{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T22:43:50Z","timestamp":1787784230726,"version":"build-2784847793"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002413","name":"Egyptian Petroleum Research Institute","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002413","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Predicting permeability along and between wells to obtain the 3D spatial distribution is challenging in reservoir modeling. Permeability measurements using core sampling are time-consuming and accord too limited subsurface coverage. This study presents a systematic approach based on artificial neural networks (ANNs) to integrate core data and well logs to overcome these challenges and construct a robust 3D permeability model for the Lower Safa sand reservoir, JG field, Abu El-Gharadig basin, Egypt. The routine core analysis was performed to determine the reservoir quality and heterogeneity. Core-log depth match was performed, and the influential well logs, including effective porosity, bulk density, and deep resistivity logs, were selected to build the permeability model. ANN is applied to integrate the core permeability with the logs to predict permeability logs in the two studied wells. The dataset is randomly split into training, validation, and testing sets to evaluate the model's performance, and different hyperparameters are tuned to improve the prediction accuracy. A detailed comparison between the conventional method and the proposed ANN approach is provided in this study. Results demonstrate that the proposed ANN model was able to improve the permeability prediction performance by capturing the complex relationships between permeability and well logging data. The model achieves outstanding performance, where the coefficient of determination (R<jats:sup>2<\/jats:sup>) between the predicted permeability and core permeability is 0.90, 0.91, and 0.88 for the training, validation, and testing datasets, respectively. The developed model was used to predict the permeability logs along the reservoir intervals of the two studied wells. Ultimately, the Sequential Gaussian Simulation algorithm was used to populate the predicted logs in 3D. The outcomes of the study will aid users of deep learning to make informed choices on the appropriate ANN models to use in clastic reservoir characterization for more accurate permeability prediction with limited available data.<\/jats:p>","DOI":"10.1007\/s12145-025-01713-3","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T05:57:31Z","timestamp":1738303051000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving permeability modeling using artificial neural networks in Lower Safa sand reservoir, JG field, Abu El-Gharadig basin, Western Desert, Egypt"],"prefix":"10.1007","volume":"18","author":[{"given":"Mostafa S.","family":"Khalid","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"1713_CR1","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.petrol.2016.11.033","volume":"150","author":"KO Akande","year":"2017","unstructured":"Akande KO, Owolabi TO, Olatunji SO, AbdulRaheem A (2017) A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir. J Petrol Sci Eng 150:43\u201353","journal-title":"J Petrol Sci Eng"},{"key":"1713_CR2","doi-asserted-by":"publisher","unstructured":"Al-Ajmi FA, Aramco S, Holditch SA (2000) Permeability estimation using hydraulic flow units in a central Arabia reservoir. SPE Annual Technical Conference and Exhibition? SPE, pp. SPE-63254-MS. https:\/\/doi.org\/10.2118\/63254-MS","DOI":"10.2118\/63254-MS"},{"key":"1713_CR3","doi-asserted-by":"publisher","first-page":"110573","DOI":"10.1016\/j.petrol.2022.110573","volume":"214","author":"N Alizadeh","year":"2022","unstructured":"Alizadeh N, Rahmati N, Najafi A, Leung E, Adabnezhad P (2022) A novel approach by integrating the core derived FZI and well logging data into artificial neural network model for improved permeability prediction in a heterogeneous gas reservoir. J Petrol Sci Eng 214:110573","journal-title":"J Petrol Sci Eng"},{"issue":"1","key":"1713_CR4","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s11053-018-9370-y","volume":"28","author":"WJ Al-Mudhafar","year":"2019","unstructured":"Al-Mudhafar WJ (2019) Bayesian and LASSO regressions for comparative permeability modeling of sandstone