The Experts below are selected from a list of 3024 Experts worldwide ranked by ideXlab platform
Ahmed Abdulhamid Mahmoud - One of the best experts on this subject based on the ideXlab platform.
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determination of the total organic carbon toc based on conventional well Logs using artificial neural network
International Journal of Coal Geology, 2017Co-Authors: Mohamed Abouelresh, Ahmed Abdulhamid Mahmoud, Mohamed Mahmoud, Abdulazeez AbdulraheemAbstract:Abstract Total organic carbon (TOC) is the measure of the amount of carbon available in an organic compound and is often used as an essential factor for unconventional shale resources evaluation. Previous models for TOC determination based on density Log data considered the presence of organic matter is proportional to the formation bulk density. Also model those based on: Resistivity Log, sonic, or density Logs as well as the formation level of maturity (LOM) were used to determine the TOC. These models assumed linear relation between Resistivity and porosity Logs. Previous correlations showed very low coefficient of determination of the estimated TOC compared to the actual laboratory data. The objective of this paper is to develop an empirical correlation to determine the TOC for Barnett and Devonian shale formations based on conventional Logs using artificial neural network (ANN). Core TOC data (442 data points) and well Logs (Resistivity, gamma ray, sonic transit time, and bulk density) from Barnett shale were used to develop the ANN model. For the first time an empirical correlation for TOC was developed based on the weights and biases of the ANN model. The developed correlation was then applied to estimate TOC for Devonian shale. The developed ANN model predicted the TOC based on conventional well-Log data with high accuracy. The average absolute deviation (AAD) and coefficient of determination (R2) for the predicted TOC compared with the measured TOC for Barnett shale are 0.91 wt% and 0.93, respectively. The developed model outperformed the previous available models in estimating TOC for Devonian shale as well with AAD of 0.99 wt% and R2 of 0.89 compared to AAD of 1.16 wt% or more and R2 of 0.65 or less for the available correlations. The developed empirical correlation was used to estimate the TOC with high accuracy for Barnett shale and Devonian shale formations. The developed correlation will help the geoLogical and reservoir engineers predict the TOC using well Logs without the need to measure TOC in the laboratory.
Abdulazeez Abdulraheem - One of the best experts on this subject based on the ideXlab platform.
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determination of the total organic carbon toc based on conventional well Logs using artificial neural network
International Journal of Coal Geology, 2017Co-Authors: Mohamed Abouelresh, Ahmed Abdulhamid Mahmoud, Mohamed Mahmoud, Abdulazeez AbdulraheemAbstract:Abstract Total organic carbon (TOC) is the measure of the amount of carbon available in an organic compound and is often used as an essential factor for unconventional shale resources evaluation. Previous models for TOC determination based on density Log data considered the presence of organic matter is proportional to the formation bulk density. Also model those based on: Resistivity Log, sonic, or density Logs as well as the formation level of maturity (LOM) were used to determine the TOC. These models assumed linear relation between Resistivity and porosity Logs. Previous correlations showed very low coefficient of determination of the estimated TOC compared to the actual laboratory data. The objective of this paper is to develop an empirical correlation to determine the TOC for Barnett and Devonian shale formations based on conventional Logs using artificial neural network (ANN). Core TOC data (442 data points) and well Logs (Resistivity, gamma ray, sonic transit time, and bulk density) from Barnett shale were used to develop the ANN model. For the first time an empirical correlation for TOC was developed based on the weights and biases of the ANN model. The developed correlation was then applied to estimate TOC for Devonian shale. The developed ANN model predicted the TOC based on conventional well-Log data with high accuracy. The average absolute deviation (AAD) and coefficient of determination (R2) for the predicted TOC compared with the measured TOC for Barnett shale are 0.91 wt% and 0.93, respectively. The developed model outperformed the previous available models in estimating TOC for Devonian shale as well with AAD of 0.99 wt% and R2 of 0.89 compared to AAD of 1.16 wt% or more and R2 of 0.65 or less for the available correlations. The developed empirical correlation was used to estimate the TOC with high accuracy for Barnett shale and Devonian shale formations. The developed correlation will help the geoLogical and reservoir engineers predict the TOC using well Logs without the need to measure TOC in the laboratory.
