Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network
Abstract Waterflooding can lead to formation damage, which is affected by various parameters. This study experimentally examined changes in the permeability of rock samples due to calcium sulfate precipitation over a wide range of temperatures, sulfate ion concentrations, injection rates, and inject...
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SpringerOpen
2025-01-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
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| Online Access: | https://doi.org/10.1007/s13202-024-01898-1 |
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| author | Azizollah Khormali Soroush Ahmadi Aleksandr Nikolaevich Aleksandrov |
| author_facet | Azizollah Khormali Soroush Ahmadi Aleksandr Nikolaevich Aleksandrov |
| author_sort | Azizollah Khormali |
| collection | DOAJ |
| description | Abstract Waterflooding can lead to formation damage, which is affected by various parameters. This study experimentally examined changes in the permeability of rock samples due to calcium sulfate precipitation over a wide range of temperatures, sulfate ion concentrations, injection rates, and injected pore volumes. The results showed that with increasing temperature, ion concentration, injected pore volume, and decreasing injection rate, salt precipitation and formation damage are increased. These effects were associated with a reduction in solubility, collisions of the particles, and large numbers of ions in the porous media. Experimental rock permeability data were then used to develop a model using a multi-layer perceptron (MLP) artificial neural network (ANN). In order to create a high-performing MLP-ANN model, various transfer functions and training algorithms were evaluated. Tansig was the most effective transfer function for the hidden layers. The ANN model using the Levenberg-Marquardt (LM) algorithm showed superior performance with the lowest mean squared error (MSE) for training, testing, and all data sets. Through the implementation of this algorithm in the network, it was determined that the optimal configuration consisted of three layers - comprising of two hidden layers with eight and four neurons in the first and second layer, respectively, along with one output layer. A comparison of the predicted values of rock permeability under scaling conditions with experimental data showed that the proposed ANN model makes it possible to predict formation damage due to scaling with high accuracy. At the same time, no deviations were identified between the predicted and laboratory data. Finally, using a scale inhibitor prevented formation damage at any temperature, injection rate, and ion concentration, confirming the high effectiveness of the scale inhibitor injection for the field application. |
| format | Article |
| id | doaj-art-cdda5b39a6264527ae4f9a3549a03ad6 |
| institution | Kabale University |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-cdda5b39a6264527ae4f9a3549a03ad62025-02-09T12:13:23ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-01-0115111810.1007/s13202-024-01898-1Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural networkAzizollah Khormali0Soroush Ahmadi1Aleksandr Nikolaevich Aleksandrov2Department of Chemistry, Faculty of Basic Sciences and Engineering, Gonbad Kavous UniversityDepartment of Chemical Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf UniversityDepartment of Oil and Gas Field Development and Operation, Oil and Gas Faculty, Empress Catherine II Saint Petersburg Mining UniversityAbstract Waterflooding can lead to formation damage, which is affected by various parameters. This study experimentally examined changes in the permeability of rock samples due to calcium sulfate precipitation over a wide range of temperatures, sulfate ion concentrations, injection rates, and injected pore volumes. The results showed that with increasing temperature, ion concentration, injected pore volume, and decreasing injection rate, salt precipitation and formation damage are increased. These effects were associated with a reduction in solubility, collisions of the particles, and large numbers of ions in the porous media. Experimental rock permeability data were then used to develop a model using a multi-layer perceptron (MLP) artificial neural network (ANN). In order to create a high-performing MLP-ANN model, various transfer functions and training algorithms were evaluated. Tansig was the most effective transfer function for the hidden layers. The ANN model using the Levenberg-Marquardt (LM) algorithm showed superior performance with the lowest mean squared error (MSE) for training, testing, and all data sets. Through the implementation of this algorithm in the network, it was determined that the optimal configuration consisted of three layers - comprising of two hidden layers with eight and four neurons in the first and second layer, respectively, along with one output layer. A comparison of the predicted values of rock permeability under scaling conditions with experimental data showed that the proposed ANN model makes it possible to predict formation damage due to scaling with high accuracy. At the same time, no deviations were identified between the predicted and laboratory data. Finally, using a scale inhibitor prevented formation damage at any temperature, injection rate, and ion concentration, confirming the high effectiveness of the scale inhibitor injection for the field application.https://doi.org/10.1007/s13202-024-01898-1Rock permeabilityWaterfloodingFormation damageANNScaling |
| spellingShingle | Azizollah Khormali Soroush Ahmadi Aleksandr Nikolaevich Aleksandrov Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network Journal of Petroleum Exploration and Production Technology Rock permeability Waterflooding Formation damage ANN Scaling |
| title | Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network |
| title_full | Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network |
| title_fullStr | Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network |
| title_full_unstemmed | Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network |
| title_short | Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network |
| title_sort | analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network |
| topic | Rock permeability Waterflooding Formation damage ANN Scaling |
| url | https://doi.org/10.1007/s13202-024-01898-1 |
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