Enhanced Oil Recovery Processes Research Articles

Discrepancy in oil displacement mechanisms at equivalent interfacial tensions: Differentiating contributions from surfactant and nanoparticles on interfacial activities

Discrepancies in oil displacement mechanisms at equivalent interfacial tensions were principally explored in the current study, with a novel emphasis on distinguishing the contributions of surfactants and nanoparticles in interfacial activities. The hypothesis was that if both chemicals exhibit similar interfacial activities, the oil displacement outcomes at the capillary scale should be consistent. Otherwise, differences would indicate distinct interfacial behaviors. Fluid displacement experiments were conducted using a two-dimensional micromodel, where nanofluids and surfactant solutions were tested at equivalent interfacial tensions. In the high interfacial tension pair (20 mN/m), the surfactant solution displaced oil more efficiently and rapidly than nanofluids, achieving greater ultimate oil recovery. This difference was attributed to the effective reinforcement of capillary forces in the surfactant system, which drove the displacement process. In contrast, the nanofluids did not achieve the same level of performance due to their limited ability to modify interfacial forces. This finding highlighted the two chemicals’ fundamentally different interfacial activities and oil displacement mechanisms. Furthermore, in the lowest interfacial tensions pair, where the surfactant achieved 6.5 mN/m and the nanofluids 15.6 mN/m, both systems unexpectedly displayed similar oil displacement efficiencies and fingering-like behaviors. This similarity, however, arose from distinct underlying mechanisms: capillary instability drove fingering in the surfactant system, while expansive layer flow induced fingering-like in the nanofluids. These findings challenge the assumption that reducing interfacial tension with nanoparticles is the primary mechanism in enhanced oil recovery (EOR). The study highlighted the need for a more refined understanding of the action of nanoparticle interfacial activities within EOR processes. To further validate these insights, future research should scale up fluid displacement studies to Darcy’s scale using core-flooding tests, enabling a more comprehensive examination of complex two-phase flow dynamics.

CO2 foam structure and displacement dynamics in a Hele–Shaw cell

The substitution of CO2 gas with CO2 foam for improved mobility control has recently attracted research attentions in enhanced oil recovery (EOR) as well as aquifer and soil remediation. However, the interplay between CO2 foam properties and oil displacement remains less investigated. In this work, using clear visualizations, we systematically investigate the dynamic advection-dominated foam morphology, its corresponding viscosity, and the efficiency and dynamics of oil displacement process by CO2 foam in a Hele–Shaw cell under the effects of gas ratio (Rg), fluid injection rates (Qt), and surfactant type. Clear visualization enables particle image velocimetry to calculate actual, rather than nominal, foam velocity that significantly affects the estimated foam viscosity. Our results demonstrate that at elevated gas ratios under constant total injection rate, larger and more uniform bubbles with less number density and greater interfacial area are obtained. Increasing the injection rates leads to finer foam texture at a constant gas ratio. Different foam structures have an impact on the foam viscosity, with a general increasing trend of viscosity for larger bubbles as Rg increases from 0.5 to 0.85. The higher the foam viscosity, the more stable displacement interfaces with less viscous fingers are observed, leading to improved sweeping rates. The green surfactants (saponin + Cellulose NanoFibers) provide foams with higher viscosity and, thus, more stable displacement interfaces. These findings highlight the important effect of Rg–dependent foam structure on its viscosity, which in turn is crucial for controlling the mobility of CO2 foam to maximize oil recovery rate during EOR processes.

Active ions’ impact in the enhanced oil recovery process: a microfluidic-based approach

Active ions’ impact in the enhanced oil recovery process: a microfluidic-based approach

New Insights into the Understanding of High-Pressure Air Injection (HPAI): The Role of the Different Chemical Reactions

High-pressure air injection (HPAI) is an enhanced oil recovery process in which compressed air is injected into deep, light oil reservoirs, with the expectation that the oxygen in the injected air will react with a fraction of the reservoir oil at an elevated temperature to produce carbon dioxide. The different chemical reactions taking place can be grouped into oxygen addition, thermal cracking, oxygen-induced cracking, and bond scission reactions. The latter reactions involve the combustion of a flammable vapor as well as the combustion of solid fuel, commonly known as “coke”. Since stable peak temperatures observed during HPAI experiments are typically below 300 °C, it has been suggested that thermal cracking and combustion of solid fuel may not be important reaction mechanisms for the process. The objective of this work is to assess the validity of that hypothesis. Therefore, this study makes use of different oxidation and combustion HPAI experiments, which were performed on two different light oil reservoir samples. Modeling of those tests indicate that thermal cracking is not an important reaction mechanism during HPAI and can potentially be ignored. The work also suggests that the main fuel consumed by the process is a flammable vapor generated by the chemical reactions. This represents a shift from the original in situ combustion paradigm, which is based on the combustion of coke.

Stability analysis of CO2 microbubble for CO2 sequestration and mobility control in enhanced oil recovery

This study addresses the challenge of CO2 microbubble stability in enhanced oil recovery (EOR), with a focus on the combined effects of surfactant concentration, brine concentration, brine-oil ratio, and gas flow rate. Microbubble destabilization, driven by phase separation and improper brine-oil ratios, is a critical issue in EOR. The novelty of this research lies in the use of Acacia concinna, a natural, non-ionic surfactant, to enhance microbubble stability sustainably. The study systematically investigates foamability, drainage kinetics, surface tension, rheology, and bubble size distribution to assess microbubble stability. The results showed that increasing surfactant concentration and brine-oil ratio significantly impacted foam stability and bubble size distribution, with optimal stability conditions observed at 2 wt% surfactant concentration (53.6 min), 5000 ppm brine concentration (47.6 min), a brine-oil ratio of 1:2 (52.4 min), and a CO2 gas flow rate of 1 lpm. Key findings also indicate that increasing NaCl concentration from 5000 to 20,000 ppm caused an increase in microbubble diameter, while higher gas flow rates reduced bubble size. The work introduces new empirical correlations for predicting bubble size distribution based on column diameter, surfactant and brine concentrations, viscosity, density, and gas superficial velocity. These correlations provide a more accurate means of optimizing EOR processes, improving oil recovery efficiency while enhancing CO2 sequestration, contributing to both energy production and environmental sustainability.

Enhancing the emulsification and demulsification efficiency of switchable surfactants through molecular dynamics simulation: The roles of surfactant concentration, salinity, and structure

This study aims to investigate the impacts of a surfactant structure, surfactant concentration, and salt content on switchable emulsification processes through molecular dynamics (MD) simulations. Specifically, we focus on assessing the properties and behaviors of water/tetradecane systems containing CO2-switchable acetamidine surfactant N’-dodecyl-N, N-dimethylacetamidine (C12DMAA) and C18 naphthalene sulfonate (C18PS), both of which are relevant to enhanced oil recovery processes. Utilizing MD simulations, we comprehensively explore the influence of the molecular composition of switchable surfactants, salinity, and surfactant concentration on the reversible processes of emulsification and demulsification in a complex oil/water/C18PS/C12DMAA system. This system can be activated through the injection of CO2 or N2 gas. Various analyses, including molecule mobility, hydration behavior, void volume analysis, a solvent accessible surface area (SASA), a diffusion coefficient, and relative concentration profiles, are employed to gain insights into the emulsification and demulsification processes. Our study reveals that lower surfactant concentrations result in the formation of partial emulsions, while the presence of salt disrupts surfactant hydration and weakens emulsification properties. Additionally, we observe that the impact of hydrogen bonding interactions is less pronounced at lower surfactant concentrations. Furthermore, the MD simulations provided insights into the interplay of a surfactant monomer number and alkyl phenyl introduction with a solvent-accessible surface area (SASA) and a void volume. Understanding these factors is crucial for designing and optimizing emulsion systems, particularly in oil recovery processes. The findings advance our understanding of CO2/N2-switchable surfactants, offering insights into their potential for sustainable development in the petroleum industry. This research contributes to the optimization of switchable surfactants, providing a foundation for improved emulsification processes in enhanced oil recovery applications.

A Pore-Level Study of Dense-Phase CO2 Foam Stability in the Presence of Oil

The ability of foam to reduce CO2 mobility in CO2 sequestration and CO2 enhanced oil recovery processes relies on maintaining foam stability in the reservoir. Foams can destabilize in the presence of oil due to mechanisms impacting individual lamellae. Few attempts have been made to measure the stability of CO2 foams in the presence of oil in a realistic pore network at reservoir pressure. Utilizing lab-on-a-chip technology, the pore-level stability of dense-phase CO2 foam in the presence of a miscible and an immiscible oil was investigated. A secondary objective was to determine the impact of increasing surfactant concentration and nanoparticles on foam stability.In the absence of oil, all surfactant-based foaming solutions generated fine-textured and strong foam that was less stable both when increasing surfactant concentrations and when adding nanoparticles. Ostwald ripening was the primary destabilization mechanism both in the absence of oil and in the presence of immiscible oil. Moreover, foam was less stable in the presence of miscible oil, compared to immiscible oil, where the primary destabilization mechanism was lamellae rupture. Overall, direct pore-scale observations of dense-phase CO2 foam in realistic pore network revealed foam destabilization mechanisms at high-pressure conditions.

A Comprehensive Approach to Modeling Air Injection-Based Enhanced Oil Recovery Processes

Summary Modeling of air-injection-based processes for enhanced oil recovery (EOR) is a challenging task, mostly due to the complexity of the chemical reactions taking place. Also, the applicability of currently available kinetic models is limited to the reservoir systems they were originally developed for. The objective of this study is to derive a general chemical reaction framework that could be used to develop a kinetic model for a variety of crude oils (i.e., light or heavy oils). The work is based on the modeling of high-pressure ramped temperature oxidation (HPRTO) experiments, and combustion tube (CT) tests, performed on two different oil systems: a volatile oil that is near critical at reservoir conditions (44 °API), and a bitumen sample (10 °API). The HPRTO test is a kinetic experiment that intends to mimic the flow conditions within the reservoir and allows the determination of kinetic parameters of the different reactions. On the other hand, the CT test is meant to provide quantitative information on the combustion performance that can be expected in the field. Therefore, a kinetic model was derived for each of the cases based on the history match of an HPRTO experiment. The resulting model was validated by history matching a CT test for each of the oils. An important feature of these experiments is that they were performed at representative reservoir pressure conditions. The modeling approach chosen is an extension of the methodology originally proposed by Belgrave et al. in 1993, which is arguably the most comprehensive kinetic model available in the air injection literature. However, their model was developed from experiments performed on Athabasca bitumen, and it fails to represent the high-pressure air injection process as it occurs in light oil reservoirs, which are typically encountered at higher pressure conditions. For example, Belgrave’s model is based on the deposition and combustion of semisolid residue commonly known as “coke,” which is rarely present during the combustion of light oils at high pressure. As in Belgrave’s model, this study also describes the original composition of the oil in terms of maltenes and asphaltenes. The main difference lies in the presence and importance of oxygen-induced cracking reactions, as well as the combustion of a liquid-vapor flammable hydrocarbon mixture that is generated by cracking and oxidation reactions, which take place in the gas phase. Also, a unique feature of these simulations is that, apart from history-matching traditional variables such as thermocouple temperatures, fluid recovery, and produced gas composition, they also capture changes in the physical properties of the produced oil, such as viscosity and density, as well as the amount of the residual phases in the post-test core. This enhancement to Belgrave’s reactions allows modeling the air injection process in cases where coke is not the main source of fuel, such as in high-pressure light oil reservoirs. This work changes a paradigm deeply rooted in the original in-situ combustion (ISC) theory, by deriving a general chemical reaction framework that is used to develop a kinetic model for two crude oils, which are at opposite ends of the density spectrum. This allows the consolidation of a new and comprehensive general theory for the description of the ISC process as applied to oil reservoirs. Moreover, as the pseudocomponents representing the fuel are not present in the original oil, the method is not limited to a fluid characterization in terms of maltenes and asphaltenes but could potentially be applied along with any type of characterization of the original oil.

Study of the Adsorption of Anionic Surfactants on Carbonate Rocks: Characterizations, Experimental Design, and Parameter Implementation

