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Research Papers: Petroleum Engineering

Determination of Total Organic Carbon Content in Shale Formations With Regression Analysis

[+] Author and Article Information
Jianguo Wang

College of Petroleum Engineering,
China University of Petroleum (Beijing),
Beijing 102249, Changping, China;
Petroleum Systems Engineering,
Faculty of Engineering and
Applied Science,
University of Regina,
Regina, SK S4S 0A2, Canada

Daihong Gu

College of Petroleum Engineering,
China University of Petroleum (Beijing),
Beijing 102249, Changping, China

Wei Guo

Research Institute of Petroleum
Exploration and Development,
PetroChina,
Beijing 100083, Haidian, China

Haijie Zhang

Chongqing Shale Gas Exploration and
Development Company Limited,
PetroChina,
Chongqing 401121, China

Daoyong Yang

Petroleum Systems Engineering,
Faculty of Engineering and Applied Science,
University of Regina,
Regina, SK S4S 0A2, Canada
e-mail: tony.yang@uregina.ca

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 25, 2018; final manuscript received June 12, 2018; published online August 9, 2018. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 141(1), 012907 (Aug 09, 2018) (15 pages) Paper No: JERT-18-1291; doi: 10.1115/1.4040755 History: Received April 25, 2018; Revised June 12, 2018

By correcting both the positive and negative ΔlogR separation resulting from the resistivity in organic-deficient shales, the traditional ΔlogR correlation is modified, validated, and applied to determine the total organic carbon (TOC) content in shale formations. The TOC content is determined once the Fisher distribution, which represents the significance of each model, and Student's t-distribution, which denotes the significance of every variable in the models, have achieved values equal to or higher than their respective threshold values at a confidence level of 95%. Using a total of 45 sets of logging measurements, the newly proposed correlation is found to be able to reproduce the measured TOC values with a root mean-squared absolute difference (RMSAD) of 0.30 wt % and root mean-squared relative difference (RMSRD) of 23.8%, respectively. Uranium concentration, apart from interval transit time and resistivity, is found to be key in determining the TOC content in organic-rich shale without other radioactive minerals. By combining the reading of DGR (i.e., the difference between the spectral gamma ray with the radioactivity and the computed gamma ray without uranium), the traditional ΔlogR technique has now been improved and extended to the negative ΔlogR separation resulting from the resistivity in organic-deficient shale higher than that in organic-rich shale.

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Figures

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Fig. 1

(a) Comparison of ΔlogR and DGR for the Longmaxi shale in Well V201. Track #1 (leftmost): two types of gamma-ray logs; track #2: resistivity and sonic logs; track #3: relative depth with vertical scale 1:1000; track #4: DGR and the measured TOCs; and track #5 (rightmost): DGR and ΔlogR separation calculated by using Eq. (2b). (b) Mineral content from log interpretation in Well V201. Track #1 (leftmost): clay content; track #2: quartz plus feldspar content; track #3: relative depth with vertical scale 1:1000; track #4: carbonate content; and Track #5 (rightmost): pyrite content.

Grahic Jump Location
Fig. 2

(a) Comparison of various ΔlogR calculation methods for the Longmaxi shale in Well N209. Track #1 (leftmost): two types of gamma-ray logs; track #2: resistivity and sonic logs; track #3: relative depth with vertical scale 1:2000; track #4: DGR and the measured TOCs; track #5: DGR and ΔlogR separation calculated by using Eq. (2c); track #6 (rightmost): DGR and ΔlogR separation calculated by using Eq. (2c). (b) Mineral content from log interpretation in Well N209. Track #1 (leftmost): clay content; track #2: quartz plus feldspar content; track #3: relative depth with vertical scale 1:1000; track #4: carbonate content; and track #5 (rightmost): pyrite content.

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Fig. 3

Cross-plot of TOC and DGR in Well V201 (red) and Well N209 (black)

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Fig. 4

Cross-plot of DGR and ΔlogR in Well V201 (One data point is to represent the average value of DGR and ΔlogR two meters interval of Track #5 in Fig.1(a))

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Fig. 5

Cross-plot of DGR and ΔlogR2 in Well N209 (One data point is to represent the average value of DGR and ΔlogR two meters interval of (a) track #5 in Figs. 2(a) and 2(b) track #6 in Fig. 2(a))

Grahic Jump Location
Fig. 6

Comparison of possible resistivity logarithm cycles and Δt scale in Well V201. Track #1 (leftmost): two types of gamma-ray logs; track #2: relative depth with vertical scale 1:500; tracks #3–7: five possible combinations of resistivity and sonic logs.

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Fig. 8

Schematic structural map of the Sichuan Basin, Southern China

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Fig. 9

Paleozoic stratigraphic column of the Southern Sichuan Basin

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Fig. 7

Flowchart for multiple regression models in shales

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Fig. 10

Comparison of various TOC estimation methods and core data for the Longmaxi shale in Well N209. Track #1 (leftmost): Two types of gamma-ray logs; track #2: Relative depth with vertical scale 1:1000; track #3: Resistivity and sonic logs. Track #4: TOC calculated from DGR with Eq. (7a); track #5: TOC calculated with the sonic/resistivity ΔlogR with Eq. (7b); Track #6: TOC calculated with the MLR method with Eq. (8a); track #7 (rightmost): TOC calculated with the MNLR method with Eq. (8c).

Grahic Jump Location
Fig. 11

Comparison of various TOC estimation methods and core data for the Longmaxi shale in Well N210. Track #1 (leftmost): two types of gamma-ray logs; track #2: relative depth with vertical scale 1:1000; track #3: resistivity and sonic logs; track #4: TOC calculated from DGR with Eq. (7a); track #5: TOC calculated with the sonic/resistivity ΔlogR with Eq. (7b); track #6: TOC calculated with the MLR method with Eq. (8a); track #7 (rightmost): TOC calculated with the MNLR method with Eq. (8c).

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Fig. 12

Comparison of the measured TOCs and calculated values using the MLR method (i.e., Eq. (8a))

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