Automatic event identification and extraction from daily drilling reports using an expert system and artificial intelligence

Cinelli, Lucas Pinheiro; Oliveira, Jonice; Pinho, Vinicius M. de; Passos, Wesley L.; Padilla, Rafael; Braz, Patrick F; Galves, Breno; Dalvi, Domenica P.; Lewenfus, Gabriela; Ferreira, Jonathas O.; Ji, Anthony Y.Y.; Oliveira, F. L.; Gonçalves, Clemente; Netto, Sérgio L.; Silva, Eduardo A. B. da; Campos, Marcello L. R. de

doi:10.1016/j.petrol.2021.108939

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…The construction of event knowledge graphs hinges on a fundamental task known as event extraction, which involves identifying events mentioned within a text document. In addition to recognizing events, many systems performing event extraction also aim to identify the event types and their associated attributes [96][97][98]. These attributes may include details about the entities participating in an event, the event's location, timing, and more.…”

Section: Event Extractionmentioning

confidence: 99%

“…For instance, PALKA could produce a pattern like "(perpetrator) attack (target) with (instrument)", where the attack event would encompass three attributes: perpetrator, target, and instrument. Additionally, several other systems have been developed employing manually defined rules and patterns for event extraction and argument identification [97,[102][103][104][105][106][107].…”

Section: Event Extraction Using Pattern Matchingmentioning

confidence: 99%

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Event-Centric Temporal Knowledge Graph Construction: A Survey

Knez,

Žitnik

2023

Mathematics

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Textual documents serve as representations of discussions on a variety of subjects. These discussions can vary in length and may encompass a range of events or factual information. Present trends in constructing knowledge bases primarily emphasize fact-based common sense reasoning, often overlooking the temporal dimension of events. Given the widespread presence of time-related information, addressing this temporal aspect could potentially enhance the quality of common-sense reasoning within existing knowledge graphs. In this comprehensive survey, we aim to identify and evaluate the key tasks involved in constructing temporal knowledge graphs centered around events. These tasks can be categorized into three main components: (a) event extraction, (b) the extraction of temporal relationships and attributes, and (c) the creation of event-based knowledge graphs and timelines. Our systematic review focuses on the examination of available datasets and language technologies for addressing these tasks. An in-depth comparison of various approaches reveals that the most promising results are achieved by employing state-of-the-art models leveraging large pre-trained language models. Despite the existence of multiple datasets, a noticeable gap exists in the availability of annotated data that could facilitate the development of comprehensive end-to-end models. Drawing insights from our findings, we engage in a discussion and propose four future directions for research in this domain. These directions encompass (a) the integration of pre-existing knowledge, (b) the development of end-to-end systems for constructing event-centric knowledge graphs, (c) the enhancement of knowledge graphs with event-centric information, and (d) the prediction of absolute temporal attributes.

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Section: Event Extractionmentioning

confidence: 99%

Section: Event Extraction Using Pattern Matchingmentioning

confidence: 99%

Event-Centric Temporal Knowledge Graph Construction: A Survey

Knez,

Žitnik

2023

Mathematics

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“… 1 − 3 Transformer oil is widely used as an insulating material in substations. 4 − 6 However, jet fire accidents often occur due to the thermal runaway of transformer oil. 7 − 9 Many scholars have been puzzled by substation safety problems.…”

Section: Introductionmentioning

confidence: 99%

“…A substation is an important carrier of power transmission. − Transformer oil is widely used as an insulating material in substations. − However, jet fire accidents often occur due to the thermal runaway of transformer oil. − Many scholars have been puzzled by substation safety problems . Gómez-Mares et al investigated 84 jet fire events and found that 50% of the events induced a domino effect.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of the Combustion Behavior of Transformer Oil Jet Flame

Sun

Liu

et al. 2022

ACS Omega

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Transformer oil jet fire is one of the most dangerous types of fires in substations. The combustion behavior of transformer oil jet fire produces uncontrollable hazards to personnel and equipment and even triggers a domino effect. However, the jet fire combustion behavior of such materials as transformer oil has not been revealed before. Investigation of the combustion behavior of transformer oil jet fire has positive implications for the prevention and control of substation fires. In this paper, KI25X transformer oil was used as fuel. A series of transformer oil jet fire experiments were conducted with variable orifice diameters (5, 10, and 15 mm) with heat release rates ranging from 200 to 659.2 kW. The results showed that the entrainment coefficient of transformer oil jet fire was greater than that of pure gas phase jet fire. The entrainment coefficient of transformer oil jet fire was 0.029. Using dimensionless theory, it was proposed that the imaginary point source was proportional to the 0.317 power of Froude number. Based on the point source model, a dimensional analysis model with Reynolds number was developed. The radiation fraction of transformer oil jet fire was proportional to the −0.133 power of Reynolds number. This study played an important role in improving the jet combustion behavior of transformer oil.

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References

2022

Artificial Intelligence and Data Analytics for Energy Exploration and Production

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Automatic event identification and extraction from daily drilling reports using an expert system and artificial intelligence

Cited by 11 publications

References 12 publications

Event-Centric Temporal Knowledge Graph Construction: A Survey

Event-Centric Temporal Knowledge Graph Construction: A Survey

Experimental Investigation of the Combustion Behavior of Transformer Oil Jet Flame

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