Comparison of Cementation Factor Determination by Artificial Neural Network Method and Optimized Experimental Relations in Carbonate Rocks
The cementation factor is one of the basic parameters for calculating water saturation and then hydrocarbon saturation of reservoirs. The best way to determine the cementation factor is through laboratory measurements. To generalize this coefficient for samples without laboratory measurements, exper...
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| Format: | Article |
| Language: | English |
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Reaserch Institute of Petroleum Industry
2022-05-01
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| Series: | Journal of Petroleum Science and Technology |
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| Online Access: | https://jpst.ripi.ir/article_1288_9919ae3b3b0efffc8ce90d6cfa111e4b.pdf |
| _version_ | 1797893073121837056 |
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| author | Jafar Vali Farnusch Hajizadeh |
| author_facet | Jafar Vali Farnusch Hajizadeh |
| author_sort | Jafar Vali |
| collection | DOAJ |
| description | The cementation factor is one of the basic parameters for calculating water saturation and then hydrocarbon saturation of reservoirs. The best way to determine the cementation factor is through laboratory measurements. To generalize this coefficient for samples without laboratory measurements, experimental relationships versus petrophysical properties by researchers can be somewhat helpful. The method of artificial neural networks, with the help of training, validation, and data analysis, has given better results in determining the cementation factor of carbonate samples. It is one of the best methods to use petrophysical data as training data and make acceptable predictions with analytical methods. Therefore, laboratory measurement of the cementation factor has been performed for 159 carbonate cores from the Sarvak formation in southwest Iran. For the studied samples, the cementation factor in porosity was determined as a quadratic equation with the highest correlation coefficient. In this study, the compatibility of the experimental relationship shows better conformity by considering the permeability of each sample. Improvement of empirical relationships by the authors, correlation coefficients between the laboratory data, and the experimental relationships have been increased. Therefore, it is better to use improved experimental relationships for the studied carbonate samples. Artificial neural network methods have been used to process the data, best adapt the laboratory data, and present a suitable model. The Bayesian Regularization algorithm with five hidden layers has the least error in the test, validation, and testing stages. |
| first_indexed | 2024-04-10T06:47:06Z |
| format | Article |
| id | doaj.art-43b8cf2876354f2d8f0a50c08198570d |
| institution | Directory Open Access Journal |
| issn | 2251-659X 2645-3312 |
| language | English |
| last_indexed | 2024-04-10T06:47:06Z |
| publishDate | 2022-05-01 |
| publisher | Reaserch Institute of Petroleum Industry |
| record_format | Article |
| series | Journal of Petroleum Science and Technology |
| spelling | doaj.art-43b8cf2876354f2d8f0a50c08198570d2023-02-28T11:57:32ZengReaserch Institute of Petroleum IndustryJournal of Petroleum Science and Technology2251-659X2645-33122022-05-01122172510.22078/jpst.2023.4840.18081288Comparison of Cementation Factor Determination by Artificial Neural Network Method and Optimized Experimental Relations in Carbonate RocksJafar Vali0Farnusch Hajizadeh1Department of Mining Engineering, Urmia University, Urmia, IranDepartment of Mining Engineering, Urmia University, Urmia, IranThe cementation factor is one of the basic parameters for calculating water saturation and then hydrocarbon saturation of reservoirs. The best way to determine the cementation factor is through laboratory measurements. To generalize this coefficient for samples without laboratory measurements, experimental relationships versus petrophysical properties by researchers can be somewhat helpful. The method of artificial neural networks, with the help of training, validation, and data analysis, has given better results in determining the cementation factor of carbonate samples. It is one of the best methods to use petrophysical data as training data and make acceptable predictions with analytical methods. Therefore, laboratory measurement of the cementation factor has been performed for 159 carbonate cores from the Sarvak formation in southwest Iran. For the studied samples, the cementation factor in porosity was determined as a quadratic equation with the highest correlation coefficient. In this study, the compatibility of the experimental relationship shows better conformity by considering the permeability of each sample. Improvement of empirical relationships by the authors, correlation coefficients between the laboratory data, and the experimental relationships have been increased. Therefore, it is better to use improved experimental relationships for the studied carbonate samples. Artificial neural network methods have been used to process the data, best adapt the laboratory data, and present a suitable model. The Bayesian Regularization algorithm with five hidden layers has the least error in the test, validation, and testing stages.https://jpst.ripi.ir/article_1288_9919ae3b3b0efffc8ce90d6cfa111e4b.pdfcarbonate rockcementation factorartificial neural networkempirical relationshipsarvak formation |
| spellingShingle | Jafar Vali Farnusch Hajizadeh Comparison of Cementation Factor Determination by Artificial Neural Network Method and Optimized Experimental Relations in Carbonate Rocks Journal of Petroleum Science and Technology carbonate rock cementation factor artificial neural network empirical relationship sarvak formation |
| title | Comparison of Cementation Factor Determination by Artificial Neural Network Method and Optimized Experimental Relations in Carbonate Rocks |
| title_full | Comparison of Cementation Factor Determination by Artificial Neural Network Method and Optimized Experimental Relations in Carbonate Rocks |
| title_fullStr | Comparison of Cementation Factor Determination by Artificial Neural Network Method and Optimized Experimental Relations in Carbonate Rocks |
| title_full_unstemmed | Comparison of Cementation Factor Determination by Artificial Neural Network Method and Optimized Experimental Relations in Carbonate Rocks |
| title_short | Comparison of Cementation Factor Determination by Artificial Neural Network Method and Optimized Experimental Relations in Carbonate Rocks |
| title_sort | comparison of cementation factor determination by artificial neural network method and optimized experimental relations in carbonate rocks |
| topic | carbonate rock cementation factor artificial neural network empirical relationship sarvak formation |
| url | https://jpst.ripi.ir/article_1288_9919ae3b3b0efffc8ce90d6cfa111e4b.pdf |
| work_keys_str_mv | AT jafarvali comparisonofcementationfactordeterminationbyartificialneuralnetworkmethodandoptimizedexperimentalrelationsincarbonaterocks AT farnuschhajizadeh comparisonofcementationfactordeterminationbyartificialneuralnetworkmethodandoptimizedexperimentalrelationsincarbonaterocks |