This paper explores how dissolution and precipitation reactions are coupled in batch reactor experimental systems at elevated temperatures. This is the fifth paper in our series of ‘‘Coupled Alkali Feldspar Dissolut...This paper explores how dissolution and precipitation reactions are coupled in batch reactor experimental systems at elevated temperatures. This is the fifth paper in our series of ‘‘Coupled Alkali Feldspar Dissolution and Secondary Mineral Precipitation in Batch Systems.'' In the previous four papers we presented batch experiments of alkali-feldspar hydrolysis and explored the coupling of dissolution and precipitation reactions(Fu et al. in Chem Geol91:955–964, 2009; Zhu and Lu in Geochim Cosmochim Acta 73:3171–3200, 2009; Zhu et al.in Geochim Cosmochim Acta 74:3963–3983, 2010; Lu et al. in Appl Geochem30:75–90, 2013). Here, we present the results of additionalK-rich feldspar hydrolysis experiments at 150 °C. Our solution chemistry measurements have constrained feldspar dissolution rates, and our high resolution transmission electron microscopy work has identified boehmite precipitation. Reaction path modeling of K-feldspar dissolution and boehmite precipitation simulated the coupled reactions, but only with forced changes of boehmite rate law in the middle of experimental duration. The results which are reported in this article lend further support to our hypothesis that slow secondary mineral precipitation explains part of the wellknown apparent discrepancy between lab measured and field estimated feldspar dissolution rates(Zhu et al. in Water–rock interaction, 2004).展开更多
Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources.Channels are among the most important geological features interpreters analyze to loca...Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources.Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs.However,manual channel picking is both time consuming and tedious.Moreover,similar to any other process dependent on human intervention,manual channel picking is error prone and inconsistent.To address these issues,automatic channel detection is both necessary and important for efficient and accurate seismic interpretation.Modern systems make use of real-time image processing techniques for different tasks.Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies.In this paper,we propose an innovative automatic channel detection algorithm based on machine learning techniques.The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process.The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches.We provide a field data example to demonstrate the performance of the new algorithm.The training phase gave a maximum accuracy of 84.6%for the classifier and it performed even better in the testing phase,giving a maximum accuracy of 90%.展开更多
基金grant from the State Key Laboratory of Ore Deposits at the Institute of Geochemistry, Chinese Academy of Sciences
文摘This paper explores how dissolution and precipitation reactions are coupled in batch reactor experimental systems at elevated temperatures. This is the fifth paper in our series of ‘‘Coupled Alkali Feldspar Dissolution and Secondary Mineral Precipitation in Batch Systems.'' In the previous four papers we presented batch experiments of alkali-feldspar hydrolysis and explored the coupling of dissolution and precipitation reactions(Fu et al. in Chem Geol91:955–964, 2009; Zhu and Lu in Geochim Cosmochim Acta 73:3171–3200, 2009; Zhu et al.in Geochim Cosmochim Acta 74:3963–3983, 2010; Lu et al. in Appl Geochem30:75–90, 2013). Here, we present the results of additionalK-rich feldspar hydrolysis experiments at 150 °C. Our solution chemistry measurements have constrained feldspar dissolution rates, and our high resolution transmission electron microscopy work has identified boehmite precipitation. Reaction path modeling of K-feldspar dissolution and boehmite precipitation simulated the coupled reactions, but only with forced changes of boehmite rate law in the middle of experimental duration. The results which are reported in this article lend further support to our hypothesis that slow secondary mineral precipitation explains part of the wellknown apparent discrepancy between lab measured and field estimated feldspar dissolution rates(Zhu et al. in Water–rock interaction, 2004).
文摘Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources.Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs.However,manual channel picking is both time consuming and tedious.Moreover,similar to any other process dependent on human intervention,manual channel picking is error prone and inconsistent.To address these issues,automatic channel detection is both necessary and important for efficient and accurate seismic interpretation.Modern systems make use of real-time image processing techniques for different tasks.Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies.In this paper,we propose an innovative automatic channel detection algorithm based on machine learning techniques.The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process.The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches.We provide a field data example to demonstrate the performance of the new algorithm.The training phase gave a maximum accuracy of 84.6%for the classifier and it performed even better in the testing phase,giving a maximum accuracy of 90%.
