Image motion velocity field model of space camera with large field and analysis on three-axis attitude stability of satellite

2016 ◽  
Vol 24 (9) ◽  
pp. 2173-2182 ◽  
Author(s):  
路朋罗 LU Peng-luo ◽  
李永昌 LI Yong-chang ◽  
金龙旭 JIN Long-xu ◽  
李国宁 LI Guo-ning ◽  
武奕楠 WU Yi-nan ◽  
...  
Keyword(s):  
Velocity Field ◽  
Field Model ◽  
Image Motion ◽  
Large Field ◽  
Motion Velocity ◽  
Attitude Stability

Image motion velocity field of off-axis TMA space camera with large field of view

2016 ◽  
Vol 45 (5) ◽  
pp. 0513001 ◽  
Author(s):  
李永昌 Li Yongchang ◽  
金龙旭 Jin Longxu ◽  
武奕楠 Wu Yinan ◽  
王文华 Wang Wenhua ◽  
吕增明 Lv Zengming ◽  
...  
Keyword(s):  
Velocity Field ◽  
Field Of View ◽  
Image Motion ◽  
Large Field ◽  
Motion Velocity

Image Motion Velocity Field for Wide View Remote Sensing Camera and Detectors Exposure Integration Control

2013 ◽  
Vol 33 (5) ◽  
pp. 0511002 ◽  
Author(s):  
王翀 Wang Chong ◽  
尤政 You Zheng ◽  
邢飞 Xing Fei ◽  
张高飞 Zhang Gaofei
Keyword(s):  
Remote Sensing ◽  
Velocity Field ◽  
Image Motion ◽  
Motion Velocity

Image Motion Compensation of Space Camera with Large Field of View Using Earth Ellipsoid

2013 ◽  
Vol 33 (5) ◽  
pp. 0528001
Author(s):  
武星星 Wu Xingxing ◽  
刘金国 Liu Jinguo ◽  
周怀得 Zhou Huaide
Keyword(s):  
Motion Compensation ◽  
Field Of View ◽  
Image Motion ◽  
Large Field

Calculation of image motion velocity for agile satellite dynamic imaging to changed continuously attitude point

2012 ◽  
Vol 20 (12) ◽  
pp. 2812-2820
Author(s):  
黄群东 HUANG Qun-dong ◽  
杨芳 YANG Fang ◽  
赵键 ZHAO Jiang
Keyword(s):  
Dynamic Imaging ◽  
Image Motion ◽  
Motion Velocity ◽  
Agile Satellite

scholarly journals Precessional Constant Determined from Optical Astrometry

1995 ◽  
Vol 10 ◽  
pp. 199-199
Author(s):  
M. Miyamoto
Keyword(s):  
Steady State ◽  
Velocity Field ◽  
Heliocentric Distance ◽  
Field Model ◽  
Velocity Fields ◽  
Galactic Rotation ◽  
Proper Motions ◽  
Plane Parallel ◽  
Initial Trial

We have analyzed proper motions of about 30000 K-M giants chosen from the astrometrie catalogue ACRS Part 1 (Corbin and Urban 1991). To avoid localized velocity fields, these K-M giants are chosen from the heliocentric distance interval 0.5 - 1.0 kpc. Starting from the initial trial of the luni-solar precessional correction Δρ ~ —0”.3/cy, we have found that the motion of stars are well expressed in terms of the Oort constants for the K-M giants. After confirming that the K-M giants are in a steady-state, we have applied the velocity-field model of the plane-parallel galactic rotation with non-zero Oort constants. Then, we have found a rational set of the corrections to the FK5 system: Δρ = -0”.27 ± 0”.03/cy and Δe + Δλ = —0”.12 ± 0”.03/cy, as is shown in Table 1 (Miyamoto and Sóma 1993). In consequence, the FK5 system is still rotating.

Streamlining the Well Location Optimization Process - An Automated Approach Applied to a Large Onshore Carbonate Field

2021 ◽  
Author(s):  
Bruno Roussennac ◽  
Gijs van Essen ◽  
Bert-Rik de Zwart ◽  
Claus von Winterfeld ◽  
Erika Hernandez ◽  
...  
Keyword(s):  
Field Model ◽  
Optimization Algorithms ◽  
Large Field ◽  
Design Constraints ◽  
Full Field ◽  
Location Optimization ◽  
Well Location ◽  
Well Design ◽  
Infill Drilling ◽  
Multiple Scenarios

Abstract Infill drilling is a proved strategy to improve hydrocarbon recovery from reservoirs to increase production and maximize field value. Infill drilling projects address the following questions: 1) Where should the wells be drilled? 2) What should be their optimum trajectories? 3) What are the realistic ranges of incremental production of the infill wells? Answering these questions is important yet challenging as it requires the evaluation of multiple scenarios which is laborious and time intensive. This study presents an integrated workflow that allows the optimization of drilling locations using an automated approach that comprises cutting-edge optimization algorithms coupled to reservoir simulation. This workflow concurrently evaluates multiple scenarios until they are narrowed down to an optimum range according to pre-set objectives and honoring pre-established well design constraints. The simultaneous nature of the workflow makes it possible to differentiate between acceleration and real incremental recovery linked to proposed locations. In addition, the technology enables the optimization of all the elements that are relevant to the selection of drilling candidates, such as location, trajectory, inclination, and perforation interval. The well location optimization workflow was applied to a real carbonate large field; heavily faulted; with a well count of +400 active wells and subject to waterflooding. Hence the need for an automated way of finding new optimal drilling locations enabling testing of many locations. Also due to the significant full field model size; sector modelling capability was used such that the optimization, i.e. running many scenarios; could be carried out across smaller scale models within a reasonable time frame. Using powerful hardware and a fully parallelized simulation engine were also important elements in allowing the efficient evaluation of ranges of possible solutions while getting deeper insights into the field and wells responses. As a result of the study, 8 out of the original 9 well locations were moved to more optimal locations. The proposed optimized locations generate an incremental oil recovery increase of more than 70% compared to the original location (pre-optimization). In addition, the project was completed within 2 weeks of equivalent computational time which is a significant acceleration compared to a manual approach of running optimization on a full field model and it is significantly more straight forward than the conventional location selection process. The novelty of the project is introduced by customized python scripts. These scripts allow to achieve practical ways for placing the well locations to explore the solution space and at the same time, honor well design constraints, such as maximum well length, maximum step-out from the surface well-pad, and well perforation interval. Such in-built flexibility combined with automation and highly advanced optimization algorithms helped to achieve the project goals much easier and faster.

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