Enhanced Oil Recovery Operations Research Articles

Optimization of Well Placement in Carbon Capture and Storage (CCS): Bayesian Optimization Framework under Permutation Invariance

Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations being already an established practice for enhanced oil recovery operations. The overarching objective of CCS is to protract the operational viability and sustainability of platforms and oilfields, thereby facilitating a seamless transition towards sustainable practices. This study introduces a comprehensive framework for optimizing well placement in CCS operations, employing a derivative-free method known as Bayesian Optimization. The development plan is tailored for scenarios featuring aquifers devoid of flow boundaries, incorporating production wells tasked with controlling pressure buildup and injection wells dedicated to CO2 sequestration. Notably, the wells operate under group control, signifying predefined injection and production targets and constraints that must be adhered to throughout the project’s lifespan. As a result, the objective function remains invariant under specific permutations of the well locations. Our investigation delves into the efficacy of Bayesian Optimization under the introduced permutation invariance. The results reveal that it demonstrates critical efficiency in handling the optimization task extremely fast. In essence, this study advocates for the efficacy of Bayesian Optimization in the context of optimizing well placement for CCS operations, emphasizing its potential as a preferred methodology for enhancing sustainability in the energy sector.

Performance evaluation of ferro-fluids flooding in enhanced oil recovery operations based on machine learning

The process of enhanced oil recovery (EOR) and core flooding involves various challenges such as preserving cores, configuring experiment setup, scaling from the laboratory to the field, interpreting experimental data, handling interactions between multiple EOR agents, and performing cost-effective evaluations. However, recent advancements in artificial intelligence and deep learning have facilitated the prediction of EOR efficiency, simplifying the process while saving time and money - two vital aspects of petroleum engineering. This study employed deep learning to determine the impact of ferro-fluids on EOR efficiency, which reduces excessive laboratory analysis costs and saves time. The findings may aid in optimizing injectable materials for future EOR projects. Four machine learning (ML) algorithms were utilized to predict the efficacy of ferro-fluids in carbonate and sandstone core flooding: support vector machine (SVM) for classification, multilayer perceptron (MLP), radial basis function (RBF), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) for regression. The models were trained initially, followed by creating a set of validating measurements through classification to determine the regression trend of ferro-fluids. The SVM accurately classified all data points and the resulting regressions were satisfactory, with MLP and ANFIS producing R2 values of 0.9957 and 0.9922, respectively.

Optimizing Well Completion for Polymer Flooding in Conjunction with Waterflood Flow Control Valves

This work presents the proposal design for the completion of a polymer flooding injector well with waterflood flow regulator valves (FRV) in a Colombian field, based on experimental evaluations at the laboratory, intending to reduce the mechanical degradation suffered by the polymer solution at the time of injection, which allows to maintain the design parameters of the improved recovery project and reach the expected recovery factor. An analysis of the parameters and variables that influence the mechanical degradation of the polymer solution during the injection process (polymer solution concentration and the diameters of the FRV) was carried out using one laboratory methodology based on the recommended practices for the evaluation of polymers used in enhanced oil recovery operations API RP63. This work focuses on the following highlights: Evaluation of a waterflood flow regulator valve through experimental tests for polymer flooding and the designing of an initial well completion strategy to minimize mechanical degradation. The proposed valve and diameter resulted in a reduction of only 15 percentage points in the mechanical degradation of the polymeric solution when compared to a commercial water valve.

Molecular insights of condensate trapping mechanism in shale oil reservoirs and its implications on lean gas enhanced oil recovery

The heterogeneous pore structure and the confinement effect of the narrow pore space lead to the differential release/retention phenomenon of hydrocarbons in unconventional reservoirs. So far, the mechanism of this phenomenon, in particular the dynamic process, requires further investigation since it affects the design of gas-based enhanced oil recovery (EOR) operations. In this work, we aim to numerically study the capillary condensate trapping mechanism that causes the differential release phenomenon by conducting molecular dynamics (MD) simulations.In our work, a two-bath MD model is set up, in which two baths are used to mimic the bulk pores and a nano-channel connecting the two baths is used to model the pore throat. Methane (C1) and decane (C10) molecules are used to represent the light and heavy component of the in-situ oil. The MD results show that at the early stage of the primary depletion, the light components release faster than the heavier components, leading to increasing gas oil ratio (GOR) above the system’s bubble point. As depletion continues, heavier hydrocarbons accumulate in the nano-channel due to the confinement effect, while light hydrocarbons tend to remain in the dead-end pores. During the gas EOR process, the injected gas ‘dissolves’ the liquid in the nano-channel and thus enhances the recovery of the ‘trapped’ hydrocarbons.This work systematically investigates the impact of pore throat on the differential release and trapping effect. We demonstrate that a significant amount of light hydrocarbons may be ‘trapped’ during the primary depletion of unconventional reservoirs, due to the capillary condensate trapping effect. The trapped hydrocarbons can be partly recovered through gas injection. Our study has implications for practical EOR operations.

