Perforation friction modeling in limited entry fracturing using artificial neural network
The effect of perforation friction on hydraulic fracturing treating pressure is normally considered negligible. However; this is not the case in limited entry fracturing. In limited entry fracturing, perforation friction is utilized to attain large frictional pressure drop. During the treatment inje...
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| Format: | Article |
| Language: | English |
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Egyptian Petroleum Research Institute
2019-09-01
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| Series: | Egyptian Journal of Petroleum |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110062118304628 |
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| author | Ahmed ElGibaly Mohamed Abdalla Osman |
| author_facet | Ahmed ElGibaly Mohamed Abdalla Osman |
| author_sort | Ahmed ElGibaly |
| collection | DOAJ |
| description | The effect of perforation friction on hydraulic fracturing treating pressure is normally considered negligible. However; this is not the case in limited entry fracturing. In limited entry fracturing, perforation friction is utilized to attain large frictional pressure drop. During the treatment injection, the frictional pressure offsets the stress differences between the zones to ensure fluid injection through every perforation in each interval. The major constraint with this diversion mechanism is perforations erosion when proppant laden slurry pumped through it. The purpose of this paper is to provide a new model to assess the perforation friction after perforation erosion by proppant laden slurry. The methodology involves the application of artificial neural network (ANN) to predict the final hydraulic perforation diameter. In order to train, validate and test the proposed ANN model; real field data of limited entry fracturing treatments have been used. The paper reviews the previous research progress on the assessment of perforation friction. A comparison between the new proposed ANN model and the previous perforation friction correlations demonstrates that the results from ANN model are the most reliable estimation of perforation friction real data. Keywords: Perforation friction, Hydraulic fracturing, Limited-entry, Neural network |
| format | Article |
| id | doaj-art-d848adbc158a4584bc324742f742cf85 |
| institution | Kabale University |
| issn | 1110-0621 |
| language | English |
| publishDate | 2019-09-01 |
| publisher | Egyptian Petroleum Research Institute |
| record_format | Article |
| series | Egyptian Journal of Petroleum |
| spelling | doaj-art-d848adbc158a4584bc324742f742cf852025-08-20T03:24:36ZengEgyptian Petroleum Research InstituteEgyptian Journal of Petroleum1110-06212019-09-0128329730510.1016/j.ejpe.2019.08.001Perforation friction modeling in limited entry fracturing using artificial neural networkAhmed ElGibaly0Mohamed Abdalla Osman1Faculty of Petroleum and Mining Engineering, Suez University, EgyptQarun Pet. Co., Egypt; Corresponding author.The effect of perforation friction on hydraulic fracturing treating pressure is normally considered negligible. However; this is not the case in limited entry fracturing. In limited entry fracturing, perforation friction is utilized to attain large frictional pressure drop. During the treatment injection, the frictional pressure offsets the stress differences between the zones to ensure fluid injection through every perforation in each interval. The major constraint with this diversion mechanism is perforations erosion when proppant laden slurry pumped through it. The purpose of this paper is to provide a new model to assess the perforation friction after perforation erosion by proppant laden slurry. The methodology involves the application of artificial neural network (ANN) to predict the final hydraulic perforation diameter. In order to train, validate and test the proposed ANN model; real field data of limited entry fracturing treatments have been used. The paper reviews the previous research progress on the assessment of perforation friction. A comparison between the new proposed ANN model and the previous perforation friction correlations demonstrates that the results from ANN model are the most reliable estimation of perforation friction real data. Keywords: Perforation friction, Hydraulic fracturing, Limited-entry, Neural networkhttp://www.sciencedirect.com/science/article/pii/S1110062118304628 |
| spellingShingle | Ahmed ElGibaly Mohamed Abdalla Osman Perforation friction modeling in limited entry fracturing using artificial neural network Egyptian Journal of Petroleum |
| title | Perforation friction modeling in limited entry fracturing using artificial neural network |
| title_full | Perforation friction modeling in limited entry fracturing using artificial neural network |
| title_fullStr | Perforation friction modeling in limited entry fracturing using artificial neural network |
| title_full_unstemmed | Perforation friction modeling in limited entry fracturing using artificial neural network |
| title_short | Perforation friction modeling in limited entry fracturing using artificial neural network |
| title_sort | perforation friction modeling in limited entry fracturing using artificial neural network |
| url | http://www.sciencedirect.com/science/article/pii/S1110062118304628 |
| work_keys_str_mv | AT ahmedelgibaly perforationfrictionmodelinginlimitedentryfracturingusingartificialneuralnetwork AT mohamedabdallaosman perforationfrictionmodelinginlimitedentryfracturingusingartificialneuralnetwork |