Data mining/analysis
This paper describes an approach to creating a digital, interconnected workspace that aligns sensor data with operational context to place the completions engineer back into a central role.
This paper demonstrates how the integration of multiphysics downhole imaging with machine-learning techniques provides a significant advance in perforation-erosion analysis.
This paper presents a workflow that leverages a multiagent conversational system to integrate data, analytics, and domain expertise for improved completion strategies.
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At the 2016 Gulf of Mexico Deepwater Technical Symposium in New Orleans, a presentation discussed the application of sensors and analytics in pipeline integrity management systems. -
Young Technology Showcase—Top-Down Modeling: A Shift in Building Full-Field Models for Mature FieldsData-driven, or top-down, modeling uses machine learning and data mining to develop reservoir models based on measurements, rather than solutions of governing equations.
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Well control is built around huge steel machines, but the future of the business is digital. Data have become a critical asset as operators and service companies work to increase the safety and reliability of their products and operations.
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In an effort to foster collaboration in an area where there is currently very little, researchers at the University of Texas at Austin (UT) created a new web-based application for storing and sharing CT images of rocks.
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A data-driven approach to successfully analyze and evaluate production-fluid impact during facility system divert events is presented. The work flow effectively identifies opportunities for prompt event mitigation and system optimization.
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The big data approach will allow new types of data-driven models to bypass traditional bottlenecks. It is also expected to lead to different views of standard models, thus providing new and valuable insights in the process.
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There is a lot of information buried in drilling reports written every day, but little of it appears in computer databases.
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There is talk about digital oil fields and big data and some striking examples of their power. But in real oil fields, a lot of operators are still running fields with systems relying on big paper.
