New polymer rheology models based on machine learning

dc.contributor.advisorBalhoff, Matthew T.
dc.creatorAlqahtani, Abdulwahab Saeed
dc.date.accessioned2020-05-04T22:09:40Z
dc.date.available2020-05-04T22:09:40Z
dc.date.created2019-08
dc.date.issued2019-09-17
dc.date.submittedAugust 2019
dc.date.updated2020-05-04T22:09:40Z
dc.description.abstractA successful polymer-type EOR project relies upon many factors, including an adequate characterization, description, and prediction of the polymer’s rheology. A high polymer viscosity can improve the mobility and sweep efficiency, but can also lead to poor injectivity. Polymers are generally non-Newtonian and the rheology is a function of in-situ shear rate, polymer concentration, salinity, temperature, molecular weight, and molecular structure. A priori estimation of polymer rheology using models is important for design of polymer floods and prediction using numerical reservoir simulators. Existing models require many fitting parameters, are purely empirical, and can rarely be used for a priori estimation. The objective of this work was to develop new models to predict the viscosity of HPAM polymers used in enhanced oil recovery (EOR) and implement them into a chemical flooding numerical reservoir simulator. The study uses a combination of fundamental, physical models and machine learning methods to develop new predictive models. The data used in the study includes the measured polymer rheology at various polymer concentrations, molecular weights and types, temperatures, and brine salinity and hardness. Data are first fit to the 4-parameter Carreau’s model and then advanced machine learning techniques are used to develop the models of the Carreau parameters with the aforementioned solution properties. The models are then used to predict the rheology of new samples which are validated against data measured on an ARES G2 rheometer. All data fit the 4-parameter Carreau model well. The new models for the zero-shear viscosity, shear thinning index, and time constant are a function of temperature, polymer concentration, salinity, hardness, and molecular weight using less than ten parameters
dc.description.departmentPetroleum and Geosystems Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/81161
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/8174
dc.language.isoen
dc.subjectMachine learning
dc.subjectPolymer rheology
dc.subjectEOR
dc.subjectChemical EOR
dc.subjectPolymer flooding
dc.titleNew polymer rheology models based on machine learning
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentPetroleum and Geosystems Engineering
thesis.degree.disciplinePetroleum Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
ALQAHTANI-THESIS-2019.pdf
Size:
29.52 MB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.85 KB
Format:
Plain Text
Description: