Using a Business Intelligence Tool for Drilling Data Analysis in the Permian Basin

2018 ◽  
Author(s):  
Zhenyu Chen ◽  
Allen Lo ◽  
Maria Neves Carrasquilla ◽  
Zhiguo Zhao ◽  
Tanveer Shahid
Keyword(s):  
Data Analysis ◽  
Business Intelligence ◽  
Permian Basin ◽  
Drilling Data

Expand Excellence: Business Intelligence Tools for Drilling Data Analysis as Enhanced Efficiency Approach

2021 ◽  
Author(s):  
Farit Rakhmangulov ◽  
Andrey Garipov ◽  
Mikhail Chertenkov
Keyword(s):  
Programming Languages ◽  
Business Intelligence ◽  
Data Model ◽  
Information Needs ◽  
Horizontal Section ◽  
Report Generation ◽  
Suggested Approach ◽  
Drilling Data ◽  
Main Driver ◽  
Key Issues

Abstract The business intelligence tools allow you to work with databases containing historical drilling records. This gives a possibility to collect data into a single visualized shell, to see patterns, and, most importantly, to identify the main issues. A database among Yamal region projects was taken as an example. This process automation allows you to reduce the time for collecting information and makes it possible to conduct a more detailed and thoughtful analysis of project indicators. In turn, this results in making quick and effective decisions. At the initial stage, historical drilling data over three years was analyzed, namely information on more than 800 drilling runs. A model was formed from various sources in reliance on the database, and a visual working dashboard of the project was compiled. The dashboard allowed us to recognize the main issues that were plain to see. Awareness of plain-to-see issues gives the possibility to find out what information needs to be added to the data model in order to perform more thoughtful analysis. Collecting all the data in one place is not enough. In order to bring the data together, they were collated and visualized in the most explicit way. The bulk data collected in a single model allowed us to see the whole picture of what was happening on the project and to concentrate on solving the root problem. Major issues resulted in equipment failures due to vibrations in the horizontal section. A more detailed vibration analysis showed that the bit was the main driver of vibrations. Hence, the data on rock strength and bottom hole assembly (BHA) vibrations were added to the model for further analysis. However, the complete package of data does not guarantee success. It becomes problematic to see the patterns in some situations, since analogous wells are not always available. While business intelligence tools make it easy to manage filters and find analogy, that is only fair if you have enough offset data. Based on the analysis, the most efficient bit designs were selected and proposed. The updated range of bits allowed to increase ROP in the horizontal section and reduced failures due to vibrations, so the well construction cycle was shortened. These tools are accessible for technicians, which means there is no need-to-know programming languages. Suggested approach allows to accelerate decision-making and identification of key issues. In addition, existing tools give the possibility to monitor key performance indicators continuously within a single project as well as throughout the country. In contrast to the well-known program with tables, the main advantage is the automatic update of the already built data model. This fact speeds up the analytical process and report generation by times.

Multi Well Analysis Data Processing for Event Analysis and Mitigation

2021 ◽  
Author(s):  
Vallet Laurent ◽  
Gutarov Pavel ◽  
Chevallier Bertrand ◽  
Converset Julien ◽  
Paterson Graeme ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
New Technologies ◽  
Early Stage ◽  
Mitigation Measures ◽  
Construction Process ◽  
Field Development ◽  
Drilling Data ◽  
Lost Time ◽  
Productive Time

