{"title":"Evaluation of Algerian Reservoir Petrophysics Properties by Principal Components Analysis: Case Study of Illizi Basin","authors":"Djamel Chehili, Kaddour Sadek, Badr Eddine Rahmani, Benaoumeur Aour, Mehdi Bendali, Abdelmoumen Bacetti, Brahmi Serhane","doi":"10.1007/s11053-025-10502-0","DOIUrl":null,"url":null,"abstract":"<p>Optimizing hydrocarbon recovery in the Illizi Basin requires precise reservoir characterization. Traditional methods face challenges in efficiently handling large datasets from multiple wells. This paper employs principal components analysis (PCA) to evaluate the petrophysical properties of the reservoir intervals (IV-3, IV-1b, IV-1a) using wells P8, P4, and P6, situated in the northern, center, and south of our reservoir, respectively. PCA reduced the dimensionality of the data, while preserving original information, facilitating the analysis of the reservoir's geological and sedimentological features. The results showed that unit IV-3 has the highest average porosity (average NET porosity) and the lowest average water saturation (average PAY log sw) across all wells, indicating significant hydrocarbon production potential. In contrast, units IV-1b and IV-1a exhibited higher water saturations, suggesting less favorable conditions for hydrocarbon extraction. Strong negative correlations between petrophysical properties and water saturation in unit IV-3 highlighted its potential for hydrocarbon production. PCA correlation circles illustrated these relationships, with unit IV-3 showing predominantly hydrocarbon saturation, Unit IV-1b exhibited mixed saturation, whereas unit IV-1a was characterized by high water saturation. These findings demonstrate the effectiveness of PCA in guiding hydrocarbon resource management and exploitation strategies in the Illizi Basin; therefore, we recommend prioritizing drilling in zones with optimal reservoir properties, as identified through PCA. These zones are likely to have higher porosity, permeability, and lower water saturation, we also recommend Considering implementing suitable enhanced oil recovery techniques, such as waterflooding, polymer flooding, or gas injection, to improve recovery factors, especially in low-permeability zones. Finally, we recommend implementing a robust monitoring system to track reservoir performance and adjust production strategies as needed. This may involve real-time monitoring of pressure, temperature, and flow rates. These recommendations, can significantly enhance hydrocarbon recovery from unit IV-3, maximizing economic benefits, while minimizing environmental impact. This study demonstrates the practical application of PCA in reservoir characterization and provides valuable insights for optimizing field development and production strategies in the Illizi Basin.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"22 1","pages":""},"PeriodicalIF":4.8000,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Resources Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s11053-025-10502-0","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Optimizing hydrocarbon recovery in the Illizi Basin requires precise reservoir characterization. Traditional methods face challenges in efficiently handling large datasets from multiple wells. This paper employs principal components analysis (PCA) to evaluate the petrophysical properties of the reservoir intervals (IV-3, IV-1b, IV-1a) using wells P8, P4, and P6, situated in the northern, center, and south of our reservoir, respectively. PCA reduced the dimensionality of the data, while preserving original information, facilitating the analysis of the reservoir's geological and sedimentological features. The results showed that unit IV-3 has the highest average porosity (average NET porosity) and the lowest average water saturation (average PAY log sw) across all wells, indicating significant hydrocarbon production potential. In contrast, units IV-1b and IV-1a exhibited higher water saturations, suggesting less favorable conditions for hydrocarbon extraction. Strong negative correlations between petrophysical properties and water saturation in unit IV-3 highlighted its potential for hydrocarbon production. PCA correlation circles illustrated these relationships, with unit IV-3 showing predominantly hydrocarbon saturation, Unit IV-1b exhibited mixed saturation, whereas unit IV-1a was characterized by high water saturation. These findings demonstrate the effectiveness of PCA in guiding hydrocarbon resource management and exploitation strategies in the Illizi Basin; therefore, we recommend prioritizing drilling in zones with optimal reservoir properties, as identified through PCA. These zones are likely to have higher porosity, permeability, and lower water saturation, we also recommend Considering implementing suitable enhanced oil recovery techniques, such as waterflooding, polymer flooding, or gas injection, to improve recovery factors, especially in low-permeability zones. Finally, we recommend implementing a robust monitoring system to track reservoir performance and adjust production strategies as needed. This may involve real-time monitoring of pressure, temperature, and flow rates. These recommendations, can significantly enhance hydrocarbon recovery from unit IV-3, maximizing economic benefits, while minimizing environmental impact. This study demonstrates the practical application of PCA in reservoir characterization and provides valuable insights for optimizing field development and production strategies in the Illizi Basin.
期刊介绍:
This journal publishes quantitative studies of natural (mainly but not limited to mineral) resources exploration, evaluation and exploitation, including environmental and risk-related aspects. Typical articles use geoscientific data or analyses to assess, test, or compare resource-related aspects. NRR covers a wide variety of resources including minerals, coal, hydrocarbon, geothermal, water, and vegetation. Case studies are welcome.