Energy and Spectrum of Transient Electromagnetic Responses for Deep-Reading Looking Ahead LWD Tools

The total electromagnetic (EM) fields consist of the primary fields excited and propagated in the background model and the scattering fields reflected from the resistivity anomaly. As the resistivity anomaly is the detection target in the logging, it is advantageous to detect the resistivity anomalies by directly investigating the scattering EM fields instead of the total EM fields. The transient electromagnetics (TEM) method measures the scattering EM fields and is promising to largely enhance the detection of the resistivity anomaly layer ahead of the drill bit. However, it is impossible to directly process the time-domain TEM data downhole as the computational resource downhole is limited or transmit it to surface for further processing as the bandwidth of the borehole transmission system is limited. With developments in computational technologies, it becomes promising to process the frequency-domain TEM (FTEM) data downhole, as it requires less downhole computational resources than the time-domain TEM data. Thus, we apply the FTEM method to a borehole-conveyed logging tool for deep-reading look ahead. We first define the energy of time-domain signals (dBz(t)_dt) by implementing the Hilbert transform on it. Then, the time-domain original signals and their energy signals are transformed into the frequency domain for further investigation. Finally, we use attenuations of original signals and their energy signals in the frequency domain to detect the formation boundary ahead of the drill bit. They could provide formation information at very low frequencies and are of significant usefulness to detect a distant formation boundary ahead of the drill bit. Moreover, we propose a theoretical instrument consisting of one transmitter sub and one receiver sub hanging in the top layer of a two-layer model, as shown in Fig. 1a. The transmission waveform is shown in Fig. 1b. The measurements of original signals in the time domain are shown in Fig. 1c, and the corresponding measurements in the frequency domain are shown in Fig. 1d. The energy signals in the time domain are shown in Fig. 1e, and the energy signals in the frequency domain are shown in Fig. 1f. The attenuations of original signals and attenuations of energy signals are estimated by the ratio of signals with different distances (3 to 40 m) between the receiver and the layer boundary and signals with a distance of 50 m and are shown in Figs. 1g to 1h, respectively. Figure 1d shows that the original signals have a maximum amplitude of around 1k to 2k Hz, while Fig. 1f shows that the energy signals have a maximum amplitude of around 0 Hz. And the amplitude shown in Fig. 1f is larger than that in Fig. 1d. It reveals that the signals in the frequency domain could provide formation information at a much lower frequency, which is useful for detecting a distant formation boundary ahead of the drill bit. Figures 1g to 1h shows the dynamic range of the signal attenuations is large, although that of original signals attenuations is a little smaller than that of energy signals. These two figures reveal that the attenuations of both signals in the frequency domain are sensitive to the formation boundary ahead of the drill bit. Furthermore, the depth of detection of the tool could reach more than 30 m ahead of the drill bit. We show that such an FTEM tool may be used to image the formation boundary at comparatively large distances from the drill bit while keeping the tool itself relatively compact. Therefore, it would be instrumental in the optimal placement of a well in a hydrocarbon reservoir.