Technology Focus: Seismic (February 2022)

Abstract

The past couple of years has been a rollercoaster for energy professionals, but it did not stop the incredible achievements in machine-learning techniques, particularly neural networks to improve seismic imaging and multiple attribute extraction to decipher reservoir rock properties and fluid contact movements in areas with data paucity and increasing difficulty in finding new or remaining hydrocarbon accumulations. As I reviewed the seismic-related papers, I was impressed at the innovative ML methods that were developed and successfully deployed to improve already existing data and augment sometimes scarce data to aid understanding of risks and uncertainties, enhance hydrocarbon recovery, reduce cost, increase production, and, in some cases, redefine the geological understanding of the play. Apart from the fields of seismic imaging, reservoir characterization, and hydrocarbon recovery, there were many papers devoted to new tectonic models, improved geohazards assessment, microporosity estimates, and data-acquisition methods—vertical seismic profiles, shear wave, 3D/4D ocean bottom cable, electromagnetic survey, and high-resolution broadband seismic. It was difficult to select the top three papers for synopsis because there were so many interesting topics to choose from, but I ensured that readers receive a taste of the major topics covered, selecting some that are must-reads and recommending a few others for your reading pleasure. I hope you find them as enjoyable as I did. Recommended additional reading at OnePetro: www.onepetro.org. IPTC 19810 - Hydrocarbon Exploration and Production Potential of the Gulf of Suez Basin in the Framework of the New Tectonostratigraphic Model by Janpieter Van Dijk, Dragon Oil, et al. OTC 30620 - Improving Drilling Geohazards Assessments of the Overburden Section by Performing Seismic Multiattribute Analysis by Yosmel Sanchez, Fugro, et al. SPE 200761 - Accounting for Model Errors of Rock Physics Models in 4D Seismic History-Matching Problems: A Perspective of Machine Learning by Xiaodong Luo, NORCE, et al. SPE 202651 - The Houta Prospect Hammamet Basin, Offshore Tunisia, as an Example of a New Play in an Old Basin by Janpieter van Dijk, GGS, et al. SPE 202753 - A Machine-Learning Approach to Removing Highly Variable Surface Waves for a Large Shallow-Water Survey Offshore Abu Dhabi by Al Hajeri Badreia, ADNOC, et al.