AAPG Geoscience Technology Workshop

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Electrofacies modes and novel method for lithofacies identification of microbial carbonate: a case study of the Sinian Dengying Formation in Moxi-Gaoshiti, Sichuan Basin, China

Abstract

The giant carbonate gas reserve deposited in the Sinian Dengying Formation was discovered in Chuanzhong paleo-uplift of Sichuan Basin in 2011, which considered as an important deep reservoir in China. As for pilot area of Moxi-Gaoshiti region, the gas-bearing intervals of the Dengying Formation characterized by dissolved pores and vugs, where facies of algal mounds and grain shoals determined development of good reservoirs. However, genetic variations and complex petrophysical responses of carbonates lead to the issue of lithofacies identification, which affect facies analysis and reservoir evaluation. Therefore, based on cores from over 100 meters in three wells, combing with petrologic observations and well logging data, the integrated electrofacies modes and a novel method of lithofacies identification for microbial carbonates were introduced. There are over 8 lithofacies within intervals, and four dominant dolomite lithofacies representing major depositional settings were classified, including algae dolomite, grainstone, mudstone and argillaceous mudstone. The calibration between cores and well logs showed algae dolomite was characterized by low gamma ray (GR), long ultrasonic transit time (AC), and medium reading in deep measurements of borehole resistivity (RD). Grainstone was characterized by extremely low GR, medium AC and medium to high RD. However, these electrofacies were unfavorably applied to identify lithofacies, since sensitively logging responses failed to display within 2-D or 3-D cross-plots and similar responses among lithofacies could not be well divided. Therefore, an alternative method called color modes were designed. Firstly, the most sensitive parameters (GR, AC and RD) were normalized for individual lithofacies. Secondly, the normalized data were segmented by four ranges with corresponding colors of blue, yellow, red and green, which means data array were converted into visible color associations. Finally, after calibration with detailed core descriptions, a total of 53 meaningful color associations representing lithofacies and color modes of four dominant dolomites were acquired. The novel electrofacies modes were successfully applied to determine lithofacies with coincidence rate of above 80% among core wells. Six wells in pilot region illustrated this novel method could achieve direct and rapid lithofacies interpretation, which considered as an effective tool for facies analysis and reservoir prediction for microbial carbonate.