Mohamed Mahmoud - One of the best experts on this subject based on the ideXlab platform.
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determination of the total organic carbon toc based on conventional well Logs using artificial neural network
International Journal of Coal Geology, 2017Co-Authors: Mohamed Abouelresh, Ahmed Abdulhamid Mahmoud, Mohamed Mahmoud, Abdulazeez AbdulraheemAbstract:Abstract Total organic carbon (TOC) is the measure of the amount of carbon available in an organic compound and is often used as an essential factor for unconventional shale resources evaluation. Previous models for TOC determination based on density Log data considered the presence of organic matter is proportional to the formation bulk density. Also model those based on: Resistivity Log, sonic, or density Logs as well as the formation level of maturity (LOM) were used to determine the TOC. These models assumed linear relation between Resistivity and porosity Logs. Previous correlations showed very low coefficient of determination of the estimated TOC compared to the actual laboratory data. The objective of this paper is to develop an empirical correlation to determine the TOC for Barnett and Devonian shale formations based on conventional Logs using artificial neural network (ANN). Core TOC data (442 data points) and well Logs (Resistivity, gamma ray, sonic transit time, and bulk density) from Barnett shale were used to develop the ANN model. For the first time an empirical correlation for TOC was developed based on the weights and biases of the ANN model. The developed correlation was then applied to estimate TOC for Devonian shale. The developed ANN model predicted the TOC based on conventional well-Log data with high accuracy. The average absolute deviation (AAD) and coefficient of determination (R2) for the predicted TOC compared with the measured TOC for Barnett shale are 0.91 wt% and 0.93, respectively. The developed model outperformed the previous available models in estimating TOC for Devonian shale as well with AAD of 0.99 wt% and R2 of 0.89 compared to AAD of 1.16 wt% or more and R2 of 0.65 or less for the available correlations. The developed empirical correlation was used to estimate the TOC with high accuracy for Barnett shale and Devonian shale formations. The developed correlation will help the geoLogical and reservoir engineers predict the TOC using well Logs without the need to measure TOC in the laboratory.
Mohamed Abouelresh - One of the best experts on this subject based on the ideXlab platform.
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determination of the total organic carbon toc based on conventional well Logs using artificial neural network
International Journal of Coal Geology, 2017Co-Authors: Mohamed Abouelresh, Ahmed Abdulhamid Mahmoud, Mohamed Mahmoud, Abdulazeez AbdulraheemAbstract:Abstract Total organic carbon (TOC) is the measure of the amount of carbon available in an organic compound and is often used as an essential factor for unconventional shale resources evaluation. Previous models for TOC determination based on density Log data considered the presence of organic matter is proportional to the formation bulk density. Also model those based on: Resistivity Log, sonic, or density Logs as well as the formation level of maturity (LOM) were used to determine the TOC. These models assumed linear relation between Resistivity and porosity Logs. Previous correlations showed very low coefficient of determination of the estimated TOC compared to the actual laboratory data. The objective of this paper is to develop an empirical correlation to determine the TOC for Barnett and Devonian shale formations based on conventional Logs using artificial neural network (ANN). Core TOC data (442 data points) and well Logs (Resistivity, gamma ray, sonic transit time, and bulk density) from Barnett shale were used to develop the ANN model. For the first time an empirical correlation for TOC was developed based on the weights and biases of the ANN model. The developed correlation was then applied to estimate TOC for Devonian shale. The developed ANN model predicted the TOC based on conventional well-Log data with high accuracy. The average absolute deviation (AAD) and coefficient of determination (R2) for the predicted TOC compared with the measured TOC for Barnett shale are 0.91 wt% and 0.93, respectively. The developed model outperformed the previous available models in estimating TOC for Devonian shale as well with AAD of 0.99 wt% and R2 of 0.89 compared to AAD of 1.16 wt% or more and R2 of 0.65 or less for the available correlations. The developed empirical correlation was used to estimate the TOC with high accuracy for Barnett shale and Devonian shale formations. The developed correlation will help the geoLogical and reservoir engineers predict the TOC using well Logs without the need to measure TOC in the laboratory.
