Next Article in Journal
The Control of Renewable Energies to Improve the Performance of Multisource Heat Pump Systems: A Two-Case Study
Previous Article in Journal
A Measurement-Based Message-Level Timing Prediction Approach for Data-Dependent SDFGs on Tile-Based Heterogeneous MPSoCs
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Data-Driven Analyses of Low Salinity Waterflooding in Carbonates

School of Mining and Geosciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(14), 6651; https://doi.org/10.3390/app11146651
Submission received: 2 June 2021 / Revised: 5 July 2021 / Accepted: 7 July 2021 / Published: 20 July 2021
(This article belongs to the Section Materials Science and Engineering)

Abstract

Low salinity water (LSW) injection is a promising Enhanced Oil Recovery (EOR) technique that has the potential to improve oil recovery and has been studied by many researchers. LSW flooding in carbonates has been widely evaluated by coreflooding tests in prior studies. A closer look at the literature on LSW in carbonates indicates a number of gaps and shortcomings. It is difficult to understand the exact relationship between different controlling parameters and the LSW effect in carbonates. The active mechanisms involved in oil recovery improvement are still uncertain and more analyses are required. To predict LSW performance and study the mechanisms of oil displacement, data collected from available experimental studies on LSW injection in carbonates were analyzed using data analysis approaches. We used linear regression to study the linear relationships between single parameters and the incremental recovery factor (RF). Correlations between rock, oil, and brine properties and tertiary RF were weak and negligible. Subsequently, we analyzed the effect of oil/brine parameters on LSW performance using multivariable linear regression. Relatively strong linear correlations were found for a combination of oil/brine parameters and RF. We also studied the nonlinear relationships between parameters by applying machine learning (ML) nonlinear models, such as artificial neural network (ANN), support vector machine (SVM), and decision tree (DT). These models showed better data fitting results compared to linear regression. Among the applied ML models, DT provided the best correlation for oil/brine parameters, as ANN and SVM overfitted the testing data. Finally, different mechanisms involved in the LSW effect were analyzed based on the changes in the effluent PDIs concentration, interfacial tension, pH, zeta potential, and pressure drop.
Keywords: low salinity waterflooding; carbonates; data-driven analysis; machine learning; SVM; ANN; DT low salinity waterflooding; carbonates; data-driven analysis; machine learning; SVM; ANN; DT

Share and Cite

MDPI and ACS Style

Salimova, R.; Pourafshary, P.; Wang, L. Data-Driven Analyses of Low Salinity Waterflooding in Carbonates. Appl. Sci. 2021, 11, 6651. https://doi.org/10.3390/app11146651

AMA Style

Salimova R, Pourafshary P, Wang L. Data-Driven Analyses of Low Salinity Waterflooding in Carbonates. Applied Sciences. 2021; 11(14):6651. https://doi.org/10.3390/app11146651

Chicago/Turabian Style

Salimova, Rashida, Peyman Pourafshary, and Lei Wang. 2021. "Data-Driven Analyses of Low Salinity Waterflooding in Carbonates" Applied Sciences 11, no. 14: 6651. https://doi.org/10.3390/app11146651

APA Style

Salimova, R., Pourafshary, P., & Wang, L. (2021). Data-Driven Analyses of Low Salinity Waterflooding in Carbonates. Applied Sciences, 11(14), 6651. https://doi.org/10.3390/app11146651

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop