In oil and gas exploration, artificially generated pressure waves are used to extract important information about the subsurface physical properties as well as the rocks geometrical appearance. In seismic inversion you formulate the forward problem, in order to make synthetic data, and try to fit this data with the observed data (field data). The closer synthetic data resembles field data, the closer we are to estimating the true model parameters. In this study we will investigate how we can use state of the art algorithms from both geoscience and datascience in order to predict a model which is closer to the true model. The proposed doctoral work will focus on combining methods used in geophysical processing and inversion with methods within the field of machine learning. The candidate will conduct research towards the use of numerical methods for optimizing and adapting methods and algorithms within these fields. The motivation and overall goal is to develop machine learning workflows in order to improve the efficiency of seismic processing and inversion processes. In addition, as high resolution seismic, such as Broadband seismic, has become more and more sought after in the industry, attention will be directed towards research within the use of non-conventional streamer configurations, such as variable depth streamers and special VSP setups, and how they relate to seismic resolution. Case studies on field data will be implemented in this thesis work.
Project leader: Odd Kolbjørnsen
Institution: LUNDIN ENERGY NORWAY AS