R/interpolation.R

In f0nzie/rNodal: Oil and Gas Well Nodal Analysis for Production Petroleum Engineering

#' Interpolation for Fancher and Brown correlation
#'
#' It will calculate the friction Factor by interpolating in the digitized plot
#' produced by Fancher and Brown. The values provided for the interpolation are
#' the ___ product on the x-axis, the GLR and the degree of the poliynomial.
#'
#' @param drhov    Duns and Ros parameters. See equation on x-axis
#' @param GLR      gas liquid ratio
#' @param degree   degree of polynomial
#' @importFrom dplyr filter
#' @importFrom stats lm predict
#' @export
#' @examples
#' # 7. call the interpolation function with degree = 4 from Fig 2.41
#' interp.fanbr(drhov = 10, GLR = 3200, degree = 4)$ff
interp.fanbr <- function(drhov, GLR, degree) {
    # curves.0241 <- NULL
    # load("./data/curves.0241.rda")
    if (GLR < 1500) curve = "LT1500"
    if (GLR > 3000) curve = "GT3000"
    if (GLR <= 3000 & GLR >= 1500) curve = "15003000"

    data <- curves.0241 %>%
        filter(GLR == curve)

    x <- log(data$drhov)
    y <- log(data$FF)

    model <- model.select(x, y, degree)
    val <- data.frame(x = log(drhov))

    # df = data.frame(x = log(seq(1, 81, 1)))     # dataframe with drhov sequence
    # y.df <- as.numeric(predict(model, df))
    # ff.df <- exp(y.df)

    y.val <- as.numeric(predict(model, val))
    ff <- exp(y.val)

    named.list( drhov, GLR, degree, curve, model, y.val, ff )
}

model.select <- function(x, y, degree) {
    models <- list(
        lm(y ~ x),
        lm(y ~ x + I(x^2)),
        lm(y ~ x + I(x^2) + I(x^3)),
        lm(y ~ x + I(x^2) + I(x^3) + I(x^4)),
        lm(y ~ x + I(x^2) + I(x^3) + I(x^4) + I(x^5) )
    )
    sel.mod <- models[[degree]]
    return(sel.mod)
}