[CCoE Notice] Fw: PETR DISSERTATION ANNOUNCEMENT

[Knudsen, Rachel W]

The Petroleum Engineering Department


Invites the Cullen College of Engineering

To the

PhD Dissertation Defense


Predicting Static Data Using Dynamic Data and Quantitative Sample Characterization

Abdullah Bilal


Date: Thursday, November 14, 2019

Location: Technology Bridge (Formerly ERP) Building 9, Auditorium 135

Time: 10:00 am – 12:00 pm

Committee Chair: Dr. Lori Hathon

Committee Members:

Dr. Michael Myers, Dr. Guan Qin, Dr. Konstantinos Kostarelos, Dr. Nishank Saxena


Abstract


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.

-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://Bug.EGR.UH.EDU/pipermail/engi-dist/attachments/20191105/91fd96c6/attachment-0001.html 
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Thesis announcement- Abdullah Bilal- fa19.docx
Type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
Size: 14733 bytes
Desc: Thesis announcement- Abdullah Bilal- fa19.docx
Url : http://Bug.EGR.UH.EDU/pipermail/engi-dist/attachments/20191105/91fd96c6/attachment-0001.bin