It was gratifying to see that, despite the challenges inflicted by the pandemic this past year, technologies in the seismic world continued to advance. As an example, the machine-learning theme again received considerable focus.
As I reviewed all the SPE seismic papers this time, the most noticeable thing was the diversity of themes and case histories that were covered.
Thus, in addition to selecting three papers to be synopsized this year, I expanded the list of recommended readings to showcase that diversity. For instance, gravity and electromagnetics (and their integration with seismic) are also featured. I hope you will find this broad range of topics useful.
This Month's Technical Papers
Sub-Basalt Imaging Reveals Deeper Plays Offshore India
Applications of Artificial Neural Networks for Seismic Facies Classification
Machine-Learning Method Determines Salt Structures From Gravity Data
Recommended Additional Reading
IPTC 19700 Using Multicomponent 3D Seismic Data To Define Current Fluid Contacts in Stacked Limestone Reservoirs of a Partially Depleted Oil Field by Falah Al-Enazi, KJO, et al.
IPTC 20119 Near-Surface Characterization in Southern Oman: Multiwave Inversion by Machine Learning by Masclet Sylvain, CGG, et al.
OTC 30294 Saturated Gas Hydrate Drilling-Hazard Prediction Using Controlled Source Electromagnetic Method by Raghava Tharimela, EMGS Asia Pacific, et al.
OTC 30316 Application of Compressive Seismic Imaging Technology for Multicomponent Ocean-Bottom Nodes Seismic Survey Acquisition in K Field, Sabah Offshore, and Reconstruction of Multicomponent Data by Abdul Aziz Abdul Rahim, Kebabangan Petroleum Operating Company, et al.
SPE 199758 Understanding the Role of Well Sequencing in Managing Reservoir Stress Response in the Permian: Implications for Child-Well Completions Using High-Resolution Microseismic Analysis by Hannah Chittenden, Diamondback Energy, et al.
SPE 201750 Quantitative 4D Seismic-Assisted History Matching Using Ensemble-Based Methods on the Vilje Field by Konrad Wojnar, Resoptima, et al.
SPE 202786 Quantification of Reservoir Thickness Reduction Using Seismic Attributes and Inversion by David Contreras, ADNOC, et al.
