Chevron and Science Applications International Corporation (SAIC) are working together to implement Asset Decision Environments (ADEs) across business units as part of the Chevron i-field™ initiative. This paper describes lessons learned from the program to date, covering types of ADE, challenges faced, solutions delivered and benefit realized. It goes on to discuss the future development of the ADE concept. Chevron has explored ADE value and usage through the implementation of different types of environments targeted at different problems, and this paper will outline the logic behind this multi-strand approach, and the benefits delivered. Examples will include Decision Support Centers, collaboration environments, and training environments. As the initial implementations deliver success and collaborative working becomes more widespread, questions are being asked about how far we can go with collaborative working and decision-making. The paper will provide some insight into the vision for the ADE in the future. This paper will set out practical lessons learned on how to design an ADE to address current problems, deliver value and still remain flexible to developments in the future. It will describe challenges and solutions for many aspects of ADE design and implementation, highlighting issues, solutions and benefits delivered. The paper will share a vision for a multi-functional and multi-asset operational ADE of the future, including key challenges that still need to be resolved. Decision and collaboration environments are being deployed by companies to address challenges associated with response to real-time data, scarcity of experienced resources, and integration and collaboration between disciplines and locations. This paper provides a Chevron and SAIC perspective on the questions: How can decision environments deliver value, and how might they evolve in the future to deliver more integration and collaboration? Introduction Over the past three years, Chevron has been working with Science Applications International Corporation (SAIC) on the design and implementation of various types of Asset Decision Environments (ADEs) across multiple business units. An ADE is Chevron's term for a collaboration environment or decision environment. This paper sets out some of the key features of the ADE program, the design approach, lessons learned and results, and provides some insight into the future direction of the ADE.
This paper explains why collaboration is a cornerstone of so many successful Intelligent Energy (IE) programs, and how organisations can use what has been learnt about collaboration to support their IE activities, whether they have a mature program or are just starting on their journey. The paper will look at evidence for the importance of collaboration and why it is so frequently seen as a key element of transformational IE programs. The results from 24 IE assessments across different companies and assets point to collaboration as the most commonly-recommended opportunity area for inclusion within IE initiatives. We will review the current state of collaboration to identify value that has been delivered, and common principles and lessons that can be extracted from multiple implementations. We will then consider future directions for collaboration. A Collaboration Maturity Model and Roadmap will be introduced to explain the current state and potential future developments of collaboration across the industry. Although there has been much success delivered from collaboration to date, we believe that there is significantly more that could be achieved through further technical, visualisation, process and organisational innovation. The model will be used to help illustrate and explain potential future developments, and consider how organisations at all stages of maturity can increase the effectiveness of their collaboration activities. Collaboration has been one of the key successes of Intelligent Energy; however, as an industry we are still in the early stages on the journey of where collaboration and IE could take us. This paper charts our progress on that journey and sets out how we can use today's knowledge to accelerate and direct further developments to transform our business in the future.
The concept of Integrated Operations (IO) has matured significantly over the past eight years since the landmark study by CERA in 2002. Many companies, including Chevron, now have established programs with demonstrated results delivered in multiple assets. With that track record, companies are now asking what's next and how can they build on this foundation for the future.Given what we've learned to date, how can we accelerate results and where can IO add the most value? What business and technology trends will emerge both to help and hinder integrated workflow solutions over the next 5-10 years? This paper will address these questions by looking at the history of the Chevron i-field program to understand what has been accomplished, the promises realized and the challenges uncovered in the journey. We will examine trends in the industry, to understand future opportunities and challenges more clearly.The paper will look at how different elements of Integrated Operations can be deployed more effectively, and how to address the challenges of delivering and sustaining the change within the current and future workforce. Delivering solutions is usually difficult, but sustaining and supporting them provide new levels of challenge. How do we ensure that Integrated Operations becomes the way we work in the future instead of a passing technology fashion fad? Revisiting Expectations: History and Current State of Chevron's i-field™ Figure 1: Chevron's i-field Program Has Been Running Since 2002Chevron's Integrated Operations (IO) program, called i-field™ 1 , is now in its eighth year (Figure 1). Much has been accomplished in that time. Chevron has to begun to think differently about how to use digital technology and processes to change the way we operate key assets. We are starting to realize the potential of the way we can collaborate and use information to make better investment and operational decisions. Many major operators and service providers in the E&P industry have made similar progress with their own programs.But a lot more is left to do, before we achieve the ultimate goal of transformation. As we try to forecast the future, it would be prudent to first look back on early industry expectations, to see how well we have done.An early analysis of the potential of IO came from CERA in the form of a multi-client study on the Digital Oil Field of the Future (DOFF). They predicted DOFF challenges coming in the areas of: technology development, demonstrating business case, workforce adoption, work process redesign, data and application integration and collaboration between service and E&P firms.
Summary The first 10 years of intelligent energy (IE) have been interesting and rewarding. The oil and gas industry has seen many successful IE implementations with significant value delivered. We have established improved communication and collaboration, and seen changes enabled by real-time data and new technologies such as predictive analytics. However, we have not yet delivered the level and scale of transformation that were envisioned at the start. Our industry has moved more slowly at the same time that the "outside" world— our own homes and other industries—has moved much more quickly. As individuals, we consume information and communicate very differently from 10 years ago (e.g., through online booking, social media, and the use of map-based solutions). There is an ease of connecting across the world and "things talk"—but not in the oil patch to any large degree. [See Chui et al. (2010) for a broader discussion of the "Internet of Things."] In this paper, and on the basis of more than 10 years of experience working exclusively with IE and with more than a dozen of the key industry actors, we present an analysis of the IE domain, lessons learned, and suggestions for where we should go from here. We consider the reasons for our current state, attempting to answer why it might be more difficult to transform in the oil and gas industry than elsewhere. Why have some of the barriers been much bigger than we expected? The business case is as strong as it was 10 years ago, the technology is more robust, and we have more young people in the industry as well as a higher level of acceptance of technology and change in our personal lives. Thus, we should find it easier to make more-rapid progress now than in the past. What must change to achieve that progress?
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.