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<b><span style="font-size:18.0pt; font-family:"Times New Roman",serif; color:black">MS Thesis Defense</span></b></p>
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<b><span style="font-size:18.0pt; font-family:"Times New Roman",serif; color:black">Petroleum Engineering Department</span></b></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"> </span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"></span></p>
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<b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black">Application of Data Analytics to Prediction of Initial Production in Tight Oil Reservoir </span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"></span></p>
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<b><span style="font-size:14.0pt; font-family:"Times New Roman",serif; color:black">Nathan Tran</span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"></span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#1F497D"> </span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"></span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">Date: </span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">Tuesday, December 4, 2018</span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"> </span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"></span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">Location: </span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">ConocoPhillips Petroleum Engineering Building 9, Rm 104</span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">Energy Research Park</span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">Time: </span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">11:00 am</span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"> </span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"></span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">Committee Chair:</span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"></span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">Dr. Ganesh C. Thakur</span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"> </span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"></span></p>
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<b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">Committee Members:</span></b><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black"></span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">Dr. Guan Qin</span></p>
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<span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:black">Dr. Adwait Chawathe</span></p>
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<span style="font-size:11.5pt; font-family:"Times New Roman",serif; color:#222222">Fields J, K and N have been discovered and mostly produced in primary recovery stage. Infill drilling has been a strategy to provide incremental oil production for the fields.
Predicting Initial production (IP) rate for new infill wells is important because it is used for decline curve analysis to forecast future production hence helpful for economic analysis. The main objective of this study is to develop a quick predictive tool
to forecast IP for wells in LT reservoir in the three fields J, K and N.</span></p>
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<span style="font-size:11.5pt; font-family:"Times New Roman",serif; color:#222222">In conclusion, PCA and linear regression can be used to develop a prediction model for IP. In this case, components with the most correlation to IP contribute to a better prediction
model than do the first components with greatest variance. Thorough analysis and quality check on data are important before incorporating them into the prediction model. The model can be used to predict future wells’ initial production which is a very important
parameter for economic analysis. Missing data should be handled with understanding the behavior of the parameters (e.g, handling missing pressure data and skin data). As expected, the more data samples in the training set, the more robust the prediction model
becomes.</span><span style="font-size:12.0pt; font-family:"Times New Roman",serif; color:#222222"></span></p>
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