Enhanced Oil Recovery Techniques Research Articles

Investigating the vaporization mechanism's effect on interfacial tension during gas injection into an oil reservoir

AbstractIn gas injection, which is one of the fascinating enhanced oil recovery techniques, the main mechanism involves decreasing interfacial tension (IFT). Although various mechanisms can affect the IFT of a system, in most experimental and numerical studies, condensation is considered the dominant mechanism among condensation-vaporization and vaporization. Investigating the impact of each mechanism is crucial as they can influence the IFT of the system and, consequently, the effectiveness of the gas injection method. This study introduces a novel model to assess the influence of different mechanisms on system IFT. The model defines system IFT, adjusts fluid relative permeability to represent miscible, immiscible, and near-miscible states, and utilizes the Buckley–Leverett method to analyze gas fractional flow and saturation profiles when injecting carbon dioxide (CO2), methane (CH4), and nitrogen (N2). Furthermore, the research explores the impact of injection pressure and IFT at minimum miscible pressure (IFT0) on gas injection efficiency. Based on our results, for both live and dead oil, the condensation mechanism reduces IFT and near-miscible pressure; switching to a condensing-vaporizing mechanism increases these parameters. This trend was consistent across all gases studied (N2, CO2, CH4), with a more significant effect observed on the CH4-live oil system compared to N2 and CO2. Controlling the condensing mechanism in IFT measurements enhances gas flow rate and relative permeability curve within the medium. Higher injection pressure in the condensing mechanism and IFT0 = 0.5 leads to faster fluid movement and improved relative permeability due to increased driving forces. Higher IFT0 accelerates the relative permeability of fluids and gas movement within the medium by promoting miscibility sooner. The impact of IFT0 was more pronounced on the dead oil–gas system compared to the live oil–gas system in this study.

BSEO-Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices.

Microfluidic models have become essential instruments for studying enhanced oil recovery techniques through fluid and chemical injection into micromodels to observe interactions with pore structures and resident fluids. The widespread use of cost-effective lab-on-a-chip devices, known for efficient data extraction and minimal reagent usage, has driven demand for efficient data management methods crucial for high-performance data and image analyses. This article introduces a semiautomatic method for calculating oil recovery in polymeric nanofluid flooding experiments based on the background subtraction (BSEO). It employs the background subtraction technique, generating a foreground binary mask to detect injected fluids represented as pixel areas. The pixel difference is then compared to a threshold value to determine whether the given pixel is foreground or background. Moreover, the proposed method compares its performance with two other representative methods: the ground truth (manual segmentation) and Fiji-ImageJ software. The experiments yielded promising results. Low values of mean-squared error (MSE), mean absolute error (MAE), and root-mean-squared error (RMSE) indicate minimal prediction errors, while a substantial coefficient of determination (R2) of 98% highlights the strong correlation between the method's predictions and the observed outcomes. In conclusion, the presented method emphasizes the viability of BSEO as a robust alternative, offering the advantages of reduced computational resource usage and faster processing times.

Low-Salinity Waterflooding for EOR in Field A of Western Offshore Basin: A Pilot Study Analysis with Laboratory and Simulation Studies—Early Observations

Carbonate reservoirs hold vast oil reserves, but their complex properties make traditional enhanced oil recovery (EOR) methods challenging. This study explores the application of low-salinity water flooding (LSWF) as a novel EOR method for India’s largest offshore carbonate oil field. Conventional EOR techniques were deemed unsuitable due to reservoir heterogeneity, pressure decline, high temperature, and the offshore location. Favorable factors for LSWF included successful seawater flooding history, medium-weight crude oil, and existing infrastructure. Following core flooding experiments demonstrating a 6–16% increase in oil recovery, a multi-pronged evaluation process was implemented. Single-well chemical tracer tests (SWCTT) and reservoir simulations confirmed the potential of LSWF. A specific target area was chosen based on reservoir characteristics, production data, and available facilities. Simulations predicted a 1.5% incremental oil recovery using diluted seawater (25% salinity) at 30% pore volume injection. After a positive techno-economic analysis, the first offshore LSWF project in India was completed within 3 years. Initial monitoring results are encouraging. This study highlights the successful journey of LSWF from concept to field deployment in a challenging carbonate reservoir, showcasing its potential for revitalizing such fields. Furthermore, this work provides valuable data relevant to Indian offshore environments, where factors like salinity, mineralogy, and crude oil composition pose unique challenges compared to other LSWF applications. These detailed data fill a critical gap in the existing literature.

Advanced machine learning-based modeling of interfacial tension in the crude oil-brine-diethyl ether system: Insights into the effects of temperature and salinity

Solvent injection, a well-established method for enhanced oil recovery (EOR), has demonstrated significant improvements in oil recovery when compared with conventional water flooding techniques. The interfacial tension (IFT) is pivotal in determining the displacement efficiency and overall performance of innovative techniques like dimethyl ether-enhanced waterflooding (DEW), which has gained substantial attention in recent years. In this study, following laboratory measurements of IFT, six advanced machine learning (ML) techniques were employed: Generalized Linear Model (GLM), Generalized Additive Model (GAM), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Boosted Regression Tree (BRT) to model the IFT in both oil-brine and oil-brine-diethyl ether (DEE) systems. The analysis is based on an extensive dataset comprising 7,017 data points for oil-brine and 6,949 data points for oil-brine-DEE systems obtained from experimental studies. The findings indicate that the developed RF model excels in predicting IFT, boasting a remarkable coefficient of determination (R2 = 0.99) along with the lowest root mean squared error (RMSE = 0.2), mean squared error (MSE = 0.04), and mean absolute error (MAE = 0.13). The study underscores the significance of optimizing salinity levels to achieve the most substantial reduction in IFT. This reduction is attributed to the enhanced migration of polar components, such as asphaltene molecules, to the interface of the oil-brine system. Moreover, the research highlights a synergistic decrease in IFT when both DEE and soluble ions are present, resulting in the lowest IFT at around 2 mN/m in 40,000 ppm salinity (S2) at 70 °C (T3). This indicates that the adsorption of DEE at the water–oil interface forms a layer capable of adsorbing ions, thereby enhancing the layer's thickness. As a result, the oil–solvent-ion layer becomes thicker compared to the oil-ion layer, leading to the maximum decrease in IFT. Additionally, with increasing temperature up to 70 °C, the IFT of both systems demonstrated a downward trend, as evidenced by all experiments. The outcomes of this study have the potential to enhance our comprehension of the underlying mechanisms involved in water-soluble solvent EOR techniques.

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose a novel approach to overcome the above challenge by taking advantage of the Power-Law Committee Machine (PLCM) technique optimized by Particle Swam Optimization (PSO) to combine the output of five cutting-edge machine learning methods with different types of learning algorithms. The PLCM method has not been used in previous studies for EOR screening. The machine learning models include the Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The CatBoost is used for the first time in this work for screening of EOR methods. The role of the PSO is to find the optimal values for the coefficients and exponents of the power-law model. In this study, a bigger dataset than those in previous studies, including 2563 successful worldwide EOR experiences, was gathered. A bigger dataset improves the generalization of the data-driven methods and prevents overfitting. The hyperparameters of the individual machine-learning models were tuned using the fivefold cross-validation technique. The results showed that all the individual methods could predict the suitable EOR method for unseen cases with an average score of 0.868. Among the machine learning models, the KNN and SVM had the highest scores with a value of 0.894 and 0.892, respectively. Nonetheless, after combining the output of the models using the PLCM method, the score of the predictions improved to 0.963, which was a substantial increase. Finally, a feature importance analysis was conducted to find out the most influential parameters on the output. The novelty of this work is having shown the ability of the PLCM technique to construct an accurate model to overcome the class-imbalance issue in EOR screening by utilizing different types of data-driven models. According to feature importance analysis, oil gravity and formation porosity were recognized as the most influential parameters on EOR screening.

Pore scale investigation of chelating agents flooding for enhanced oil recovery

Recently, laboratory tests have shown a positive response to chelating agent brines in sandstone and carbonate reservoirs as a new enhanced oil recovery technique. There is little research on direct and visual evidence of underlying mechanisms behind the chelating agent effects. In this study, a microfluidic method was established using glass micromodel to investigate the micro-mechanisms of displacement under ethylenediaminetetraacetic acid (EDTA) and diethylenetriaminepentaacetic acid (DTPA) chelating agent at the pore-scale level. Furthermore, for the study of fluid/fluid interactions, EDTA brine and DTPA brine were brought into contact with two different crude oils to examine the possible compositional changes. For this purpose, sample tests, Fourier Transform Infrared Spectroscopy (FTIR), microscopic image analysis, and dynamic interfacial tension (IFT) measurements were conducted. The FTIR data showed EDTA brine and DTPA brine induced changes in the oil sample composition compared to seawater, which can be related to the formation of water-in-oil micro-dispersions. Microscopic image analysis on samples confirmed the formation of these micro-dispersions. The visual study by the micromodel demonstrated the micro-dispersion formation in the tertiary injection of EDTA and DTPA brine. Based on the observation made, the pore-scale mechanisms of chelating agent flooding involve the chelating agent brine penetrating the crude oil, resulting in micro-dispersion formation. These micro-dispersions tend to alter wettability to a more water-wet state, facilitate oil detachment from the surface, redistribution of oil saturation, and thus leading to improved oil recovery. The observed IFT reduction aids the penetration of EDTA and DTPA brine into the crude oil to form micro-dispersions.

