Putting a Value Tag on Well Data Management When Designing a Plug & Abandonment Operation

2020 ◽  
Author(s):  
Livio Caramanico ◽  
Eric Déliac ◽  
Ali Mirza ◽  
Paolo D'Alesio
Keyword(s):  
A Value ◽  
2004 ◽  
Vol 1 (2) ◽  
pp. 129-131 ◽  
Author(s):  
Ning Li ◽  
Mingchao Wang ◽  
Jian Cui ◽  
Jianqiang Wang ◽  
Caizhi Wang

2017 ◽  
Vol 1 (3) ◽  
pp. 145
Author(s):  
Aniq Noviciatie Ulfah ◽  
Wing Wahyu Winarno

Data center was developed related to data security as one of the assets of the organization in addressing the data management for operational purposes as secondary storage media and data distribution. Safety management is part of the framework of the data center that should be assessed by the manager to determine whether compliance with the standards so as to minimize the likelihood of the risk of adverse effects on the organization. This prompted the University XYZ to evaluate the safety management to determine the extent of the implementation of safety management in the data center in their environtment. In evaluating the safety management of the data center in the University XYZ is using the standard ISO 22301: 2012. ISO 22301 is a standard that specifically to plan, establish, implement, operate, monitor, review, maintain and improve a documented management system to protect or reduce the possibility of the risk, be on the alert, handle and recover the time of the incident. The sources of data was obtained from 9 respondents who are heads / staff from each division in the data center University XYZ. The data that have been obtained will be used to measure the maturity level of each clause of the ISO 22301: 2012 and as an evaluation tools. The results obtained in this study indicate that the University XYZ has been implementing safety management in the data center with a value for each clause 5, 6, 7, 8, and 9 are sequential ie 2:42, 2:41, 1:21, 1.67, and 1.65.


2020 ◽  
Vol 54 ◽  
pp. 102174 ◽  
Author(s):  
Konstantin L. Wilms ◽  
Stefan Stieglitz ◽  
Björn Ross ◽  
Christian Meske

Web Services ◽  
2019 ◽  
pp. 459-472
Author(s):  
Himyar Ali Al Jabri ◽  
Ali H. Al-Badi ◽  
Oualid Ali

Big Data has recently become a very hot topic in the field of Information Technology and Data Management. Data generated by the company's daily operations through different resources such as social media, etc. is very important because it can bring a value that will lead to a competitive advantage. The objectives of this research are to: 1) Explore the analytical tools used to manipulate Big Data in Omani telecom industry, 2) Present the benefits of using these tools, the extent of use, and the features specifically promoted these tools, and 3) Highlight the challenges/obstacles that the telecom industry in Oman facing in adopting/using Big Data analytical tools. To achieve the research objectives two case studies were conducted among the main telecom operators in Oman. This research concluded that both studied telecom operators in Oman are not ready for the DBAs. Both operators need to invest in developing the capabilities that enable them to use these tools. Once that is satisfied, then other components like the infrastructure, tools, and data can be managed very well.


1990 ◽  
Vol 1 (2) ◽  
pp. 219-225
Author(s):  
Norio MATSUMOTO ◽  
Yoshio WATANABE

2020 ◽  
Vol 17 (1) ◽  
pp. 50-57
Author(s):  
Darno Darno ◽  
Riska Anggraeni

Prove that there is a relationship between cash flow and production capacity at PT. Vista Partners. The sample used in this study is financial data and production data of PT. Vista Partners. This study uses a quantitative approach with the aim to provide an overview of how PT Vista Mitra manages their cash flow and how it affects the production process. According to the data that I process, cash flow at PT Vista Mitra shows good performance because PT Vista Mitra is able to pay the company's obligations well. Data collection is carried out by the author to obtain information on cash flow processing activities carried out in three ways, namely (a) documentation, (b) observation, and (c) interview. Analysis Techniques used in this study use (a) the classic assumption test, (b) t test, and (c) f test with the help of SPSS software. The hypothesis I use is accepted if significant t is less than α = 0.05. The results of this study prove that production capacity. Payment of Receivables (X₁) has a t value of 1,216 with a significance probability value of 0.236. Significant t is greater than α = 0.05. Means that payment of receivables (X₁) production capacity. Product Sales Results (X₂) has a value of t-0.276 with a significance value of 0.785. Significant α = 0.05. Means that product sales (X₂) have no significant effect on production capacity. Payment of debt (X₃) has a value of t 2,562 with a significance value of 0.017. Significant t is smaller than α = 0.05. This means that debt repayment (X₃) has a production capacity. The cost of raw materials (X₄) has a t value of 1.193 with a significance probability value of 0.244. Significant t is greater than α = 0.05. Means the cost of raw materials (X₄). Labor costs (X₅) has a t value of 1,274 with a significance probability value of 0.215. Significant t is greater than α = 0.05. Means that labor costs (X₅) have no significant effect on production capacity. Other costs (X6) have a value of t 1.091 with a significance probability value of 0.286. Significant t is greater than α = 0.05. Means other costs (X₅) have no significant effect on production capacity. 


2017 ◽  
Vol 30 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Himyar Ali Al Jabri ◽  
Ali H. Al-Badi ◽  
Oualid Ali

Big Data has recently become a very hot topic in the field of Information Technology and Data Management. Data generated by the company's daily operations through different resources such as social media, etc. is very important because it can bring a value that will lead to a competitive advantage. The objectives of this research are to: 1) Explore the analytical tools used to manipulate Big Data in Omani telecom industry, 2) Present the benefits of using these tools, the extent of use, and the features specifically promoted these tools, and 3) Highlight the challenges/obstacles that the telecom industry in Oman facing in adopting/using Big Data analytical tools. To achieve the research objectives two case studies were conducted among the main telecom operators in Oman. This research concluded that both studied telecom operators in Oman are not ready for the DBAs. Both operators need to invest in developing the capabilities that enable them to use these tools. Once that is satisfied, then other components like the infrastructure, tools, and data can be managed very well.


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