scholarly journals Prediction of Casing Damage: A Data-Driven, Machine Learning Approach

2021 ◽  
Vol 14 ◽  
pp. 1046-1053
Author(s):  
Yanhong Zhao ◽  
Hanqiao Jiang ◽  
Hongqi Li
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Data Driven ◽  
Learning Approach ◽  
Damage Prediction ◽  
Number Of Factors ◽  
Static Data ◽  
Casing Damage ◽  
Machine Learning Approach ◽  
And Performance

Casing damage is the result of a number of factors in the long process of oilfield development, so it must be correctly judged and repaired in time to ensure the normal production of the oil fields. With the development of data science, it has always been an imperative problem remained to be solved. In this paper, we adopt a data-driven and the machine learning approach to casing damage forecasts. Firstly, from the fields of geology, engineering and development, a lot of history data is collected and processed. Then, based on these dynamic and static data samples, the random forest algorithm is used to create the casing damage prediction model. Finally, after the model is tested in two fault blocks, the results indicate that accuracy rates are 91% and 75%, which proves the validity and performance of the mode.

scholarly journals Clustering suicides: A data-driven, exploratory machine learning approach

2019 ◽  
Vol 62 ◽  
pp. 15-19 ◽  
Author(s):  
Birgit Ludwig ◽  
Daniel König ◽  
Nestor D. Kapusta ◽  
Victor Blüml ◽  
Georg Dorffner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Analysis Data ◽  
The Other ◽  
Data Driven ◽  
Learning Approach ◽  
Suicide Methods ◽  
Machine Learning Approach ◽  
The One ◽  
Methods Of Suicide

Abstract Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into “violent” versus “non-violent” method. Interestingly, since the proposition of this dichotomous differentiation, no further efforts have been made to question the validity of such a classification of suicides. This study aimed to challenge the traditional separation into “violent” and “non-violent” suicides by generating a cluster analysis with a data-driven, machine learning approach. In a retrospective analysis, data on all officially confirmed suicides (N = 77,894) in Austria between 1970 and 2016 were assessed. Based on a defined distance metric between distributions of suicides over age group and month of the year, a standard hierarchical clustering method was performed with the five most frequent suicide methods. In cluster analysis, poisoning emerged as distinct from all other methods – both in the entire sample as well as in the male subsample. Violent suicides could be further divided into sub-clusters: hanging, shooting, and drowning on the one hand and jumping on the other hand. In the female sample, two different clusters were revealed – hanging and drowning on the one hand and jumping, poisoning, and shooting on the other. Our data-driven results in this large epidemiological study confirmed the traditional dichotomization of suicide methods into “violent” and “non-violent” methods, but on closer inspection “violent methods” can be further divided into sub-clusters and a different cluster pattern could be identified for women, requiring further research to support these refined suicide phenotypes.

Deep Optimization of Catalyst Layer Composition via Data-Driven Machine Learning Approach

2020 ◽  
Author(s):  
Bowen Wang ◽  
Biao Xie ◽  
Jin Xuan ◽  
Wen Gu ◽  
Dezong Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Catalyst Layer ◽  
Data Driven ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Layer Composition

Classification of different skarn deposits based on the compositional variability of associated grandite garnets: a data science and Machine Learning approach

2021 ◽  
Author(s):  
Urmi Ghosh ◽  
Tuhin Chakraborty
Keyword(s):  
Machine Learning ◽  
Trace Element ◽  
Data Science ◽  
Training Dataset ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Skarn Deposits

<p>Rapid technological improvements made in in-situ analysis techniques, including LA-ICPMS, have transformed the field of analytical geochemistry. This has a far-reaching impact for different petrogenetic and ore-genetic studies where minute major and trace element compositional changes between different mineral zones within a single crystal can now be demarcated. Minerals such as garnet although robust are quite sensitive to the changing P-T and fluid conditions during their formation. These minerals have become powerful tools to characterize mineralization types. Previously, Meinert (1992) has used in-situ major element EPMA analysis results to classify different skarn deposit based on the end-member composition of hydrothermal garnets. Alternatively, Tian et al. (2019) used the garnet trace element composition for the similar purpose. However, these discrimination plots/ classification schemes show major overlap in different skarn deposits, such as Fe, Cu, Zn, and Au. The present study is an attempt to use machine learning approach on available garnet data to found a more potent classification scheme for skarn deposits, thus reaffirming garnet as a faithful indicator for hydrothermal ore deposits. We have meticulously collected major and trace element data of Ca-rich garnets, associated with different skarn deposits worldwide from 40 publications. This collected data is then used to train a model for fingerprinting the skarn deposits. Stratified random sampling method has been used on the dataset with 80% of the samples as test set and the rest 20 % as training dataset. We have used K-nearest neighbour (KNN), Support Vector Machine (SVM) and Random Forest algorithms on the data by using Python as a platform. These ML classification algorithm performs better than the earlier existing models available for classification of ore types based on garnet composition in skarn system. Factor importance is calculated that shows which elements play a pivotal role in classification of the ore type. Our results depict that multiple garnet forming elements taken together can reliably be used to discriminate between different ore formation settings.</p>

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