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Data Science and Digital Engineering in Upstream Oil and Gas (DSDE) is an online publication from SPE that presents the evolving landscape of data management and data use in the industry.

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FEATURES


The Case for Collaboration: Data Science Is Done Best When an Operator Works With a Data Scientist

Studies conducted through collaboration between an operator that knows the physical reality and a data-science company that knows the best machine-learning methods yield good practical results.


Data Is Not Scarce, but Oil Companies Hoard It as if It Were

June Newsletter

A growing number of oil industry leaders are saying that data sharing across the industry is needed, but change is coming slowly.

Digital Technologies Shake Up the Safety Status Quo

Digitalization and new technologies are disrupting old practices and shaping a new safety landscape for the oil and gas industry. At OTC, a panel sponsored by the Center for Offshore Safety looked at the effects of some of these new technology trends.
 

IN THE INDUSTRY


Offshore and Onshore Reliability Data Project Goes Digital

Vast amounts of offshore and onshore reliability data will be made available as the project by the Norwegian Petroleum Directorate provides access to its now-digital data in an effort to improve accessibility and efficiency for oil and gas industry reliability data.

Augmented Artificial Intelligence Improves Data Analytics in Heavy-Oil Reservoirs

The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields.

IN THEORY


Katy, Texas, Is Now Home to One of the World’s Fastest Supercomputers

The 21st century oil and gas industry thrives on hardcore computing power, crunching data derived from seismic testing to find oil deep in the ground and below the sea. Now, an Australian company will fire up a digital behemoth in a data center near the Houston suburb.

Data Science vs. Decision Science

Data science and decision science are related but still separate fields, so, at some points, it might be hard to compare them directly. This article attempts to show the commonalities, differences, and specific features of data science and decision science.

IN PRACTICE


Artificial Intelligence Can Reduce ESP Failures  

Electrical-submersible-pump (ESP) technology predominates available artificial-lift options. The risk of ESP failures can be reduced greatly with the right combination of advanced technologies, such as combining artificial intelligence with a cloud-based autonomous surveillance system.

Machine Learning Optimizes Duvernay Shale-Well Performance

This paper discusses how machine learning by use of multiple linear regression and a neural network was used to optimize completions and well designs in the Duvernay shale.

EMERGING TECHNOLOGY


Integrated Internet of Things Platform Helps Close the Gap Between Data Science and Operations

Arundo Analytics has built an integrated industrial Internet of things platform that allows data scientists to productize data-science solutions and accelerate feedback/improvement iterations between end-users and data scientists effectively.

Microsoft Launches Drag-and-Drop Machine-Learning Tool

Microsoft  announced three new services that aim to simplify the process of machine learning—an interface for a tool that automates the process of creating models; a new no-code visual interface for building, training, and deploying models; and hosted Jupyter-style notebooks for advanced users.


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