Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the g...Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.展开更多
The emergence of Y6-type nonfullerene acceptors has greatly enhanced the power conversion efficiency(PCE)of organic solar cells(OSCs).However,which structural feature is responsible for the excellent photovoltaic perf...The emergence of Y6-type nonfullerene acceptors has greatly enhanced the power conversion efficiency(PCE)of organic solar cells(OSCs).However,which structural feature is responsible for the excellent photovoltaic performance is still under debate.In this study,two Y6-like acceptors BDOTP-1 and BDOTP-2 were designed.Different from previous Y6-type acceptors featuring an A–D–Aʹ–D–A structure,BDOTP-1,and BDOTP-2 have no electron-deficient Aʹfragment in the core unit.Instead,there is an electron-rich dibenzodioxine fragment in the core.Although this modification leads to a marked change in the molecular dipole moment,electrostatic potential,frontier orbitals,and energy levels,BDOTP acceptors retain similar three-dimensional packing capability as Y6-type acceptors due to the similar banana-shaped molecular configuration.BDOTP acceptors show good performance in OSCs.High PCEs of up to 18.51%(certified 17.9%)are achieved.This study suggests that the banana-shaped configuration instead of the A–D–Aʹ–D–A structure is likely to be the determining factor in realizing high photovoltaic performance.展开更多
The Gouméré region is located in the North-East of Côte d’Ivoire and is located in the South-West of the Bui furrow. In order to highlight the geology of the area studied, 14 samples were taken for stu...The Gouméré region is located in the North-East of Côte d’Ivoire and is located in the South-West of the Bui furrow. In order to highlight the geology of the area studied, 14 samples were taken for studies using petrographic, geochemical and metallogenic methods. The study of macroscopic and microscopic petrography made it possible to highlight two major lithological units: 1) a volcano-plutonic unit, formed of gabbros, basalt, volcaniclastics and rhyodacite;2) a sedimentary unit (microconglomerate). From a geochemical point of view, the results obtained indicate that the plutonites are gabbro and gabbro diorite while the volcanics have compositions of basaltic andesites, rhyolite and dacites. The sediments have a litharenitic to sublitharenitic character. The metallogenic study made it possible to highlight hydrothermal alterations and metalliferous paragenesis on the formations studied. Hydrothermal alteration is characterized by the presence of carbonation, silicification, sericitization, sulfidation and to a lesser degree chloritization. Metalliferous paragenesis consists of pyrite, chalcopyrite, hematite and magnetite.展开更多
Climate change is an alarming global challenge, particularly affecting the least developed countries (LDCs) including Liberia. These countries, located in regions prone to unpredictable temperature and precipitation c...Climate change is an alarming global challenge, particularly affecting the least developed countries (LDCs) including Liberia. These countries, located in regions prone to unpredictable temperature and precipitation changes, are facing significant challenges, particularly in climate-sensitive sectors such as mining and agriculture. LDCs need more resilience to adverse climate shocks but have limited capacity for adaptation compared to other developed and developing nations. This paper examines Liberia’s susceptibility to climate change as a least developed country, focusing on its exposure, sensitivity, and adaptive capacity. It provides an overview of LDCs and outlines the global distribution of carbon dioxide emissions. The paper also evaluates specific challenges that amplify Liberia’s vulnerability and constrain sustainable adaptation, providing insight into climate change’s existing and potential effects. The paper emphasizes the urgency of addressing climate impacts on Liberia and calls for concerted local and international efforts for effective and sustainable mitigation efforts. It provides recommendations for policy decisions and calls for further research on climate change mitigation and adaptation.展开更多
基金funded by the National Natural Science Foundation of China(General Program:No.52074314,No.U19B6003-05)National Key Research and Development Program of China(2019YFA0708303-05)。
文摘Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.
基金the open research fund of the Songshan Lake Materials Laboratory(2021SLABFK02)the National Key Research and Development Program of China(2017YFA0206600)the National Natural Science Foundation of China(51922032 and 21961160720).
文摘The emergence of Y6-type nonfullerene acceptors has greatly enhanced the power conversion efficiency(PCE)of organic solar cells(OSCs).However,which structural feature is responsible for the excellent photovoltaic performance is still under debate.In this study,two Y6-like acceptors BDOTP-1 and BDOTP-2 were designed.Different from previous Y6-type acceptors featuring an A–D–Aʹ–D–A structure,BDOTP-1,and BDOTP-2 have no electron-deficient Aʹfragment in the core unit.Instead,there is an electron-rich dibenzodioxine fragment in the core.Although this modification leads to a marked change in the molecular dipole moment,electrostatic potential,frontier orbitals,and energy levels,BDOTP acceptors retain similar three-dimensional packing capability as Y6-type acceptors due to the similar banana-shaped molecular configuration.BDOTP acceptors show good performance in OSCs.High PCEs of up to 18.51%(certified 17.9%)are achieved.This study suggests that the banana-shaped configuration instead of the A–D–Aʹ–D–A structure is likely to be the determining factor in realizing high photovoltaic performance.
文摘The Gouméré region is located in the North-East of Côte d’Ivoire and is located in the South-West of the Bui furrow. In order to highlight the geology of the area studied, 14 samples were taken for studies using petrographic, geochemical and metallogenic methods. The study of macroscopic and microscopic petrography made it possible to highlight two major lithological units: 1) a volcano-plutonic unit, formed of gabbros, basalt, volcaniclastics and rhyodacite;2) a sedimentary unit (microconglomerate). From a geochemical point of view, the results obtained indicate that the plutonites are gabbro and gabbro diorite while the volcanics have compositions of basaltic andesites, rhyolite and dacites. The sediments have a litharenitic to sublitharenitic character. The metallogenic study made it possible to highlight hydrothermal alterations and metalliferous paragenesis on the formations studied. Hydrothermal alteration is characterized by the presence of carbonation, silicification, sericitization, sulfidation and to a lesser degree chloritization. Metalliferous paragenesis consists of pyrite, chalcopyrite, hematite and magnetite.
文摘Climate change is an alarming global challenge, particularly affecting the least developed countries (LDCs) including Liberia. These countries, located in regions prone to unpredictable temperature and precipitation changes, are facing significant challenges, particularly in climate-sensitive sectors such as mining and agriculture. LDCs need more resilience to adverse climate shocks but have limited capacity for adaptation compared to other developed and developing nations. This paper examines Liberia’s susceptibility to climate change as a least developed country, focusing on its exposure, sensitivity, and adaptive capacity. It provides an overview of LDCs and outlines the global distribution of carbon dioxide emissions. The paper also evaluates specific challenges that amplify Liberia’s vulnerability and constrain sustainable adaptation, providing insight into climate change’s existing and potential effects. The paper emphasizes the urgency of addressing climate impacts on Liberia and calls for concerted local and international efforts for effective and sustainable mitigation efforts. It provides recommendations for policy decisions and calls for further research on climate change mitigation and adaptation.
