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You have been given a field dataset from multiple fields of many different wells (project_data.csv). Your job is to analyze the data and try to create a correlation to predict solution gas-oil ratio at the bubblepoint. The field dataset has five columns of data as follows: Gas oil ratio measured at the stock tank (Rst) in standard cubic feet per stock tank barrel (scf/STB), Specific Gravity of the oil at the stock tank (SGoil) where 1.0 is the specific gravity of water,Specific Gravity of the gas at the separator (SGgas) where 1.0 is the specific gravity of air,Separator Pressure (Psp) in psiSeparator Temperature (Tsp) in Fahrenheit, You need to create a correlation for Rst, using the other values as input values. Some key points will be the following: Importing the data Performing basic statistical analysis on the dataIdentifying any issues/outliers in the dataset and cleaning themVisualizing the datasetWorking on single and multivariable correlations using the clean data setGenerating a final equation to predict Rst
_project.docx

_project.docx

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Project – 2060 Spring 2019 (20% of your final grade)
Analyzing Data for Solution Gas-Oil Ratio (GOR) at the Bubblepoint
The initial producing Gas-Oil Ratio (GOR) provides a good estimate of solution GOR for use at pressures
equal to and above the bubble point pressure (the point where oil changes phase from liquid to gas) of a
black oil. This will not be true if free gas from a gas cap or another formation is produced with the oil.
Field data often exhibit a great deal of scatter; however, a trend of constant GOR usually can be
discerned before reservoir pressure drops below the bubblepoint.
Assignment:
You have been given a field dataset from multiple fields of many different wells (project_data.csv). Your
job is to analyze the data and try to create a correlation to predict solution gas-oil ratio at the
bubblepoint. The field dataset has five columns of data as follows:





Gas oil ratio measured at the stock tank (Rst) in standard cubic feet per stock tank barrel
(scf/STB),
Specific Gravity of the oil at the stock tank (SGoil) where 1.0 is the specific gravity of water,
Specific Gravity of the gas at the separator (SGgas) where 1.0 is the specific gravity of air,
Separator Pressure (Psp) in psi
Separator Temperature (Tsp) in Fahrenheit,
You need to create a correlation for Rst, using the other values as input values. Some key points will be
the following:






Importing the data
Performing basic statistical analysis on the data
Identifying any issues/outliers in the dataset and cleaning them
Visualizing the dataset
Working on single and multivariable correlations using the clean data set
Generating a final equation to predict Rst
Hint: Transformation of all of the data by taking the logarithm is VERY common for this kind of data due
to the amount of spread.
The deliverable will be:

A Jupyter Lab file containing a very well described analysis of your dataset and any assumptions,
observations, and all work notated. I should be able to reproduce your results using the file.
Good luck.
Project – 2060 Spring 2019 (20% of your final grade)
Analyzing Data for Solution Gas-Oil Ratio (GOR) at the Bubblepoint
The initial producing Gas-Oil Ratio (GOR) provides a good estimate of solution GOR for use at pressures
equal to and above the bubble point pressure (the point where oil changes phase from liquid to gas) of a
black oil. This will not be true if free gas from a gas cap or another formation is produced with the oil.
Field data often exhibit a great deal of scatter; however, a trend of constant GOR usually can be
discerned before reservoir pressure drops below the bubblepoint.
Assignment:
You have been given a field dataset from multiple fields of many different wells (project_data.csv). Your
job is to analyze the data and try to create a correlation to predict solution gas-oil ratio at the
bubblepoint. The field dataset has five columns of data as follows:





Gas oil ratio measured at the stock tank (Rst) in standard cubic feet per stock tank barrel
(scf/STB),
Specific Gravity of the oil at the stock tank (SGoil) where 1.0 is the specific gravity of water,
Specific Gravity of the gas at the separator (SGgas) where 1.0 is the specific gravity of air,
Separator Pressure (Psp) in psi
Separator Temperature (Tsp) in Fahrenheit,
You need to create a correlation for Rst, using the other values as input values. Some key points will be
the following:






Importing the data
Performing basic statistical analysis on the data
Identifying any issues/outliers in the dataset and cleaning them
Visualizing the dataset
Working on single and multivariable correlations using the clean data set
Generating a final equation to predict Rst
Hint: Transformation of all of the data by taking the logarithm is VERY common for this kind of data due
to the amount of spread.
The deliverable will be:

A Jupyter Lab file containing a very well described analysis of your dataset and any assumptions,
observations, and all work notated. I should be able to reproduce your results using the file.
Good luck.

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