The importance of organic geochemistry and basin modeling is widely recognized and used to understand the source rock potential and hydrocarbon generation history of the Mangahewa Formation,and thereby given the found...The importance of organic geochemistry and basin modeling is widely recognized and used to understand the source rock potential and hydrocarbon generation history of the Mangahewa Formation,and thereby given the foundational role in the petroleum exploration.This study utilized the total organic carbon(TOC)content and hydrogen index(HI)to investigate the dominant kerogen type and hydrogen richness for the significance of petroleum generative potential.The Mangahewa coals and carbonaceous shales exhibit an excellent source rocks,with high total organic content(TOC)of more than 22%.The coals and carbonaceous shales were also characterised by Type Ⅱ‒Ⅲ kerogen with Type Ⅲ kerogen,promising oiland gas-prones.The Mangahewa Formation reached the main oil generation,with vitrinite reflectances between 0.53%and 1.01%.Vitrinite reflectance was also used in developing themal models and reveal the transformation(TR)of 10‒50%kerogen to oil during the Late Miocene.The models also showed that the Mangahewa source rock has a significant oil generation and little expulsion competency,with a TR of up to 54%.These findings support the substantial oil-generating potential in the Taranaki Basin's southern graben and can be used as a guide when developing strategies for an oil exploration program.展开更多
Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource identification.This review examines current AI applications,focusing on the a...Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource identification.This review examines current AI applications,focusing on the algorithms used,the challenges addressed,and the opportunities created.In addition,the review highlights the growth of machine learning applications in geothermal exploration over the past decade,demonstrating how AI has improved the analysis of subsurface data to identify potential resources.AI techniques such as neural networks,support vector machines,and decision trees are used to estimate subsurface temperatures,predict rock and fluid properties,and identify optimal drilling locations.In particular,neural networks are the most widely used technique,further contributing to improved exploration efficiency.However,the widespread adoption of AI in geothermal exploration is hindered by challenges,such as data accessibility,data quality,and the need for tailored data science training for industry professionals.Furthermore,the review emphasizes the importance of data engineering methodologies,data scaling,and standardization to enable the development of accurate and generalizable AI models for geothermal exploration.It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources.By effectively addressing key challenges and leveraging AI technologies,the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.展开更多
October oil field is one of the largest hydrocarbon-bearing fields which produces oil from the sand section of the Lower Miocene Asl Formation. Two marl (Asl Marl) and shale (Hawara Formation) sections of possible sou...October oil field is one of the largest hydrocarbon-bearing fields which produces oil from the sand section of the Lower Miocene Asl Formation. Two marl (Asl Marl) and shale (Hawara Formation) sections of possible source enrichment are detected above and below this oil sand section, respectively. This study aims to identify the content of the total organic carbon based on the density log and a combination technique of the resistivity and porosity logs (Δlog R Technique). The available geochemical analyses are used to calibrate the constants of the TOC and the level of maturity (LOM) used in the (Δlog R Technique). The geochemical-based LOM is found as 9.0 and the calibrated constants of the Asl Marl and Hawara Formation are found as 11.68, 3.88 and 8.77, 2.80, respectively. Fair to good TOC% content values (0.88 to 1.85) were recorded for Asl Marl section in the majority of the studied wells, while less than 0.5% is recorded for the Hawara Formation. The lateral distribution maps show that most of the TOC% enrichments are concentrated at central and eastern parts of the study area, providing a good source for the hydrocarbons encountered in the underlying Asl Sand section.展开更多
This study focuses on the heterogeneity of the middle Miocene syn-rift Belayim nullipore(reefal)marine sequences in the Gulf of Suez and its impacts on reservoir quality.The sequences consist of coralline algal reef l...This study focuses on the heterogeneity of the middle Miocene syn-rift Belayim nullipore(reefal)marine sequences in the Gulf of Suez and its impacts on reservoir quality.The sequences consist of coralline algal reef limestones with a highly complex dual-porosity system of primary and secondary porosities of widely varying percentages.To achieve a precise mathematical modeling of these reservoir sequences,a workflow protocol was applied to separate these sequences into a number of hydraulic flow units(HFUs)and reservoir rock types(RRTs).This has been achieved by conducting a conventional core analysis on the nullipore marine sequence.To illustrate the heterogeneity of the nullipore reservoir,the Dykstra-Parsons coefficient(V)has been estimated(V=0.91),indicating an extremely heterogeneous reservoir.A slight to high anisotropy(λ_(k))has been assigned for the studied nullipore sequences.A stratigraphic modified Lorenz plot(SMLP)was applied to define the optimum number of HFUs and barriers/baffles in each of the studied wells.Integrating the permeability-porosity,reservoir quality index-normalized porosity index(RQI-NPI)and the RQI-flow zone indicator(RQIFZI)plots,the discrete rock types(DRT)and the R35 techniques enable the discrimination of the reservoir sequences into 4 RRTs/HFUs.The RRT4 packstone samples are characterized by the best reservoir properties(moderate permeability anisotropy,with a good-to-fair reservoir quality index),whereas the RRT1 mudstone samples have the lowest flow and storage capacities,as well as the tightest reservoir quality.展开更多
基金Supporting Project number(RSP2025R92)at King Saud University,Riyadh,Saudi Arabia,for their support.
