干旱区生态环境知识资源中心(Arid): Petrophysical assessment and permeability modeling utilizing core data and machine learning approaches - A study from the Badr El Din-1 field, Egypt

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DOI	10.1016/j.marpetgeo.2021.105265
	Petrophysical assessment and permeability modeling utilizing core data and machine learning approaches - A study from the Badr El Din-1 field, Egypt
	Farouk, Sherif; Sen, Souvik; Ganguli, Shib Sankar; Abuseda, Hesham; Debnath, Akash
通讯作者	Sen, S (corresponding author), Geologix Ltd, Dynasty Bldg,Wing A,Level 4,Andheri Kurla Rd, Mumbai 400059, Maharashtra, India.
来源期刊	MARINE AND PETROLEUM GEOLOGY
ISSN	0264-8172
EISSN	1873-4073
出版年	2021
卷号	133
英文摘要	Reservoir petrophysical characterization involving permeability estimation in the laboratory using core samples is time-consuming and that too accords limited coverage of the sub-surface reservoir. Machine learning (ML) algorithms have been utilized to overcome such difficulties, which offers better and more accurate prediction models. This work integrates core data and ML approached and reports a comprehensive petrophysical evaluation of the Cenomanian Bahariya and Albian Kharita reservoirs from the Badr El Din-1 field, Western Desert of Egypt. Three distinct reservoir rock types are identified from the detailed petrographic investigation: i) glauconitic sandstones (RRT1) and ii) subfeldspathic quartz arenites (RRT2) within Bahariya Formation and iii) quartz arenites (RRT3) within Kharita Formation. Reservoir quality index (RQI), flow zone indicator (FZI), and hydraulic flow units (HFU) are interpreted from the routine core analysis, which infers that the RRT1 has poor permeability (K-H < 1 mD) and impervious (RQI < 0.25 mu m, FZI <1 mu m). Transgressive shoreface deposits of RRT2 display a wide range of porosity (5.9-18.5%) and permeability (0.82-76 mD) distribution and offer poor to fair reservoir qualities (1 mu m < FZI 5 mu m) with moderate to high permeability anisotropy. Well sorted medium grained quartz arenites (RRT3) were deposited in a braided fluvial channel environment and contributed to the excellent reservoir quality with an average K-H of 468 mD, FZI 5 mu m, and lesser permeability anisotropy. Finally, two-hybrid ML-based algorithms such as particle swarm optimization (PSO) trained neural network (NN) and least squares support vector machines (LS-SVM) have been employed to estimate the permeability. Based on the statistical performance metrics, we conclude that both the ML algorithms performed exceptionally well as compared to the conventional method, which is attributed to their enhanced competence to model the connectivity within the pore microstructures of the reservoir rocks with proper recognition of the pattern in the training data. Our results indicate that amongst the PSO-NN and LS-SVM algorithms, the latter is more superior since it is comparatively more accurate and reliable in permeability prediction.
英文关键词	Petrophysical characterization Reservoir quality Core data Machine learning Bahariya Kharita
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000698392000002
WOS关键词	MARGINAL GONDWANA BASIN ; ARTIFICIAL NEURAL-NETWORKS ; PERMIAN BARAKAR FORMATION ; NORTH-WESTERN DESERT ; BAHARIYA FORMATION ; OIL-FIELD ; SEQUENCE STRATIGRAPHY ; PREDICTION ; POROSITY ; FACIES
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364141
作者单位	[Farouk, Sherif] Egyptian Petr Res Inst EPRI, Explorat Dept, 1 Ahmed El Zomor St, Cairo, Egypt; [Sen, Souvik] Geologix Ltd, Dynasty Bldg,Wing A,Level 4,Andheri Kurla Rd, Mumbai 400059, Maharashtra, India; [Ganguli, Shib Sankar; Debnath, Akash] CSIR, Natl Geophys Res Inst, Deep Seism Res Grp, Uppal Rd, Hyderabad 500007, Telangana, India; [Abuseda, Hesham] Egyptian Petr Res Inst EPRI, Prod Dept, 1 Ahmed El Zomor St, Cairo, Egypt
推荐引用方式 GB/T 7714	Farouk, Sherif,Sen, Souvik,Ganguli, Shib Sankar,et al. Petrophysical assessment and permeability modeling utilizing core data and machine learning approaches - A study from the Badr El Din-1 field, Egypt[J],2021,133.
APA	Farouk, Sherif,Sen, Souvik,Ganguli, Shib Sankar,Abuseda, Hesham,&Debnath, Akash.(2021).Petrophysical assessment and permeability modeling utilizing core data and machine learning approaches - A study from the Badr El Din-1 field, Egypt.MARINE AND PETROLEUM GEOLOGY,133.
MLA	Farouk, Sherif,et al."Petrophysical assessment and permeability modeling utilizing core data and machine learning approaches - A study from the Badr El Din-1 field, Egypt".MARINE AND PETROLEUM GEOLOGY 133(2021).