Machine Learning Applications of 4D Seismic in Carbonate: Case Study Offshore Abu Dhabi

Mahgoub M. A., Bashir Y., Berry A. A.

Abu Dhabi International Petroleum Exhibition and Conference 2022, ADIPEC 2022, Abu Dhabi, United Arab Emirates, 31 October - 03 November 2022 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.2118/211705-ms
  • City: Abu Dhabi
  • Country: United Arab Emirates
  • Istanbul Technical University Affiliated: No


Copyright © 2022, Society of Petroleum Engineers.Seismic 4D analysis is a model for integrating different disciplines in the oil and gas industry, such as seismic, petrophysics, reservoir engineering, and production engineering. Two 3D seismic surveys were conducted in the studied area with low repeatability of the recordings: the baseline survey in 1994 and the monitoring survey in 2014. A full 4D seismic co-processing of the baseline and monitor surveys was performed for both surveys starting with the field tapes. The 4D seismic co-processing improved poor seismic acquisition repeatability and 4D seismic attributes such as NRMS and predictability showed that. 4D time-trace shift was also performed, using the baseline survey as a reference to measure the time shifts between the baseline survey and the monitor survey at 20-year intervals. Dynamic 4D trace warping was followed by seismic 4D inversion to compare the 4D difference in the seismic inverted data with the difference in seismic amplitude. The seismic inversion helped overcome noise, multiple contamination, and differences in dynamic amplitude range between the baseline and seismic monitoring measurements. Applications of machine learning in the geosciences are growing rapidly in both processing and seismic interpretation. We then examined the relationship between well logs and seismic volumes by predicting a volume of log properties at the well locations of the seismic volume. In this method, we computed a possibly nonlinear operator that can predict well logs based on the properties of the adjacent seismic data. We then tested the Deep Forward Neural Network (DFNN) on six wells to adequately train and validate the machine learning approach using baseline seismic inversion data and monitoring data. The objective of trying such a supervised machine learning approach was to predict the density and porosity of both the baseline seismic data and the monitoring seismic data to verify the accuracy of the 4D seismic inversion.