In jkennel/rbr: Assists with handling rbr (.rsk files) in R

knitr::opts_chunk$set(echo = TRUE)
library(plotly)
library(rbr)
library(DT)

Purpose

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{width=35%}

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The purpose of this vignette is to compare the transducer values to the rbr pressure measurements. This is a straight-forward arithmetic calculation that is aided by a figure. Make sure units are the same and typically the transducer value will need to be converted to equivalent (m $H_2O$).

$d_{water} = d_{trans} - (P^{port}_t - P^{atm}_t)$

• $d_{water}$ = depth to water
• $d_{trans}$ = depth to transducer
• $P^{port}_t$ = port pressure at time t
• $P^{atm}_t$ = depth to water at time t

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Meta data

Meta data is a very important component of the comparison process. Depth of the transducer, the elevation of the reference point, the units for measurements, type of measurement (barometric pressure, port pressure), file locations, and well location info. While these are important values some may not be necessary for your particular application or known early in a project. Typically, these would be stored in a couple of files. Also, when creating these files we want to use consistent and not complicated field names across files. This means try to avoid spaces, special characters, and capitilization changes. Dates should be yyyy-mm-dd hh:mm:ss.

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Well meta table

well_meta <- fread('/media/kennel/Data/phd/personnel/pat/well_meta.csv')
print(well_meta)

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Port meta table

well_name <- 'UW-1'

port_meta <- fread('/media/kennel/Data/phd/personnel/pat/transducer_depth_path.csv')
print(port_meta)

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Read in data

Select a specific period of time when a manual measurement was taken. This function reads directly from rsk files and will be fastest if a short range is used.

port <- reference_data(port_meta,
                       id_col = 'id', 
                       path_col = 'path', 
                       depth_col = 'depth', 
                       elev_unit = well_meta[well == well_name]$elev_unit,
                       start = '2014-12-08 12:00:00', 
                       end = '2014-12-08 18:00:00',
                       ref_elev = well_meta[well == well_name]$ref_elev)
print(port)

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Compare to manual measurement

When were manual water level measurements taken? It may be useful to have the measurements at the start and the end of the test. In this example we will only use the measurement at the start of the test, but to add additional comparison periods you can use addional rows in your blended data.table (you will need the corresponding dates in your port dataset for matches to be found).

# read manual water level file
blended <- fread('/media/kennel/Data/phd/personnel/pat/blended_open_hole.csv')

# convert to datetime
blended[, start := as.POSIXct(start, tz = 'UTC')]
blended[, end := as.POSIXct(end, tz = 'UTC')]

blended

# select data period from port dataset
comp <- filter_dates(port, blended, keep = TRUE, include_filt_cols = TRUE)

comp

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Calculate the average difference

It is important to see how different the range of data for the period compares. Here we clearly can see P1_19 is different from the rest, and likely is malfunctioning.

diffs <- comp[, list(mean_diff = round(mean(meas_wl - dtw, na.rm = TRUE), 3),
                     max_diff = round(max(meas_wl - dtw, na.rm = TRUE), 3),
                     min_diff = round(min(meas_wl - dtw, na.rm = TRUE), 3),
                     range = round(diff(range(meas_wl-dtw)), 3)),
              by = list(name, depth, id)]

print(diffs)

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Putting it all together

```r

library(rbr) library(data.table)

read in meta data

well_name <- 'UW-1' well_meta <- fread('/media/kennel/Data/phd/personnel/pat/well_meta.csv') port_meta <- fread('/media/kennel/Data/phd/personnel/pat/transducer_depth_path.csv')

convert data to depth to water/elevation

port <- reference_data(port_meta, id_col = 'id', path_col = 'path', depth_col = 'depth', elev_unit = well_meta[well == well_name]$elev_unit, start = '2014-12-08 12:00:00', end = '2014-12-08 18:00:00', ref_elev = well_meta[well == well_name]$ref_elev)

read manual water level file

blended <- fread('/media/kennel/Data/phd/personnel/pat/blended_open_hole.csv')

convert to datetime

blended[, start := as.POSIXct(start, tz = 'UTC')] blended[, end := as.POSIXct(end, tz = 'UTC')]

select data period from port dataset

comp <- filter_dates(port, blended, keep = TRUE, include_filt_cols = TRUE)

calculate differences for each port

diffs <- comp[, list(mean_diff = round(mean(meas_wl - dtw, na.rm = TRUE), 3), max_diff = round(max(meas_wl - dtw, na.rm = TRUE), 3), min_diff = round(min(meas_wl - dtw, na.rm = TRUE), 3), range = round(diff(range(meas_wl-dtw)), 3)), by = list(name, depth, id)]

jkennel/rbr documentation built on Feb. 15, 2023, 1:37 a.m.