Controlling or reducing the adsorption of surfactants on reservoir rock surfaces has been a challenging task in enhanced oil recovery (EOR) methods, as it directly affects the cost-effectiveness of the projects. The adsorption of surfactants on rock surfaces can modify their hydrophobicity, surface charge, and other important parameters that govern EOR processes, such as reducing the interfacial tension between water and oil and increasing permeability. Therefore, understanding the adsorption mechanism on rocks is essential for developing alternatives that improve the effectiveness of these processes. In this work, the adsorption of surfactants on carbonate materials was evaluated considering variations in temperature, contact time, and surfactant concentration. The surfactants used were derived from vegetable oils, aiming for a sustainable approach: saponified coconut oil (SCO), saponified babassu coconut oil (SBCO), and saponified castor oil (SMO). The finite bath method was used, resulting in adsorption efficiencies of 85.74%, 82.52%, and 45.30% for SCO, SBCO, and SMO, respectively. The Sips isotherm and the pseudo-second-order model were found to be suitable for characterizing these systems. The simulation of SCO adsorption isotherms on limestone by the Langmuir model was more accurate than that using the Freundlich model. The limestone showed a negative surface charge of approximately −35.0 mV at pH 6.5; this negative charge varied over a wide pH range. These zeta potential data for the samples confirmed that hydrophobic interactions played an important role in the adsorption of the surfactants. Thermodynamic evaluation indicated spontaneous and endothermic adsorption of SCO on limestone. The systems were also characterized by FTIR, TG/DTG, XRD, XRF, SEM, and zeta potential.

Performance Evaluation of Rankiya (Grewia venusta) as a Polymer for Enhanced Oil Recovery-Polymer Flooding

Polymer Flooding is an enhanced oil recovery method where high-molecular-weight polymers are added into the injected water, in order to increase the viscosity of injection fluid, improve volumetric sweep efficiency, and finally increase the oil recovery factor. Most research studies focused on the use of partially hydrolysed polyacrylamide (HPAM), xanthan gum, SPG, HEC as flooding fluid. This work focused on investigating the potential of natural polymer - Grewia venusta plant tree in formulating flooding fluid for polymer flooding operation. The polymer was prepared from the mucilage extracted from the inner stem bark of Grewia venusta. Rheological characterization shows that the formulated Grewia venusta mucilage (GVM)-based flooding fluid maintained high viscosity under very high salinity, also exhibits shear thinning behaviour which obeys power law model with fluid behaviour index n = 0.32 and consistency index K= 6.3 at highest GVM concentration of 3000 ppm, similarly revealed that GVM-based flooding fluid was stable when subjected to high salinity of 130,000 ppm and temperature, up to 90 oC an inconsequential decrease in viscosity was experienced. These indicate its potential in enhancing oil recovery process. Core flooding analysis was carried out on an outcrop core sample with porosity and permeability of 26.1% and 218 mD respectively. Core flooding analysis revealed that: with increase in GVM concentration of 500, 750, 1000 and 2000 ppm oil recovered against time were 55.1%, 62.6%, 69.2% and 72.9% respectively, beyond 2000 ppm GVM concentration (optimum concentration), increase in polymer concentration did not bring about an increase in oil recovery, this finally resulted to an incremental recovery of 29% original oil in place (OOIP).

Machine Learning Techniques in Enhanced Oil Recovery Screening Using Semisupervised Label Propagation

Summary Efficiently choosing the optimal enhanced oil recovery (EOR) technique is a critical requirement in reservoir engineering. Machine learning (ML) methods, with a well-established history of application, serve as a swift and dependable tool for EOR screening. In this paper, we aim to evaluate the effectiveness of various ML algorithms for EOR screening, utilizing a comprehensive database of nearly 1,000 EOR projects. This study delves into a comprehensive evaluation of regression and classification-based algorithms to develop a reliable screening system for EOR predictions and address challenges such as limited labeled data and missing values. Our analysis considered various EOR processes, including gas injection, chemical, and thermal EOR techniques. Various ML methods such as random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), shallow artificial neural networks (SANN), naive Bayes classifier (NBC), logistic regression (LR), and decision tree (DT) are applied, enabling both intermethod comparisons and evaluations against advanced methods, multiobjective deep artificial neural networks (MDANN), and multiobjective artificial neural networks (MANN). These advanced techniques provide the unique capability to concurrently address both regression and classification tasks. Considering that conventional methods can only be implemented on a single task, the RF, MANN, MDANN, and KNN algorithms demonstrated top-tier performance in our classification analysis. Regarding the regression task, KNN, RF, and MDANN displayed exceptional performance, signifying their prowess in predictive accuracy. However, MANN exhibited moderate performance in regression analysis. In addition, our study identified areas where certain algorithms, such as support vector regression (SVR), exhibited weaker performance, highlighting the importance of comprehensive model evaluation. This paper contributes novel insights into the application of ML techniques for EOR screening in the petroleum industry. By addressing challenges such as limited labeled data and missing values and by providing a thorough evaluation of various ML algorithms, our study offers valuable information for decision-makers in the oil and gas sector, aiding in the selection of suitable algorithms for EOR projects. In addition, the use of semisupervised label propagation and advanced techniques like KNN imputation adds to the existing body of literature, enhancing the state of knowledge in this domain.