One-dimensional modelling of air injection into abandoned oil fields for heat generation

With the global drive for net-zero emissions, it has never been more important to find clean energy sources. There are thousands of abandoned oilfields worldwide with the potential to be reactivated to produce clean energy with air injection and subsequent waste fluid sequestration. Air injection, and the development of a fire-front, may be used with enhanced geothermal systems by taking advantage of the inherent increase in heat and pressure. Conventionally used as an enhanced oil recovery technique, air injection has gained the reputation of being a high-risk intervention due to the many failures in its history. Knowledge of how petrophysical rock properties and oil physical and chemical properties control the consequences of air injection is key to optimzing the selection of late-life, or even abandoned oilfields for use in such systems. Here we use one-dimensional modelling to test the effect of varying porosity, permeability, oil viscosity and API gravity on the success of air injection. Modelling shows that the most important factor controlling temperature is the porosity of the reservoir, followed by the API gravity and then the viscosity of the oil. The most important factors controlling velocity of the fire-front are API gravity followed by oil viscosity. We show that reservoirs with high porosity and low permeability with high viscosity and low API gravity oil reach the highest fire-front temperatures. The significance of this work is that it provides several geoscience-related criteria to rank possible candidate reservoirs for reactivation and clean energy generation via air injection: the best candidates will have the highest total porosity, relatively low permeability, highest oil viscosity and lowest API gravity, such fields can then move on to bespoke and more complex simulations.

An enhanced oil recovery technology applicable to kazakhstan reserves with highly viscous oils

The subject of discussion of the article below is polymer flooding as an EOR (enhanced oil recovery) technique considered here within the context of Kazakhstan’s depleted oil-fields exploitation. The underlying factor for considerable residual hydrocarbon saturation observed in the productive reservoirs of the majority of Kazakhstan’s oil fields connected with their stratified heterogeneous structures. Such kind of Oil Reservoirs require additional developments comprising new technologies promoting enhanced oil-recovery. Keywords: viscosity; inhomogeneity; polymer flooding; polyacrylamide; degree of inhomogeneity; permeability; catholyte; trim.

An Overview of Oil Recovery Techniques: From Primary to Enhanced Oil Recovery Methods

As we all know, numerous methods have been invented for better managing of the reservoirs to recover the trapped oil from them as much as possible. These techniques included primary techniques that were implemented primarily at the beginning of this industry. As these techniques were not effective enough, secondary techniques, like; water flooding and gas injection methods were created and the amount of recovered oil were increased, as well. On the contrary, the demand for more oil was raised up and it was felt that much more effective techniques are necessary. It resulted to creation of Enhanced Oil Recovery Techniques and these techniques are included; thermal methods (steam injection, steam assisted gravity drainage and in-situ combustion), Chemical methods (alkali flooding, surfactant flooding, polymer flooding, foam flooding, and combination of alkali-surfactant-polymer flooding), and microbial EOR. The most promising technique is microbial EOR because of being cost-effective and ecofriendly. GEMEOR (Genetically Engineered MEOR) and EEOR (Enzyme Enhanced Oil Recovery) are two new trends of MEOR that own potential hopes in petroleum industry.

