A Self-Adaptive Artificial Neural Network Technique to Predict Total Organic Carbon (TOC) Based on Well Logs

2018 ◽  
Vol 44 (6) ◽  
pp. 6127-6137 ◽  
Author(s):  
Salaheldin Elkatatny
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Well Logs ◽  
Artificial Neural ◽  
Self Adaptive

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

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

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.

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.

scholarly journals A Self-Adaptive Artificial Intelligence Technique to Predict Oil Pressure Volume Temperature Properties

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3490 ◽  
Author(s):  
Salaheldin Elkatatny ◽  
Tamer Moussa ◽  
Abdulazeez Abdulraheem ◽  
Mohamed Mahmoud
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Differential Evolution Algorithm ◽  
Empirical Correlation ◽  
Percentage Error ◽  
Empirical Correlations ◽  
Input Parameters ◽  
Artificial Neural ◽  
Self Adaptive

Reservoir fluid properties such as bubble point pressure (Pb) and gas solubility (Rs) play a vital role in reservoir management and reservoir simulation. In addition, they affect the design of the production system. Pb and Rs can be obtained from laboratory experiments by taking a sample at the wellhead or from the reservoir under downhole conditions. However, this process is time-consuming and very costly. To overcome these challenges, empirical correlations and artificial intelligence (AI) models can be applied to obtain these properties. The objective of this paper is to introduce new empirical correlations to estimate Pb and Rs based on three input parameters—reservoir temperature and oil and gas gravities. 760 data points were collected from different sources to build new AI models for Pb and Rs. The new empirical correlations were developed by integrating artificial neural network (ANN) with a modified self-adaptive differential evolution algorithm to introduce a hybrid self-adaptive artificial neural network (SaDE-ANN) model. The results obtained confirmed the accuracy of the developed SaDE-ANN models to predict the Pb and Rs of crude oils. This is the first technique that can be used to predict Rs and Pb based on three input parameters only. The developed empirical correlation for Pb predicts the Pb with a correlation coefficient (CC) of 0.99 and an average absolute percentage error (AAPE) of 6%. The same results were obtained for Rs, where the new empirical correlation predicts the Rs with a coefficient of determination (R2) of 0.99 and an AAPE of less than 6%. The developed technique will help reservoir and production engineers to better understand and manage reservoirs. No additional or special software is required to run the developed technique.

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