temperature-depth gauge, because seawater conductivity is known to have a significant effect on the CSEM responses. An apparent resistivity pseudosection of the CSEM data resemb...

Inversion of porosity and fluid saturations from joint electromagnetic and elastic full‐waveform data

We present a method for obtaining the porosity and fluid saturation distributions by simultaneously inverting electromagnetic (EM) and seismic measurements. In this work multicomponent full-waveform elastic data are used. Application of the method on a surface prospecting problem, where the fluid saturation distribution are changing because of oil production, demonstrates that the method not only accurately estimates the porosity distribution, but also accurately identifies the location and magnitude of the changes in the fluid saturation distribution. This suggests that the method has a potential for monitoring fluid movement during oil production.

Nonlinear Inversion Approaches For Cross-well Electromagnetic Data Collected In Cased-Wells

The cross-well electromagnetic (EM) technique is useful in appraising reservoirs because of its deep domain of investigation that can reach deep into the reservoir (Wilt et al., 1995; Spies and Habashy, 1995). However, most of the existing wells are cased with steel pipes, which can have significant effects on the measured data as compared to those for open-holes. Nichols (2001) proposed an inversion scheme, which we refer to as the “data ratio I” inversion approach, to remove the casing effects in the inversion process. Numerical results showed that this data ratio I inversion approach has some deficiencies such as instabilities when the data are significantly noisy. To overcome these deficiencies, we propose two inversion approaches. The first method is the so-called the “data ratio II” inversion approach (Abubakar et al., 2007). In this approach, instead of using the data ratio as the reference data set, we use the numerator of the data ratio as the reference data set and the denominator as the weights for the simulated response. Compared to the previous data ratio inversion schemes, this proposed method is less sensitive to the presence of noise. In the second method, named “casing coefficient” inversion approach, the casing coefficients are estimated as part of the optimization process to reconstruct the formation conductivity. By designing the appropriate cost function, we are able to robustly estimate the casing coefficients and at the same time reconstruct the formation conductivity. The attractive feature of this second method is that it does not affect the signal to noise ratio nor does it alter the sensitivity of data to the formation conductivity. Also, this approach can also be used to calibrate the acquired data.

Resolution And Uncertainty Analysis For Marine CSEM And Cross-well EM Imaging

Methods are presented for appraising resolution and uncertainty in images generated with large-scale nonlinear EM inversion schemes where singular value decomposition or direct matrix inversion is not possible. The methods explore the computation of the model resolution matrix (MRM) and model covariance matrix (MCM) using a conjugate gradient (CG) method. The proposed methods enable the computation of the MRM and MCM for all the inversion iterations without considerable sacrifices in the total computation time. Examples of the resolution and uncertainty analysis are provided for the inversion of Marine CSEM and cross-well EM synthetic and field data.