Role of Underground Carbon Storage to Assist Reaching Net Zero by 2050: Perspectives on Petroleum Reservoirs

This article focuses on the critical role of sedimentary basins in underground carbon storage. Focusing on both depleted petroleum reservoirs, as well as sedimentary reservoirs in the field’s petroleum stratigraphy (associated sedimentary reservoirs), it highlights the importance of complete CO2 storage in saline aquifers associated with petroleum reservoirs. This paper provides a novel approach to the understanding of underground carbon sequestration (UCS) by combining the examination of target reservoirs and regulation of activities in these reservoirs. By combining this consideration of physical characteristics with legal issues arising from the regulation of UCS, and their application to emerging Australian UCS projects, this novel evaluation of the progress in UCS provides a unique insight into Australian existing and planned UCS Projects. The findings of the research indicate that depleted petroleum reservoirs are more suited to enhanced oil recovery techniques, while associated sandstone reservoirs (saline aquifers) of the same formation are more suited to UCS. The suitability of a reservoir should be considered in the regulation of UCS activities. The example of Australia presented in this paper demonstrates the difficulties in such regulation.

The Role of Silica Nanofluid as a Plugging Agent for Enhanced Oil Recovery – Experimental Approach

Many enhanced oil recovery techniques have been adopted in oil production to recover as much oil as possible. However, enhanced oil recovery techniques are complex and costly. Therefore, nanofluids have been modified in the laboratory to be consistent with reservoir fluids and rocks while also being environmentally benign. This study aims to investigate the role of silica nanofluid in plugging the high permeable zones after oil is recovered from the reservoir to divert pressure to the low permeable zone for enhanced oil production from the low permeable zones. A digital gas permeameter (DGP-300-B) was used to determine the permeability of the core by injecting nitrogen gas. In addition, a DHP-100-M Digital Helium Porosimeter was used to determine bulk volume, grain volume, and grain density porosity. The experiment was conducted with different percentages of silicon dioxide nanoparticles to observe the effect of plugging in each stage. Results show that a 52% reduction in core permeability was observed when using 0.5wt.% of silica nanoparticles, a 72% reduction in core permeability when using 1wt.% of silica nanoparticles and a 94% reduction in core permeability when using 2wt.% of silica nanoparticles.

A computational fluid dynamic method for dense-phase carbon dioxide centrifugal pump

As an advanced approach for large-scale low-carbon utilization of fossil energy, the carbon capture, utilization and storage technology is a promising means to achieve the carbon-neutrality target by 2060. Among the most widely used approaches for carbon storage is the enhanced oil recovery technique, for which the centrifugal pump is used to inject high-pressure dense-phase carbon dioxide (CO2) into the stratum. This work focuses on real fluid effects and inter-stage matching deviation from the incompressibility assumption for a dense-phase CO2 centrifugal pump, of which the inlet condition is close to the fluid critical point. With a thermophysical-state table for CO2 based on the thermophysical-property database of the National Institute of Standards and Technology embedded into a computational fluid dynamics solver, a numerical simulation method for compressible dense-phase CO2 pumps is established. The developed method is validated in the 50kW main compressor for a supercritical CO2 cycle and then applied to the simulation of a multistage dense-phase CO2 pump designed for carbon storage. The stage with the most remarkable fluid compressibility is determined, which provides a priori basis for the follow-up design optimization of the pump. This work is of promising application prospect for design of advanced dense-phase fluid machinery.

A new approach based on VIKOR and Monte-Carlo algorithms for determining the most efficient enhanced oil recovery methods: EOR screening

Most of oil reservoirs in the world have faced decrease in production and they are in the second half of their life cycle. Therefore, tertiary and enhanced oil recovery (EOR) techniques are needed for continuous production from these reservoirs. As choosing the most appropriate EOR methods for a reservoir is a challenging task for reservoir engineers, screening of EOR approaches is of high importance before any full field simulation and experiments. Enhanced oil recovery screening is a multiple criteria decision-making (MCDM) problem and hence, a systematic statistical algorithm based on MCDM can be used for this purpose. In this study, for the first time, a new EOR screening method is proposed by using VIKOR and Monte-Carlo algorithms. The approach used a large database of successful EOR projects around the world and was applied to 12 various EOR methods including a wide range of conditions and properties. Pre-processing was performed on the gathered database and then based on reservoir engineering analyses and using a pairwise comparison matrix, initial weights were considered for the parameters in each EOR method. Afterward, these weights were used in the proposed VIKOR MCDM calculation algorithm and the corresponding numerical values of EOR techniques for each reservoir were obtained. Finally, the EOR method with the highest corresponding value was selected as the most suitable method. Results demonstrated that by using the presented approach, a high classification accuracy of 98% was obtained for different cases, which shows the proficiency and robustness of the developed screening algorithm. In addition, the reliability of the developed method was validated using data obtained from 11 oil reservoirs in the southwest of Iran. Also, the results were compared with the results of previous studies and they were in a very good match. The developed approach is less expensive and faster than full field simulation method and can be used as an efficient EOR screening approach for reservoirs with different properties in the world.

