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<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 18pt;"><b>The Petroleum Engineering Department</b></span></p>
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<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14pt;"><b>I</b></span><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14pt;"><b>nvites the Cullen
College of Engineering</b></span></p>
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<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14pt;"><b>PhD Dissertation Defense</b></span></p>
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<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif;"><b>Predicting Static Data Using Dynamic Data and Quantitative Sample Characterization</b></span></p>
<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif;"><b>Abdullah Bilal</b></span></p>
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<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif;"><b>Date:
</b>Thursday, November 14, 2019</span></p>
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<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif; font-size: medium; color: rgb(0, 0, 0);"><b>Location:</b> Technology Bridge (Formerly ERP) Building 9, Auditorium 135</span><br>
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<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif;"><b>Time:</b> 10:00 am – 12:00 pm</span></p>
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<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif;"><b>Committee Chair:</b> Dr. Lori Hathon</span></p>
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<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif;"><b>Committee Members:</b></span></p>
<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif;">Dr. Michael Myers, Dr. Guan Qin, Dr. Konstantinos Kostarelos, Dr. Nishank Saxena</span></p>
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<p style="text-align: center; font-family: Times; font-size: medium;"><span style="font-family: Calibri, Helvetica, sans-serif;"><b>Abstract</b></span></p>
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<p style="font-family: Times; font-size: medium; text-align: start"><span style="font-family: Calibri, Helvetica, sans-serif;">This work develops an improved understanding of the stess-strain dependence of data along a “triaxial” stress path. “Static data”
are defined as the large strain (> 10−3) measurements on unloading and reloading along multistage tri-axial stress paths. “Dynamic data” are the small strain (< 10−6) data acquired using standard pitch and catch acoustic velocity measurement techniques. The
multistage triaxial stress path is a systematic exploration of the yield surface of a single sample by performing triaxial tests at increasing confining stress. The samples were measured “dry” i.e. equilibrated to ambient conditions. The effects of acoustic
dispersion and poroelastic effects are therefore assumed negligible. The results of the strain dependent experiments are analyzed in terms of Young’s Modulus. A quadratic fit has been applied to static data. This allows us to separate the response into linear
and nonlinear elastic terms, with coefficients M1, and M2 respectively. The rest of the strain is assumed to be induced “plastic strains”. M1 is dominated by the contact modulus and is constant throughout the entire unload and reload cycles. M2 the nonlinear
elastic term, is due to the opening and closing of generated cracks. These interpretations are based on the equality we find between M1 and the measured modulus determined from the velocity and the correlation we find between M2 with the measured total irrecoverable
strains. Acoustic velocity is predicted from the static data using the measured M1 data. A compaction model is modified to fit plastic strains. This work provides robust connection between the Young’s modulus derived from static and dynamic data to that derived
from empirically based correlations. Based on this work the strain dependence of Young’s modulus can be predicted. A model has been used to predict the static data. This work involves the use of thin section data to provide a mineralogical and textural based
methodology to predict the model parameters.</span></p>
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