Machine learning to reduce cycle time for time-lapse seismic data assimilation into reservoir management

Xue, Yang (Shell International Exploration and Production, Inc.) | Araujo, Mariela (Shell International Exploration and Production, Inc.) | Lopez, Jorge (Shell International Exploration and Production, Inc.)


4D seismic is widely deployed in offshore operations to monitor improved oil recovery methods including water flooding, yet its value for enhanced well and reservoir management (WRM) is not fully realized due to the long cycle times required for quantitative 4D seismic data assimilation into dynamic reservoir models. To shorten the cycle, we designed a simple inversion workflow to estimate reservoir property changes directly from the 4D attribute maps using Machine Learning methods. Thousands of training data sets are generated by Monte Carlo sampling from the rock physics model within reasonable ranges of the relevant parameters. Machine Learning methods are then applied to build the relationship between the rock property changes and the 4D attributes, and the learnings are used to estimate the rock property changes given the 4D attribute maps. The estimated reservoir property changes (e.g. water saturation changes dSw) can be used to analyze injection efficiency, update dynamic reservoir models, and support reservoir management decisions. The turnaround time can be reduced from months to days, allowing early engagements with reservoir engineers to enhance integration. This accelerated data assimilation removes a deterrent to the acquisition of frequent 4D surveys.

Presentation Date: Thursday, October 18, 2018

Start Time: 8:30:00 AM

Location: 204B (Anaheim Convention Center)

Presentation Type: Oral