Reservoir prescriptive management combining electric resistivity tomography and machine learning
In this paper, I introduce a comprehensive workflow aimed at optimizing oil production and CO<sub>2</sub> geological storage. I show that the same methodology can be applied to different categories of problems: a) real-time reservoir fluid mapping for predicting and delaying water breakt...
Main Author: | Paolo Dell'Aversana |
---|---|
Format: | Article |
Language: | English |
Published: |
AIMS Press
2021-04-01
|
Series: | AIMS Geosciences |
Subjects: | |
Online Access: | https://www.aimspress.com/article/doi/10.3934/geosci.2021009?viewType=HTML |
Similar Items
-
Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography
by: Tomasz Rymarczyk, et al.
Published: (2019-03-01) -
Logistic Regression for Machine Learning in Process Tomography
by: Tomasz Rymarczyk, et al.
Published: (2019-08-01) -
A calculation model for water breakthrough time of gas wells in gas reservoirs with edge water considering interlayer heterogeneity: A case study of the Lower Triassic Feixianguan gas reservoirs in the Puguang Gas Field
by: Jiqiang Li, et al.
Published: (2020-12-01) -
Exponential Models of Breakthrough Development of Industrial Systems for Achievement of Global Competitiveness
by: Tolstykh T.O., et al.
Published: (2019-01-01) -
2-D electrical resistivity tomography assessment of hydrate formation in sandy sediments
by: Yanlong Li, et al.
Published: (2020-06-01)