Project Team
Students
Tracie Butler
Petroleum Engineering
Penn State Schuylkill
Faculty Mentors
Gregory King
Penn State University Park
Petroleum Engineering
Project
https://sites.psu.edu/mcreu/files/formidable/2/MCREU-Final-Presentation-.pdf
Project Video
Project Abstract
Name: Tracie Butler
Campus Affiliation: Penn State Schuylkill
Major: Petroleum Engineering
Anticipated Graduation Date: May 2023
Mentors: Gregory King (University Park)
Partner: None
Project Title: Oil Production Forecasting in Petroleum Engineering
My Research Paper is entitled “Oil Production Forecasting in Petroleum Engineering”. This research topic investigates how to improve oil production forecasts to predict how much recoverable oil is produced in the future. There is no real problem here but more of a question instead; “How Can We Improve these Forecasts?”. There’s a large amount of collected production data that typically does not get used in the forecast. Specifically, I will focus on the GOR data collected over time. Currently, we are attempting to determine if points on a GOR plot can provide insight that can help us with the oil production forecasts. We have identified a particular point on the oil production decline curve that has been unused in oil forecasting. To implement this point, we combined the oil production decline curve with the GOR plot and used material balance analysis to incorporate this point into our forecast. Up to date, we have not obtained our final results, but we intend to investigate two GOR extrapolation methods on synthetic data to determine the better choice for GOR estimates. In our analysis we will consider the synthetic data the “Truth” data because we know all aspects of this dataset. Once we determine the best way to extrapolate the GOR data, we will be able to exploit the observed point mentioned previously on the oil decline curve. We will incorporate the GOR extrapolation into material balance analysis to help ground our approach to the “Truth”. We will then determine if this methodology is general and can be used if the “Truth” is unknown. Because of this work, we will be able to improve our oil production forecasts by incorporating data normally ignored during reservoir analysis.
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