English Russian
 
 

Events

Protecting Return on Investment Using the Big Loop Workflow, an Automated Ensemble Based Approach

July 17, 2018
Recorded Webinar
EMR_VLS.jpg

Presented by:  Samir Walia, Chief Geoscientist and Regional Manager for Subsurface Software Solutions
Featured Domains: Reservoir Modeling and Reservoir Engineering
Featured Technology: Big Loop

register_for_webinar_blue.png

Abstract

The sharp decline in oil prices is pushing exploration and production companies to revise future investment plans and ensure that investment decisions come with positive returns. Reservoir modeling is a key tool in the evaluations, as it explores different development scenarios to optimize field performance and gain the most value out of the reservoir. Traditional reservoir modeling, however, relies on a single or small number of scenarios (base, high and low cases) and introduces deterministic prediction profiles for future field performance that don’t fit with modern reservoir management guidelines. Such a modeling approach also fails to integrate the impact of uncertainties at the different reservoir modeling stages - from seismic interpretation to dynamic simulation. Using an integrated reservoir modeling and reservoir engineering workflow, the Big Loop and its ensemble statistics-based approach can generate crucial information to reduce risk and support decision making - in determining reservoir uncertainties and their ranges, planning future field development, and guiding well placement using multiple history match models.

Biography

Samir-Walia_sm.pngSamir Walia is Global Chief Geoscientist and Regional Manager for Subsurface Software Solutions at Emerson.  Samir has 22 years of experience in the oil and gas E&P industry.  With a Master's Degree in Geophysics from a prestigious university in India, he now specializes in Reservoir Characterization and Modeling.  His qualifications and achievements extend to his most recent contribution - automating the process of capturing and quantifying reservoir uncertainties, for more reliable reserve forecasts.