Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction Article Swipe
Medhat Awadalla
,
Hassan Yousef
,
Ali Al-Shidani
,
Ahmed Al-Hinai
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.24297/ijct.v15i12.4354
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.24297/ijct.v15i12.4354
This paper proposes Radial Basis and Feed-forward Neural Networks to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and the standard statistical analysis has been accomplished on the achieved results to validate the models’ prediction accuracy. For the sake of qualitative comparison, empirical modes have been developed. The obtained results show that the proposed Feed-Forward Neural Network models outperforms and capable of estimating the FBHPaccurately.The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 375 sets only to give a 3.4 RMES and 97% of the test data achieved 90% accuracy.
Related Topics
Concepts
Artificial neural network
Mean squared error
Correlation coefficient
Standard deviation
Computer science
Approximation error
Test data
Feedforward neural network
Test set
Set (abstract data type)
Algorithm
Empirical modelling
Data mining
Artificial intelligence
Statistics
Mathematics
Machine learning
Simulation
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24297/ijct.v15i12.4354
- https://cirworld.com/index.php/ijct/article/download/4354/4243
- OA Status
- diamond
- Cited By
- 5
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2519033143
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2519033143Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24297/ijct.v15i12.4354Digital Object Identifier
- Title
-
Artificial Intelligent techniques for Flow Bottom Hole Pressure PredictionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2016Year of publication
- Publication date
-
2016-09-23Full publication date if available
- Authors
-
Medhat Awadalla, Hassan Yousef, Ali Al-Shidani, Ahmed Al-HinaiList of authors in order
- Landing page
-
https://doi.org/10.24297/ijct.v15i12.4354Publisher landing page
- PDF URL
-
https://cirworld.com/index.php/ijct/article/download/4354/4243Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://cirworld.com/index.php/ijct/article/download/4354/4243Direct OA link when available
- Concepts
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Artificial neural network, Mean squared error, Correlation coefficient, Standard deviation, Computer science, Approximation error, Test data, Feedforward neural network, Test set, Set (abstract data type), Algorithm, Empirical modelling, Data mining, Artificial intelligence, Statistics, Mathematics, Machine learning, Simulation, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2022: 2, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.1.0 | 185 |
| abstract_inverted_index.1.4 | 169, 175 |
| abstract_inverted_index.375 | 214 |
| abstract_inverted_index.90% | 229 |
| abstract_inverted_index.97% | 223 |
| abstract_inverted_index.ANN | 208 |
| abstract_inverted_index.For | 90 |
| abstract_inverted_index.The | 19, 39, 101, 198 |
| abstract_inverted_index.and | 5, 52, 72, 113, 147, 160, 186, 222 |
| abstract_inverted_index.can | 210 |
| abstract_inverted_index.get | 60 |
| abstract_inverted_index.has | 77 |
| abstract_inverted_index.low | 212 |
| abstract_inverted_index.oil | 17, 37 |
| abstract_inverted_index.per | 50 |
| abstract_inverted_index.set | 155 |
| abstract_inverted_index.the | 11, 46, 53, 73, 81, 86, 91, 106, 117, 122, 148, 161, 189, 225 |
| abstract_inverted_index.two | 130 |
| abstract_inverted_index.FBHP | 125 |
| abstract_inverted_index.FFNN | 128, 137 |
| abstract_inverted_index.RMES | 221 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.With | 165 |
| abstract_inverted_index.been | 65, 70, 99 |
| abstract_inverted_index.best | 166 |
| abstract_inverted_index.data | 32, 154, 191, 203, 227 |
| abstract_inverted_index.from | 34 |
| abstract_inverted_index.give | 218 |
| abstract_inverted_index.have | 64, 69, 98 |
| abstract_inverted_index.less | 194 |
| abstract_inverted_index.mean | 157, 171 |
| abstract_inverted_index.only | 216 |
| abstract_inverted_index.rely | 24 |
| abstract_inverted_index.root | 170 |
| abstract_inverted_index.sake | 92 |
| abstract_inverted_index.sets | 192, 204, 215 |
| abstract_inverted_index.show | 104 |
