Quantitatively Evaluating the Preservation of Deep-water Channel Architecture using 3D Synthetic Seismic from Outcrop

Abstract

Summary Forward seismic reflectivity models can be used to interpret depositional architecture and stratal surfaces. However, such studies often stop short at a qualitative assessment of the link between underlying depositional architecture and seismic resolvability, lacking a quantitative assessment. This study addresses this gap with a direct quantitative comparison of 3-dimensional facies architecture predicted from seismic with a “ground truth” to quantify heterogeneity facies associations and architecture preserved in inverted seismic data. The primary goal is to quantify how facies architecture information is preserved in and predicted from inverted seismic reflectivity data. The objective is to explore what the variables are that impact correct vs incorrect facies classification. With increasing seismic frequency, channel axis becomes harder to predict while mass transport deposits became easier to predict. Facies in shallow reservoirs are easier to predict than in deep reservoirs. Disorganized channel systems show greater facies predictability than organized systems due to greater AI contrasts. This study highlights what architectural information is preserved in 3-dimensional inverted seismic data, built from outcrop data of a deep-water system, which can aid directly in interpretation, reservoir prediction, and modelling.