Interfacial tension of smart water and various crude oils

The utilization of smart water in EOR (enhanced oil recovery) operations has drawn increased attention in recent years. Nevertheless, the variety and complexity of mechanisms during smart water injection require more research endeavors. In this study, interfacial tension (IFT) for the smart water/crude oil interacting system has been investigated. The investigated parameters included various salts with different salinity, pH, asphaltene type, and aged smart water with two crude oil samples. The results showed that the hydration of ions in smart water would play a key role to discern the primary mechanism of smart water. The interaction of acidic and basic asphaltene with hydration shell of different ions can be considered as primary mechanism for efficacy of smart water. Acidic and basic asphaltenes serve as intrinsic surfactants due to their interaction with OH− in the anion hydration shell and H+ in the cation hydration shell, respectively. The propensity of acidic asphaltene to interact with anions with high hydration energy and the tendency of basic asphaltene to interact with cations with high hydration energy led to an IFT of 0.2 mN/m and 11.3 mN/m for acidic and basic oils, respectively. The impact of pH also revealed that acidic and basic asphaltenes would have lower IFT in alkaline and acidic environments. Finally, it was found that changes in IFT for the aged smart water with crude oil depend on the asphaltene type as well as salt and salinity.

Microfluidic Studies on Minimum Miscibility Pressure for n-Decane and CO2

Oil production is a complex process that can be made more efficient by applying gas enhanced oil recovery (EOR) methods. Thus, it is essential to know the minimum miscibility pressure (MMP) and minimum miscibility enrichment (MME) of gas in oil. Conventional slim-tube experiments for the measurement of MMP require hundreds of millilitres of real or recombined oil and last over 30 days. Advances in microfluidic technology allow the reduction of the amount of fluid and the time required in determining MMP (or MME), hence making the process rapid. In this study, we developed a microfluidic model with a stochastically distributed pore network, porosity of 74.6% and volume of 83.26 nanolitres. Although the volume was six orders of magnitude smaller than the slim tube, it retained the same proportions, guaranteeing a proper comparison between the tests. This microfluidic chip allowed the study of the MMP of n-decane with carbon dioxide at two different temperature conditions. The experimental results coincided with the results received both from conventional and microfluidic experiments. Furthermore, a numerical simulation of a section of the microfluidic model under the experimental conditions presented results within acceptable margins of the experimental ones. The results of the presented methodology indicate the potential to replace conventional technology for the measurement of MMP with microfluidic technology. Its promise lies in accelerating laboratory tests and increasing the reliability of experimental results and, subsequently, the quality of field gas EOR operations.

Technology Focus: EOR Operations (June 2023)

