A NOVEL APPROACH FOR PERMEABILITY PREDICTION OF TIGHT CARBONATES USING BOREHOLE ELECTRICAL IMAGING LOGGING

Fractal theory has emerged as a crucial tool for quantifying the pore space within rocks. Borehole electrical imaging logs are extensively employed for geological and pore structure analysis of formations. Consequently, introducing a novel method to calculate the fractal dimension from these logs becomes essential in accurately quantifying the complexity of the formation's pore structure. Initially, we extract porosity frequency spectra from the borehole electrical imaging logging. Subsequently, we performed reverse accumulation and normalization of the porosity frequency spectra to construct a reverse accumulation curve that exhibits a morphology similar to the capillary pressure curve. The inflection point of the fractal curve was determined by locating its maximum point on the "Pittman" curve. Finally, the fractal dimensions of the segments were determined by analyzing the segmental power-law relations of the fractal curves. Our investigation revealed a notable difference in the fractal curve of the porosity frequency spectra with 58.14% porosity in tight carbonate rocks, exhibiting three distinct segments instead of the previously observed two segments. When examining the relationship between fractal dimension and core permeability, we observed weak negative correlations between the middle pore fractal dimensions (Df_middle) and small pore fractal dimensions (Df_right) with core permeability. Conversely, the large pore fractal dimensions (Df_left) displayed a positive correlation with core permeability (R² = 0.78). These findings highlight that the complexity of the pore system and the presence of large pores are the principal factors influencing seepage capacity in tight carbonate rocks. Building upon this understanding, we developed a permeability model utilizing Df_left, which we successfully applied to the Ordovician Majiagou Formation. The resulting predicted permeability values closely matched core permeability (PPMCC = 0.93). This study introduces a novel method for quantifying the complexity of the pore structure in formations using borehole electrical imaging logs. By doing so, it enhances our comprehension of the factors that impact seepage capacity and establishes a valuable tool for predicting permeability in tight carbonate reservoirs.