Unsupervised machine learning-based multi-attributes analysis for enhancing gas channel detection and facies classification in the serpent field, offshore Nile Delta, Egypt

Abstract

The prediction of highly heterogeneous reservoir parameters from seismic amplitude data is a major challenge. Seismic attribute analysis can enhance the tracking of subtle stratigraphic features. It is challenging to investigate these subtle features, including channel systems, with conventional-amplitude seismic data. Over the past few years, the use of machine learning (ML) to analyze multiple seismic attributes has enhanced the facies analysis by mapping patterns in seismic data. The purpose of this research was to assess the efficiency of an unsupervised self-organizing map (SOM) approach supported by multi-attribute analysis that could improve gas channel detection and seismic facies classification in Serpent Field, offshore Nile Delta, Egypt. As well as evaluates the importance of several available seismic attributes in reservoir characterization rather than analyzing individual attribute volumes. In this study, the single attribute (spectral decomposition attribute) highlighted the gas channel spatial distribution using three distinct frequency magnitude values. Subsequently, we employ principal component analysis (PCA) as an attribute selection method, discovering that combining seismic attributes such as sweetness, envelope, spectral magnitude, and spectral voice as input for SOM reflects an effective method to determine facies. The clustering results distinguish between shale, shaly sand, wet sand, and gas-saturated sand and identify gas–water contact on a 2D topological map (SOM), where each pattern indicates certain facies. This is achieved by associating the SOM outputs with lithofacies determined from petrophysical logs. Reducing exploration and development risk and empowering the geoscientist to generate a more precise interpretation are the ultimate objectives of this multi-attribute analysis.