Integrating Silicate-Based Nanoparticles with Low-Salinity Water Flooding for Enhanced Oil Recovery in Sandstone Reservoirs

A review on application of nanoparticles for EOR purposes: history and current challenges

Applications of nanotechnology in several fields of petroleum industry, e.g., refinery, drilling and enhanced oil recovery (EOR), have attracted a lot of attention, recently. This research investigates the applications of nanoparticles in EOR process. The potential of various nanoparticles, in hybrid and bare forms for altering the state of wettability, reducing the interfacial tension (IFT), changing the viscosity and activation of other EOR mechanisms are studied based on recent findings. Focusing on EOR, hybrid applications of nanoparticles with surfactants, polymers, low-salinity phases and foams are discussed and their synergistic effects are evaluated. Also, activated EOR mechanisms are defined and specified. Since the stabilization of nanofluids in harsh conditions of reservoir is vital for EOR applications, different methods for stabilizing nanofluids through EOR procedures are reviewed. Besides, a discussion on different functional groups of NPs is represented. Later, an economic model for evaluation of EOR process is examined and "Hotelling" method as an appropriate model for investigation of economic aspects of EOR process is introduced in detail. The findings of this study can lead to better understanding of fundamental basis about efficiency of nanoparticles in EOR process, activated EOR mechanisms during application of nanoparticles, selection of appropriate nanoparticles, the methods of stabilizing and economic evaluation for EOR process with respect to costs and outcomes.

Data-Driven Connectionist Models for Performance Prediction of Low Salinity Waterflooding in Sandstone Reservoirs

Low salinity waterflooding (LSWF) and its variants also known as smart water or ion tuned water injection have emerged as promising enhanced oil recovery (EOR) methods. LSWF is a complex process controlled by several mechanisms and parameters involving oil, brine, and rock composition. The major mechanisms and processes controlling LSWF are still being debated in the literature. Thus, the establishment of an approach that relates these parameters to the final recovery factor (RF_f) is vital. The main objective of this research work was to use a number of artificial intelligence models to develop robust predictive models based on experimental data and main parameters controlling the LSWF determined through sensitivity analysis and feature selection. The parameters include properties of oil, rock, injected brine, and connate water. Different operational parameters were considered to increase the model accuracy as well. After collecting the relevant data from 99 experimental studies reported in the literature, the database underwent a comprehensive and rigorous data preprocessing stage, which included removal of duplicates and low-variance features, missing value imputation, collinearity assessment, data characteristic assessment, outlier removal, feature selection, data splitting (80-20 rule was applied), and data scaling. Then, a number of methods such as linear regression (LR), multilayer perceptron (MLP), support vector machine (SVM), and committee machine intelligent system (CMIS) were used to link 1316 data samples assembled in this research work. Based on the obtained results, the CMIS model was proven to produce superior results compared to its counterparts such that the root mean squared rrror (RMSE) values for both training and testing data are 4.622 and 7.757, respectively. Based on the feature importance results, the presence of Ca²⁺ in the connate water, Na⁺ in the injected brine, core porosity, and total acid number of the crude oil are detected as the parameters with the highest impact on the RF_f. The CMIS model proposed here can be applied with a high degree of confidence to predict the performance of LSWF in sandstone reservoirs. The database assembled for the purpose of this research work is so far the largest and most comprehensive of its kind, and it can be used to further delineate mechanisms behind LSWF and optimization of this EOR process in sandstone reservoirs.

Pore wettability for enhanced oil recovery, contaminant adsorption and oil/water separation: A review

Wettability, a fundamental property of porous surface, occupies a pivotal position in the fields of enhanced oil recovery, organic contaminant adsorption and oil/water separation. In this review, wettability and the related applications are systematically expounded from the perspectives of hydrophilicity, hydrophobicity and super-wettability. Four common measurement methods are generalized and categorized into contact angle method and ratio method, and influencing factors (temperature, the type and layer charge of matrix, the species and structure of modifier) as well as their corresponding altering methods (inorganic, organic and thermal modification etc.) of wettability are overviewed. Different roles of wettability alteration in enhanced oil recovery, organic contaminant adsorption as well as oil/water separation are summarized. Among these applications, firstly, the hydrophilic alteration plays a key role in recovery of the oil production process; secondly, hydrophobic circumstance of surface drives the organic pollutant adsorption more effectually; finally, super-wetting property of matrix ensures the high-efficient separation of oil from water. This review also identifies importance, challenges and future prospects of wettability alteration, and as a result, furnishes the essential guidance for selection and design inspiration of the wettability modification, and supports the further development of pore wettability application.