reservoirs. Nat Resour Res 28(1):47\u201362","journal-title":"Nat Resour Res"},{"key":"1713_CR5","doi-asserted-by":"publisher","unstructured":"Amaefule JO, Altunbay M, Tiab D, Kersey DG, Keelan DK (1993) Enhanced reservoir description: using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals\/wells. SPE annual technical conference and exhibition. OnePetro. https:\/\/doi.org\/10.2118\/26436-MS","DOI":"10.2118\/26436-MS"},{"key":"1713_CR6","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1016\/j.petrol.2019.01.110","volume":"176","author":"F Anifowose","year":"2019","unstructured":"Anifowose F, Abdulraheem A, Al-Shuhail A (2019) A parametric study of machine learning techniques in petroleum reservoir permeability prediction by integrating seismic attributes and wireline data. J Petrol Sci Eng 176:762\u2013774","journal-title":"J Petrol Sci Eng"},{"key":"1713_CR7","unstructured":"Beale MH, Hagan MT, Demuth HB (2010) Neural network toolbox. User\u2019s Guide, MathWorks 2:77-81"},{"key":"1713_CR8","volume-title":"Machine learning mastery with Python: understand your data, create accurate models, and work projects end-to-end","author":"J Brownlee","year":"2016","unstructured":"Brownlee J (2016) Machine learning mastery with Python: understand your data, create accurate models, and work projects end-to-end. Machine Learning Mastery, San Francisco"},{"key":"1713_CR9","doi-asserted-by":"publisher","unstructured":"Cannon S (2018) Reservoir modelling: a practical guide. John Wiley & Sons. https:\/\/doi.org\/10.1002\/9781119313458","DOI":"10.1002\/9781119313458"},{"key":"1713_CR10","unstructured":"Coates GR, Dumanoir JL (1973) A new approach to improved log-derived permeability, SPWLA Annual Logging Symposium. SPWLA, pp SPWLA-1973-R"},{"issue":"2","key":"1713_CR11","first-page":"54","volume":"29","author":"G Coates","year":"1981","unstructured":"Coates G, Denoo S (1981) The producibility answer product. Tech Rev 29(2):54\u201363","journal-title":"Tech Rev"},{"key":"1713_CR12","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.cageo.2011.08.001","volume":"41","author":"AA Del Castillo","year":"2012","unstructured":"Del Castillo AA, Santoyo E, Garc\u00eda-Valladares O (2012) \u0391 new void fraction correlation inferred from artificial neural networks for modeling two-phase flows in geothermal wells. Comput Geosci 41:25\u201339","journal-title":"Comput Geosci"},{"key":"1713_CR13","doi-asserted-by":"crossref","unstructured":"Desouky\u00a0SEDM\u00a0(2005) Predicting permeability in un-cored intervals\/wells using hydraulic flow unit approach. J Can Pet Technol 44(07)","DOI":"10.2118\/05-07-04"},{"issue":"147","key":"1713_CR14","first-page":"578","volume":"119","author":"CV Deutsch","year":"1992","unstructured":"Deutsch CV, Journel AG (1992) Geostatistical software library and user\u2019s guide. N Y 119(147):578","journal-title":"N Y"},{"key":"1713_CR15","doi-asserted-by":"publisher","first-page":"105027","DOI":"10.1016\/j.jafrearsci.2023.105027","volume":"206","author":"F Djebbas","year":"2023","unstructured":"Djebbas F, Ameur-Zaimeche O, Kechiched R, Heddam S, Wood DA, Movahed Z (2023) Integrating hydraulic flow unit concept and adaptive neuro-fuzzy inference system to accurately estimate permeability in heterogeneous reservoirs: Case study Sif Fatima oilfield, southern Algeria. J Afr Earth Sc 206:105027","journal-title":"J Afr Earth Sc"},{"key":"1713_CR16","doi-asserted-by":"publisher","first-page":"108350","DOI":"10.1016\/j.petrol.2021.108350","volume":"199","author":"Y Gu","year":"2021","unstructured":"Gu Y, Zhang D, Bao Z (2021) A new data-driven predictor, PSO-XGBoost, used for permeability of tight sandstone reservoirs: A case study of member of chang 4+ 5, western Jiyuan Oilfield, Ordos Basin. J Petrol Sci Eng 199:108350","journal-title":"J Petrol Sci Eng"},{"issue":"7","key":"1713_CR17","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"key":"1713_CR18","unstructured":"Jensen J (2000) Statistics for petroleum engineers and geoscientists, vol 2. Gulf Professional Publishing"},{"key":"1713_CR19","doi-asserted-by":"publisher","first-page":"105597","DOI":"10.1016\/j.marpetgeo.2022.105597","volume":"139","author":"MZ Kamali","year":"2022","unstructured":"Kamali MZ, Davoodi S, Ghorbani H, Wood DA, Mohamadian N, Lajmorak S, Rukavishnikov VS, Taherizade F, Band