Prediction of permeability and effective porosity values using ANN in Maleh field
This study presents the development of an intelligent system designed to predict permeability and effective porosity in wells where core samples are unavailable. An artificial neural network (ANN) was constructed with three hidden layers—comprising 15, 10, and 4 neurons, respectively—utilizing well...
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| Language: | English |
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Universidad Nacional Autónoma de México, Instituto de Geofísica
2025-07-01
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| Series: | Geofísica Internacional |
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| Online Access: | https://revistagi.geofisica.unam.mx/index.php/RGI/article/view/1830 |
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| author | Mohammed Essa Nassani Ali Alaji |
| author_facet | Mohammed Essa Nassani Ali Alaji |
| author_sort | Mohammed Essa Nassani |
| collection | DOAJ |
| description | This study presents the development of an intelligent system designed to predict permeability and effective porosity in wells where core samples are unavailable. An artificial neural network (ANN) was constructed with three hidden layers—comprising 15, 10, and 4 neurons, respectively—utilizing well logging parameters (CAL, VCL, NPHI, RHOB, DT) as inputs. The ANN outputs predicted permeability and effective porosity values with remarkable accuracy. The network was optimized with a learning rate of 0.05, a momentum coefficient of 0.95, and the LOGSIG activation function, applied across layers. Input values were normalized to the range of 0 to 1, and training was performed using the sequential forward backpropagation algorithm (newcf). The training phase achieved a minimum mean square error of 0.00001 within 58 seconds over 12,000 cycles, delivering a 100% recognition rate for the training data. The ANN was tested on independent data and demonstrated exceptional performance, achieving 96% accuracy for effective porosity and 98% for permeability predictions in sandstone formations. This efficient algorithm eliminates the need for core sample analysis, reducing costs and time while improving prediction reliability, making it a valuable tool for subsurface characterization and resource exploration. |
| format | Article |
| id | doaj-art-bc46e651c2f64aaf8cd46bb8c0084d05 |
| institution | OA Journals |
| issn | 0016-7169 2954-436X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Universidad Nacional Autónoma de México, Instituto de Geofísica |
| record_format | Article |
| series | Geofísica Internacional |
| spelling | doaj-art-bc46e651c2f64aaf8cd46bb8c0084d052025-08-20T02:38:06ZengUniversidad Nacional Autónoma de México, Instituto de GeofísicaGeofísica Internacional0016-71692954-436X2025-07-0164310.22201/igeof.2954436xe.2025.64.3.18301831Prediction of permeability and effective porosity values using ANN in Maleh fieldMohammed Essa Nassani0https://orcid.org/0009-0008-1020-5743Ali Alaji1https://orcid.org/0009-0001-9149-6851Damascus University, Faculty of Science, Department of Geology, PhD Student, Damascus, Syrian Arab Republic.Damascus University, Faculty of Science, Department of Geology, Associate Professor, Syrian Arab Republic.This study presents the development of an intelligent system designed to predict permeability and effective porosity in wells where core samples are unavailable. An artificial neural network (ANN) was constructed with three hidden layers—comprising 15, 10, and 4 neurons, respectively—utilizing well logging parameters (CAL, VCL, NPHI, RHOB, DT) as inputs. The ANN outputs predicted permeability and effective porosity values with remarkable accuracy. The network was optimized with a learning rate of 0.05, a momentum coefficient of 0.95, and the LOGSIG activation function, applied across layers. Input values were normalized to the range of 0 to 1, and training was performed using the sequential forward backpropagation algorithm (newcf). The training phase achieved a minimum mean square error of 0.00001 within 58 seconds over 12,000 cycles, delivering a 100% recognition rate for the training data. The ANN was tested on independent data and demonstrated exceptional performance, achieving 96% accuracy for effective porosity and 98% for permeability predictions in sandstone formations. This efficient algorithm eliminates the need for core sample analysis, reducing costs and time while improving prediction reliability, making it a valuable tool for subsurface characterization and resource exploration.https://revistagi.geofisica.unam.mx/index.php/RGI/article/view/1830maleh fieldwell loggingpermeabilityeffective porosityartificial intelligencecoresartificial neural network (ann) |
| spellingShingle | Mohammed Essa Nassani Ali Alaji Prediction of permeability and effective porosity values using ANN in Maleh field Geofísica Internacional maleh field well logging permeability effective porosity artificial intelligence cores artificial neural network (ann) |
| title | Prediction of permeability and effective porosity values using ANN in Maleh field |
| title_full | Prediction of permeability and effective porosity values using ANN in Maleh field |
| title_fullStr | Prediction of permeability and effective porosity values using ANN in Maleh field |
| title_full_unstemmed | Prediction of permeability and effective porosity values using ANN in Maleh field |
| title_short | Prediction of permeability and effective porosity values using ANN in Maleh field |
| title_sort | prediction of permeability and effective porosity values using ann in maleh field |
| topic | maleh field well logging permeability effective porosity artificial intelligence cores artificial neural network (ann) |
| url | https://revistagi.geofisica.unam.mx/index.php/RGI/article/view/1830 |
| work_keys_str_mv | AT mohammedessanassani predictionofpermeabilityandeffectiveporosityvaluesusinganninmalehfield AT alialaji predictionofpermeabilityandeffectiveporosityvaluesusinganninmalehfield |