Removal of dynamic weather conditions based on variable time window

Abstract Well test is one of the crucial steps required to forecast production investments of their fields. However, the operators face many challenges such as reduced capex, exploration budgets, and bad weather conditions that limit the well testing time window. To overcome these challenges, an automated well testing platform enabled a real time monitoring and controlling more zones in a single run for appraisal wells in the Sea of Okhotsk, Russia. This article highlights the test objectives, the job planning, and automated execution of wirelessly enabled operations in very hostile conditions and limited time period. The use of a telemetry system to well test seven zones allowed real-time data acquisition, control of critical downhole equipment, data transmission to the operator's office in town. Various operational cases will be discussed to demonstrate how automated data acquisition and downhole operations control has optimized operations for both the service company and the operator.

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Bringing Flexibility and Automation to Well Testing Operations Through Wireless Telemetry - Case Study

10.4043/31275-ms ◽

2021 ◽

Author(s):

Elias Temer ◽

Deiveindran Subramaniam ◽

Yermek Kaipov ◽

Carlos Merino ◽

Vladimirovich Latvin ◽

...

Keyword(s):

Real Time ◽

Reservoir Characterization ◽

Time Window ◽

Weather Conditions ◽

Development Planning ◽

Testing Time ◽

Well Testing ◽

Well Test ◽

Real Time Control ◽

Field Development

Abstract Dynamic reservoir data are a key driver for operators to meet the forecasted production investments of their fields. However, many challenges during well testing, such as reduced exploration and capex budgets, complex geologic structures, and inclement weather conditions that reduce the well testing time window can prevent them from gathering critical reservoir characterization data needed to make more informed field development planning decisions. To overcome these challenges, a live, downhole reservoir testing platform enabled the most representative reservoir information in real time and connected more zones of interest in a single run for appraisal wells in the Sea of Okhotsk, Russia. This paper describes the test requirements, the prejob planning, and automated execution of wirelessly enabled operations that led to the successful completion of the well test campaign in very hostile conditions, a remote area, and restricted period. The use of a telemetry system to well testing in seven zones enabled real-time control of critical downhole equipment and acquired data at surface, which in turn was transmitted to the operator's office in town in real time. Various operation examples will be discussed to demonstrate how automated data acquisition and downhole operations control has been used to optimize operations by both the service company and the operator.

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Real-time capacity requirement planning for make-to-order manufacturing with variable time-window orders

Computers & Industrial Engineering ◽

10.1016/j.cie.2012.11.003 ◽

2013 ◽

Vol 64 (2) ◽

pp. 641-652 ◽

Cited By ~ 7

Author(s):

Yi-Feng Hung ◽

Chuan-Che Huang ◽

Ying Yeh

Keyword(s):

Real Time ◽

Time Window ◽

Variable Time ◽

Make To Order

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Fixed versus variable time window warehousing strategies in real time

Progress in Artificial Intelligence ◽

10.1007/s13748-020-00215-1 ◽

2020 ◽

Vol 9 (4) ◽

pp. 315-324

Author(s):

Sergio Gil-Borrás ◽

Eduardo G. Pardo ◽

Antonio Alonso-Ayuso ◽

Abraham Duarte

Keyword(s):

Real Time ◽

Time Window ◽

Variable Time

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Classifying compound coastal storm and heavy rainfall events in the north-western Spanish Mediterranean

10.5194/hess-2020-560 ◽

2020 ◽

Author(s):

Marc Sanuy ◽

Tomeu Rigo ◽

José A. Jiménez ◽

M. Carmen Llasat

Keyword(s):

