Reservoir heterogeneity analysis using multi-directional textural attributes from deep learning-based enhanced acoustic impedance inversion: A study from Poseidon, NW shelf Australia

Abstract

Reservoir heterogeneities play a crucial role in governing reservoir performance and management. Traditionally, detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data, which is a time-intensive task. Many researchers have utilized a robust Grey-level co-occurrence matrix (GLCM)-based texture attributes to map reservoir heterogeneity. However, these attributes take seismic data as input and might not be sensitive to lateral lithology variation. To incorporate the lithology information, we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume (a rock property-based attribute) obtained from a deep convolution network-based impedance inversion. Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach, wherein seismic data is used as input. To evaluate the improvement, we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field, NW-shelf, Australia. This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach. In addition, we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization. This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines. Thus, the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity, which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.