Abstract
The first member of the Qingshankou Formation, in the Gulong Sag in the northern part of the Songliao Basin, has become an important target for unconventional hydrocarbon exploration. The organic-rich shale within this formation not only provides favorable hydrocarbon source rocks for conventional reservoirs, but also has excellent potential for shale oil exploration due to its thickness, abundant organic matter, the overall mature oil generation state, high hydrocarbon retention, and commonly existing overpressure. Geochemical analyses of the total organic carbon content (TOC) and rock pyrolysis evaluation (Rock-Eval) have allowed for the quantitative evaluation of the organic matter in the shale. However, the organic matter exhibits a highly heterogeneous spatial distribution and its magnitude varies even at the millimeter scale. In addition, quantification of the TOC distribution is significant to the evaluation of shale reservoirs and the estimation of shale oil resources. In this study, well log data was calibrated using the measured TOC of core samples collected from 11 boreholes in the study area; the continuous TOC distribution within the target zone was obtained using the △logR method; the organic heterogeneity of the shale was characterized using multiple fractal models, including the box-counting dimension (Bd), the power law, and the Hurst exponent models. According to the fractal dimension (D) calculation, the vertical distribution of the TOC was extremely homogeneous. The power law calculation indicates that the vertical distribution of the TOC in the first member of the Qingshankou Formation is multi-fractal and highly heterogeneous. The Hurst exponent varies between 0.23 and 0.49. The lower values indicate higher continuity and enrichment of organic matter, while the higher values suggest a more heterogeneous organic matter distribution. Using the average TOC, coefficient of variation (CV), Bd, D, inflection point, and the Hurst exponent as independent variables, the interpolation prediction method was used to evaluate the exploration potential of the study area. The results indicate that the areas containing boreholes B, C, D, F, and I in the western part of the Gulong Sag are the most promising potential exploration areas. In conclusion, the findings of this study are of significant value in predicting favorable exploration zones for unconventional reservoirs.
Highlights
With the growing global energy demand and the success of unconventional shale oil and gas in North America, unconventional reservoirs, an effective substitute for conventional oil-gas resources, have been of great interest in global exploration and exploitation [1]
Current exploration practices and studies indicate that the total organic carbon content (TOC) is one of the most important indicators of oil enrichment within the shale formations [3]
The vertical distribution of the content of a given element obeys a power law, which is consistent with the fractal model [9,10]
Summary
With the growing global energy demand and the success of unconventional shale oil and gas in North America, unconventional reservoirs, an effective substitute for conventional oil-gas resources, have been of great interest in global exploration and exploitation [1]. Combining mathematical theory with geological data remains the key to characterizing the organic heterogeneity of a shale formation and to evaluating its exploration potential. The vertical distribution of the content of a given element obeys a power law, which is consistent with the fractal model [9,10] These studies successfully investigate the fractal models to characterize the distribution of geochemical elements in boreholes, which is of significance to evaluate the quality and quantity of mineral resources. We characterize the vertical TOC distribution of the shale in the first member of the Qingshankou Formation, in the Gulong Sag in the northern part of the Songliao Basin, using multiple fractal models, including the box dimension model, the power law model, and the Hurst exponent model. We investigate the shale’s horizontal heterogeneity using inverse distance weighting (IDW) and predict the distribution of the shale oil exploration targets within the study area using multiple indices
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