Reservoir: A Standardized and Replicable Assisted History Matching Workflow

Speaker Jose Villa
Jose Villa is a senior reservoir engineer in the Global Water Flood Deployment team at Shell International E&P in Houston, TX. He is responsible for the deployment of assisted history matching technologies in Shell fields worldwide. Before this position, he was a reservoir engineer in the Dynamic Reservoir Modeling team ...

Jose Villa is a senior reservoir engineer in the Global Water Flood Deployment team at Shell International E&P in Houston, TX. He is responsible for the deployment of assisted history matching technologies in Shell fields worldwide. Before this position, he was a reservoir engineer in the Dynamic Reservoir Modeling team providing workflow consulting and support on Shell reservoir simulation workflows. Prior to joining Shell in 2007, Jose was a reservoir engineer at PDVSA Intevep where he conducted reservoir modeling studies for a number of fields in Venezuela.  Jose Villa holds a Bachelor's degree in Petroleum Engineer from Central University of Venezuela (1999), and a Master's degree in Petroleum Engineering from Stanford University (2003). Jose also serves in the committee of the SPE GCS Reservoir Study Group since 2011.

Full Description

Assisted History Matching (AHM) is a systematic procedure of modifying parameters of a reservoir model to reproduce its dynamic response. Unlike traditional history matching (HM), where non-systematic manual adjustments on few engineering parameters are made on a single model to match the production data, AHM is part of an integrated study where engineering and geological consistent modifications are made to a reservoir model with the aid of efficient optimization techniques allowing the incorporation of multiple data types, such as production and 4D seismic, while also providing a vehicle for uncertainty assessment.

During the last decade, Shell has developed a number of methodologies to computer-automate the process of updating the large-scale reservoir parameters. These methodologies have been standardized and replicated in multiple fields and include assisted parameter estimation, which uses MCMC sampling techniques with experimental design-based proxies, and model maturation, which uses (adjoint) gradient-based updates to identify and correct (major) omissions (under-modeling) in the static and dynamic model. In this presentation, these methodologies will be presented and the results of their application in some field examples will be demonstrated.

Organizer Juan C. Picott

When?

Thu, Jan. 22, 2015
11:30 a.m. - 1 p.m. US/Central

How Much?

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Where?

Sullivan’s Steakhouse
4608 Westhiemer rd
Houston, TX 77027
United States

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