Study on Ontology-based Knowledge Construction of Petroleum Exploitation Domain

Du, Ruishan; Li, Yang; Shang, Fanhua; Wu, Ya-Juan

doi:10.1109/aici.2010.132

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…According to the authors, the use of ontologies for an explicit description of the world has many advantages, including ease of integration and extensibility. Some authors suggest the use of ontology for geological interpretation [16], oil exploitation [17], exploration [18], and process surveillance [19]. Techniques used to achieve a better production quality of the extraction well.…”

Section: Ontologymentioning

confidence: 99%

A Multi-agent Approach for Production Management

Tanajura

Oliveira

Lepikson

2015

Lecture Notes in Electrical Engineering

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Abstract. Multi-agent systems have been successfully used to represent distributed manufacturing systems. Each part or characteristic of the system can be represented by an agent which acts independently and in a cooperative way. A better integrated operation can emerge as the result of each agent following its own rules and communicating with the others. Better decisions from a holistic viewpoint can be achieved when manufacturing asset management is supported by a multi-agent approach. A model to manage distributed manufacturing is proposed and applied to on-shore oil fields. The distributed characteristics of oil field units, such as wells, collecting stations, compressing stations and supplies mean that they have to cooperate to reach production targets. The integrated management model carries out a cost analysis and helps to identify unprofitable assets and to support decision making processes. Agents help to reduce the load of information for the operator, giving more time to focus on situations that require greater attention. Example applications are presented in which the proposed multi-agent model, its ontology and rules, could help oil field surveillance and support decision-making process.

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Section: Ontologymentioning

confidence: 99%

A Multi-agent Approach for Production Management

Tanajura

Oliveira

Lepikson

2015

Lecture Notes in Electrical Engineering

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“…The fourth industrial revolution is initiating the use of CPS (Lee et al, 2015) and is focused on the development of a new generation of intelligent and integrated technologies for smart manufacturing (Ivezic & Ljubicic, 2016), seeking to optimize its planning and usage across different industrial domains such as oil and gas industry (Du et al, 2010), (Guo & Wu, 2012), mining (Xue & Chang, 2012), energy (Teixeira et al, 2017), steel production (Dobrev et al, 2008), construction (Sorli et al, 2006), aviation (Hoppe et al, 2017;Lehmann et al, 2018), automotive industry (Phutthisathian et al, 2013), electronic industry (Liu et al, 2005a), chemical industry (Natarajan et al, 2011), and process engineering (Wiesner et al, 2010). In addition, the concept of virtual production is considered to be the key factor for modeling production aiming for zero defects (MacDonald, 2016).…”

Section: Challenges Of Industry 40mentioning

confidence: 99%

“…For this purpose, in the last decade, ontologies have been developed for one specific industrial domain such as aviation (Keller, 2016), aeropsace (Kossmann et al, 2009), construction (Liao et al, 2009), steel production (Dobrev et al, 2008), chemical engineering (Vinoth & Sankar, 2016;Feng et al, 2018), oil industry (Du et al, 2010;Guo & Wu, 2012), energy (Santos et al, 2018), and electronics (Liu et al, 2005a). Other ontologies have been used for one specific manufacturing process such as packaging (Liu et al, 2005b), process engineering (Wiesner et al, 2010), process compliance (Disi & Zualkernan, 2009), risk management (Atkinson et al, 2006), safety management (Hooi et al, 2012), customer feedback analysis (Kim and Lee, 2013;Daly et al, 2015), organizational management (Grangel-Gonzalez et al, 2016;Izhar and Apduhan, 2017), project management (Cheah et al, 2011), product development (Zhang et al, 2017), maintenance (Haupert et al, 2014), resource reconfiguration (Wan et al, 2018b), and production scheduling (Kourtis et al, 2019).…”

Section: As Well As System Diagnosismentioning

confidence: 99%

Ontologies for Industry 4.0

Ragavan

Khamis

Fiorini³

et al. 2019

The Knowledge Engineering Review

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The current fourth industrial revolution, or 'Industry 4.0' (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.

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“…For example, the Industry 4.0 knowledge graph [6], TCM health care knowledge graph [7], earth science data knowledge graph [8], large-scale engineering patent knowledge graph [9], measurement remote sensing application knowledge graph [10], and so on. In the field of oil and natural gas, Ruishan Du et al [11] introduced ontology into knowledge construction in the field of oil development, designed an ontology integration platform and realized knowledge representation and knowledge sharing. Xianming Tang et al [12] proposed a domain knowledge graph engineering construction method on the basis of traditional natural language processing based on the ontology of petroleum exploration and development to provide better knowledge services for the oil and gas industry.…”

Section: Introductionmentioning

confidence: 99%

Establishment of Technical Standard Database for Surface Engineering Construction of Oil and Gas Field

Xia,

Dai,

Huang

et al. 2023

Processes

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In recent years, oil and gas field surface engineering construction projects tend to be large in scale, large in quantity, and short in cycle. The task of surface construction management has increased significantly. In the process of project construction, corresponding standards and specifications are required to provide sufficient technical guidance and support for design, construction, and management personnel to ensure project management and control towards compliance, safety, and quality. However, the oil and gas field engineering standards are numerous and specialized, involving different levels of national standards, enterprise standards, and industry standards, which leads to the inefficiency of the actual use of standards and specifications. To solve them, this paper uses knowledge graph technology, OCR recognition, and natural language processing technology to conduct systematic research on the knowledge classification mechanism, data extraction, database construction mechanism, data structuring, and intelligent retrieval matching of oil-gas field surface engineering construction standards. In this study, the structured identification, storage, and information warehousing of standards are realized, and a highly sharable library of standards and specifications is formed, which realizes the intelligent retrieval and pushing of technical standards for surface engineering construction. This paper creates conditions for the realization of intelligent push and benchmarking management of standards and specifications, providing support for digital transformation and intelligent development of oil–gas fields.

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Study on Ontology-based Knowledge Construction of Petroleum Exploitation Domain

Cited by 3 publications

References 5 publications

A Multi-agent Approach for Production Management

A Multi-agent Approach for Production Management

Ontologies for Industry 4.0

Establishment of Technical Standard Database for Surface Engineering Construction of Oil and Gas Field

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