文摘The importance of organic geochemistry and basin modeling is widely recognized and used to understand the source rock potential and hydrocarbon generation history of the Mangahewa Formation,and thereby given the foundational role in the petroleum exploration.This study utilized the total organic carbon(TOC)content and hydrogen index(HI)to investigate the dominant kerogen type and hydrogen richness for the significance of petroleum generative potential.The Mangahewa coals and carbonaceous shales exhibit an excellent source rocks,with high total organic content(TOC)of more than 22%.The coals and carbonaceous shales were also characterised by Type Ⅱ‒Ⅲ kerogen with Type Ⅲ kerogen,promising oiland gas-prones.The Mangahewa Formation reached the main oil generation,with vitrinite reflectances between 0.53%and 1.01%.Vitrinite reflectance was also used in developing themal models and reveal the transformation(TR)of 10‒50%kerogen to oil during the Late Miocene.The models also showed that the Mangahewa source rock has a significant oil generation and little expulsion competency,with a TR of up to 54%.These findings support the substantial oil-generating potential in the Taranaki Basin's southern graben and can be used as a guide when developing strategies for an oil exploration program.
文摘Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource identification.This review examines current AI applications,focusing on the algorithms used,the challenges addressed,and the opportunities created.In addition,the review highlights the growth of machine learning applications in geothermal exploration over the past decade,demonstrating how AI has improved the analysis of subsurface data to identify potential resources.AI techniques such as neural networks,support vector machines,and decision trees are used to estimate subsurface temperatures,predict rock and fluid properties,and identify optimal drilling locations.In particular,neural networks are the most widely used technique,further contributing to improved exploration efficiency.However,the widespread adoption of AI in geothermal exploration is hindered by challenges,such as data accessibility,data quality,and the need for tailored data science training for industry professionals.Furthermore,the review emphasizes the importance of data engineering methodologies,data scaling,and standardization to enable the development of accurate and generalizable AI models for geothermal exploration.It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources.By effectively addressing key challenges and leveraging AI technologies,the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.
文摘October oil field is one of the largest hydrocarbon-bearing fields which produces oil from the sand section of the Lower Miocene Asl Formation. Two marl (Asl Marl) and shale (Hawara Formation) sections of possible source enrichment are detected above and below this oil sand section, respectively. This study aims to identify the content of the total organic carbon based on the density log and a combination technique of the resistivity and porosity logs (Δlog R Technique). The available geochemical analyses are used to calibrate the constants of the TOC and the level of maturity (LOM) used in the (Δlog R Technique). The geochemical-based LOM is found as 9.0 and the calibrated constants of the Asl Marl and Hawara Formation are found as 11.68, 3.88 and 8.77, 2.80, respectively. Fair to good TOC% content values (0.88 to 1.85) were recorded for Asl Marl section in the majority of the studied wells, while less than 0.5% is recorded for the Hawara Formation. The lateral distribution maps show that most of the TOC% enrichments are concentrated at central and eastern parts of the study area, providing a good source for the hydrocarbons encountered in the underlying Asl Sand section.
基金the Researchers Supporting Project number(RSP-2020/92),King Saud University,Riyadh,Saudi Arabia。
文摘This study focuses on the heterogeneity of the middle Miocene syn-rift Belayim nullipore(reefal)marine sequences in the Gulf of Suez and its impacts on reservoir quality.The sequences consist of coralline algal reef limestones with a highly complex dual-porosity system of primary and secondary porosities of widely varying percentages.To achieve a precise mathematical modeling of these reservoir sequences,a workflow protocol was applied to separate these sequences into a number of hydraulic flow units(HFUs)and reservoir rock types(RRTs).This has been achieved by conducting a conventional core analysis on the nullipore marine sequence.To illustrate the heterogeneity of the nullipore reservoir,the Dykstra-Parsons coefficient(V)has been estimated(V=0.91),indicating an extremely heterogeneous reservoir.A slight to high anisotropy(λ_(k))has been assigned for the studied nullipore sequences.A stratigraphic modified Lorenz plot(SMLP)was applied to define the optimum number of HFUs and barriers/baffles in each of the studied wells.Integrating the permeability-porosity,reservoir quality index-normalized porosity index(RQI-NPI)and the RQI-flow zone indicator(RQIFZI)plots,the discrete rock types(DRT)and the R35 techniques enable the discrimination of the reservoir sequences into 4 RRTs/HFUs.The RRT4 packstone samples are characterized by the best reservoir properties(moderate permeability anisotropy,with a good-to-fair reservoir quality index),whereas the RRT1 mudstone samples have the lowest flow and storage capacities,as well as the tightest reservoir quality.
