Data mining/analysis
This paper presents a robust workflow to identify optimization opportunities in gas lift wells through real-time data analysis and a surveillance-by-exception methodology.
This paper describes a data-driven well-management strategy that optimizes condensate recovery while preserving well productivity.
This paper explores the evolving role of the digital petroleum engineer, examines the core technologies they use, assesses the challenges they face, and projects future industry trends.
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The difficulty in applying traditional reservoir-simulation and -modeling techniques for unconventional-reservoir forecasting is often related to the systematic time variations in production-decline rates. This paper proposes a nonparametric statistical approach to resolve this difficulty. -
The oil and gas industry is facing an invasion of data analytics startups who saw a wide-open gap in the market a few years ago when talk of big data first began.
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A newcomer in the arena of oilfield market research has set an ambitiously high bar for itself: to speed up the oil and gas industry’s widely acknowledged and painfully slow rate of technology adoption.
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The use of intelligent software is on the rise in the industry and it is changing how engineers approach problems. A series of articles explores the potential benefits and limitations of this emerging area of data science.
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The use of intelligent software is on the rise in the industry and it is changing how engineers approach problems. A series of articles explores the potential benefits and limitations of this emerging area of data science.
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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.
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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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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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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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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.
