High-Frequency Drilling Data Analysis to Characterize Water-Ice on the Moon

2020 ◽  
Author(s):  
Deep Joshi ◽  
Alfred Eustes ◽  
Jamal Rostami ◽  
Jenna Hanson ◽  
Christopher Dreyer
Keyword(s):  
Data Analysis ◽  
High Frequency ◽  
The Moon ◽  
Water Ice ◽  
Drilling Data

Accurate Drilling Data Interpretations Brought Significant Values to a Major Drilling Project

2021 ◽  
Author(s):  
Faisal Rashid ◽  
Hamdan Mohamed Al Saadi ◽  
Shahid Yakubbhai Duivala ◽  
Steve Butt ◽  
Sultan Al Mansoori ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Frequency ◽  
Performance Enhancement ◽  
Low Frequency ◽  
High Frequency Data ◽  
Coding System ◽  
Frequency Data ◽  
Drilling Data ◽  
The Many ◽  
Drilling Project

Abstract With the launch of a mega drilling project in the Middle East, the drilling data during the execution stage was collected in two formats; Low-Frequency Data and High-Frequency Data. This paper explains the effective utilization of data in the performance enhancement scheme. The paper also demonstrates the combination of Low-frequency and High-frequency data can reveal the many secrets of the drilling operations and can open the many sides of drilling operations for improvements. Low-Frequency data was entered manually at the rig-site using an improved coding system to identify the activities start and end times. High-Frequency data was collected through real-time transmission from the different data streaming services at the rig-site. Both data forms were collected simultaneously using stringent rules and close follow-ups to make sure that data collection was free of any reporting mistakes and gaps. Later, the collected data was extracted for different types of analyses and interpretations. Low-frequency data was studied in a novel way to get the best analytical and critical outcome to make sure that the right areas for improvements were identified and actions were implemented for enhanced performance. Improved operations coding system helped the team to categorize the operations and failures in an effective way to set new standards in data analysis. More than 100 different types of analyses using the best data analysis technique, such as trailing average, normalization, trends, etc., were conducted based on the information collected during the execution phase, and many new KPIs were established with challenging milestones to be achieved in the prescribed period. High-Frequency data was split into different sets of KPIs to identify the multiple Invisible Lost Time (ILT) areas to boost the operational efficiency. Various performance enhancement schemes were developed based on High-frequency data. As a result, these schemes were proven to enhance the performance of the mega drilling project. This paper discusses the novel methods of drilling data analysis based on low and high-frequency data and shows the effectiveness of the data presented in a standardized format over a period. It deliberates how the teams were challenged to enhance the performance. Such detailed data analysis will bring valuable information for the industry to utilize the conventional database in modernized ways to get the best outcomes from the data analysis results.

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’’.

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