Redefining seismic interpretation - Machine learning for fault interpretation, enhancing efficiency, accuracy and auditability through a cloud-based approach

Abstract

Market dynamics have challenged the oil and gas industry to evolve. Modern digital technology has started to impact the entire oil?eld lifecycle from exploration to development and production, fundamentally changing the way geoscientists work by enhancing performance and enabling significant value creation. In subsurface characterization, computer-assisted seismic interpretation has been around for several decades. Over time, computer and software technology advances have improved the speed and quality of seismic interpretation, but these advances have coincided with an exponential growth in the volume of data to be interpreted. Consequently, the critical task of performing seismic interpretation is both time-consuming and laborious. Moreover, friction in accessing data and relevant technologies, along with a lack of insight due to inefficient collaboration, increases the uncertainty of the results. Due to these factors, it takes several months to mature an interpretation and build a 3D digital representation of the subsurface which is required to support a drilling decision. This paper is primarily focused on seismic fault identification, which is a key component in subsurface characterization and modelling workflows, using a combination of cloud technology and new machine learning techniques.