Drake Wells What I did Lab 3 Written Report PETE 301 For this lab I am tasked to employ the provided Python code to compute the gas z factor by analyzing historical pressure in conjunction with historical production data I ll then create a p z vs Gp plot With the available data I ll carry out a linear regression to project the EUR and determine the recovery factor for the well I first renamed the given python file and excel file to Lab3 DDW in order to match so the zfact py function can be run I then created a table of Z as a function of pressure in the worksheet titled Given z factor Moving into the worksheet called Production Data I used the given pressures of 5000 3500 2500 and 2100 psia in column C to find their corresponding z factors using the python function The z values are shown in column D I then used excel s built in mathematical operations to divide pressure by z values column C column D to obtain p z values as shown in column E This will be our y axis and the Gp values in column B will be on the x values on the x axis It is worth noting that parameters were set as T 590 degR grav 0 76 air 1 and delta p 100 psia The x and y values were brought over to columns L and M for a easily accessible view of the data Using equations 17 1 17 6 and 17 7 from the textbook I used the built in excel operations including Sum and Average to calculate slope and intercept of the linear trendline a1 the slope was calculated using eq 17 6 column H followed by calculating for a0 the intercept of the linear trendline column I by plugging in the slope to eq 17 7 I got a slope of 387 209 and a y intercept of 5249 828 To check my work I used the excel Slope function in column P and got the same value of 387 209 for the slope Next I created a graphical representation of this data I constructed a scatter plot of p z vs Gp showing a linear trendline displaying the linear equation to 6 significant digits and expanding the boundaries of the x axis so it could graphically include the x intercept where the line passed through the x axis Seeing the trendline equation display values of 387 209 and 5249 828 for the slope and y intercept further allowed me to check my previous work I then calculated G the original gas in place by substituting 0 in the equation for y and solving for x Simplify this gives G as equal to the negative intercept divided by the slope This gave a G value of 13 55811 Bscf as shown in column F Based on the graph this appears to be the x intercept Lastly the abandonment pressure is the reservoir pressure at which production operations are no longer economically viable and are therefore halted Given an abandonment pressure of 1000 psi we are able to calculate the EUR reserves and recovery factor as of 1 1 2022 The EUR is the Gp value when p z is equal to 0 Using the linear regression equation p z m Gp b where m is the slope of the regression line and b is the intercept The EUR can be calculated with the abandonment pressure as follows EUR pabandonment zabandonment b m I plugged this formula in to excel to automate this calculation using its built in mathematical operations Drake Wells At 1000 psi based on our python function the z factor is 0 858867 Plugging in these two values for p and z abandonment in the above equation and using the m and b values calculated earlier we can get an EUR of 10 55 Bscf as shown in column C below the previous data The reserves represent the remaining hydrocarbons expected to be produced from this point onwards until abandonment The reserves are the EUR OIGP G value which yields about 3 Bscf as shown below the EUR in the worksheet The recovery factor RF is the fraction of the original gas in place that has been produced RF OIGP EUR which yields 1 2849 as shown below the reserves in the worksheet Finally since my boss told me that the company may be able to apply artificial lift to reduce the abandonment pressure to 300 psi we can apply the same procedure with 300 psi Abandonment pressure of 300 psi corresponds with a z factor of 0 955514 according to the python function in the given z factor worksheet With these p and z abandonment values and the previous m and b values the formula EUR pabandonment zabandonment b m can be employed which gives a new predicted EUR of 12 7472 Bscf Results can be found directly below the previous results Reserves EUR OIGP which gives 0 8108 Bscf as shown below the EUR The recovery factor is equal to OIGP EUR which is equal to 1 063 as shown below the reserves In conclusion these quantities offer invaluable insights for decision making and financial assessments related to oil wells The Estimated Ultimate Recovery EUR plays a crucial role in determining the economic viability of an oil well influencing both infrastructure planning and the potential return on investments On the other hand reserves represent an oil company s tangible assets driving its stock market value informing production timelines and forecasting the company s long term output prospects The recovery factor provides a lens into how effectively oil can be extracted serving as a comparative tool for different extraction strategies and establishing targets for optimization