干旱区生态环境知识资源中心(Arid): Thermal maturity and TOC prediction using machine learning techniques: case study from the Cretaceous-Paleocene source rock, Taranaki Basin, New Zealand

Arid

DOI	10.1007/s13202-020-00906-4
	Thermal maturity and TOC prediction using machine learning techniques: case study from the Cretaceous-Paleocene source rock, Taranaki Basin, New Zealand
	Shalaby, Mohamed Ragab; Malik, Owais Ahmed; Lai, Daphne; Jumat, Nurhazwana; Islam, Md Aminul
通讯作者	Shalaby, MR
来源期刊	JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
ISSN	2190-0558
EISSN	2190-0566
出版年	2020
卷号	10 期号:6 页码:2175-2193
英文摘要	Thermal maturity, organic richness and kerogen typing are very important parameters to be evaluated for source rock characterization. Due to the difficulties of high cost geochemical analyses and the unavailability of rock samples, it was necessary to examine and test many different method and techniques to help in the prediction of TOC values as well as other maturity indicators in case of missing or absence of geochemical data. Integrated study of machine learning techniques and well-log data has been applied on Cretaceous-Paleocene formations in the Taranaki Basin, New Zealand. A novel approach of maturity prediction using T-max and vitrinite reflectance (VR%) is the first and preliminary objective of this research. Moreover, the organic richness or the total organic carbon (TOC) content has been predicted as well. Geochemical and well-log data collected from the Cretaceous Rakopi and North Cape formations and Paleocene Mangahewa Formation have been processed and prepared to apply the machine learning techniques. Five machine learning techniques, namely Bayesian regularization for feed-forward neural networks (BRNNs), random forest (RF), support vector machine (SVM) for regression, linear regression (LR) and Gaussian process regression (GPR), were employed for prediction of TOC, T-max and VR, and their results have been compared. For TOC prediction, the best model achieved the coefficient of determination (R-2) value of 0.964 using RF model. For T-max prediction, BRNN with one hidden layer achieved the R-2 value of 0.828. BRNN with two hidden layers produced the best model for VR prediction achieving R-2 = 0.636. A comparison of five ML techniques showed that all of these techniques performed exceedingly well for TOC prediction with a value of R-2 > 0.96. In contrast, BRNN with one hidden layer was the only ML technique able to achieve R-2 > 0.8 for T-max and BRNN with two hidden layers was the only ML technique able to achieve R-2 > 0.6 for VR prediction. Therefore, this research provides a strong empirical evidence that ML techniques can capture the nonlinear relationship between the well-log data and TOC as well as the maturity indicators which may not be fully understood by existing linear models.
英文关键词	Machine learning Neural networks Random forest Support vector machine Linear regression Well-logging Geochemical analysis Taranaki Basin New Zealand
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000532625400001
WOS关键词	ORGANIC-CARBON CONTENT ; NORTHERN WESTERN DESERT ; APPALACHIAN DEVONIAN SHALES ; SUPPORT-VECTOR-REGRESSION ; JURASSIC SOURCE ROCKS ; WELL LOGS ; GEOCHEMICAL CHARACTERISTICS ; NEURAL-NETWORKS ; WIRELINE LOGS ; DEPOSITIONAL ENVIRONMENT
WOS类目	Energy & Fuels ; Engineering, Petroleum ; Geosciences, Multidisciplinary
WOS研究方向	Energy & Fuels ; Engineering ; Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324443
作者单位	[Shalaby, Mohamed Ragab; Jumat, Nurhazwana; Islam, Md Aminul] Univ Brunei Darussalam, Dept Phys & Geol Sci, Jalan Tungku Link, BE-1410 Gadong, Brunei; [Shalaby, Mohamed Ragab] Tanta Univ, Fac Sci, Geol Dept, Tanta 31527, Egypt; [Malik, Owais Ahmed; Lai, Daphne] Univ Brunei Darussalam, Dept Comp Sci, Jalan Tungku Link, BE-1410 Gadong, Brunei; [Malik, Owais Ahmed; Lai, Daphne] Univ Brunei Darussalam, Inst Appl Data Analyt, Jalan Tungku Link, BE-1410 Gadong, Brunei
推荐引用方式 GB/T 7714	Shalaby, Mohamed Ragab,Malik, Owais Ahmed,Lai, Daphne,et al. Thermal maturity and TOC prediction using machine learning techniques: case study from the Cretaceous-Paleocene source rock, Taranaki Basin, New Zealand[J],2020,10(6):2175-2193.
APA	Shalaby, Mohamed Ragab,Malik, Owais Ahmed,Lai, Daphne,Jumat, Nurhazwana,&Islam, Md Aminul.(2020).Thermal maturity and TOC prediction using machine learning techniques: case study from the Cretaceous-Paleocene source rock, Taranaki Basin, New Zealand.JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY,10(6),2175-2193.
MLA	Shalaby, Mohamed Ragab,et al."Thermal maturity and TOC prediction using machine learning techniques: case study from the Cretaceous-Paleocene source rock, Taranaki Basin, New Zealand".JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 10.6(2020):2175-2193.