a novel approach to total organic carbon content...
TRANSCRIPT
Accepted Manuscript
A novel approach to total organic carbon content prediction in shale gas reservoirswith well logs data, Tonghua Basin, China
Pan Wang, Suping Peng, Taohua He
PII: S1875-5100(18)30147-1
DOI: 10.1016/j.jngse.2018.03.029
Reference: JNGSE 2521
To appear in: Journal of Natural Gas Science and Engineering
Received Date: 2 November 2017
Revised Date: 16 March 2018
Accepted Date: 30 March 2018
Please cite this article as: Wang, P., Peng, S., He, T., A novel approach to total organic carbon contentprediction in shale gas reservoirs with well logs data, Tonghua Basin, China, Journal of Natural GasScience & Engineering (2018), doi: 10.1016/j.jngse.2018.03.029.
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MANUSCRIP
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ACCEPTED
ACCEPTED MANUSCRIPTA Novel Approach to Total Organic Carbon Content Prediction in 1
Shale Gas Reservoirs with Well Logs Data, Tonghua Basin, China 2
Pan Wang a,b, Suping Peng a*, Taohua He c 3
a State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Beijing 100083, China 4
b College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China 5
c School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong 266580, China 6
ABSTRACT 7
As a geochemical parameter for charactering source rock in shale gas reservoirs, estimation of 8
total organic carbon (TOC) is a main task of geophysical and geochemical studies. TOC can be used 9
to evaluate the hydrocarbon generation potential of source rocks. Artificial intelligence (AI) 10
methods have been proposed recently to obtain TOC from well logs. This avoids expensive and 11
time-consuming core analysis of geochemical experiments. However, the optimal combination 12
(selected) of logs should be determined to get the highest accuracy TOC. We used a well-trained 13
least square support vector machine model to select appropriate well log inputs for intelligent model 14
based on mean impact values. We used a conventional 9 logs obtained from a shale gas well in 15
Tonghua Basin, China, to test our method. The results were compared with 215 TOC values from 16
core analysis. In addition, we tested three AI of models, including artificial neural network based on 17
backpropagation algorithm (ANN-BP), least square support vector machine (LSSVM), and particle 18
swarm optimization-least square support vector machine (PSO-LSSVM). For these three models, 19
both selected logs and all logs are used for comparison. The TOC results, obtained from different 20
calculations, showed that selected logs significantly improved the TOC accuracy in each AI model. 21
By comparing different AI model, we found that PSO-LSSVM model outperforms the other two 22
models. The TOC obtained from the PSO-LSSVM can benchmark with core analysis results. 23
Keywords: 24
Total organic carbon (TOC), PSO-LSSVM, Geophysical logs, ANN-BP, Mean impact value (MIV), 25
Shale gas reservoirs 26
