Quantitative evaluation of organic richness from correlation of well logs and geochemical data: a case study of the Lower Permian Taiyuan shales in the southern North China Basin

doi:10.1007/s11707-021-0930-9

Front. Earth Sci.

2021, Vol. 15

Issue (2) : 360-377 https://doi.org/10.1007/s11707-021-0930-9

RESEARCH ARTICLE

Quantitative evaluation of organic richness from correlation of well logs and geochemical data: a case study of the Lower Permian Taiyuan shales in the southern North China Basin

Shuai TANG¹, Jinchuan ZHANG², Weiyao ZHU¹(

)

¹. Civil and Resource Engineering School, University of Science and Technology Beijing, Beijing 100083, China
². School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China

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Abstract

Marine-continental transitional shale is a potential energy component in China and is expected to be a realistic field in terms of increasing reserves and enhancing the natural gas production. However, the complex lithology, constantly changing depositional environment and lithofacies make the quantitative determination of the total organic carbon (TOC) suitable for marine shales not necessarily applicable to transitional shales. Thus, the identification of marine-continental transitional organic-rich shales and the mechanism of organic matter enrichment need to be further studied. As a typical representative of transitional shale, samples from Well MY-1 in the Taiyuan Formation in the southern North China Basin, were selected for TOC prediction using a combination of experimental organic geochemical data and well logging data including natural gamma-ray (GR), density (DEN), acoustic (AC), neutron (CNL) and U spectral gamma-ray (U), and TH spectral gamma-ray (TH). The correlation coefficient, coefficient of determination, standard deviation, mean squared error (MSE) and root mean squared error (RMSE) were selected to conduct the error analysis of the evaluation of different well log-based prediction methods, involving U spectral gamma logging, ΔlogR, and multivariate fitting methods to obtain the optimal TOC prediction method for the Taiyuan transitional shale. The plots of TOC versus the remaining volatile hydrocarbon content and the generation potential from Rock Eval show good to excellent potentials for hydrocarbon generation. The integrated results obtained from the various log-based TOC estimation methods indicate that, the multivariate fitting method of GR-U-DEN-CNL combination is preferable, with the correlation coefficients of 0.78 and 0.97 for the entire and objective interval of the Taiyuan Formation respectively, and with the minimum MSE and RMSE values. Specifically, the U spectral gamma logging method based on single logging parameter is also a better choice for TOC prediction of the high-quality intervals. This study provides a reference for the exploration and development of unconventional shale gas such as transitional shale gas.

Keywords marine-continental transitional shales total organic carbon thermal maturity well logs

Corresponding Author(s): Weiyao ZHU

Online First Date: 30 September 2021 Issue Date: 26 October 2021

Cite this article:

Shuai TANG,Jinchuan ZHANG,Weiyao ZHU. Quantitative evaluation of organic richness from correlation of well logs and geochemical data: a case study of the Lower Permian Taiyuan shales in the southern North China Basin[J]. Front. Earth Sci., 2021, 15(2): 360-377.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0930-9
https://academic.hep.com.cn/fesci/EN/Y2021/V15/I2/360

Fig.1 Simplified structural map of the southern North China Basin, showing well locations (modified from Sun, 1996, with permission from the Chinese Journal of Petroleum Exploration and Development).

Fig.2 Stratigraphic column showing the porosity, vitrinite reflectance, gas potential, brittleness, and spectral gamma-ray log data for the Lower Permian Taiyuan Formation (modified from Tang et al., 2016, with permission from Elsevier).

Fig.3 Workflow adopted in this study.

Tab.1 TOC, Rock-Eval, vitrinite reflectance and maceral composition data for the Taiyuan samples

Fig.4 Cross-plot of TOC versus remaining volatile hydrocarbon content showing the hydrocarbon generation potentials of selected Taiyuan samples.

Fig.5 Cross-plot of TOC versus generation potential showing the source rock characterization of selected Taiyuan samples.

Tab.2 Pearson’s correlation analysis of relationships between TOC and logging data for single parameter fitting in the entire interval and objective interval of Taiyuan Formation

Tab.3 Pearson’s correlation coefficient (R) between the TOC and multi-logging data for the studied samples from Well MY-1 in the entire interval and objective interval of Taiyuan Formation

Fig.6 Log data and comparison of the TOC prediction results obtained using different methods for the Taiyuan Formation in Well MY-1.

Fig.7 Log data and comparison of the TOC prediction results obtained using different methods for the Taiyuan Formation in Well MY-1 at the target depths of 2930–2952 m.

Taiyuan Formation	Regression statistics	U spectral gamma logging method	ΔlogR method	Multivariate fitting method
Entire interval	Calculated TOC	$1.45 — 3.62 2.27$ *	$− 3.19 — 21.52 3.10$ *	$1.26 — 4.40 2.27$ *
	Correlation coefficient (R)	0.55	−0.24	0.78
	Coefficient of determination (R²)	0.31	0.06	0.61
	Standard deviation/%	0.58	6.05	0.81
	Mean squared error/%	0.75	41.42	0.43
	Root mean squared error/%	0.87	6.44	0.66
	Observations	17	17	17
Objective interval	Calculated TOC	$1.32 — 5.01 2.53$ *	$− 0.89 — 21.52 6.25$ *	$1.38 — 5.31 2.53$ *
	Correlation coefficient (R)	0.91	−0.53	0.97
	Coefficient of determination (R²)	0.83	0.28	0.94
	Standard deviation/%	1.16	7.40	1.23
	Mean squared error/%	0.26	80.06	0.09
	Root mean squared error/%	0.51	8.95	0.30
	Observations	8	8	8

Tab.4 Linear regression statistics for the prediction of the TOC within the Taiyuan Formation in the study area

Fig.8 Comparison of the predicted TOC values obtained using the well logging methods and the measured TOC for (a) the entire interval and (b) objective interval; the trend lines with different colors represent the corresponding best-fit line for each prediction method.

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