Masters Theses


"This work examines how physical and chemical heterogeneity can affect reactive and non-reactive transport in porous media. The effect of heterogeneity of the porous media is investigated both on dissolution rate of magnesite and attenuation time of nonreactive contaminants in non-reactive media. Various spatial distribution were created using statistical parameters in PETREL.A total of 6793 transport modeling simulations were run using CrunchFlow. Lasso regression was used to select most significant features and those features are then used in linear regression and deep learning models.

The magnesite dissolution simulations were performed under different permeability ratios (magnesite /sand permeability) and inlet pH. The variables used for building different realizations of porous media are mineral abundance, major direction anisotropy and minor direction anisotropy. Overall, permeability ratio had the most significant impact on dissolution rate. Deep learning captured 89.0 % of the variance in the data while linear regression only captured 73.2%.

The bromide transport simulations were conducted under various flow rates and transverse dispersivity values. Different spatial distributions were created with different permeability standard deviations and major and minor direction anisotropies. Standard deviation proved to have the most significant impact on attenuation time, followed by major and minor direction anisotropies A more heterogeneous and anisotropic distribution resulted in a slower concentration reduction. The effect of anisotropies were trivial in a relatively homogenous distributions. The linear model can describe 70.83 % of the variance in the data."--Abstract, page iv.


Heidari, Peyman

Committee Member(s)

Grote, Katherine R.
Rogers, J. David


Geosciences and Geological and Petroleum Engineering

Degree Name

M.S. in Geological Engineering


Missouri University of Science and Technology

Publication Date

Summer 2017

Journal article titles appearing in thesis/dissertation

  • Prediction of magnesite dissolution rate in heteogenous porous media using deep learning
  • Data-driven study of non-reactive contaminant attenuation time in heterogenous porous media


xi, 78 pages

Note about bibliography

Includes bibliographical references.


© 2017 Mahta Gholizadeh Ansari, All rights reserved.

Document Type

Thesis - Open Access

File Type




Thesis Number

T 11167

Electronic OCLC #