While supply disruptions and price volatility are not new to the oil and gas industry, the dramatic drop in oil prices because of the COVID-19 pandemic severely affected global enhanced oil recovery (EOR) projects. To add fuel to the fire, the current geopolitical scenario leading to a surge in commodity prices and supply chain disruptions exacerbated this situation by negating any chances of recovery, at least in the short term. Concurrently, the rise in energy consumption and increasing demand for energy security worldwide warrants the need to increase global hydrocarbon production. However, rising environmental concerns with regard to greenhouse gas emissions, coupled with a decrease in the availability of easy-to-produce hydrocarbon resources, mandate the need for a gradual shift toward optimized recovery strategy in a sustainable and cost-efficient manner. As a result, current circumstances dictate focusing on low-cost and low-carbon EOR barrels to secure energy supply in the shorter term while continuing to develop innovative technologies and strategies for enhancing production in the longer term. Energy consumption for mature waterfloods increases sharply at higher water cuts primarily because of the recirculation of water through the existing swept pathways. Lower volumetric sweep in reservoirs leads to excessive water production and less oil recovery. To curb the menace of excess water production, conventional EOR techniques such as polymerflooding or in-depth conformance-control techniques are mostly implemented. Both methods have demonstrated cost savings because of reduced watercuts and reductions in CO2 generation by lowering reinjection of produced water. Additional costs of injectants, transport, and topside facilities, however, contribute to higher project costs. Early optimization measures for existing waterfloods using machine-learning (ML) approaches could enable optimal reservoir management and production by delineating reservoir heterogeneities more efficiently and predicting more-effective interwell connectivity. Furthermore, ML could contribute directly to the optimization and surveillance of EOR applications, which would subsequently affect the performance and the cost of such projects, thereby supporting the achievement of low-cost and low-carbon EOR barrels. Recommended additional reading at OnePetro: www.onepetro.org. SPE 214268 A Novel Approach To Combine Models To Evaluate Interwell Connectivity in a Waterflooded Reservoir With Limited Injection History by Yanfidra Djanuar, Dragon Oil, et al. SPE 210657 A Laboratory-to-Field Approach and Evaluation of Low-Salinity Waterflooding Process for High-Temperature/High-Pressure Carbonate Reservoirs by Hemanta Kumar Sarma, University of Calgary, et al. IPTC 22733 Like Cures Like Microbial Enhanced Oil Recovery in Biodegraded Crude by Thanapong Ketmalee, PTTEP, et al.

Synergistic Effect of Low Salinity Surfactant Nanofluid on the Interfacial Tension of Oil–Water Systems, Wettability Alteration, and Surfactant Adsorption on the Quartz Surface

Interfacial tension (IFT) and wettability are the two major factors influencing oil recovery. The synergistic effect of low salinity water (LSW) with chemical agents such as surfactants, alkalis, and polymer is an emerging technology for enhanced oil recovery (EOR). Recently, the use of nanoparticles for EOR has become the attractive option. This study examines the impact of low salinity surfactant–silica nanofluid (LSS–SNF) on interfacial tension (IFT) and wettability alteration in an oil–water–quartz system using various types of oils, viz., n-heptane, n-decane, benzene, toluene, model oil A, model oil B, and crude oil. Besides, the adsorption of surfactants on the quartz substrate in the presence of silica nanoparticles is also studied. A quartz substrate is particularly chosen to mimic the sandstone formation. It has been observed that the type of oil has a crucial role in the IFT behavior, and different trends have been observed for model oil A and model oil B in the presence of LSS–SNF. Also, a prominent wettability shift is observed on the quartz substrate for the acidic (model oil A) than the other oil employed. Further, the adsorption studies using an ultraviolet–visible (UV–vis) spectrometer confirm the reduction in surfactant adsorption with an increase in silica nanoparticle concentration. The adsorption of surfactants is also confirmed using scanning electron microscopy (SEM) and energy-dispersive X-ray analysis (EDXA), and the order of adsorption is identical to the Freundlich adsorption isotherm. The outcomes of this study will help us understand the application of silica nanofluid in EOR operation.

Modeling Viscosity of CO2-N2 Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM.

Carbon dioxide (CO2) has an essential role in most enhanced oil recovery (EOR) methods in the oil industry. Oil swelling and viscosity reduction are the dominant mechanisms in an immiscible CO2-EOR process. Besides numerous CO2 applications in EOR, most oil reservoirs do not have access to natural CO2, and capturing it from flue gas and other sources is costly. Flue gases are available in huge quantities at a significantly lower price and can be considered economically viable agents for EOR operations. In this work, four powerful machine learning algorithms, namely, extra tree (ET), random forest (RF), gradient boosting (GBoost), and light gradient boosted machine (LightGBM) were utilized to accurately estimate the viscosity of CO2-N2 mixtures. To this aim, a databank was employed, containing 3036 data points over wide ranges of pressures and temperatures. Temperature, pressure, and CO2 mole fraction were applied as input parameters, and the viscosity of the CO2-N2 mixture was the output. The RF smart model had the highest precision with the lowest average absolute percent relative error (AAPRE) of 1.58%, root mean square error (RMSE) of 2.221, and determination coefficient (R 2) of 0.9993. The trend analysis showed that the RF model could precisely predict the real physical behavior of the CO2-N2 viscosity variation. Finally, the outlier detection was performed using the leverage approach to demonstrate the validity of the utilized databank and the applicability area of the developed RF model. Accordingly, nearly 96% of the data points seemed to be dependable and valid, and the rest of them were located in the suspected and out-of-leverage data zones.

Assessing reservoir connectivity with pressure/rate surveillance data

Estimating reservoir connectivity is critical for assessing infill-drilling prospects and initiating fluid injection in enhanced oil recovery operations. Several methods have appeared in the literature over decades to meet these business needs, given that all tools, including seismic imaging, have limitations. Besides imaging, geochemical fingerprinting constitutes a powerful tool to gauge the compartmentalization question. However, real-time pressure/rate surveillance data allows assessing interwell connectivity vis-à-vis the overall drainage volume. This study presents a simplified approach to using the reciprocal-productivity index (RPI) vs. the total-material-balance time ( t ¯ T M B ) plot. This tool exhibits the same slope for those wells in the same compartment beyond the start of the boundary-dominated flow (BDF) period. The wells showing different slopes imply that they are in separate drainage volumes. The early-time transient period remains muted to minimize confusion on this Cartesian plot. We validated the proposed tool's efficacy with 2D and 3D models with increased degrees of reservoir complexity, followed by the verification phase with four field examples. The use of other analytical tools complemented our findings.

A novel stochastic programming model under endogenous uncertainty for the CCS-EOR planning problem

Carbon-capture-and-storage (CCS) is one of the leading technologies to reduce CO2 emissions. A commercial way to deploy CCS on a large scale is to sequestrate CO2 in depleted oil reservoirs and to combine it with enhanced oil recovery (EOR) operations. In this manner, not only the CO2 emission is reduced, but also the oil production increases. The collaborative CCS-EOR planning problem determines the proper allocation of available CO2 to depleted reservoirs and the scheduling of the EOR operations. This problem is of great importance, especially when there are multiple oil reservoirs. This paper presents a deterministic mixed-integer linear programming model as an improvement of an existing model in the literature. Then, it is extended to a multistage stochastic model with endogenous uncertainty in which the parameters expressing the initial oil yields and the periodic depletion factor of oil yields associated with reservoirs are uncertain, and the time of uncertainty realization is decision-dependent. Our deterministic model is computationally more efficient than the existing model in the literature, due to the reduction of binary variables to about one-third. Also, providing the possibility of selecting pipeline types among different options as well as incorporating uncertainty may lead to a significant cost-saving. The proposed models are examined over two case-studies taken from the literature. The results indicate that in comparison to the deterministic model, the cost-saving achieved by incorporating uncertainty is about 8.8%, on average.

Effect of chemicals on the phase and viscosity behavior of water in oil emulsions

Due to population growth, the need for energy, especially fossil fuels, is increased every year. Since the costs of exploring new reservoirs and drilling new wells are very high, most reservoirs have passed their first and second periods of life, and it is necessary to use EOR methods. Water-based enhanced oil recovery (EOR) methods are one of the popular methods in this field. In this method, due to the possibility of emulsion formation is high, and by creating a stable emulsion, viscosity and mobility improved. In this study, the parameters affecting the stability and viscosity of the emulsion have been investigated step by step. In the first step, 50% (v/v) of water has been selected as the best water cut. The type of salt and its best concentration was evaluated in the second step by measuring the average droplets size. The third step investigated the effect of SiO2 nanoparticles and surfactant (span80) on emulsion stability and viscosity. According to the results, the best amount of water cut was 50% due to the maximum viscosity. In salts the yield was as follows: MgCl2 > CaCl2 > MgSO4 > Na2SO4 > NaCl. The best yield was related to MgCl2 at a concentration of 10,000 ppm. Finally, it was shown that the synergy of nanoparticles and surfactants resulted in higher stability and viscosity than in the case where each was used alone. It should be noted that the optimal concentration of nanoparticles is equal to 0.1% (w/w), and the optimal concentration of surfactant is equal to 200 ppm. In general, a stable state was obtained in 50% water-cut with MgCl2 salt at a concentration of 10,000 ppm and in the presence of SiO2 nanoparticles at a concentration of 0.1% and span 80 surfactants at a concentration of 200 ppm. The results obtained from this study provide important insights for optimal selection of the water-based EOR operation parameters. Viscosity showed a similar trend with stability and droplet size. As the average particle size decreased (or stability increased), the emulsion viscosity increased.

Tunnel-Structured ζ-V2O5 as a Redox-Active Insertion Host for Hybrid Capacitive Deionization

Much of the earth's water has a salt content that is too high for human consumption or agricultural use. Enhanced oil recovery operations generate massive volumes of produced water waste with a high mineral content that can substantially exacerbate water distress. Current deionization techniques such as reverse osmosis function by removing the water (majority phase) from the salt (minority phase) and are thus exceedingly energy-intensive. Furthermore, these methods are limited in their ability to selectively extract high-value ions from produced water waste and brine streams. Hybrid capacitive deionization holds promise for enabling both desalination and resource recovery. In this work, we demonstrate the construction of a hybrid capacitive deionization cell that makes use of tunnel-structured ζ-V2O5 as a redox-active positive electrode material. By augmenting surface adsorption with Faradaic insertion processes, a 50% improvement in the ion removal capacity for K and Li ions is obtained as compared to a capacitive high-surface-area carbon electrode. The extracted ions are accommodated in surface sites and interstitial sites within the one-dimensional tunnel framework of ζ-V2O5. The kinetics of ion removal depend on the free energy of hydration, which governs the ease of desolvation at the electrode/electrolyte interface. The overall ion removal capacity additionally depends on the solid-state diffusion coefficient. ζ-V2O5 positive electrodes show substantial selectivity for Li+ removal from mixed flow streams and enrichment of the Li-ion concentration from produced water waste derived from the Permian Basin.

Investigating Enhanced Oil Recovery in Unconventional Reservoirs Based on Field Case Review, Laboratory, and Simulation Studies

Aimed at advancing gas injection enhanced oil recovery (EOR) technologies in unconventional reservoirs, this study comprised a series of activities to bridge the gap between the theoretical study and actual field applications. Twenty-four EOR pilot tests were collected from the major unconventional plays in North America to evaluate the performance of different EOR technologies. Fit-for-purpose experiments and simulations were performed to investigate the effects of injection rate and pressure on EOR performance, as well as to reveal the effectiveness of huff “n” puff (HnP) cycles in actual field operations. The selection of injection rate and pressure as key parameters for investigation was based on field observations and communications with oil and gas operators because these two parameters play critical roles in both facility design and overall cost for an EOR project. Results showed that miscible EOR with a high gas injection rate and pressure is required for field operations because the injected gas needs to penetrate and extract oil from the tight matrix. Experimental results indicated that there is a correlation between oil recovery and the logarithm of core volume for miscible EOR. Immiscible gas EOR could not yield a satisfactory EOR response in actual reservoirs because the injected gas tends to flow through fractures instead of penetrating the matrix to interact with oil. Results also showed that reaching minimum miscibility pressure (MMP) does not guarantee an optimum EOR operation in unconventional reservoirs. Pressure higher than MMP is preferred in field operations. When designed properly, up to a tenfold oil production rate boost is achievable in field applications within a short period. However, such a high-performance operation is only effective in the first several HnP cycles due to the limited gas penetration depth into the rock matrix.

A novel apparatus and method for lab-scale study of wellbore integrity using CT imaging and analysis

Well drilling, completions, stimulation, and enhanced oil recovery operations induce downhole conditions that may negatively impact the integrity of the annular seal and consequently hinder zonal isolation. Thus, the ability to accurately quantify the evolution of the annular seal in response to the prevailing downhole environment is critical for the optimal design of the annular barrier for the life of a well. Thanks to increased accessibility and recent advancements in computing power and techniques, X-ray computed tomography has gained popularity as a non-destructive analysis method in materials science and geomechanics due to its ability to reveal details about the interior volume of objects in real-time without physical disassembly.Therefore, in this study, a novel apparatus is presented for the construction of a lab-scale wellbore, with the purpose of simulating downhole processes while simultaneously monitoring wellbore elements of interest in real-time via x-ray computed tomography. The benefits of this novel setup for wellbore integrity are demonstrated via applications to two test cases: the mechanical evolution of annular cement under stresses induced by cyclic water injection as a function of the mechanical properties of the cased and cemented wellbore system; the evaluation of nano magnesium oxide performance as an additive for autogenous shrinkage mitigation in annular cement.The results of the studies presented illustrate the benefits of combining x-ray computed tomography with lab-scale wellbore process simulations. The results of the cyclic water injection study suggest that residual strain in the cement is the major factor in annular seal degradation under cyclic downhole pressure fluctuations. Nano magnesium oxide is also shown to be very effective in preventing autogenous shrinkage of Class H cement. However, more study is required to characterize its effectiveness in a wider range of cement formulations. Finally, suggestions are offered on how to improve the experimental procedure presented while future potential applications of the apparatus are discussed.

