PETMiner—A Visual Analysis Tool for Petrophysical Properties of Core Sample Data

Harrison, Dave Graham; Efford, N. D.; Fisher, Q.J.; Ruddle, Roy A.

doi:10.1109/tvcg.2017.2682865

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Logfiles are widely used to evaluate visualisation tools 48. QualDash logfiles will record information about the user (job title and so on), data used (audit, year), overall time spent using QualDash, time spent interacting with different QualCards (including new QualCards that have been created), functionality used and whether QualDash visualisations were downloaded.…”

Section: Methods and Analysismentioning

confidence: 99%

How, in what contexts, and why do quality dashboards lead to improvements in care quality in acute hospitals? Protocol for a realist feasibility evaluation

et al. 2020

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IntroductionNational audits are used to monitor care quality and safety and are anticipated to reduce unexplained variations in quality by stimulating quality improvement (QI). However, variation within and between providers in the extent of engagement with national audits means that the potential for national audit data to inform QI is not being realised. This study will undertake a feasibility evaluation of QualDash, a quality dashboard designed to support clinical teams and managers to explore data from two national audits, the Myocardial Ischaemia National Audit Project (MINAP) and the Paediatric Intensive Care Audit Network (PICANet).Methods and analysisRealist evaluation, which involves building, testing and refining theories of how an intervention works, provides an overall framework for this feasibility study. Realist hypotheses that describe how, in what contexts, and why QualDash is expected to provide benefit will be tested across five hospitals. A controlled interrupted time series analysis, using key MINAP and PICANet measures, will provide preliminary evidence of the impact of QualDash, while ethnographic observations and interviews over 12 months will provide initial insight into contexts and mechanisms that lead to those impacts. Feasibility outcomes include the extent to which MINAP and PICANet data are used, data completeness in the audits, and the extent to which participants perceive QualDash to be useful and express the intention to continue using it after the study period.Ethics and disseminationThe study has been approved by the University of Leeds School of Healthcare Research Ethics Committee. Study results will provide an initial understanding of how, in what contexts, and why quality dashboards lead to improvements in care quality. These will be disseminated to academic audiences, study participants, hospital IT departments and national audits. If the results show a trial is feasible, we will disseminate the QualDash software through a stepped wedge cluster randomised trial.

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Section: Methods and Analysismentioning

confidence: 99%

How, in what contexts, and why do quality dashboards lead to improvements in care quality in acute hospitals? Protocol for a realist feasibility evaluation

et al. 2020

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VRGE: An Immersive Visualization Application for the Geosciences

Hyde

Hall

Caers

2018

2018 IEEE Scientific Visualization Conference (SciVis)

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Convolutional Neural Networks for the Classification of the Microstructure of Tight Sandstone

Flores

Fisher

Lorinczi

2021

Day 9 Wed, March 31, 2021

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Tight gas sandstone reservoirs vary widely in terms of rock type, depositional environment, mineralogy and petrophysical properties. For this reason, estimating their permeability is a challenge when core is not available because it is a property that cannot be measured directly from wire-line logs. The aim of this work is to create an automatic tool for rock microstructure classification as a first step for future permeability prediction. Permeability can be estimated from porosity measured using wire-line data such as derived from density-neutron tools. However, without additional information this is highly inaccurate because porosity-permeability relationships are controlled by the microstructure of samples and permeability can vary by over five orders of magnitude. Experts can broadly estimate porosity-permeability relationships by analysing the microstructure of rocks using Scanning Electron Microscopy (SEM) or optical microscopy. Such estimates are, however, subjective and require many years of experience. A Machine Learning model for the automation of rock microstructure determination on tight gas sandstones has been built using Convolutional Neural Networks (CNNs) and trained on backscattered images from cuttings. Current results were obtained by training the model on around 24,000 Back Scattering Electron Microscopy (BSEM) images from 25 different rock samples. The obtained model performance for the current dataset are 97% of average of both validation and test categorical accuracy. Also, loss of 0.09 and 0.089 were obtained for validation and test correspondingly. Such high accuracy and low loss indicate an overall great model performance. Other metrics and debugging techniques such Gradient-weighted Class Activation Mapping (Grad-CAM), Receiver Operator Characteristics (ROC) and Area Under the Curve (AUC) were considered for the model performance evaluation obtaining positive results. Nevertheless, this can be improved by obtaining images from new already available samples and make the model generalizes better. Current results indicate that CNNs are a powerful tool and their application over thin section images is an answer for image analysis and classification problems. The use of this classifier removes the subjectivity of estimating porosity-permeability relationships from microstructure and can be used by non-experts. The current results also open the possibility of a data driven permeability prediction based on rock microstructure and porosity from well logs.

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PETMiner—A Visual Analysis Tool for Petrophysical Properties of Core Sample Data

Cited by 3 publications

References 36 publications

How, in what contexts, and why do quality dashboards lead to improvements in care quality in acute hospitals? Protocol for a realist feasibility evaluation

How, in what contexts, and why do quality dashboards lead to improvements in care quality in acute hospitals? Protocol for a realist feasibility evaluation

VRGE: An Immersive Visualization Application for the Geosciences

Convolutional Neural Networks for the Classification of the Microstructure of Tight Sandstone

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