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

Abstract This work introduces a relevant non-linear approach to well log data processing and interpretation, based on the analysis of fractal dimension values and singularity spectrum attributes, and aims at studying the behavior of these values and attributes for hydrocarbon zone's delineating and fluids' nature detecting. 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 a multifractal analysis using wavelet transform allowed us to compute the singularity spectrum from which we could extract the main attributes, namely width, right height, left height, αpeak, αmin, αmax, f(αpeak), f(αmin), f(αmax) 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 and meaningful discrimination between oil and gas. It should be noted that the oil and gas widths which 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. Our finding showed the oil has the broader range that manifests remarkably in all values and attributes as compared to the gas, the gas occupies high regularization dimensions, low box dimensions, low width, low right heights and low left heights which means high sets of fractal dimension, low α peak, low α max and usually high α 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 the well 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.