A new multifractal analysis-based for identifying the reservoir fluid nature

This work introduces a relevant non-linear approach to well log data processing and interpretation. The suggested method studies the behavior of fractal dimension values together with singularity spectrum attributes. It aims to delimit the hydrocarbon zones, including the detection of fluids' nature. The analysis was carried out on different datasets of 1085 well logging records related to 93 wells drilled in various geological contexts, located in different sedimentary basins in southern Algeria, both box and regularization dimensions have been calculated, and amultifractal analysis usingwavelet transform allowed us to compute the singularity spectrum from which we could extract the main attributes, namely width, right height, left height, alpha(peak), alpha(min), alpha(max), f(alpha(peak)), f(alpha(min)), f(alpha(max)) and C value. Based on the obtained values and attributes, an analytical investigation using descriptive statistics, Agglomeration Hierarchical Clustering (AHC), cross plots and scatter plots, has been performed. The obtained results illustrate that the fractal dimensions and singularity spectrum attributes can effectively characterize reservoir fluids, and are importantly affected by the fluid change, resulting in important differences andmeaningful discrimination between oil and gas. It should be noted that the oil and gas widthswhich are significantly different from zero confirm that well logs exhibit multifractal behavior and can, therefore, be suitably investigated to explain such behavior related to physical properties and fluid qualities of reservoirs. The attained results show the oil has the broader range that manifests remarkably in all values and attributes as compared to the gas. Our findings exhibit the gas occupies high regularization dimensions, low box dimensions, low width, low right heights, low left heights which means high sets of fractal dimension, low alpha(peak), low alpha(max), and usually high alpha(min) as compared to oil. In addition, the widths are affected by the strength of singularity, with lower widths in gas, the gas known by strong singularities as compared to the oil which represents high widths. It is worth noting that these values and attributes should be used in combination to provide more precision in the analysis, and enhance the interpretation process of gas and oil detection. In conclusion, the obtained results demonstrate the relevance of the suggested method in detecting the nature of the fluid; it might be very interesting to examine the multiscale characteristics of thewell log data and enrich the conventional analysis. However, additional investigations using multiple mathematical and statistical tools are needed to confirm these findings, obtain more results as well as improve our analysis and interpretation process. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The study utilizes fractal dimensions and singularity spectrum attributes to characterize reservoir fluids, highlighting significant differences between oil and gas. Gas typically shows high regularization dimensions, low box dimensions, and strong singularities, while oil exhibits a broader range of attributes and high box dimensions.