Identification of Strata from Irregularities in Well Logs Using Chaos Quantifiers

Abstract

Summary Estimating a transition between rock types from well logs using traditional methods can be challenging and time-consuming. Developing new approaches to improving the quality of the estimation as well as saving time becomes necessary. This paper presents a new methodology that uses elements of chaos theory to evaluate the variability of well logs to identify rock layers. Four different parameters that quantify chaos were used in the present study: fractal correlation dimension, sample entropy, Hurst exponent, and Lyapunov exponent. Each of them describes a different property of a well log. The method presented in this paper uses all of them together for an extensive characterization of well log irregularities. The study was carried out on a set of 68 well logs from six wells in the Pluto gas field (Australia). The logs were divided into segments of 25 m. A computer program was written to calculate the chaos parameter values of each interval. The parameters were then analyzed statistically. Hierarchical methods and k-means clustering were used to create dendrograms and clusters. The statistical analysis of the results has shown that the well log variability can be used to successfully differentiate rock formations by showing which intervals on a log are similar. In addition, the intervals that correspond to Mungaroo sandstones, which are the reservoir rock of the Pluto gas field, were particularly distinguished from other parts of the log. Therefore, the presented methodology could prove useful to estimate zones of interest in terms of hydrocarbon potential. The presented algorithm accounts for the variability of the well log readings, not the log values themselves. It does not point exactly to a depth where rock layers interface, but it rather allows similar (in terms of irregularities), consecutive intervals to be grouped together. Based on that, one can draw a conclusion that a lithology differs between groups of intervals.