Improvement and application of ultrasonic cement bond quality evaluation method

2016 ◽  
Author(s):  
Hou Zhenyong* ◽  
Hao Xiaoliang* ◽  
Ma Huanying* ◽  
Li Jiling* ◽  
Niu Peng* ◽  
...  
Keyword(s):  
Quality Evaluation ◽  
Evaluation Method ◽  
Bond Quality ◽  
Cement Bond Quality

scholarly journals Cement bond quality evaluation based on acoustic variable density logging

2016 ◽  
Vol 43 (3) ◽  
pp. 514-521 ◽  
Author(s):  
Jun TANG ◽  
Chengguang ZHANG ◽  
Bixing ZHANG ◽  
Fangfang SHI
Keyword(s):  
Quality Evaluation ◽  
Variable Density ◽  
Bond Quality ◽  
Cement Bond Quality

Objective quality evaluation method of stereo image based on steerable pyramid

2013 ◽  
Vol 32 (3) ◽  
pp. 710-714
Author(s):  
Jin-jin WEI ◽  
Su-mei LI ◽  
Wen-juan LIU ◽  
Yan-jun ZANG
Keyword(s):  
Quality Evaluation ◽  
Evaluation Method ◽  
Stereo Image ◽  
Objective Quality ◽  
Steerable Pyramid

Multi-exposure X-ray image fusion quality evaluation based on CSF and gradient amplitude similarity

2021 ◽  
pp. 1-13
Author(s):  
Yanjie Qi ◽  
Zehui Yang ◽  
Lin Kang
Keyword(s):  
Image Fusion ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Dynamic Range ◽  
Reference Image ◽  
Subjective Perception ◽  
Steel Component ◽  
Imaging Device ◽  
X Ray ◽  
Gradient Amplitude

Due to the limitation of dynamic range of the imaging device, the fixed-voltage X-ray images often produce overexposed or underexposed regions. Some structure information of the composite steel component is lost. This problem can be solved by fusing the multi-exposure X-ray images taken by using different voltages in order to produce images with more detailed structures or information. Due to the lack of research on multi-exposure X-ray image fusion technology, there is no evaluation method specially for multi-exposure X-ray image fusion. For the multi-exposure X-ray fusion images obtained by different fusion algorithms may have problems such as the detail loss and structure disorder. To address these problems, this study proposes a new multi-exposure X-ray image fusion quality evaluation method based on contrast sensitivity function (CSF) and gradient amplitude similarity. First, with the idea of information fusion, multiple reference images are fused into a new reference image. Next, the gradient amplitude similarity between the new reference image and the test image is calculated. Then, the whole evaluation value can be obtained by weighting CSF. In the experiments of MEF Database, the SROCC of the proposed algorithm is about 0.8914, and the PLCC is about 0.9287, which shows that the proposed algorithm is more consistent with subjective perception in MEF Database. Thus, this study demonstrates a new objective evaluation method, which generates the results that are consistent with the subjective feelings of human eyes.

Cement Conundrum: Valuable Lessons Learned for Sustaining Production

2021 ◽  
Author(s):  
Siti Najmi Farhan binti Zulkipli
Keyword(s):  
New Technology ◽  
Processing Parameters ◽  
Lessons Learned ◽  
Critical Element ◽  
Well Performance ◽  
Bond Quality ◽  
Quality Issue ◽  
Cement Integrity ◽  
Production Period ◽  
Cement Bond Quality

Abstract Addressing wellbore integrity through cement evaluation has been an evergreen topic which frequently catches major operators by surprise due to premature water or gas breakthrough causing low production attainability from the wells. Managing idle well strings arising from integrity issues is also a challenge throughout the production period. The remedial solutions to these issues do not come conveniently and require high cost during late life well intervention which often erodes the well economic limit. A critical element of wellbore barrier which is cement integrity evaluation is proposed to be uplifted and given a new perspective to define success criteria for producer wells to achieve certain reserves addition and production recovery. This paper will highlight integrated factors affecting cement bond quality, impact to well production, potential remedies for poor cement bond observed leveraging on the enhanced workflow and new technology and way forward to proactively prevent the unwanted circumstances in the first opportunity taken. A set of recommendations and prioritization criteria for future cement improvement will be also highlighted. Several case specific wells logged with variable cement bond evaluation tools are re-assessed and deep-dived to trace the root causes for unsatisfactory cement bond quality observed which include reservoir characteristics, understanding anomalies during drilling and cementing operation, identifying cement recipe used, log processing parameters applied and observing best practices during cementing operation to improve the quality. New and emerging cement evaluation technology inclusive of radioactive-based logging to meet specific well objectives will be also briefly discussed in terms of differences and technical deliverables. Looking at each spectrum, results show that there are several interdependent factors contributing to poor cement bond quality observed. Accurate understanding of formation behavior, designing fit-for-purpose cement recipe and adequate planning for cementing operation on well-by-well basis are among the top- notch approaches to be applied for an acceptable cement bond quality and placement. Statistics show that 27% to 64% of production attainability is achieved by wells with good cement quality within the first 3 months of production and this increases to 85% to 98% up until 7 months of production period, while only 12% production attainability achieved for those wells with adverse cement quality issue. In another well, water cut as high as 47% since the first day of production is observed which keeps increasing up to 40% thereafter. In a nutshell, cement evaluation exercise shall not be treated as vacuum, instead it requires an integrated foundation and close collaboration to materialize the desired outcomes. Arresting the issue with the right approach in the first place will be the enabler for optimum well performance and productivity to exceed the recovery target.

Quantitative image quality evaluation method for UDC (Under Display Camera)

2021 ◽  
Keyword(s):  
Image Quality ◽  
International Symposium ◽  
System Performance ◽  
Quality Evaluation ◽  
Fast Track ◽  
Evaluation Method ◽  
Image Quality Evaluation ◽  
Electronic Imaging ◽  
Quantitative Image

Fast track article for IS&T International Symposium on Electronic Imaging 2021: Image Quality and System Performance XVIII proceedings.

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Data Quality ◽  
Health Monitoring ◽  
Structural Damage ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Statistical Characteristics ◽  
Structural Health

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>

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