SS (2022) Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling. Mar Pet Geol 139:105597","journal-title":"Mar Pet Geol"},{"issue":"2","key":"1713_CR20","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/s13202-019-00758-7","volume":"10","author":"M Khalid","year":"2020","unstructured":"Khalid M, Desouky SE-D, Rashed M, Shazly T, Sediek K (2020) Application of hydraulic flow units\u2019 approach for improving reservoir characterization and predicting permeability. J Pet Explor Prod Technol 10(2):467\u2013479","journal-title":"J Pet Explor Prod Technol"},{"key":"1713_CR21","doi-asserted-by":"crossref","unstructured":"Lucia FJ (2007) Petrophysical rock properties. Carbonate reservoir characterization: An integrated approach, pp 1\u201327","DOI":"10.1007\/978-3-662-03985-4_1"},{"key":"1713_CR22","unstructured":"Lundberg S (2017) A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874"},{"key":"1713_CR23","doi-asserted-by":"publisher","unstructured":"Ma YZ, Zhang X (2019) Quantitative geosciences: Data analytics, geostatistics, reservoir characterization and modeling. Springer. https:\/\/doi.org\/10.1007\/978-3-030-17860-4","DOI":"10.1007\/978-3-030-17860-4"},{"issue":"4","key":"1713_CR24","doi-asserted-by":"publisher","first-page":"2555","DOI":"10.1007\/s13202-019-00745-y","volume":"9","author":"H Mahmoud","year":"2019","unstructured":"Mahmoud H, Lotfy H, Bakr A (2019) Structural evolution of JG and JD fields, Abu Gharadig basin, Western Desert, Egypt, and its impact on hydrocarbon exploration. J Pet Explor Prod Technol 9(4):2555\u20132571","journal-title":"J Pet Explor Prod Technol"},{"key":"1713_CR25","first-page":"29","volume-title":"3rd symposium on the sedimentary basins of Libya (The geology of East Libya)","author":"A Moustafa","year":"2008","unstructured":"Moustafa A, Abd El-Aziz M, Gaber W (2008) 3rd symposium on the sedimentary basins of Libya (The geology of East Libya). Earth Science Society of Libya, Tripoli, pp 29\u201346"},{"key":"1713_CR26","unstructured":"Negnevitsky M (2005)\u00a0Artificial intelligence. Pearson Education India"},{"key":"1713_CR27","doi-asserted-by":"publisher","unstructured":"Olea RA (2000) Geostatistics for engineers and earth scientists. Taylor & Francis. https:\/\/doi.org\/10.1007\/978-1-4615-5001-3","DOI":"10.1007\/978-1-4615-5001-3"},{"key":"1713_CR28","volume-title":"Geostatistical reservoir modeling","author":"MJ Pyrcz","year":"2014","unstructured":"Pyrcz MJ, Deutsch CV (2014) Geostatistical reservoir modeling. Oxford University Press, USA"},{"key":"1713_CR29","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.coal.2012.06.001","volume":"100","author":"MR Shalaby","year":"2012","unstructured":"Shalaby MR, Hakimi MH, Abdullah WH (2012) Geochemical characterization of solid bitumen (migrabitumen) in the Jurassic sandstone reservoir of the Tut Field, Shushan Basin, northern Western Desert of Egypt. Int J Coal Geol 100:26\u201339","journal-title":"Int J Coal Geol"},{"key":"1713_CR30","unstructured":"Tiab D, Donaldson EC (2015) Petrophysics: theory and practice of measuring reservoir rock and fluid transport properties. Gulf Professional Publishing"},{"key":"1713_CR31","unstructured":"Timur A (1968) An investigation of permeability, porosity, & residual water saturation relationships for sandstone reservoirs.\u00a0The Log Analyst\u00a09(04)"},{"key":"1713_CR32","first-page":"113","volume":"16","author":"MP Tixier","year":"1949","unstructured":"Tixier MP (1949) Evaluation of permeability from electric-log resistivity gradients. Oil Gas J 16:113\u2013133","journal-title":"Oil Gas J"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01713-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-025-01713-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01713-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T23:21:49Z","timestamp":1757460109000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-025-01713-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,31]]},"references-count":32,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1713"],"URL":"https:\/\/doi.org\/10.1007\/s12145-025-01713-3","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,31]]},"assertion":[{"value":"14 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author declares no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"220"}}