Cloud computing has become the buzz word in the last few years. All the service industries from different fields are using cloud computing based data analytics to optimize their operations in order to improve the customer user experiences and the overall efficiency. It is mainly due to the use of high volume computing backed by significant development in advanced hardware capabilities. This paper describes the ‘what’, ‘why’ and ‘how’ based questions on cloud computing. To start with, a brief introduction of cloud computing has been discussed along with the history of computation usage in oil industry. Followed by that a brief introduction highlighting the significance of Artificial intelligence and machine learning in the current computing environment has been explained. As the industry moves towards more and more usage of digital oilfield techniques, the dominance of high end computing and data analytics in the oil and gas industry is also showcased. Once cloud computing is established as a standard, the paper further discusses different modes of cloud delivery service models- Infrastructure as a service (IaaS), Platform as a service (PaaS) and Software as a Service (SaaS). The paper also identifies the challenges and concerns/issues that comes as a part of cloud computing methodology and then describes how these all challenges are being addresses. This paper elucidates the comprehensive view of the cloud computing landscape in oil and gas arena through a review of available noteworthy open source literature
Having a good understanding of the offset wells is the key for successful planning and execution of any well, both from the risk management point of view as well as from equipment and operations planning. In both cases of congested or simple fields the amount of the manual work is significant, which further affected by potential human mistakes. The manuscript aims to provide the detailed explanation of the digitalization of the offset well risk analysis (ORA) implemented in several drilling projected, what lead to almost complete elimination of the manual work and allowed to improve the quality and the quantity of the offset data. At the project kick-off the manual work performed by different parties (drilling engineer as well as drilling fluid, directional, bits engineers etc.) was mapped in the different detailed workflows. This allowed to understand the final result of every tasks. As next step the massive database of the end of well reports, post-job reports, daily drilling reports, etc was created with few tens of millions entry points. Further the artificial intelligence in combination with data analytics was used to replicate the previously mapped workflows. As the result entire manual work was replaced by the digital, leading to receive higher number of outputs with superior quality. The direct benefit was a reduction of the time required to get the final result, when previously a detailed analysis was completed in 3 to 4 days, and now it is done within minutes, allowing to dedicate the man-hours to more other valuable tasks. The manuscript provides the novel information on ability to use digital technologies to eliminate manual work and avoid costly human mistakes. The proposed solution can be implemented in any other drilling project worldwide, as well as in any other activity requiring performance of the repetitive tasks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.