Multi-Attribute Analysis For Tight Gas Sand Reservoir Characterization

Nowadays unconventional resources are steadily turning into the leading trend of the oil and gas industry.Although common exploration and exploitation approaches were successful in the past,traditional methods are evolving into more assertive solutions to address the unconventional reservoir characterization.Tight gas sands are a strategic worldwide energy resource that accounts for more than 7,500 trillion cubic feet in reserves.Low permeability and natural fractures are the key factors to undertake in the asset depiction.Notwithstanding,post-stack seismic attributes analysis delivers valuable information for identifying sweet spots,predicting reservoir properties,reducing drilling uncertainties and boosting fracking stimulation designs.The study on the Sulige Gas Field features an augmented workflow for multi-attribute analysis,based on the extraction of post-stack seismic attributes to identify high prospective drilling zones.The methodology discriminates and blends the utmost significance of complex trace seismic attributes,such as acoustic impedance,curvature,similarity,gray-level co-occurrence matrix textures and spectral decomposition,to escalate the characterization accuracy.Furthermore,the procedure evaluates multi-linear regression analysis and non-linear transformations,by including multilayer feedforward neural network,radial basis function neural network,and probabilistic neural network to generate pseudo properties volumes.Experimental results show a strong correlation and validation relevance to forecast P-wave velocity,density,and permeability values.