Abstract In the current economic environment, delivering wells on time and on budget is paramount. Well construction is a significant cost of any field development and it is more important than ever to minimize these costs and to avoid unnecessary lost time and non-productive time. Invisible lost time and non-productive time can represent as much as 40% of the cost of well construction and can lead to more severe issues such as delaying first oil, losing the well or environmental impact. There has been much work developing systems to optimize well construction, but the industry still fails to routinely detect and avoid problematic events such as stuck pipe, kicks, losses and washouts. Standardizing drilling practice can help also to improve the efficiency, this practice has shown a 30% cost reduction through repetitive and systematic practices, automation becomes the key process to realize it and Machine Learning introduced by new technologies is the key to achieve it. Drilling data analysis is key to understanding reasons for bad performances and detecting at an early stage potential downhole events. It can be done efficiently to provide to the user tools to look at the well construction process in its whole instead of looking at the last few hours as it is done at the rig site. In order to analyze the drilling data, it is necessary to have access to reliable data in Real-Time to compare with a data model considering the context (BHA, fluids, well geometry). Well planning, including multi-well offset analysis of risks, drilling processes and geology enables a user to look at the full well construction process and define levels of automation. This paper applies machine learning to a post multi-well analysis of a deepwater field development known for its drilling challenges. Minimizing the human input through automation allowed us to compare offset wells and to define the root cause for non-productive time. In our case study an increase of the pressure while drilling should have led to immediate mitigation measures to avoid a wiper trip. This paper presents techniques used to systematize surface data analysis and a workflow to identify at an early stage a near pack off which was spotted in an automatic way. The application of this process during operations could have achieved a 10%-time reduction of the section 12 ¼’’.

scholarly journals Evaluation of application of new decision-making methods in selected companies: the use of business intelligence in practice

ACC Journal ◽  
2020 ◽  
Vol 26 (2) ◽  
pp. 29-40
Author(s):  
Petra Kašparová
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Business Intelligence ◽  
Questionnaire Survey ◽  
Human Life ◽  
Utilization Rate ◽  
Decision Making Process ◽  
Short Introduction ◽  
Business Decision

Growing pressure on increasing decision-making speed in all spheres of human life is one of the basic phenomena of today. Immediately after the first wave of the coronavirus pandemic, we can consider the ability of making good decisions quickly as one of the most important aspects of our being. The main objective of this article is to find out the utilization rate of several basic decision-making approaches in selected companies with an emphasis on newly used methods such as data analysis and business intelligence tools. The first part of the article presents a short introduction of the decision-making process and an overview of hitherto known and used tools facilitating the whole procedure. The submitted study of available literature leads to the presentation of own classification of the most widely used decisionmaking methods. Based on a questionnaire survey, in the second section, the pilot research examines the involvement of five different groups of methods in business decision-making, such as intuition and previous experiences, consultation with colleagues, data analysis (historical), MCDM methods and consultation with experts. Afterwards, the most common obstacles that employees must face in introducing new tools have been identified. In general, the results show that time and the associated pressure on decision-making speed play a crucial role in the decision-making process.

Data Analytics Tools: A User Perspective

2019 ◽  
Vol 18 (01) ◽  
pp. 1950002 ◽  
Author(s):  
Paul Town ◽  
Fadi Thabtah
Keyword(s):  
Data Analysis ◽  
Business Intelligence ◽  
Data Analytics ◽  
End Users ◽  
Point Of View ◽  
Future Research ◽  
User Perspective ◽  
Evaluative Criteria ◽  
Decision Making Processes ◽  
User Groups

Business Intelligence Tools (BI Tools) can be an intelligent way for individuals to undertake data analysis and reporting for guiding decision-making processes. There are many different BI Tools available in the market today, as well as information to assist organisations in evaluating their effectiveness. This paper focusses on two commercially available BI Tools: Tableau and Microsoft Power BI. It aims to determine which BI Tool is better for data analysis and reporting from an end user’s point of view. This paper undertakes an evaluation of both tools and compares which is more suitable for students using interface (navigation), cost, presence in the market, and available training and help as the evaluative criteria. Results produced in this paper found that overall, Tableau was more highly ranked than Power BI based on the evaluative criteria for end users for data analysis and reporting at least among the samples of the study. Tableau ranked higher than Power BI with its presence in the market, and available training and help. Power BI was rated more highly on its interface and both BI Tools were ranked the same in terms of cost to end users. This research is exploratory and may assist in formulating future research on BI Tools for specific user groups.

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