Dynamic monitoring of coalbed methane reservoirs using Super-Low Frequency electromagnetic prospecting

• We extract electromagnetic radiation anomalies to dynamically monitor reservoirs.
• Electromagnetic radiation wave packets are used to identify 10 m reservoirs.
• Passive low frequency magnetic background amplitudes are calculated in theory.
• Polynomial fits are used to distinguish the stationary background field.
• Empirical mode decomposition and wavelet transform are combined in de-noising.

Dynamic monitoring of coalbed methane (CBM) reservoirs plays an important role in reservoir evaluation, production estimation, exploitation and development planning in order to efficiently operate producing wells. This paper proposes a passive Super-Low Frequency (SLF) electromagnetic prospecting and monitoring method, which helps us derive electromagnetic radiation (EMR) anomalies from reservoirs to directly identify and dynamically analyze CBM reservoirs. The modeling study shows that the SLF magnetic responses are sensitive to high resistivity layers. These responses turn out to be approximately stationary and can be seen as simply a component of the background field. This stationary background field can be clearly distinguished and then dynamic anomaly extraction would be completed. In order to suppress cultural noise and high frequency (HF) random noise, the methods of empirical mode decomposition (EMD) and wavelet transform are used in data processing. The reconstructed curves are employed to identify EMR anomalies at corresponding depths of reservoirs, and subsequently help directly interpret and dynamically monitor reservoirs. The SLF prospecting method is validated using the field data observed from CBM wells in the years from 2007 to 2013 in Qinshui Basin, China. The results present that the high EMR “wave packets” contribute conceivably to the CBM reservoir identification. Compared with audio-magnetotelluric (AMT) inversion results, the SLF identification resolution is greatly improved. The dynamic characteristics of producing reservoirs are revealed using EMR anomalies, and agree with production histories and other surveys.

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