Machine learning and multi-agent systems in oil and gas industry applications: A survey

Hanga, Khadijah Muzzammil; Kovalchuk, Yevgeniya

doi:10.1016/j.cosrev.2019.08.002

Cited by 120 publications

(51 citation statements)

References 42 publications

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“…In recent years collaborative robots have become major market drivers in industry 5.0 [49], which aims to incorporate them alongside humans in a wide array of industries and applications such as assembly lines, inspection and control of operations [50][51][52], automated advising [53,54], rehabilitation, and search-and-rescue tasks [55]. In collaborative environments involving human-agent teams, sharing of cognitive elements is essential.…”

Section: Discussionmentioning

confidence: 99%

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Mizrahi

Zuckerman

Laufer

2020

Sensors

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In recent years collaborative robots have become major market drivers in industry 5.0, which aims to incorporate them alongside humans in a wide array of settings ranging from welding to rehabilitation. Improving human–machine collaboration entails using computational algorithms that will save processing as well as communication cost. In this study we have constructed an agent that can choose when to cooperate using an optimal strategy. The agent was designed to operate in the context of divergent interest tacit coordination games in which communication between the players is not possible and the payoff is not symmetric. The agent’s model was based on a behavioral model that can predict the probability of a player converging on prominent solutions with salient features (e.g., focal points) based on the player’s Social Value Orientation (SVO) and the specific game features. The SVO theory pertains to the preferences of decision makers when allocating joint resources between themselves and another player in the context of behavioral game theory. The agent selected stochastically between one of two possible policies, a greedy or a cooperative policy, based on the probability of a player to converge on a focal point. The distribution of the number of points obtained by the autonomous agent incorporating the SVO in the model was better than the results obtained by the human players who played against each other (i.e., the distribution associated with the agent had a higher mean value). Moreover, the distribution of points gained by the agent was better than any of the separate strategies the agent could choose from, namely, always choosing a greedy or a focal point solution. To the best of our knowledge, this is the first attempt to construct an intelligent agent that maximizes its utility by incorporating the belief system of the player in the context of tacit bargaining. This reward-maximizing strategy selection process based on the SVO can also be potentially applied in other human–machine contexts, including multiagent systems.

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Section: Discussionmentioning

confidence: 99%

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Mizrahi

Zuckerman

Laufer

2020

Sensors

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“…However, these tools can analyse a multitude of data, which were left aside earlier, and propose statistically sound action plans for the current and future workforce. With the advancement of industry 4.0 and rapid spread of IoT deployment [135], big data deployments [136], [137], and artificial intelligent and machine learning algorithm deployment [138], [139] in the O&G industry will allow current and future workers to collect more data from their operating assets, feed them into the digital twin system, analyse the data using a range of data analytic tools and make more informed decisions promptly.…”

Section: Decision Support Through Big Data Analyticsmentioning

confidence: 99%

Human Centric Digital Transformation and Operator 4.0 for the Oil and Gas Industry

et al. 2021

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Working at an oil and gas facility, such as a drilling rig, production facility, processing facility, or storage facility, involves various challenges, including health and safety risks. It is possible to leverage emerging digital technologies such as smart sensors, wearable or mobile devices, big data analytics, cloud computing, extended reality technologies, robotic systems, and drones to mitigate the challenges faced by oil and gas workers. While these technologies are not new to the oil and gas industry, most of its existing digital transformation initiatives follow business or process-centric approaches, in which the critical driver of the technology adoption is the enhancement of production, efficiency, and revenue. As a result, they may not address the challenges faced by the workers. As oil and gas workers are among the essential assets in the oil and gas industry, it is vital to address the challenges faced by these workers. This paper proposes a human-centric digital transformational framework for the oil and gas industry to deploy existing digital technologies to enhance their workers' health, safety, and working conditions. The paper outlines the critical challenges faced by oilfield workers, introduces a system architecture to implements a human-centric digital transformation, discusses the opportunities of the proposed framework, and summarizes the key impediment for the proposed framework.

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“…Although multi-agents systems can provide significant benefits, a lack of knowledge or suitable tools can slow down the introduction in practice. Hanga et al have described potential use of ML and multi-agent systems in oil and gas industry applications [ 37 ]. In the oil and gas industry, the use of ML and multi-agent systems are looked into as tools for streamlining processes and cutting costs, but so far their use has been limited by a lack of knowledge, suitable development tools, and standards, investment costs and cautious attitude toward new technologies replacing existing systems.…”

Section: Related Workmentioning

confidence: 99%

Metrological Challenges in Collaborative Sensing: Applicability of Digital Calibration Certificates

Mustapää

Nikander

Hutzschenreuter

et al. 2020

Sensors

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IoT systems based on collaborative sensor networks are becoming increasingly common in various industries owing to the increased availability of low-cost sensors. The quality of the data provided by these sensors may be unknown. For these reasons, advanced data processing and sensor network self-calibration methods have become popular research topics. In terms of metrology, the self-calibration methods lack the traceability to the established measurement standards of National Metrology Institutes (NMIs) through an unbroken chain-link of calibration. This problem can be solved by the ongoing digitalization of the metrology infrastructure. We propose a conceptual solution based on Digital Calibration Certificates (DCCs), Digital SI (D-SI), and cryptographic digital identifiers, for validation of data quality and trustworthiness. The data that enable validation and traceability can be used to improve analytics, decision-making, and security in industrial applications. We discuss the applicability and benefits of our solutions in a selection of industrial use cases, where collaborative sensing has already been introduced. We present the remaining challenges in the digitization and standardization processes regarding digital metrology and the future work required to address them.

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Machine learning and multi-agent systems in oil and gas industry applications: A survey

Cited by 120 publications

References 42 publications

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Human Centric Digital Transformation and Operator 4.0 for the Oil and Gas Industry

Metrological Challenges in Collaborative Sensing: Applicability of Digital Calibration Certificates

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