{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:54:10Z","timestamp":1747191250874,"version":"3.40.5"},"reference-count":61,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2023,2,20]]},"abstract":"<jats:p>The naturally fractured reservoirs are one of the most challenging due to the tectonic movements that are caused to increase the permeability and conductivity of the fractures. The instability of the permeability and conductivity effects on the fluid flow path causes problems during the transfer of the fluids from the matrix to the fractures and fluid losses during production. In addition, these complications made it difficult for engineers to estimate fluid flow during production. The fracture properties\u2019 study is important to model the fluid flow paths such as the fracture porosity, permeability, and the shape factor, which are considered essential in the stability of fluid flow. To examine this, this research introduced new models including decision tree (DT), random forest (RF), K-nearest regression (KNR), ridge regression (RR), and LASSO regression model,. The research studied the fracture properties in naturally fractured reservoirs like the fracture porosity (FP) and the shape factor (SF). The datasets used in this study were collected from previous studies \u201ci.e., Texas oil and gas fields\u201d to build an intelligence-based predictive model for fluid flow characteristics. The prediction process was conducted based on interporosity flow coefficient, storativity ratio, wellbore radius, matrix permeability, and fracture permeability as input data. This study revealed a positive finding for the adopted machine learning (ML) models and was superior in using statistical accuracy metrics. Overall, the research emphasized the implementation of computer-aided models for naturally fractured reservoir analysis, giving more details on the extensive execution techniques, such as injection or the creation of artificial cracks, to minimize hydrocarbon losses or leakage.<\/jats:p>","DOI":"10.1155\/2023\/7953967","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:35:20Z","timestamp":1676939720000},"page":"1-19","source":"Crossref","is-referenced-by-count":0,"title":["Fluid Flow Behavior Prediction in Naturally Fractured Reservoirs Using Machine Learning Models"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1151-2231","authenticated-orcid":true,"given":"Mustafa Mudhafar","family":"Shawkat","sequence":"first","affiliation":[{"name":"Department of Petroleum Engineering, School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8030-7851","authenticated-orcid":true,"given":"Abdul Rahim Bin","family":"Risal","sequence":"additional","affiliation":[{"name":"Department of Petroleum Engineering, School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4566-3145","authenticated-orcid":true,"given":"Noor J.","family":"Mahdi","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8687-837X","authenticated-orcid":true,"given":"Ziauddin","family":"Safari","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Engineering, Takhar University, Taleqan, Afghanistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8850-1242","authenticated-orcid":true,"given":"Maryam H.","family":"Naser","sequence":"additional","affiliation":[{"name":"Building and Construction Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8137-5829","authenticated-orcid":true,"given":"Ahmed W.","family":"Al Zand","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia"}]}],"member":"311","reference":[{"key":"1","article-title":"Naturally fractured reservoir characterization","volume":"112","author":"W. 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