Permeability and Hydraulic Flow Units Prediction Using Neural Network Technique of The Clastic Mamuniyat Reservoir (Upper Ordovician), Murzuq Basin-Libya - Bahia M. Ben Ghawar; Fathi M. Salloum; Mahmud A. Al Tarhouni; Mousa G. El Shamli (Scientific Contributions Oil and Gas, 2026)
Delineation of the hydraulic flow unit (HFU) and a petrofacies parameter (R35) are essential reservoir characteristics, which are functions of the routine core analysis (CCA) and well log data. Thus, this work has been carried out with the Neural Network (NN) application to predict the HFU, pore size radius (R35), and permeability (KHFU) in uncored well 3 within an average thickness of 133 feet of the clastic Mamuniyat reservoir Murzuq basin (Libya). This prediction is based on data of two wells (1 and 2) producing from the same reservoir. Hence, log analysis demonstrates about 12% effective porosity (Øe) and not exceeding 14% of the shale content (Vsh). Also, 11% and 96 mD are averages of core porosity (Øcore) and permeability (Kcore), respectively. Whereas micropores, mesopores, and macropores are three pore throat radius (R35) classifications recognized in the reservoir and six HFUs in well 1 and five in well 2. Furthermore, gamma ray (GR) logs correlate among three wells and reveal a similarity between wells 1 and 3 that sustain the HFUs of well 3 by using the NN model. Whereas, the comparison between the predicted KHFU and two approved empirical permeability equations manifests furnishing high agreement results with some exceptions. However, the NN provides predictions supported by data of Mamuniyat reservoir quality (HFU, R35, and K) within the uncored reservoir section from the nearby wells that reveal consistent and reliable results.