New Technique to Determine the Total Organic Carbon Based on Well Logs Using Artificial Neural Network (White Box)

2017 ◽  
Author(s):  
Ahmed Abdulhamid Mahmoud ◽  
Salaheldin ElKatatny ◽  
Abdulazeez Abdulraheem ◽  
Mohamed Mahmoud ◽  
Mohamed Omar Ibrahim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Well Logs ◽  
New Technique ◽  
Artificial Neural ◽  
Carbon Based

Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network

2017 ◽  
Vol 179 ◽  
pp. 72-80 ◽  
Author(s):  
Ahmed Abdulhamid A. Mahmoud ◽  
Salaheldin Elkatatny ◽  
Mohamed Mahmoud ◽  
Mohamed Abouelresh ◽  
Abdulazeez Abdulraheem ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Well Logs ◽  
Artificial Neural

scholarly journals Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Osama Siddig ◽  
Ahmed Abdulhamid Mahmoud ◽  
Salaheldin Elkatatny ◽  
Pantelis Soupios
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Gamma Ray ◽  
Gas Production ◽  
Validation Dataset ◽  
Percentage Error ◽  
Empirical Correlations ◽  
Artificial Neural

Due to high oil and gas production and consumption, unconventional reservoirs attracted significant interest. Total organic carbon (TOC) is a significant measure of the quality of unconventional resources. Conventionally, TOC is measured experimentally; however, continuous information about TOC is hard to obtain due to the samples’ limitations, while the developed empirical correlations for TOC were found to have modest accuracy when applied in different datasets. In this paper, data from Devonian Duvernay shale were used to develop an optimized empirical correlation to predict TOC based on an artificial neural network (ANN). Three wells’ datasets were used to build and validate the model containing over 1250 data points, and each data point includes values for TOC, density, porosity, resistivity, gamma ray and sonic transient time, and spectral gamma ray. The three datasets were used separately for training, testing, and validation. The results of the developed correlation were compared with three available models. A sensitivity and optimization test was performed to reach the best model in terms of average absolute percentage error (AAPE) and correlation coefficient (R) between the actual and predicted TOC. The new correlation yielded an excellent match with the actual TOC values with R values above 0.93 and AAPE values lower than 14%. In the validation dataset, the correlation outperformed the other empirical correlations and resulted in less than 10% AAPE, in comparison with over 20% AAPE in other models. These results imply the applicability of this correlation; therefore, all the correlation’s parameters are reported to allow its use on different datasets.

Artificial neural network enabled capacitance prediction for carbon-based supercapacitors

2018 ◽  
Vol 233 ◽  
pp. 294-297 ◽  
Author(s):  
Shan Zhu ◽  
Jiajun Li ◽  
Liying Ma ◽  
Chunnian He ◽  
Enzuo Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Carbon Based

scholarly journals Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1548
Author(s):  
Edyta Puskarczyk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Thin Layers ◽  
Well Logs ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Artificial Neural ◽  
Detailed Interpretation ◽  
Group B

The main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and secondly, the artificial neural network, to identify and discriminate the facies from well log data. Determination of electrofacies was done in two ways: firstly, analysis was performed for two wells separately, secondly, the neural network learned and trained on data from the W-1 well was applied to the second well W-2 and a prediction of the facies distribution in this well was made. In both wells, located in the area of the Carpathian Foredeep, thin-layered sandstone-claystone formations were found and gas saturated depth intervals were identified. Based on statistical analyses, there were recognized presence of thin layers intersecting layers of much greater thickness (especially in W-2 well), e.g., section consisting mainly of claystone and sandstone formations with poor reservoir parameters (Group B) is divided with thin layers of sandstone and claystone with good reservoir parameters (Group C). The highest probability of occurrence of hydrocarbons exists in thin-layered intervals in facies C.

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