Decarbonization of Alkali–Surfactant–Polymer Flooding Enhanced Oil Recovery Process with Hydrogen under Uncertainty

Decarbonization of Alkali–Surfactant–Polymer Flooding Enhanced Oil Recovery Process with Hydrogen under Uncertainty

Experimental investigation and machine learning modeling of diethylenetriaminepentaacetic acid agents in sandstone rock wettability alteration: Implications for enhanced oil recovery processes

Chelating agents have garnered increasing attention due to their unique capabilities in addressing challenges associated with chemical enhanced oil recovery (CEOR) materials. This research investigates the transformative potential of diethylenetriaminepentaacetic acid (DTPA) chelating agent in altering sandstone rock wettability. Wettability and zeta potential measurements were used to assess the impact of crucial factors including DTPA concentration, salinity, and potential determining ions (PDIs) on contact angle and rock surface charges. The findings revealed that there is an optimal level for DTPA concentration and salinity that can shift rock wettability from an oil-wet to strongly water-wet state. Moreover, while PDIs in their natural concentrations had no significant impact on DTPA performance, a threefold increase in their concentration was found to adversely affect DTPA efficacy. After conducting rock wettability tests, two advanced machine learning (ML) techniques, namely Random Forest (RF) and Boosted Regression Tree (BRT), were employed to assess rock/oil contact angle based on the aforementioned factors. Data were collected from 240 experimental datasets of contact angles. The findings indicated that both models performed well, but BRT exhibited exceptional performance. Sensitivity analysis, conducted using the Jackknife method, revealed the order of importance for parameters affecting wettability as follows: PDIs > salinity > time > DTPA concentration.

Developing statistical and machine learning models for predicting CO2 solubility in live crude oils

CO2 solubility in crude oil primarily affects the viscosity and swelling capacity of oil and determines the displacement efficiency of CO2 flooding during the enhanced oil recovery process. Dissolution of CO2 into crude oil also ensures safe and permanent storage of CO2 in depleted formations via solubility trapping. This study employed statistical and machine learning algorithms to develop precise and stable predictive models for estimating CO2 solubility in crude oils with dissolved gas. Recognizing the limitations of existing experimental data on CO2 solubility for live oils (crude oils with dissolved gas), we introduced a novel approach employing statistical and machine learning techniques for developing accurate and robust predictive models. Our methodology combined regularization, k-fold cross-validation, and statistical significance tests to ensure the robustness of the models, practices not considered in the previous studies. A linear regression model with L2 regularization is developed as the base model and compared with a 2nd order polynomial model and an ensemble feed-forward neural network (FNN) model integrated with gradient boosting (GB). The incorporation of GB into the neural network capitalizes on the additive sequence of FNN models, thereby enhancing the accuracy and reliability of predictions. Moreover, an improved user-accessible and easily implementable correlation is proposed to estimate CO2 solubility in live crude oils at diverse operating conditions as a function of reservoir temperature, saturation pressure, bubble-point pressure, oil molecular weight, and specific gravity. The performances of the developed model and the correlation are compared against the literature models and correlations. The FNN model with an overall R2 score of 0.9978, average absolute relative deviation (AARD) of 1.54 %, and root mean squared error (RMSE) of 0.0083 outperformed the previously published models and correlations. The new correlation, while being relatively simpler than previously published correlations, showed similar predictive performance with an overall R2 score of 0.9817, AARD of 4.98 %, and RMSE of 0.0225. The model and the correlation can be used to estimate CO2 solubility in live crude oils with 0.67 to 0.97 specific gravity for reservoir temperatures and pressures in the range of 28 to 124 °C and 3.23 to 32.76 MPa, respectively.

