FractVis: Visualizing Microseismic Events

Mostafa, Ahmed E.; Carpendale, Sheelagh; Brazil, Emilio Vital; Eaton, David W.; Sharlin, Ehud; Sousa, Mário Costa

doi:10.1007/978-3-642-41939-3_37

Cited by 3 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…11. Examples of our interactive microseismic visualization and analytics tool (Mostafa et al 2012(Mostafa et al , 2013a. Key interface and visualization components include: (a) attributes and parameters selection; (b) 3D point-cloud visualization; (c) time-varying attribute of the point cloud; and (d) parallel coordinates with each vertical axis corresponding to one out of n existing attributes and each coloured line corresponding to an event (point sample) from the microseismic point cloud.…”

Section: Example 3: Interpretive Visualization Of Fused Hydrocarbon Mmentioning

confidence: 99%

See 1 more Smart Citation

Scalable and interactive visual computing in geosciences and reservoir engineering

Sousa

Brazil

Sharlin

2014

Self Cite

View full text Add to dashboard Cite

Visual computing technologies enable more intuitive data modelling, visualization and analysis, facilitating these processes by real-time interactive visual interfaces. These technologies are essential for software in the oil and gas industry, allowing users to gain insights and actionable information when dealing with increasingly complex, multidisciplinary datasets and processes. In the context of the oil and gas industry, interactive visual computing should also scale well with the growing data size and other components of a data analytics pipeline. We present key problems and challenges motivating the research and development of scalable and interactive visual computing systems, followed by a classification of the most important research themes and related topics. Eleven case studies developed with the industry are also presented, highlighting the main cutting edge findings, limitations and achievements.

show abstract

Section: Example 3: Interpretive Visualization Of Fused Hydrocarbon Mmentioning

confidence: 99%

“…To address some of these challenges, we developed FractVis, an interactive visualization and analytics prototype (Mostafa et al 2012(Mostafa et al , 2013a, providing a multiple coordinated views approach using parallel coordinates (Inselberg 1985) and interactive 3D visualizations (Fig. 11).…”

Section: Example 3: Interpretive Visualization Of Fused Hydrocarbon Mmentioning

confidence: 99%

Scalable and interactive visual computing in geosciences and reservoir engineering

Sousa

Brazil

Sharlin

2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…These factors collectively contribute to inaccurate visualizations and limit insights that can be derived from the data. Moreover, when dealing with exceptionally large high-dimensional datasets, computational constraints further exacerbate the difficulties in effectively visualizing the information [127], [128].…”

Section: ) Visualization Of High Dimensional Datamentioning

confidence: 99%

Machine Learning in Oil and Gas Exploration: A Review

Lawal,

Yang,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

A comprehensive assessment of machine learning applications is conducted to identify the developing trends for Artificial Intelligence (AI) applications in the oil and gas sector, specifically focusing on geological and geophysical exploration and reservoir characterization. Critical areas, such as seismic data processing, facies and lithofacies classification, and the prediction of essential petrophysical properties (e.g., porosity, permeability, and water saturation), are explored. Despite the vital role of these properties in resource assessment, accurate prediction remains challenging. This paper offers a detailed overview of machine learning's involvement in seismic data processing, facies classification, and reservoir property prediction. It highlights its potential to address various oil and gas exploration challenges, including predictive modelling, classification, and clustering tasks. Furthermore, the review identifies unique barriers hindering the widespread application of machine learning in the exploration, including uncertainties in subsurface parameters, scale discrepancies, and handling temporal and spatial data complexity. It proposes potential solutions, identifies practices contributing to achieving optimal accuracy, and outlines future research directions, providing a nuanced understanding of the field's dynamics. Adopting machine learning and robust data management methods is crucial for enhancing operational efficiency in an era marked by extensive data generation. While acknowledging the inherent limitations of these approaches, they surpass the constraints of traditional empirical and analytical methods, establishing themselves as versatile tools for addressing industrial challenges. This comprehensive review serves as an invaluable resource for researchers venturing into lesscharted territories in this evolving field, offering valuable insights and guidance for future research.INDEX TERMS Oil and gas exploration, machine learning, petrophysical properties prediction, facies and lithofacies classification, seismic data processing.

show abstract