Passive Super-Low Frequency electromagnetic prospecting technique

doi:10.1007/s11707-017-0597-4

Front. Earth Sci.

2017, Vol. 11

Issue (2) : 248-267 https://doi.org/10.1007/s11707-017-0597-4

RESEARCH ARTICLE

Passive Super-Low Frequency electromagnetic prospecting technique

Nan WANG^1,², Shanshan ZHAO¹, Jian HUI¹, Qiming QIN¹(

)

¹. Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China
². Key Laboratory of Technology in Geospatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China

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Abstract

The Super-Low Frequency (SLF) electromagnetic prospecting technique, adopted as a non-imaging remote sensing tool for depth sounding, is systematically proposed for subsurface geological survey. In this paper, we propose and theoretically illustrate natural source magnetic amplitudes as SLF responses for the first step. In order to directly calculate multi-dimensional theoretical SLF responses, modeling algorithms were developed and evaluated using the finite difference method. The theoretical results of three-dimensional (3-D) models show that the average normalized SLF magnetic amplitude responses were numerically stable and appropriate for practical interpretation. To explore the depth resolution, three-layer models were configured. The modeling results prove that the SLF technique is more sensitive to conductive objective layers than high resistive ones, with the SLF responses of conductive objective layers obviously showing uprising amplitudes in the low frequency range. Afterwards, we proposed an improved Frequency-Depth transformation based on Bostick inversion to realize the depth sounding by empirically adjusting two parameters. The SLF technique has already been successfully applied in geothermal exploration and coalbed methane (CBM) reservoir interpretation, which demonstrates that the proposed methodology is effective in revealing low resistive distributions. Furthermore, it siginificantly contributes to reservoir identification with electromagnetic radiation anomaly extraction. Meanwhile, the SLF interpretation results are in accordance with dynamic production status of CBM reservoirs, which means it could provide an economical, convenient and promising method for exploring and monitoring subsurface geo-objects.

Keywords Super-Low Frequency (SLF) three-dimensional modeling frequency-depth transformation geothermal exploration coalbed methane (CBM) electromagnetic radiation (EMR)

Corresponding Author(s): Qiming QIN

Just Accepted Date: 12 January 2017 Online First Date: 20 March 2017 Issue Date: 19 May 2017

Cite this article:

Nan WANG,Shanshan ZHAO,Jian HUI, et al. Passive Super-Low Frequency electromagnetic prospecting technique[J]. Front. Earth Sci., 2017, 11(2): 248-267.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-017-0597-4
https://academic.hep.com.cn/fesci/EN/Y2017/V11/I2/248

Fig.1 Model design and numerical algorithm evaluations. (a) 1-D model with four layers; (b) apparent resistivity responses of the four-layer model in 1-D, 2-D (TE mode), and 3-D responses (XY mode); (c) apparent resistivity responses of COMMEMI Model 3D-1 using our 2-D, 3-D algorithms and other algorithms from Russia, USA, and Germany (Zhdanov et al., 1997).

Fig.2 Normalized SLF magnetic amplitude responses of the layered model (Fig. 1(a)) calculated by 1-D, 2-D, and 3-D numerical algorithms.

Fig.3 3-D geo-electrical model and its normalized SLF magnetic amplitude responses. (a) XOZ profile; (b) XOY profile. Two black points represent acquisition sites; (c) 2-D and 3-D SLF magnetic responses in different modes and directions for the site above the low resistive anomaly; (d) 2-D and 3-D SLF magnetic responses in different modes and directions for the site above the high resistive anomaly.

Fig.4 Depth resolution analysis of the SLF technique using the three-layer model. (a) SLF magnetic responses of models with different objective resistivity; (b) SLF magnetic responses of “high resistive objective layer” models with different burial depths; (c) SLF magnetic responses of “low resistive objective layer” models with different burial depths. The dotted lines in all figures represent the trend lines.

Fig.5 Comparative results using Bostick inversion and improved Bostick inversion with the proposed Frequency-Depth transformation. (a) The objective layer is 1 W·m; (b) the objective layer is 10 W·m; (c) the objective layer is 500 W·m.

Fig.6 (a) 1-D synthetic models with three layers (the first layers have different thicknesses); (b) the SLF responses in the depth domain (depth of the second objective layers’ were set as 200 m, 400 m, and 1000 m). The star represents the acquisition site. Different dotted lines represent the trend lines and the arrows point at the intersection points of trend lines and SLF responses.

Fig.7 Super-Low Frequency remote sensing system: (a) high-resolution remote sensor; (b) the processing unit; (c) the portable power supply.

Fig.8 Working flowchart of the SLF EM prospecting technique.

Fig.9 (a) Geological settings of the Peking University geothermal area. The red text represents the position of Well jr168; (b) Drilling data of Well jr168.

Tab.1 Drilling data of Well jr168

Fig.10 (a) The raw SLF signal with 50 Hz interference indicated by the black dotted circle; (b) the SLF interpreted curve is obtained by the EMD method and wavelet de-noising methods.

Fig.11 SLF interpretation result of the geothermal reservoir in Well jr168. The black dotted line defines the slightly changing trend line. The red line defines the cubic polynomial fitting curve, and the black arrows shows the key turning points. ? is Cambrian System and Q_n is Qingbaikou System. Three forms in the Jixian System (J_x) are J_xt, J_xh, and J_xw (geothermal reservoirs).

Fig.12 (a) Drilling data of a well test in the study area; (b) gas content distribution map of No. 3 coal layer in a district of Qinshui Basin, as well as sites of two coalbed methane wells (i.e., red points). The line represented the fault F1.

Fig.13 SLF interpretation results of two producing coalbed methane wells. (a) The SLF interpretation result of Well 01; (b) the SLF interpretation result of Well 02. The dotted lines show slow-varying trend lines, and the red lines show cubic polynomial fitting curves of the SLF results. The dotted boxes represent the interpreted reservoir distributions.

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