Shale elastic property relationships as a function of total organic carbon content using synthetic samples

2015 ◽  
Vol 133 ◽  
pp. 392-400 ◽  
Author(s):  
Y. Altowairqi ◽  
R. Rezaee ◽  
B. Evans ◽  
M. Urosevic
Keyword(s):  
Organic Carbon ◽  
Elastic Property ◽  
Total Organic Carbon ◽  
Carbon Content ◽  
Total Organic Carbon Content ◽  
Organic Carbon Content

scholarly journals Relationship between Color CIEL∗a∗b∗ and Total Organic Carbon in Compost

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Jhon Jairo Palechor-Tróchez ◽  
Luis Eduardo Ordoñez Santos ◽  
Hector Samuel Villada-Castillo
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Carbon Content ◽  
Total Organic Carbon Content ◽  
Organic Carbon Content ◽  
Particle Sizes ◽  
Surface Color ◽  
Total Organic Carbon Concentration ◽  
Organic Carbon Concentration ◽  
Pearson Correlations

The CIEL∗a∗b∗ coordinates and the total organic carbon content in compost were correlated. Two particle sizes of 0.5 and 2 mm were obtained in the compost samples; the surface color was analyzed with a CIEL∗a∗b∗ colorimeter and the total organic carbon content by spectrophotometry at 588.9 nm. The results indicate that all chromaticity values were significantly affected (p<0.001) by particle size. Chromaticity values a∗, b∗, C∗, and h° showed significantly strong Pearson correlations (r>0.95). The coordinates a∗ (r=−0.992) and b∗ (r=0.968) have the potential to be used in estimating the total organic carbon concentration in the compost samples analyzed.

scholarly journals Machine Learning-Based Production Prediction Model and Its Application in Duvernay Formation

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5509
Author(s):  
Zekun Guo ◽  
Hongjun Wang ◽  
Xiangwen Kong ◽  
Li Shen ◽  
Yuepeng Jia
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Carbon Content ◽  
Gas Production ◽  
Fluid Volume ◽  
Total Organic Carbon Content ◽  
Organic Carbon Content ◽  
Gas Ratio

The production of a single gas well is influenced by many geological and completion factors. The aim of this paper is to build a production prediction model based on machine learning technique and identify the most important factor for production. Firstly, around 159 horizontal wells were collected, targeting the Duvernay Formation with detailed geological and completion records. Secondly, the key factors were selected using grey relation analysis and Pearson correlation. Then, three statistical models were built through multiple linear regression (MLR), support vector regression (SVR), gaussian process regression (GPR). The model inputs include fluid volume, proppant amount, cluster counts, stage counts, total horizontal lateral length, gas saturation, total organic carbon content, condensate-gas ratio. The model performance was assessed by root mean squared errors (RMSE) and R-squared value. Finally, sensitivity analysis was applied based on best performance model. The analysis shows following conclusions: (1) GPR model shows the best performance with the highest R-squared value and the lowest RMSE. In the testing set, the model shows a R-squared of 0.8 with a RMSE of 280.54 × 104 m3 in the prediction of cumulative gas production within 1st 6 producing months and gives a R-squared of 0.83 with a RMSE of 1884.3 t in the prediction of cumulative oil production within 1st 6 producing months (2) Sensitivity analysis based on GPR model indicates that condensate-gas ratio, fluid volume, and total organic carbon content are the most important features to cumulative oil production within 1st 6 producing months. Fluid volume, Stages, and total organic carbon content are the most significant factors to cumulative gas production within 1st 6 producing months. The analysis progress and results developed in this study will assist companies to build prediction models and figure out which factors control well performance.

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