Assessment of bacterial biosurfactant production and application in Enhanced Oil Recovery (EOR)—A green approach

The present work attempts to investigate production of biosurfactant, in a modified synthetic media, by less explored bacterial strain isolated from iron factory waste effluent from West Bengal, India and its application in enhanced oil recovery operations. The screened isolate was identified to be Pseudomonas mendocina by 16s rDNA gene homology (NCBI-Genebank accession no. MH091331). It showed a high emulsification of 68.3 ± 2.86%, lowering surface tension of water to 31 mN/ m and CMC of 125 mg l −1 and found to be alkalophillic (pH 6 to 12) and halophilic (up to 6% NaCl) in nature. Batch study in optimized media led to maximum yield of 3.09 ± 0.12 g l −1 . Production kinetic analysis by Luedeking Piret model successfully predicted mixed growth associated biosurfactant production with α and β values of 0.73 and 1.60 respectively and a yield coefficient of 0.82. Cost estimation for production of biosurfactant was calculated under lab scale conditions. Characterization of the extracted biosurfactant by anthrone test and Lowry’s protein estimation, TLC, FTIR, H 1 NMR and FESEM revealed the presence of sugar, peptides and lipid moieties. The biosurfactant obtained is extremely robust with high stability, sustaining wide range of pH (4 to 12), salinity (0.5 to 10% NaCl) and temperature (up to 100 °C). Toxicity assessment by Vigna radiata seed bioassay and MTT assay using and HEK293 cell line showed the non-toxic nature of the biosurfactant. Crude biosurfactant could play effective role in oil recovery with 44.3 ± 0.5 % enhanced oil recovery using sand packed column. • Biosurfactant (BS) from newly isolated halo-alkalophillic bacterial strain. • BS production in economically suitable optimized media. • Notably stable BS, tolerating extreme physiological conditions. • BS exhibiting presence of both glycolipids and lipopeptides: blend nature. • Potential use of crude biosurfactant for enhanced oil recovery.

Partially hydrolyzed polyacrylamide: enhanced oil recovery applications, oil-field produced water pollution, and possible solutions.

Polymers, such as partially hydrolyzed polyacrylamide (HPAM), are widely used in oil fields to enhance or improve the recovery of crude oil from the reservoirs. It works by increasing the viscosity of the injected water, thus improving its mobility and oil recovery. However, during such enhanced oil recovery (EOR) operations, it also produces a huge quantity of water alongside oil. Depending on the age and the stage of the oil reserve, the oil field produces ~ 7–10 times more water than oil. Such water contains various types of toxic components, such as traces of crude oil, heavy metals, and different types of chemicals (used during EOR operations such as HPAM). Thus, a huge quantity of HPAM containing produced water generated worldwide requires proper treatment and usage. The possible toxicity of HPAM is still ambiguous, but its natural decomposition product, acrylamide, threatens humans’ health and ecological environments. Therefore, the main challenge is the removal or degradation of HPAM in an environmentally safe manner from the produced water before proper disposal. Several chemical and thermal techniques are employed for the removal of HPAM, but they are not so environmentally friendly and somewhat expensive. Among different types of treatments, biodegradation with the aid of individual or mixed microbes (as biofilms) is touted to be an efficient and environmentally friendly way to solve the problem without harmful side effects. Many researchers have explored and reported the potential of such bioremediation technology with a variable removal efficiency of HPAM from the oil field produced water, both in lab scale and field scale studies. The current review is in line with United Nations Sustainability Goals, related to water security–UNSDG 6. It highlights the scale of such HPAM-based EOR applications, the challenge of produced water treatment, current possible solutions, and future possibilities to reuse such treated water sources for other applications.

Computing contact angles for oil-water-rock systems via thermodynamic integration.

