rOpenserver

The goal of rOpenserver is providing an R interface to Petex (Petroleum Experts) applications Prosper, GAP and MBAL to perform automated tasks, generate datasets for statistical analysis, and advanced fine control of solvers and calculations.

Installation

rOpenserver is not in CRAN yet but in the meantime, you can install it from GtiHub using the devtools package, from the rOpenserver repository with:

install.packages("devtools")
devtools::install_github("og-analytics/rOpenserver", dependencies = TRUE)

The argument dependencies has the role of downloading and installing packages that are key for rOpenserver, such as R6 and RDCOMClient. If you want to install RDCOMClient separately you could install it from its repository via devtools as well. Then after you could proceed to install rOpenServer.

devtools::install_github("omegahat/RDCOMClient")

Indicating files and folders

In R, as in Python or any other programming languages, there are some conventions to deal with the filenames and folder or directories. In Windows, we should use a double-backslash to separate the paths. In Unix operating systems, like Linux and MacOS, we use a single slash.

For instance, in the following example, we set the folder where typically the Prosper examples are. Then , we use slash / or the double-backslash \\ to separate the folder names, otherwise you will get error. We use both ways in the following two code blocks.

# point to the well model
prosper_folder = "C:/Program Files (x86)/Petroleum Experts/IPM 10/Samples/prosper"
model_file <- "T00_Integrated_Oil_Well.Out"
model_filename <- file.path(prosper_folder, model_file)
file.exists(model_filename)
#> [1] TRUE

Prosper example: read reservoir pressure and temperature

This short example will read some values from the Reservoir pressure and temperature from a model residing in the Examples folder: T00_Integrated_Oil_Well.Out.

# load OpenServer
library(rOpenserver)


# Initialize OpenServer
prosper_server <- openserver()

# Start Prosper
cmd = "PROSPER.START"
prosper_server$DoCmd(cmd)
#> [1] 0

# point to the well model
prosper_folder = system.file("models", package = "rOpenserver")
model_file <- "T00_Integrated_Oil_Well.Out"
model_filename <- file.path(prosper_folder, model_file)
file.exists(model_filename)
#> [1] TRUE

# open Prosper model
open_cmd <- "PROSPER.OPENFILE"
open_cmd <- paste0(open_cmd, "('", model_filename, "')")
DoCmd(prosper_server, open_cmd)
#> [1] 0


# get reservoir pressure
cmd <- "PROSPER.SIN.IPR.Single.Pres"
print(prosper_server$DoGet(cmd))
#> [1] "5200.000000000"

# get reservoir temperature
cmd <- "PROSPER.SIN.IPR.Single.Tres"
print(DoGet(prosper_server, cmd))      # S3 class: another way of getting values
#> [1] "210.000000000"


# shutdown Prosper
Sys.sleep(3)
command = "PROSPER.SHUTDOWN"
prosper_server$DoCmd(command)
#> [1] 0
prosper_server <- NULL

Uses of rOpenserver

The previous example just shows a spark of the functionality of rOpenserver: starting Prosper, opening a well model, read values from it, and close Prosper. This is what we call an instance.

There are plenty more examples that make use of rOpenserver. During my years as Production Engineer working on well models and optimization we practically had endless use for this interface. Okay, you could call it a data science oriented solution, but not all is really data science. The use cases go beyond that. Let me cite a few, just for Prosper:

• Batch automation for reading all the Prosper well models residing in your disk, or the network drive

• Convert all the well models in Prosper to a rectangular or tidy dataset, very much like an Excel spreadsheet: a dataframe, which is a more powerful data structure for performing data analysis

• Perform an inventory of all the fluid properties correlations that work in your field

• Read specific variables inside the well models and presenting them in dataframes

• Perform statistical analysis of missing values and outliers across all the variables that were entered in the well model

• Treat the well models as observations (rows) instead of binary files, where the well properties are the variables or features.

• Extract well variables for a particular well system, such as well tests, bottomhole data, correlation curves, pressure and temperature at depth, gas lift properties, or any other artificial lift method

• Perform corrections of well data in batch, always keeping in mind that each well is a row, not a file

• Export the well model from binary to CSV or another format of your preference. Excel, maybe.

• Find and diagnose quicker a well model that refuses to run

• Perform the calibration of the well model in an automated fashion

• Connect the well model using a unique identifier to the master production database of the company

• Use R as a common platform to connect the well models to SQL queries of the company production database

• Reduce drastically the time of calibrating all the well model with new well test data, from months to weeks, or even days

• Run multiple instances of Prosper to analyze wells that have multiple strings, such as dual gas wells: one Prosper instance for the Short string, and a second instance for the Long string