The history and results of petroleum exploration in the Santos Basin, Brazil are reviewed. The regularity of hydrocarbon enrichment and the key exploration technologies are summarized and analyzed using the seismic, g...The history and results of petroleum exploration in the Santos Basin, Brazil are reviewed. The regularity of hydrocarbon enrichment and the key exploration technologies are summarized and analyzed using the seismic, gravity, magnetic and drilling data. It is proposed that the Santos Basin had a structural pattern of two uplifts and three depressions and the Aram-Uirapuru uplift belt controlled the hydrocarbon accumulation. It is believed that the main hydrocarbon source kitchen in the rift period controlled the hydrocarbon-enriched zones, paleo-structures controlled the scale and quality of lacustrine carbonate reservoirs, and continuous thick salt rocks controlled the hydrocarbon formation and preservation. The process and mechanism of reservoirs being transformed by CO_(2)charging were revealed. Five key exploration technologies were developed,including the variable-velocity mapping for layer-controlled facies-controlled pre-salt structures, the prediction of lacustrine carbonate reservoirs, the prediction of intrusive/effusive rock distribution, the detection of hydrocarbons in lacustrine carbonates, and the logging identification of supercritical CO_(2)fluid. These theoretical recognitions and exploration technologies have contributed to the discovery of deep-water super-large reservoirs under CNODC projects in Brazil, and will guide the further exploration of deep-water large reservoirs in the Santos Basin and other similar regions.展开更多
Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and dependencies.This research uses deep learning,convolutional neural net...Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and dependencies.This research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack detection.Twelve deep-learning models are trained on 192 crack images.This research aims to provide up-to-date detecting techniques to solve dam crack problems.The finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%accuracy.The study’s pre-trained designs help to identify and to determine the specific locations of cracks.展开更多
A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including ...A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including 3 brace damage cases and 2 joint damage cases,were simulated by removing braces and weakening beam鈥揷olumn connections in the structure. The limited acceleration response data generated by hammer impact were used for system identification,and modal parameters were extracted by using the eigensystem realization algorithm. In the first stage,the possible damaged locations are determined by using the damage index and the characteristics of the analytical model itself,and the extent of damage for those substructures identified at stage I is estimated in the second stage by using a second-order eigen-sensitivity approximation method. The main contribution of this paper is to test the two-stage method by using the real dynamic data of a complicated spatial model structure with limited sensors. The analysis results indicate that the two-stage approach is ableto detect the location of both damage cases,only the severity of brace damage cases can be assessed,and the reasonable analytical model is critical for successful damage detection.展开更多
An approach is described that has been developed for auxiliary monitoring of technical condition of hydropower plant dams. It is based on analysis of changes in dynamic characteristics of dams obtained by an automated...An approach is described that has been developed for auxiliary monitoring of technical condition of hydropower plant dams. It is based on analysis of changes in dynamic characteristics of dams obtained by an automated monitoring and earthquake registration system that records microseismic vibrations of structures. The configuration of the system as well as the results of seismometric monitoring of the dam of Krasnoyarsk hydroelectric power plant are described. To study behavior of the dam under normal and extreme loads it was proposed to develop a model of the dam with the use of the finite element method.展开更多
基金Supported by the CNPC Basic and Prospective Key Scientific and Technological Project (2021DJ24)。
文摘The history and results of petroleum exploration in the Santos Basin, Brazil are reviewed. The regularity of hydrocarbon enrichment and the key exploration technologies are summarized and analyzed using the seismic, gravity, magnetic and drilling data. It is proposed that the Santos Basin had a structural pattern of two uplifts and three depressions and the Aram-Uirapuru uplift belt controlled the hydrocarbon accumulation. It is believed that the main hydrocarbon source kitchen in the rift period controlled the hydrocarbon-enriched zones, paleo-structures controlled the scale and quality of lacustrine carbonate reservoirs, and continuous thick salt rocks controlled the hydrocarbon formation and preservation. The process and mechanism of reservoirs being transformed by CO_(2)charging were revealed. Five key exploration technologies were developed,including the variable-velocity mapping for layer-controlled facies-controlled pre-salt structures, the prediction of lacustrine carbonate reservoirs, the prediction of intrusive/effusive rock distribution, the detection of hydrocarbons in lacustrine carbonates, and the logging identification of supercritical CO_(2)fluid. These theoretical recognitions and exploration technologies have contributed to the discovery of deep-water super-large reservoirs under CNODC projects in Brazil, and will guide the further exploration of deep-water large reservoirs in the Santos Basin and other similar regions.
基金supported by the National Natural Science Foundation of China(Grant Nos.61972136,41874148,and 42174178)the Natural Science and Foundation of Hubei Province(No.2020CFB497)+4 种基金the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(Nos.T201410 and T2020017)the Natural Science Foundation of Education Department of Hubei Province(No.B2020149)the Science and Technology Research Project of the Education Department of Hubei Province(No.Q20222704)Natural Science Foundation of Xiaogan City(Nos.XGKJ2022010095 and XGKJ2022010094)The funding is a foreign expert project of Henan Province(No.HNGD2023027).
文摘Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and dependencies.This research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack detection.Twelve deep-learning models are trained on 192 crack images.This research aims to provide up-to-date detecting techniques to solve dam crack problems.The finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%accuracy.The study’s pre-trained designs help to identify and to determine the specific locations of cracks.
基金supported by the National Natural Science Foundation of China (90815025, 90715032 and 50808013)
文摘A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including 3 brace damage cases and 2 joint damage cases,were simulated by removing braces and weakening beam鈥揷olumn connections in the structure. The limited acceleration response data generated by hammer impact were used for system identification,and modal parameters were extracted by using the eigensystem realization algorithm. In the first stage,the possible damaged locations are determined by using the damage index and the characteristics of the analytical model itself,and the extent of damage for those substructures identified at stage I is estimated in the second stage by using a second-order eigen-sensitivity approximation method. The main contribution of this paper is to test the two-stage method by using the real dynamic data of a complicated spatial model structure with limited sensors. The analysis results indicate that the two-stage approach is ableto detect the location of both damage cases,only the severity of brace damage cases can be assessed,and the reasonable analytical model is critical for successful damage detection.
文摘An approach is described that has been developed for auxiliary monitoring of technical condition of hydropower plant dams. It is based on analysis of changes in dynamic characteristics of dams obtained by an automated monitoring and earthquake registration system that records microseismic vibrations of structures. The configuration of the system as well as the results of seismometric monitoring of the dam of Krasnoyarsk hydroelectric power plant are described. To study behavior of the dam under normal and extreme loads it was proposed to develop a model of the dam with the use of the finite element method.
