SHAIR will create a new software service that will deliver highly accurate interpretation of seismic data to determine the likely presence of oil or gas. We will achieve this by a novel Machine Learning implementation that combines two known but disparate tools. The output of each tool can be used as the input of the other tool, so by looping round we can produce more and more accurate results, cancelling out noise and errors to continuously improve accuracy of results. (See Fig. 12 Annex) RRG's technology currently contains an AI driven horizon interpretation with minimal human interpretation. It takes in migrated stacks in the time domain. It uses their improved Geophysics-guided Neural Networks (GNNs) innovation of Convolutional Neural Networks (CNNs) to detect discontinuities in the rock structure, and interprets these as 'horizons'. S3's Technology, XWI, is the result of 20 years development covered by many patents. It is an advancement of traditional Full-Waveform Inversion (FWI). It produces high quality 3D Velocity models in assessing shallow anomalies. The main result will be a novel suite of tools available to oil companies as a SaaS product via which the companies will pay to more accurately analyse their data. The new technology will combine S3's XWI technology making use of velocity models to better constrain the automatic interpretation done by RRG's GNN. Pre-processed and migrated data will be the inputs for the AI interpretation. The main result will be a new service using our experience to date of ML to create a service that would be more cost effective for oil companies than the traditional method of analysis to discover oil. Oil companies will become more profitable via better analysis and a higher rate of success. This improved interpretation of data should reduce the likelihood of drilling of oil or gas which results in none being found and therefore can help the environment and waste of such incidents.
Project leader: Åsmund Heir
Institution: RAGNAROCK GEO AS