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<meta http-equiv="Content-Type" content="text/html; charset=Windows-1252"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; color: rgb(0, 0, 0); font-size: 14px; font-family: Calibri, sans-serif; "><div><br></div><span id="OLK_SRC_BODY_SECTION"><div xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:dt="uuid:C2F41010-65B3-11d1-A29F-00AA00C14882" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40"><div lang="EN-US" link="blue" vlink="purple"><div class="WordSection1"><p class="MsoNormal" align="center" style="mso-margin-top-alt:auto;margin-bottom:6.0pt;text-align:center;line-height:18.0pt"><a name="_Toc434675189"><b><span style="font-size: 14pt; font-family: 'Times New Roman', serif; color: black; ">PhD DEFENSE STUDENT: </span></b></a><span style="font-size: 14pt; font-family: 'Times New Roman', serif; color: black; ">Aaditya Khanal</span><span style="font-family: 'Times New Roman', serif; color: black; "><o:p></o:p></span></p><p class="MsoNormal" align="center" style="mso-margin-top-alt:auto;margin-bottom:6.0pt;text-align:center;line-height:18.0pt"><b><span style="font-size: 14pt; font-family: 'Times New Roman', serif; color: black; ">DATE: </span></b><span style="font-size: 14pt; font-family: 'Times New Roman', serif; color: black; ">Friday, November 18, 2016</span><span style="font-family: 'Times New Roman', serif; color: black; "><o:p></o:p></span></p><p class="MsoNormal" align="center" style="mso-margin-top-alt:auto;margin-bottom:6.0pt;text-align:center;line-height:18.0pt"><b><span style="font-size: 14pt; font-family: 'Times New Roman', serif; color: black; ">TIME:
</span></b><span style="font-size: 14pt; font-family: 'Times New Roman', serif; color: black; ">11:30 AM</span><span style="font-family: 'Times New Roman', serif; color: black; "><o:p></o:p></span></p><p class="MsoNormal" align="center" style="mso-margin-top-alt:auto;margin-bottom:6.0pt;text-align:center;line-height:18.0pt"><b><span style="font-size: 14pt; font-family: 'Times New Roman', serif; color: black; ">PLACE: </span></b><span style="font-size: 14pt; font-family: 'Times New Roman', serif; color: black; ">1A, Energy Research Park (ERP), Room 191D</span><span style="font-family: 'Times New Roman', serif; color: black; "><o:p></o:p></span></p><p class="MsoNormal" align="center" style="mso-margin-top-alt:auto;margin-bottom:6.0pt;text-align:center;line-height:18.0pt"><b><span style="font-size: 14pt; font-family: 'Times New Roman', serif; color: black; ">DISSERTATION CHAIR: </span></b><span style="font-size: 14pt; font-family: 'Times New Roman', serif; color: black; ">Dr. John Lee</span><span style="font-family: 'Times New Roman', serif; color: black; "><o:p></o:p></span></p><div class="MsoNormal" align="center" style="margin-bottom:0in;margin-bottom:.0001pt;text-align:center"><b><span style="font-size: 14pt; line-height: 115%; font-family: 'Times New Roman', serif; color: black; "><hr size="2" width="100%" align="center"></span></b></div><p class="MsoNormal" align="center" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;text-align:center"><b><span style="font-size: 14pt; line-height: 115%; font-family: 'Times New Roman', serif; color: black; ">TITLE:</span></b><span style="font-size: 12pt; line-height: 115%; font-family: 'Times New Roman', serif; color: black; "><o:p></o:p></span></p><p class="MsoNormal" align="center" style="margin-bottom:0in;margin-bottom:.0001pt;text-align:center;line-height:150%"><b><span style="font-size: 14pt; line-height: 150%; font-family: 'Times New Roman', serif; color: black; ">Numerical Study and Production Forecasting of Unconventional Liquid-Rich Shale Reservoirs<o:p></o:p></span></b></p><p class="MsoNormal" align="center" style="margin-bottom:0in;margin-bottom:.0001pt;text-align:center;line-height:150%"><b><span style="font-size: 12pt; line-height: 150%; font-family: 'Times New Roman', serif; color: black; "><o:p> </o:p></span></b></p><p class="gmail-abstractsectionheading" style="margin:0in;margin-bottom:.0001pt;text-align:justify;line-height:150%">
Use of horizontal drilling and hydraulic fracturing of unconventional shale formations have changed the landscape of US oil and gas production. Accurate production performance evaluation and forecasting in shales during the early stages of development can play
an important role in minimizing uncertainty. Arps’ hyperbolic decline model and the modified Arps model are widely used to estimate ultimate recovery for both conventional and unconventional reservoirs. However, Arps’ model is applicable only to reservoirs
in boundary-dominated flow, which appears only after a significant time in ultra-low permeability reservoirs.
<o:p></o:p></p><p class="gmail-abstractsectionheading" style="margin:0in;margin-bottom:.0001pt;text-align:justify;line-height:150%">
<o:p></o:p></p><p class="gmail-abstractsectionheading" style="margin:0in;margin-bottom:.0001pt;text-align:justify;line-height:150%">
In this study, reservoir simulation was used to identify the effect of several uncertain parameters and their interaction with each other on estimated ultimate recovery (EUR) of liquid rich shale (LRS) gas condensate reservoirs. Analytical tri-linear flow model,
derived for a single-phase flow case, was modified for multiphase flow with simplifying assumptions. It was seen that gas condensate wells in shales exhibit a long transitional period between the end of linear flow and the start of boundary dominated flow.
Pressure normalization was found to be an effective method to identify flow regimes in gas condensate reservoirs. Results showed that transient linear flow model with no modification for boundary-dominated flow overestimates the production forecast in almost
all cases. Finally, compositional reservoir model was used to create several iterations of synthetic production histories from liquid rich shales (LRS) wells based on Monte Carlo simulation with predefined probability distributions. Cumulative gas, gas rate,
and condensate-to-gas ratio (CGR) for the simulated cases were decomposed by principal component analysis (PCA) and were used to recreate the original data. The dataset was cross-validated to check its ability to predict the missing production data. This workflow
was verified for field data from the Eagle Ford Shale.<o:p></o:p></p><p class="gmail-abstractsectionheading" style="margin:0in;margin-bottom:.0001pt;text-align:justify;line-height:150%">
<o:p></o:p></p><p class="MsoNormal" style="margin-bottom:0in;margin-bottom:.0001pt;text-align:justify;line-height:150%"><span style="font-size: 12pt; line-height: 150%; font-family: 'Times New Roman', serif; ">Given the uncertainty in forecasts using traditional models, the work-flow presented in this study, which involves reservoir simulation and data-driven approach, improves
the accuracy of production forecasts from new wells or wells with limited production history. This workflow demonstrated in this work can be readily automated to analyze a large set of production data from conventional and unconventional reservoirs.<b><span style="color:black"><o:p></o:p></span></b></span></p></div></div></div></span><style><!--
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