Extreme Events ◽

Heavy Rainfall ◽

Time Window ◽

Weather Conditions ◽

Flash Floods ◽

Coastal Storms ◽

The North ◽

Catalan Coast ◽

Nw Mediterranean ◽

The Impact

Abstract. The Northwest (NW) Mediterranean coastal zone is a populous and well-developed area in which the impact of natural hazards like flash floods and coastal storms can result in frequent and significant damages. Although the occurrence and impacts of such hazards have been widely covered, few studies have considered their combined impact on the region, which would result in more damage. Within this context, this study analyses the occurrence and characteristics of compound extreme events of heavy rainfall episodes (as a proxy for flash floods) and coastal storms (using the maximum significant wave height) along the Catalan coast as a paradigm of the NW Mediterranean. Two different types of events are considered: multivariate, in which the two hazards occur at the same location, and spatially compounding, in which they occur within the same limited time window and their impacts accumulate at distinct and separate locations. The analysis is regionally performed along a coastline extension of about 600 km by considering seven coastal sectors and their corresponding river catchment basins. Once the compound events are analysed, the synoptic atmospheric pressure fields are analysed to determine the prevailing weather conditions that generated them. Finally, a Bayesian network is used to fully characterise these events over the territory. The obtained results show that the NW Mediterranean, represented by the Catalan coast, has a high probability of experiencing compound extreme events (3.4 events per year). Despite the relatively small size of the study area (600 km of coastline), there are significant variations in the event characteristics along the territory, with the most frequent type being spatially compound, except in the northernmost sectors where multivariate events dominate. These northern sectors also present the highest correlation in the intensity of both hazards. Three representative synoptic situations have been identified as dominant for the occurrence of these events, with different relative importance levels of the compounding drivers (rainfall and waves) and different distributions of impacts across coastal basins. Overall, the results indicate that heavy rainfall has the more significant damage impact despite the wave damage having a larger spatial reach.

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Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

Complexity ◽

10.1155/2021/2889115 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Chun Liu ◽

Jian Li

Keyword(s):

Time Window ◽

Time Windows ◽

Target Object ◽

Variable Time ◽

Ship Detection ◽

Detection Model ◽

Maritime Surveillance ◽

Model Training ◽

Direction Judgment ◽

Deep Learning Model

Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. YOLOv3 pretraining model is used for model training with sample images for ship detection. The ship detection model is built by adjusting and optimizing parameters. Combining the target HSV color histogram features and LBP local features’ target, object recognition and selection are realized by using the deep learning model due to its efficiency in extracting object characteristics. Since tracking targets are subject to drift and jitter, a self-correction network that composites both direction judgment based on regression and target counting method with variable time windows is designed, which better realizes automatic detection, tracking, and self-correction of moving object numbers in water. The method in this paper shows stability and robustness, applicable to the automatic analysis of waterway videos and statistics extraction.

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Research and application of waveform clustering with variable time window in dolomite distribution prediction

10.1190/segam2021-3594700.1 ◽

2021 ◽

Author(s):

Yunfeng Huang ◽

Zhanlong Yang ◽

Wang YanFang ◽

Xu YunZe ◽

Lei Lv

Keyword(s):

Time Window ◽

Variable Time

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Exploring the relation between aerosol optical depth and PM<sub>2.5</sub> at Cabauw, the Netherlands

Atmospheric Chemistry and Physics ◽

10.5194/acp-9-909-2009 ◽

2009 ◽

Vol 9 (3) ◽

pp. 909-925 ◽

Cited By ~ 161

Author(s):

M. Schaap ◽

A. Apituley ◽

R. M. A. Timmermans ◽

R. B. A. Koelemeijer ◽

G. de Leeuw

Keyword(s):

The Netherlands ◽

Time Window ◽

Screening Method ◽

Ground Level ◽

Weather Conditions ◽

Modis Aod ◽

Lidar Measurements ◽

Mixed Layer Height ◽

The Relationship ◽

Cloud Screening

Abstract. Estimates of PM2.5 distributions based on satellite data depend critically on an established relation between AOD and ground level PM2.5. In this study we performed an experiment at Cabauw to establish a relation between AOD and PM2.5 for the Netherlands. A first inspection of the AERONET L1.5 AOD and PM2.5 data showed a low correlation between the two properties. The AERONET L1.5 showed relatively many observations of high AOD values paired to low PM2.5 values, which hinted cloud contamination. Various methods were used to detect cloud contamination in the AERONET data to substantiate this hypothesis. A cloud screening method based on backscatter LIDAR observations was chosen to detect cloud contaminated observations in the AERONET L1.5 AOD. A later evaluation of AERONET L2.0 showed that the most data that are excluded in the update from L1.5 to L2.0 were also excluded by our cloud screening, which provides confidence in both our cloud-screening method as well as the final screening in the AERONET procedure. The use of LIDAR measurements in conjunction with the CIMEL AOD data is regarded highly beneficial. Contra-intuitively, the AOD to PM2.5 relationship was shown to be insensitive to inclusion of the mixed layer height. The robustness of the relation improves dependent on the time window during the day towards noon. The final relation found for Cabauw is PM2.5=124.5×AOD−0.34 and is valid for fair weather conditions. The relationship found between bias corrected MODIS AOD and PM2.5 at Cabauw is very similar to the analysis based on the much larger dataset from ground based data only. We applied the relationship to a MODIS composite map to assess the PM2.5 distribution over the Netherlands for the first time. The verification of the derived map is difficult because ground level artefact free PM2.5 data are lacking. The validity and utility of our proposed mapping methodology should be further investigated.

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