| abstract_inverted_index.test | 190, 226 |
| abstract_inverted_index.than | 136, 195 |
| abstract_inverted_index.that | 105, 121 |
| abstract_inverted_index.used | 205 |
| abstract_inverted_index.with | 129, 138 |
| abstract_inverted_index.3.4Â | 220 |
| abstract_inverted_index.99.4% | 187 |
| abstract_inverted_index.Basis | 4, 144 |
| abstract_inverted_index.error | 173, 180 |
| abstract_inverted_index.large | 27 |
| abstract_inverted_index.layer | 132, 141 |
| abstract_inverted_index.model | 133, 150, 209 |
| abstract_inverted_index.modes | 97 |
| abstract_inverted_index.paper | 1, 119 |
| abstract_inverted_index.terms | 152 |
| abstract_inverted_index.used, | 156 |
| abstract_inverted_index.using | 127 |
| abstract_inverted_index.(STD), | 181 |
| abstract_inverted_index.Neural | 7, 109 |
| abstract_inverted_index.Radial | 3, 143 |
| abstract_inverted_index.actual | 35 |
| abstract_inverted_index.amount | 28 |
| abstract_inverted_index.been | 78 |
| abstract_inverted_index.better | 135 |
| abstract_inverted_index.error, | 159 |
| abstract_inverted_index.error. | 164, 197 |
| abstract_inverted_index.hidden | 131, 140 |
| abstract_inverted_index.layer, | 51 |
| abstract_inverted_index.model, | 142 |
| abstract_inverted_index.models | 23, 111 |
| abstract_inverted_index.neural | 21, 43, 145 |
| abstract_inverted_index.number | 41, 47, 54, 201 |
| abstract_inverted_index.showed | 120 |
| abstract_inverted_index.single | 139 |
| abstract_inverted_index.square | 158, 172 |
| abstract_inverted_index.wells. | 18 |
| abstract_inverted_index.(RMSE), | 174 |
| abstract_inverted_index.Network | 110 |
| abstract_inverted_index.capable | 114 |
| abstract_inverted_index.fields. | 38 |
| abstract_inverted_index.flowing | 12 |
| abstract_inverted_index.layers, | 45 |
| abstract_inverted_index.minimum | 199 |
| abstract_inverted_index.network | 22, 44 |
| abstract_inverted_index.neurons | 49 |
| abstract_inverted_index.predict | 10 |
| abstract_inverted_index.results | 83, 103, 167 |
| abstract_inverted_index.samples | 57 |
| abstract_inverted_index.trained | 56 |
| abstract_inverted_index.Networks | 8 |
| abstract_inverted_index.accuracy | 123 |
| abstract_inverted_index.achieved | 82, 193, 228 |
| abstract_inverted_index.analysis | 76 |
| abstract_inverted_index.measured | 33 |
| abstract_inverted_index.network, | 146 |
| abstract_inverted_index.obtained | 102 |
| abstract_inverted_index.pressure | 14 |
| abstract_inverted_index.proposed | 107 |
| abstract_inverted_index.proposes | 2 |
| abstract_inverted_index.relative | 179 |
| abstract_inverted_index.required | 58 |
| abstract_inverted_index.standard | 74, 176 |
| abstract_inverted_index.training | 207 |
| abstract_inverted_index.validate | 85 |
| abstract_inverted_index.vertical | 16 |
| abstract_inverted_index.Intensive | 67 |
| abstract_inverted_index.accuracy. | 89, 230 |
| abstract_inverted_index.available | 30 |
| abstract_inverted_index.conducted | 71 |
| abstract_inverted_index.developed | 20 |
| abstract_inverted_index.deviation | 177 |
| abstract_inverted_index.different | 36 |
| abstract_inverted_index.empirical | 96, 149 |
| abstract_inverted_index.obtained. | 66 |
| abstract_inverted_index.developed. | 100 |
| abstract_inverted_index.estimating | 116 |
| abstract_inverted_index.estimation | 126 |
| abstract_inverted_index.historical | 31 |
| abstract_inverted_index.prediction | 88 |
| abstract_inverted_index.sufficient | 200 |
| abstract_inverted_index.bottom-hole | 13 |
| abstract_inverted_index.coefficient | 163, 183 |
| abstract_inverted_index.comparison, | 95 |
| abstract_inverted_index.correlation | 162, 182 |
| abstract_inverted_index.experiments | 68 |
| abstract_inverted_index.outperforms | 112 |
| abstract_inverted_index.outstanding | 62 |
| abstract_inverted_index.performance | 63 |
| abstract_inverted_index.qualitative | 94 |
| abstract_inverted_index.statistical | 75 |
| abstract_inverted_index.unsurpassed | 40 |
| abstract_inverted_index.Feed-Forward | 108 |
| abstract_inverted_index.Feed-forward | 6 |
| abstract_inverted_index.accomplished | 79 |
| abstract_inverted_index.models’ | 87 |
| abstract_inverted_index.FBHPaccurately.The | 118 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.10113981 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |