Electromagnetic dispersion and sensitivity characteristics of carbonate reservoirs

Abstract

The need to develop a new measurement method for the identification of favorable hydrocarbon-producing zones in carbonate reservoirs is of great importance due to their complex pore structure and lithology. We have evaluated fundamental experimental data acquired from carbonate formation samples in a buried-hill reservoir to demonstrate the magnitude of the complex resistivity (CR) dispersion effect in oil-saturated carbonate rocks and the possibility of identifying hydrocarbon-bearing carbonate sections. Our experimental data set included CR measurements on four carbonate core samples with various degrees of oil saturation, and at each saturation level, CR measurements were acquired in a frequency range between 10 Hz and 100 kHz. The experimental data indicated that the intensity of the CR dispersion effect increased with oil saturation above a specific critical excitation frequency. The experimental spectra of the carbonate samples were fitted with the Debye model and the Cole-Cole model (CCM), which can be used to interpret the dispersion effect over a wide frequency range. Based on the experimental data and the inverted CCM parameters, a sensitivity study was carried out with the crosswell frequency-domain electromagnetic (EM) modeling. Multifrequency EM data were acquired directly by calculating the Maxwell equation with the CR. Our experimental data and forward modeling results indicated that measurement of induced polarization and EM coupling effects can be an effective means for identifying carbonate hydrocarbon reservoirs and for quantitatively evaluating the effect of formation saturation on favorable conditions.

The frequency exponent of artificial sandstone's complex resistivity spectrum

“…Gao et al . (2016) also found a similar pattern in the limestone sample as shown in Figure 8(b) even they focused on another frequency band (10 Hz–100 kHz).…”

“…Relationships between frequency exponents and water saturations of (a) sample 3‐3 when the pore space contains CaCl 2 solution with a salinity of 200,000 ppm; (b) limestone sample from Gao et al . (2016) when the pore space contains NaCl solution with a salinity of 4000 ppm.…”

“…The simulation results of Maineult et al . (2018) suggest a negative linear relationship between frequency exponent and water saturation, which is not consistent with our and Gao's (Gao et al ., 2016) experimental results as shown in Figure 8. Maineult et al .…”

Automatic karst cave detection from seismic images via a convolutional neural network and transfer learning

“…Carbonate reservoir plays an important role in oil and gas exploration due to its characteristics of high yield, large scale, and high quality of crude oil and gas (Gao et al, 2016). Many efforts have been made to study the prediction and characterization of the carbonate-karst reservoirs (Loucks 1999;Qi and Yun, 2010;Chen et al, 2011;He et al, 2019).…”