A comprehensive review direct methods to overcome the limitations of gas injection during the EOR process

Among the Enhanced Oil Recovery (EOR) methods, gas-based EOR methods are very popular all over the world. The gas injection has a high ability to increase microscopic sweep efficiency and can increase production efficiency well. However, it should be noted that in addition to all the advantages of these methods, they have disadvantages such as damage due to asphaltene deposition, unfavorable mobility ratio, and reduced efficiency of macroscopic displacement. In this paper, the gas injection process and its challenges were investigated. Then the overcoming methods of these challenges were investigated. To inhibit asphaltene deposition during gas injection, the use of nanoparticles was proposed, which were examined in two categories: liquid-soluble and gas-soluble, and the limitations of each were examined. Various methods were used to overcome the problem of unfavorable mobility ratio and their advantages and disadvantages were discussed. Gas-phase modification has the potential to reduce the challenges and limitations of direct gas injection and significantly increase recovery efficiency. In the first part, the introduction of gas injection and the enhanced oil recovery mechanisms during gas injection were mentioned. In the next part, the challenges of gas injection, which included unfavorable mobility ratio and asphaltene deposition, were investigated. In the third step, gas-phase mobility control methods investigate, emphasizing thickeners, thickening mechanisms, and field applications of mobility control methods. In the last part, to investigate the effect of nanoparticles on asphaltene deposition and reducing the minimum miscible pressure in two main subsets: 1- use of nanoparticles indirectly to prevent asphaltene deposition and reduce surface tension and 2- use of nanoparticles as a direct asphaltene inhibitor and Reduce MMP of the gas phase in crude oil was investigated.

New models for estimating minimum miscibility pressure of pure and impure carbon dioxide using artificial intelligence techniques

CO2 miscible flooding stands out as one of the most-commonly employed and promising processes in enhanced oil recovery. The key factor for ensuring success in designing and executing CO2 injection projects lies in the precise determination of the Minimum Miscibility Pressure (MMP). Despite significant research endeavors in measuring and predicting the MMP through the use of empirical, analytical, numerical, and experimental approaches, the need for a comprehensive, accurate, efficient, and easy-to-use model remains. In response to this gap, our study developed various predictive models with 205 datasets, including well-established ones such as Artificial Neural Network (ANN), Random Forest (RF), and K-Nearest Neighbors (KNN) along with employing the Gradient Boosting Algorithm (GBA) with the Grid Search (GS) as a novel approach to predict the MMP of both pure and impure CO2. The performance of these models was validated by comparing them with previously published models, using relevant performance metrics such as R2 (coefficient of determination) and MAPE (mean absolute percentage error). Our results demonstrated that the GBA model with GS optimization exhibited remarkable superiority in terms of performance and accuracy in both scenarios, outperforming previously published models as well as the developed ANN, RF, and KNN models. For pure CO2, it achieved an impressive R2 value of 0.999 on the training set and a relatively high value of 0.910 on the validation set. Additionally, the model showed remarkably low MAPE values of 0.0705 % on the training set and 9.238 % on the validation set. Similarly, for impure CO2, the GBA model with GS optimization achieved an R2 value of 0.999 on the training set and a relatively high value of 0.865 on the validation set. The model also exhibited low MAPE values of 1.66 % on the training set and 6.798 % on the validation set. These findings highlight the potential of the GBA model with GS optimization as a valuable approach for accurately estimating the MMP, offering a cost-effective and efficient approach for designing and optimizing CO2 flooding operations in enhanced oil recovery processes.

Assessment of Biopolymers Rheological Properties and Flooding Results Under Reservoir Conditions for Enhanced Oil Recovery Processes

Assessment of Biopolymers Rheological Properties and Flooding Results Under Reservoir Conditions for Enhanced Oil Recovery Processes

Chevron Applies Some Unconventional Thinking To Try To Make Shale EOR a Standard Treatment