Géraud Yves - One of the best experts on this subject based on the ideXlab platform.
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Quantification of bound water content, interstitial porosity and fracture porosity in the sediments entering the North Sumatra subduction zone from Cation Exchange Capacity and IODP Expedition 362 Resistivity data
'Elsevier BV', 2020Co-Authors: Dutilleul Jade, Bourlange Sylvain, Conin Marianne, Géraud YvesAbstract:In this study, we investigate porosity evolution through the sedimentary input section of the North Sumatra Subduction zone by quantifying interstitial porosity, bound water content and fracture porosity based on IODP Expedition 362 data and post-cruise chemical analyses. During IODP Expedition 362, total porosity of the sedimentary section entering the North Sumatra subduction zone was measured. This total porosity is derived from the total water content of core samples thus including pore water and water bound to hydrous minerals like smectite. Clay mineral composition varies over the sedimentary section and is mainly kaolinite/illite in the Nicobar Fan units and smectite/illite in the prefan pelagic unit below. The prefan pelagic unit shows anomalously high total porosity values and is stratigraphically correlated to a high amplitude negative polarity (HANP) seismic reflector located landward. This HANP reflector has been previously interpreted as a porous fluid-rich layer where the d{\'e}collement may develop along parts of the margin as a consequence of pore pressure buildup. We estimate clay bound water content from Cation Exchange Capacity (CEC) which gives information about the smectite/illite composition and soluble chloride content data. Interstitial porosity corresponds to onboard total porosity corrected from clay bound water and is more relevant in terms of sediment compaction state and fluid flow properties. Interstitial porosity versus vertical effective stress curve shows no evidence of undercompaction and suggests that the input section 2 has been experiencing normal consolidation due to high sediment accumulation rate. The porosity anomaly observed in the prefan pelagic unit results from the local occurrence of water-bearing minerals like smectite rather than excess pore pressure, which might, however, buildup more landward in the basin. We also estimate fracture porosity using a Resistivity model for shales used in previous works based on wireline Resistivity Log and show that fracture porosity yields 4-6% in damaged parts of the sedimentary section investigated
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Quantification of bound water content, interstitial porosity and fracture porosity in the sediments entering the North Sumatra subduction zone from Cation Exchange Capacity and IODP Expedition 362 Resistivity data
'Elsevier BV', 2020Co-Authors: Dutilleul Jade, Bourlange Sylvain, Conin Marianne, Géraud YvesAbstract:International audienceIn this study, we investigate porosity evolution through the sedimentary input section of the North Sumatra Subduction zone by quantifying interstitial porosity, bound water content and fracture porosity based on IODP Expedition 362 data and post-cruise chemical analyses. During IODP Expedition 362, total porosity of the sedimentary section entering the North Sumatra subduction zone was measured. This total porosity is derived from the total water content of core samples thus including pore water and water bound to hydrous minerals like smectite. Clay mineral composition varies over the sedimentary section and is mainly kaolinite/illite in the Nicobar Fan units and smectite/illite in the prefan pelagic unit below. The prefan pelagic unit shows anomalously high total porosity values and is stratigraphically correlated to a high amplitude negative polarity (HANP) seismic reflector located landward. This HANP reflector has been previously interpreted as a porous fluid-rich layer where the décollement may develop along parts of the margin as a consequence of pore pressure buildup. We estimate clay bound water content from Cation Exchange Capacity (CEC) which gives information about the smectite/illite composition and soluble chloride content data. Interstitial porosity corresponds to onboard total porosity corrected from clay bound water and is more relevant in terms of sediment compaction state and fluid flow properties. Interstitial porosity versus vertical effective stress curve shows no evidence of undercompaction and suggests that the input section 2 has been experiencing normal consolidation due to high sediment accumulation rate. The porosity anomaly observed in the prefan pelagic unit results from the local occurrence of water-bearing minerals like smectite rather than excess pore pressure, which might, however, buildup more landward in the basin. We also estimate fracture porosity using a Resistivity model for shales used in previous works based on wireline Resistivity Log and show that fracture porosity yields 4-6% in damaged parts of the sedimentary section investigated
