Application of machine learning techniques for identifying productive zones in unconventional reservoir
Unconventional reservoirs are the productive zones in other words the rock quality and the mechanical properties of the rocks this process is devastating if humans or people try to search for the best reservoirs. So we can use machine learning (ML) algorithms to help us find and search easily and fa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-01-01
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| Series: | International Journal of Intelligent Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666603022000094 |
| _version_ | 1811212271743926272 |
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| author | Amir Gharavi Mohamed Hassan Jebraeel Gholinezhad Hesam Ghoochaninejad Hossein Barati James Buick Karrar A. Abbas |
| author_facet | Amir Gharavi Mohamed Hassan Jebraeel Gholinezhad Hesam Ghoochaninejad Hossein Barati James Buick Karrar A. Abbas |
| author_sort | Amir Gharavi |
| collection | DOAJ |
| description | Unconventional reservoirs are the productive zones in other words the rock quality and the mechanical properties of the rocks this process is devastating if humans or people try to search for the best reservoirs. So we can use machine learning (ML) algorithms to help us find and search easily and fast for the best reservoirs with less human interaction as possible. The objectives of this paper is to use machine learning (ML) techniques to predict and classify the reservoirs based on the properties of each reservoirs and choose the best reservoir. In this paper we have made a comparison between the different types of machine learning algorithm and described how we get the best and worst result for each one, the comparison we made gave us that the AdaBoost algorithm gave the worst performance measured in the accuracy while the random forest (RF) algorithm gave the best performance, this paper aim to make improvement of the process of searching for productive zones using ML algorithms. |
| first_indexed | 2024-04-12T05:26:24Z |
| format | Article |
| id | doaj.art-1193e16b0cf242228bb383b17db1199d |
| institution | Directory Open Access Journal |
| issn | 2666-6030 |
| language | English |
| last_indexed | 2024-04-12T05:26:24Z |
| publishDate | 2022-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Intelligent Networks |
| spelling | doaj.art-1193e16b0cf242228bb383b17db1199d2022-12-22T03:46:15ZengKeAi Communications Co., Ltd.International Journal of Intelligent Networks2666-60302022-01-01387101Application of machine learning techniques for identifying productive zones in unconventional reservoirAmir Gharavi0Mohamed Hassan1Jebraeel Gholinezhad2Hesam Ghoochaninejad3Hossein Barati4James Buick5Karrar A. Abbas6Corresponding author.; University of Southampton, School of Chemical Engineering and Chemistry, Southampton, University Road, Highfield Campus, Southampton, SO17 1BJ, United KingdomUniversity of Southampton, School of Chemical Engineering and Chemistry, Southampton, University Road, Highfield Campus, Southampton, SO17 1BJ, United KingdomUniversity of Southampton, School of Chemical Engineering and Chemistry, Southampton, University Road, Highfield Campus, Southampton, SO17 1BJ, United KingdomUniversity of Southampton, School of Chemical Engineering and Chemistry, Southampton, University Road, Highfield Campus, Southampton, SO17 1BJ, United KingdomUniversity of Southampton, School of Chemical Engineering and Chemistry, Southampton, University Road, Highfield Campus, Southampton, SO17 1BJ, United KingdomUniversity of Southampton, School of Chemical Engineering and Chemistry, Southampton, University Road, Highfield Campus, Southampton, SO17 1BJ, United KingdomUniversity of Southampton, School of Chemical Engineering and Chemistry, Southampton, University Road, Highfield Campus, Southampton, SO17 1BJ, United KingdomUnconventional reservoirs are the productive zones in other words the rock quality and the mechanical properties of the rocks this process is devastating if humans or people try to search for the best reservoirs. So we can use machine learning (ML) algorithms to help us find and search easily and fast for the best reservoirs with less human interaction as possible. The objectives of this paper is to use machine learning (ML) techniques to predict and classify the reservoirs based on the properties of each reservoirs and choose the best reservoir. In this paper we have made a comparison between the different types of machine learning algorithm and described how we get the best and worst result for each one, the comparison we made gave us that the AdaBoost algorithm gave the worst performance measured in the accuracy while the random forest (RF) algorithm gave the best performance, this paper aim to make improvement of the process of searching for productive zones using ML algorithms.http://www.sciencedirect.com/science/article/pii/S2666603022000094Machine learningQuick analyserExploratory data analysisFeature importanceHyperparameter tuningFeature engineering |
| spellingShingle | Amir Gharavi Mohamed Hassan Jebraeel Gholinezhad Hesam Ghoochaninejad Hossein Barati James Buick Karrar A. Abbas Application of machine learning techniques for identifying productive zones in unconventional reservoir International Journal of Intelligent Networks Machine learning Quick analyser Exploratory data analysis Feature importance Hyperparameter tuning Feature engineering |
| title | Application of machine learning techniques for identifying productive zones in unconventional reservoir |
| title_full | Application of machine learning techniques for identifying productive zones in unconventional reservoir |
| title_fullStr | Application of machine learning techniques for identifying productive zones in unconventional reservoir |
| title_full_unstemmed | Application of machine learning techniques for identifying productive zones in unconventional reservoir |
| title_short | Application of machine learning techniques for identifying productive zones in unconventional reservoir |
| title_sort | application of machine learning techniques for identifying productive zones in unconventional reservoir |
| topic | Machine learning Quick analyser Exploratory data analysis Feature importance Hyperparameter tuning Feature engineering |
| url | http://www.sciencedirect.com/science/article/pii/S2666603022000094 |
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