In the shale business, the closest thing now to enhanced oil recovery (EOR) is improved techniques. During an earnings call last November an analyst asked EOG Resources executives about their enhanced completion technique which EOG reported was adding 20% to first-year well production in the Permian. Improved completions have allowed operators to significantly increase early production year after year, but after that, steep declines are a given. Back in 2016, EOG was talking about how it was increasing oil production significantly by injecting millions of cubic feet of gas a day into wells in the Eagle Ford. It triggered an EOR field-testing boom by competitors hoping to match reported reserve increases of “30 to 70%.” In a 2017 JPT story, Deepak Devegowda, a petroleum engineering professor at the University of Oklahoma, said, “This is the name of the game. Everybody is talking about EOR and pumping money into trials of EOR.” Now the only mention of the acronym EOR on EOG’s website is an item in its corporate history timeline for 2016: “We commercialized the first enhanced oil recovery process, or EOR, in shale.” In recent years, reported shale EOR work has been mostly in the form of occasional papers describing production uplift by companies selling ways to increase production by injecting gas or chemicals. EOR effectiveness isn’t the issue, according to Todd Hoffman, a petroleum engineering professor at Montana Tech University who wrote two papers evaluating EOG’s methods cited in two Chevron papers. “The EOG field work showed us that these projects can produce significant additional oil and be economically positive,” he said. The problem is that drilling and fracturing wells delivers “higher economic returns than the EOG-style EOR projects with the huge compressors, high gas rates, and high injection pressures.” Last year in the middle of this EOR drought, Chevron did something different. It delivered two papers revealing a major company-scale effort to find ways to use chemical and gas injections to economically produce more oil. The papers presented at the 2023 Unconventional Resources Technology Conference (URTeC) reported on field tests of surfactants and natural gas injection on Permian Basin wells which delivered sufficiently encouraging results to justify an expanded testing program. (URTeC 3870505 and URTeC 3871386). Chevron described a systematic effort by its corporate technical unit and its Mid-Continent business unit to rethink shale EOR methods based on the unconventional nature of flow through fractured reservoirs and the economic realities in a business where new EOR technology is competing with the profitable status quo. Its methods challenge accepted notions about the role of EOR. In SPE’s disciplines, EOR normally falls under the production topic, “marginal aging fields.” What Chevron tested is better described by the topic, “well interventions.” Rather than looking at these techniques as a way to eke out the last barrels from old wells, the papers describe methods that can be deployed earlier in the life of these short-lived wells.

Molecular Dynamics Simulations of Supramolecular Polymers within Nanoconfinements for Enhanced Oil Recovery.

Supramolecular polymers offer promising potential for enhanced oil recovery (EOR) advancing techniques. Current instrumental analyses face limitations in capturing instantaneous intracomplex motions due to temporal and spatial constraints. The molecular mechanism of supramolecular polymer transport behavior within nanoconfinement is not yet fully understood. Therefore, the self-assembly mechanism of β-cyclodextrin (β-CD) and adamantane (ADA)-modified supramolecular polymers (p-AA-β-CD-ADA) was delved into in this work. Further exploration focuses on the translocation dynamics of p-AA-β-CD-ADA within nanoconfinement under external driving forces. Results suggest that β-CD and ADA in p-AA-β-CD-ADA were assembled into nodes in the form of a host and a guest, combining with a "node-rebar-cement" interaction model encapsulating the formation mechanism of these supramolecular polymers. The heightened density of the hydrate layers at the nanoscale pore throats acts as a constraining factor, resulting in restricted mobility and altered dynamics of the supramolecular polymers. During passage through nanopore throats, host-guest molecules within the supramolecular polymer experience noncovalent dissociation. Notably, these supramolecular polymers exhibit remarkable self-healing capabilities, reinstating their assembly state upon traversing pore throats. This work provides a molecular-level comprehension of the potential utility of supramolecular polymers in EOR processes, offering valuable information for the molecular design of polymers employed for EOR in low-permeability reservoirs.

New Trends in Biosurfactants: From Renewable Origin to Green Enhanced Oil Recovery Applications.

Enhanced oil recovery (EOR) processes are technologies used in the oil and gas industry to maximize the extraction of residual oil from reservoirs after primary and secondary recovery methods have been carried out. The injection into the reservoir of surface-active substances capable of reducing the surface tension between oil and the rock surface should favor its extraction with significant economic repercussions. However, the most commonly used surfactants in EOR are derived from petroleum, and their use can have negative environmental impacts, such as toxicity and persistence in the environment. Biosurfactants on the other hand, are derived from renewable resources and are biodegradable, making them potentially more sustainable and environmentally friendly. The present review intends to offer an updated overview of the most significant results available in scientific literature on the potential application of biosurfactants in the context of EOR processes. Aspects such as production strategies, techniques for characterizing the mechanisms of action and the pros and cons of the application of biosurfactants as a principal method for EOR will be illustrated and discussed in detail. Optimized concepts such as the HLD in biosurfactant choice and design for EOR are also discussed. The scientific findings that are illustrated and reviewed in this paper show why general emphasis needs to be placed on the development and adoption of biosurfactants in EOR as a substantial contribution to a more sustainable and environmentally friendly oil and gas industry.