In f0nzie/petroleum.ml: Machine Learning Applications in Machine Learning

ML techniques organized by supervised and unspervised

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Warning in .Internal(exists(x, envir, mode, inherits)) :
  closing unused connection 3 (https://www.onepetro.org/search?q="machine+learning"AND"economics"AND"GPR"&peer_reviewed=&published_between=&from_year=&to_year=&dc_type=conference-paper&start=-1000&rows=1000)

major <- c("machine learning")
discipline <- c("reservoir", "production", "surface facilities", "metering",
           "logging", "pvt", "completion", "intervention", "workover",
           "drilling",  "geology", "petrophysics", "geophysics", "seismic",
           "economics")
ml_technique.1 <- c("random forest",
                    "GPR", " Gaussian Process Regression",
                    "PCA", "principal component analysis",
                    "boosting", 
                    "discriminant analysis")
by.ml_technique.1 <- join_keywords(major, discipline, ml_technique.1, 
                                   get_papers = TRUE, sleep = 3)

by.ml_technique.1

library(petro.One)

PROJHOME <- rprojroot::find_rstudio_root_file()


major <- c("machine learning")
discipline <- c("reservoir", "production", "surface facilities", "metering",
           "logging", "pvt",
           "completion", "intervention", "workover", "drilling", 
           "geology", "petrophysics", "geophysics", "seismic",
           "economics")

ml_technique <- c("boosting", 
                    "discriminant analysis", 
                    "kernel approximation",
                    "genetic algorithm",
                    "SVM", "Support Vector Machine", 
                    "nearest neighbor", "k-nearest neighbor",
                    "deep learning", 
                    "convolutional network", "convolutional neural",
                    "kernel density estimation",
                    "naive bayes", 
                    "logistic regression",
                    "neural network", "neural nets", 
                    "decision tree",
                    "Gradient Boosting Tree",
                    "linear regression", 
                    "random forest",
                    "GPR", " Gaussian Process Regression",
                    "PCA", "principal component analysis",
                    "SVD", "Singular Value Decomposition",
                    "c-means", "fuzzy c-means",
                     "k-means",  
                    "gaussian mixture", "gaussian mixture model",
                    "reinforcement learning",
                    "hierarchical", "hierarchical clustering",

                    "fuzzy logic", 
                    "hidden markov",

                    "SVR", "Support Vector Regression"
                   )

by.ml_technique <- join_keywords(major, discipline, ml_technique, 
                                   get_papers = TRUE, sleep = 3)

by.ml_technique
save(by.ml_technique, 
     file = file.path(PROJHOME, "data", "ml_technique.rda")) 

# 8609 papers

# only the  papers dataframe
write.csv(by.ml_technique.4$papers, file = "ml_papers_4.csv")  # just in case

ml.keywords.4 <- by.ml_technique.4$keywords
ml.papers.4   <- by.ml_technique.4$papers

Two levels: top and discipline

library(petro.One)


top <- c("machine learning")
discipline <- c("reservoir", "production", "surface facilities", "metering",
           "logging", 
           "completion", "intervention", "workover", "drilling", 
           "geology", "petrophysics", "geophysics", "seismic",
           "economics")

by.discipline <- join_keywords(top, discipline, 
                                   get_papers = TRUE, sleep = 3)
by.discipline

by.discipline.ml <- by.discipline

by.discipline.ml$uid <- 

library(petro.One)


top <- c("neural network")
discipline <- c("reservoir", "production", "surface facilities", "metering",
           "logging", 
           "completion", "intervention", "workover", "drilling", 
           "geology", "petrophysics", "geophysics", "seismic",
           "economics")

by.discipline.nn <- join_keywords(top, discipline, 
                                   get_papers = FALSE, sleep = 3)
by.discipline.nn

# data driven is the same as data-driven
library(petro.One)


top <- c("data driven")
discipline <- c("reservoir", "production", "surface facilities", "metering",
           "logging", 
           "completion", "intervention", "workover", "drilling", 
           "geology", "petrophysics", "geophysics", "seismic",
           "economics")

by.discipline.dd <- join_keywords(top, discipline, 
                                   get_papers = FALSE, sleep = 3)
by.discipline.dd

f0nzie/petroleum.ml documentation built on May 12, 2019, 6:22 p.m.