Seismic Interpretation Technologies Advancement and its Impact on Interactive and Iterative Interpretation Workflows

Seismic interpretation is a key task and foundation for hydrocarbons exploration and field development. Seismic data provides coverage from basin to reservoir scale workflows for identifying regional structures, delineate prospects and calculate rock properties. In this paper we discuss the evolution of seismic structural and stratigraphic interpretation through key technological milestones. This covers a broad spectrum, from conventional 2D interpretation methodologies to processes that help us see below the quarter wavelength resolution. We have captured the workflows that are redefining seismic interpretation landscape. These include wavelet based interpretation, multi-attribute analysis, spectral decomposition, geobody extraction, cognitive interpretation, pre-stack interpretation and applications of machine learning to seismic interpretation. We also present advancements in the computing environment that provided a paradigm shift in interpretation workflows. We demonstrate how the conventional workflows migrate into interactive and iterative processes at user desktops with multi-domain data access and analysis. We also discuss the hardware enablers such as high end desktop central processing units (CPUs) powered with graphic processing units (GPUs) that were not possible a few years ago. The advancement in technology comes with increased expectation from geoscientists. The workflow that were once considered in specialist domain are now being practiced by early to mid-career professionals. This is made possible with huge strides both in hardware infrastructure powered by clusters and cloud and software technologies. The cognitive interpretation, big data analysis, artificial intelligence, machine and deep learning workflows are becoming embedded components of seismic interpretation. We observe the advancement in 6 key areas that are responsible in transforming the seismic interpretation. The computing technology to handle large datasets and process at much faster pace, visualization technology leading to cognitive interpretation, ability to integrate multidisciplinary and multiscale data, interpretive processing utilizing pre-stack data, global interpretation methods leading to relative geologic time model (RGT) allowing the efficient use of every sample of seismic cube and ability to integrate the machine and deep learning processes that augment seismic interpretation. We present examples of using these technologies to maximize the benefit from seismic interpretation. The future of geoscience data storage as common opensource data format and applying the AI at scale offered through deploying enterprise AI platform is also discussed. The advantages of adopting the modern workflows driven by technology are helping in developing a shared integrated earth modelling environment. This allows the multi-disciplinary teams to use pre and post stack seismic data, rock properties, reservoir models and real-time drilling updates to make informed decisions. This is also helping both in exploration and field development to drill long reach horizontal wells maximizing the reservoir contacts assisted by machine and deep learning.