Oil and Gas Reservoir Modeling in the Cloud

Emerson’s Tyson Bridger has a fascinating article, Cloud Computing Can Be Applied For Reservoir Modeling, in E&P magazine. The phrase, “cloud computing” gets bandied about quite a bit, so I turned to Wikipedia for the latest definition:

Cloud computing is computation, software, data access, and storage services that do not require end-user knowledge of the physical location and configuration of the system that delivers the services.

The idea has been catching on with many businesses as they move essential services such as email and websites off their own servers over to providers such as Amazon, Google, IBM, Microsoft, and Rackspace.

Tyson describes oil & gas reservoir management as an application that can benefit from this move from internal servers to external cloud computing providers. He writes that there is:

…an important need to reduce risk and better quantify uncertainty in reservoir management today – something that is becoming more challenging as reservoirs become more geologically complex and difficult to reach.

Reservoir modeling requires tremendous computational power to generate realizations and stochastic models of the oil & gas reservoirs. It’s difficult to get enough computational horsepower without distributing the load:

…through multiple clusters of computers, often termed “distributed computing.”

Because of the tremendous amounts and size of the data involved in reservoir modeling, the workflow for collaboration between geographically dispersed reservoir engineers has been difficult. Tyson notes:

A transparent and structured reservoir modeling workflow through cloud computing also can act as a repository for years of expertise and modeling advances (particularly important given the number of people leaving the industry over the next few years), help publicize and enforce best practices, and foster a uniform style and standard of work across the operating company and across physical locations.

Security concerns of the application and associated data have held oil and gas companies back from adopting a cloud-computing model for reservoir management. Similar types of security practices applied to inside-the-firewall applications can be applied to cloud computing based applications to mitigate these risks.

Tyson notes that Emerson’s reservoir modeling, simulation, and history-matching cluster-enabled software runs on the Linux platform, based on a thin-client architecture, which lends itself well to the elastic scaling of cloud-based virtual servers. He cites an example [hyperlinks added]:

…the Roxar Tempest simulator deploys simulations across multiple computer nodes, and the automated history-matching tool, Roxar EnABLE, generates multiple realizations and multiple simulator instances across computer nodes. Elastic cloud computing allows the reservoir modeler to scale the cluster according to the size of the problem.

Tyson describes how the Roxar team has tested the reservoir management applications with the Amazon EC2 (Elastic Compute Cloud) [hyperlink added]:

Using Roxar RMS on the .rox platform, jobs can be distributed transparently to the cloud. Data can be distributed using source.rox on Amazon S3, Amazon’s storage service. Computations can be performed on the correctly sized virtual cluster on Amazon EC2 with control of the job taking place through Amazon Web Services. Reservoir modelers also can choose to run the jobs locally or in the cloud, depending on their need.

I imagine that we’ll see more applications that fit the strengths of rapidly scalable virtual servers join these reservoir modeling applications out in the cloud to help process manufacturers improve the way they run their production operations.

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