Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges

Abstract

With the emergence of industry 4.0, the oil and gas (O&G) industry is now considering a range of digital technologies to enhance productivity, efficiency, and safety of their operations while minimizing capital and operating costs, health and environment risks, and variability in the O&G project life cycles. The deployment of emerging technologies allows O&G companies to construct digital twins (DT) of their assets. Considering DT adoption, the O&G industry is still at an early stage with implementations limited to isolated and selective applications instead of industry-wide implementation, limiting the benefits from DT implementation. To gain the full potential of DT and related technological adoption, a comprehensive understanding of DT technology, the current status of O&G-related DT research activities, and the opportunities and challenges associated with the deployment of DT in the O&G industry are of paramount importance. In order to develop this understanding, this paper presents a literature review of DT within the context of the O&G industry. The paper follows a systematic approach to select articles for the literature review. First, a keywords-based publication search was performed on the scientific databases such as Elsevier, IEEE Xplore, OnePetro, Scopus, and Springer. The filtered articles were then analyzed using online text analytic software (Voyant Tools) followed by a manual review of the abstract, introduction and conclusion sections to select the most relevant articles for our study. These articles and the industrial publications cited by them were thoroughly reviewed to present a comprehensive overview of DT technology and to identify current research status, opportunities and challenges of DT deployment in the O&G industry. From this literature review, it was found that asset integrity monitoring, project planning, and life cycle management are the key application areas of digital twin in the O&G industry while cyber security, lack of standardization, and uncertainty in scope and focus are the key challenges of DT deployment in the O&G industry. When considering the geographical distribution for the DT related research in the O&G industry, the United States (US) is the leading country, followed by Norway,

Is Digital Twin Technology Supporting Safety Management? A Bibliometric and Systematic Review

“…Through the use of the IoT, the gap between the physical and virtual worlds is filled by bridging a physical component's sensors and actuators with its digital counterpart [57]. In the safety domain, a DT provides an opportunity to train employees in virtual environments, thus helping to achieve accident prevention and to reduce the probability of accidents that may occur during on-the-job training [58].…”

Cleaning Up Our Act: Systems Engineering to Promote Business Model Innovation for the Offshore Exploration and Production Supply Chain Operations

“…The vessels involved in the offshore E & P SC to complete these activities are major contributors to CO 2 emissions in the offshore E & P industry [1] and most sustainable initiatives in this context are linked to the reduction of CO 2 emission from the vessels utilized in transportation, optimal vessel and route allocation, vessel fuel usage, and route optimization for transportation [14,63,64]. Lately, the adoption of technology for this purpose has been widely discussed, such as the implementation of 3D printing of spare parts for local supply to avoid transportation; and the adoption of Blockchain, Internet of Things (IoT), and digital twins for supply chain transparency [19,65,66]. For vessel and deck space optimization, the adoption of big data analytics and machine learning/artificial intelligence has been widely discussed [67][68][69].…”

Digital Twin models in industrial operations: State‐of‐the‐art and future research directions

“…Implementation of DT still faces challenges including lack of detailed methodology and standards, difficulties in collecting and storing large amounts of data [44,70,71], developing data acquisition system, synchronisation problems, modelling of a complex system, lack of awareness, resistance of companies to adopting the technology [59], and difficulties in constructing, understanding, controlling, and simulating real-time changes in the system. High-fidelity models are required to simulate and test the product or process in a virtual environment by reducing development time and cost [26,72] The issue of high investment cost and data security is still a hindrance to many companies making DT part of their daily life [55].…”

“…Cybersecurity is another challenge to DT use and there are several pillars including security, data encryption, security audit, monitoring live events, and responding to incidents, identity, and management of devices [44,74]. To ensure data security, DT should be supported by a security audit for the visibility of the transaction and to identify the devices and users.…”