Spark map reduce based framework for seismic facies classification

Seismic facies analysis provides an efficient way to identify the structure and geology of reservoir units. In the past two decades, seismic attributes have been widely used to highlight the geological features of interest. The combined benefits of multiple seismic attributes have been explored to estimate seismic events accurately. However, with the increase in the number of seismic attributes and size of seismic data, the traditional methods of seismic facies interpretation become very difficult and time-consuming. To address this issue, several computer-assisted facies classification algorithms including k-Means, self-organizing map and generative topographic map were introduced to capture the spatial distribution of seismic facies. The automated facies classification methods accurately quantify the characteristics of geological facies. However, these methods are computationally very time-expensive. To resolve this problem, we provide Spark Map-Reduce based framework for unsupervised classification of seismic facies. The study aims to reduce the computational time complexity of various existing unsupervised facies classification techniques. Further, to demonstrate the applicability, the proposed parallel frameworks are applied to the seismic dataset of Dutch Sector (North Sea) and the classification results are compared in terms of run-time complexity and accuracy.