The Petroleum Engineering Profession A Reality or a Contradiction?

1962 ◽  
Vol 14 (11) ◽  
pp. 1195-1198
Author(s):  
Douglas H. Harnish
Keyword(s):  
Petroleum Engineering ◽  
Engineering Profession

Future organisation of the engineering profession

1974 ◽  
Vol 53 (12) ◽  
pp. 490
Author(s):  
A.J. Horsell
Keyword(s):  
Engineering Profession

Brakes along the Line

1992 ◽  
Vol 206 (2) ◽  
pp. 79-91
Author(s):  
M J Leigh
Keyword(s):  
United Kingdom ◽  
Automobile Industry ◽  
Engineering Profession ◽  
The United Kingdom ◽  
Multiple Units

The Railway Division Chairman reviews his career in railway braking with Davies and Metcalfe, including his apprenticeship in the automobile industry. He outlines his involvement with various multiple units in the United Kingdom and Australia, discussing the brake blending with its possibilities. The outcome of locomotive brake competition in the direct release market is described and the advantages of two-pipe brake systems for both direct and graduable braking are highlighted. He concludes by mentioning training and the engineering profession.

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

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