Wettability of rock surfaces with respect to oil and water, which is characterized by the contact angle, is an important factor that determines the efficacy of enhanced oil recovery operations. Experimental determination of contact angles for oil-water-rock systems is expensive and time-consuming due to the extremely long times needed for the establishment of adsorption equilibrium at the liquid-solid interface. Hence, molecular simulations form an attractive tool for computing contact angles. In this work, we use the cleaving wall technique that was developed previously in our group [R. K. R. Addula and S. N. Punnathanam, J. Chem. Phys. 153, 154504 (2020)] to compute solid-liquid interfacial free energy, which is then combined with Young's equation to compute the oil-water contact angle on silica surfaces. The silica surface is modeled with the INTERFACE force field that has been developed to accurately reproduce experimental data. We have considered three different surface chemistries of silica, namely, Q2, Q3, and Q4, in this study. Our calculations reveal that while the Q2 and Q3 surfaces are completely wetted by water, the Q4 surface is partially non-wetted by water. All the simulations needed for this calculation can be performed using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular package. This should facilitate wider adoption of the Young's equation route to compute contact angles for systems comprised of complex molecules.

Low-field NMR investigation on interaction of ZnO nanoparticles with reservoir fluids and sandstone rocks for enhanced oil recovery

The use of nanoparticles (NPs) can considerably benefit enhanced oil recovery (EOR) by changing the wettability of the rock, improving the mobility of the oil drop, and decreasing the interfacial tension (IFT) between oil and water. Prior to the application of nanoparticles in oil fields, it is essential to conduct measurements at the laboratory scale. However, the estimation of reservoir wettability is difficult in most laboratory experiments. Practicably, ZnO NPs were used to modify the rock surface wettability, lower the IFT at the oil/water interface, and reduce the interaction of chemical adsorption, such as (surfactant) onto reservoir rock surface to solve various challenges in oil production and EOR operations. Upon confining both ZnO-based nanofluid and the crude oil into sandstone, deviations from the corresponding pure bulk dynamical behaviors were observed with low-field nuclear magnetic resonance (LF-NMR) relaxometry. The expected deviations from the pure bulk behaviors were attributed to the well-known confinement effect. The wettability test results before and after surface variations of formation water (FW) with the addition of three different NP concentrations (0.05, 0.075, and 0.1) wt% ZnO reflected significant changes to its wettability. Among the treatments of Berea sandstone cores with ZnO NPs, the percentage of clay-bound H2O/free fluid index was maximum in 1.0 pore volume (PV) NP treatment. The ratio of NMR relaxations, which determines the affinity of fluids toward solids, by the 1.0 PV NP treatment is reported to have the most potential with higher affinity for FW and less affinity for crude oil toward the pore walls. Hence, LF-NMR allows monitoring of nanofluid and crude oil characteristics in the pores of rock samples and may potentially be applied in further EOR studies.

Fluorescent-tagged water with carbon dots derived from phenylenediamine as an equipment-free nanotracer for enhanced oil recovery

Chemical enhanced oil recovery (EOR) through waterflooding is the most commonly used method to improve crude oil displacement and extraction however; the impact of environmental side effects may remain ambiguous. Regarding, flooding tagged water with tracers provides a better understanding of the fate of injected water and the reservoir conditions more than oil recovery. This study's main focus is the proposed carbon dots (CDs) to develop fluorescent-tagged with dual functions as a sensing and an enhancing agent for EOR operations. Different physicochemical and optical properties were obtained for CDs by tuning the surface chemistry of phenylenediamine (PD) isomers and tartaric acid (TA) via the solvothermal method which leads to green, and yellow fluorescent emissions. Size distribution and colloidal and thermal stability of the prepared nanofluids carrying CDs were controlled by atomic force microscope (AFM), transmission electron microscopy (TEM), dynamic light scattering (DLS), zeta potential, and thermogravimetric analysis (TGA). Long-time emission stability in high temperature and salinity such as conditions found in the oil reservoirs was precisely detected by fluorescence spectroscopy and a portable UV cabinet as the on-site detection method to confirm the sensing ability of CDs. While, rheological parameters of nanofluids such as viscosity, wettability alteration, and fluid/crude oil interfacial tension were evaluated to support the potential of CDs as an enhancing agent to sweep crude oil on the carbonate rock reservoirs. The oil displacement mechanism was monitored on the micromodel pattern by recording 27.8 % and 20.5 % displacement factors for the prepared nanofluids carrying 200 ppm CDs.