Experimental investigation of in situ combustion (ISC) in heavy oil thermal recovery

With the intensification of global climate change, there is an urgent need to change the energy structure of oil and gas to reduce the impact on the environment. As a thermally enhanced oil recovery technique, in situ combustion (ISC) is very important for reducing carbon emissions in the petroleum industry through the comprehensive utilization of oil displacement and storage in reservoirs. In this paper, the similarity parameters of a three-dimensional (3D) physical simulation of ISC were determined, the temperature field changes in the combustion process were monitored in real time, and a 3D field map was obtained. The oxidation characteristics of heavy oil were examined via thermogravimetry (TG), and the results showed that the oxidation process of heavy oil had three stages. The experimental results indicated that ISC could greatly increase the reservoir temperature, improve the quality of crude oil through high-temperature cracking, reduce the viscosity of crude oil and enhance the oil fluidity. The recovery factor from the ISC reached 53.27%. The formation of the coking zone was caused by various chemical and physical changes that occurred at high temperatures. Moreover, the formation process led to a concentration of the remaining oil in the small-aperture channels in the coking zone; this reduced the fluidity and increased the production difficulty. This study could provide guidance for the development of heavy oil reservoirs.

Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms

Enhanced oil recovery (EOR) is a complex process which has high investment cost and involves multiple disciplines including reservoir engineering, chemical engineering, geological engineering, etc. Finding the most suitable EOR technique for the candidate reservoir is time consuming and critical for reservoir engineers. The objective of this research is to propose a new methodology to assist engineers to make fast and scientific decisions on the EOR selection process by implementing machine learning algorithms to worldwide EOR projects. First, worldwide EOR project information were collected from oil companies, the extensive literature, and reports. Then, exploratory data analysis methods were employed to reveal the distribution and relationships among different reservoir/fluid parameters. Random forest, artificial neural networks, naïve Bayes, support vector machines, and decision trees were applied to the dataset to establish classification models, and five-fold cross-validation was performed to fully apply the dataset and ensure the performance of the model. Utilizing random search, we optimized the model’s hyper parameters to achieve optimal classification results. The results show that the random forest classification model has the highest accuracy and the accuracy of the test set increased from 88.54% to 91.15% without or with the optimization process, achieving an accuracy improvement of 2.61%. The prediction accuracy in the three categories of thermal flooding, gas injection, and chemical flooding were 100%, 96.51%, and 88.46%, respectively. The results also show that the established RF classification model has good capability to make recommendations of the EOR technique for a new candidate oil reservoir.

Matching method between nanoparticle displacement agent size and pore throat in low permeability reservoir

Nano-particles possess desirable attributes such as small particle size, excellent injectivity, and migration performance, making them highly compatible and adaptable for addressing the water flooding requirements of the low-permeability oil reservoir. When selecting an oil displacement agent for enhancing water flooding and improving oil recovery, factors such as injectivity and migration need to be carefully considered. In this study, through a comprehensive analysis of the mechanism and technical characteristics of nano-particle oil displacement agents, the plugging and profile control mechanisms recognized by the mainstream of nano-particles are elucidated. By examining various elements including outcrop fractures, natural micro-fractures, artificial support fractures, and dynamic monitoring data, a reevaluation of the dominant channel scale governing water drive in low permeability reservoirs is conducted, thereby defining the target entities for profile control and flooding operations. Drawing upon Darcy’s percolation law and leveraging enhanced oil recovery techniques based on the classical Kozeny equation, a profile control and flooding mechanism is proposed that focuses on increasing the specific surface area of polymer particles while simultaneously reducing reservoir permeability. This innovative approach establishes a novel matching method between nano-polymer particles and the diverse media found within the reservoir. Lastly, the application of nanoparticle flooding technology in Changqing Oilfield is presented, highlighting its practical implementation and benefits.

Tight and Shale Oil Exploration: A Review of the Global Experience and a Case of West Siberia

Shale and tight oil reservoirs, with horizontal wells and hydraulic fractures, typically have a recovery ratio of around 10%. The exploration of tight oil and shale in North America has proven economically viable, thanks to advancements, such as horizontal wells, hydraulic fracturing, and other enhanced oil recovery techniques. Taking inspiration from the global experience (the North American shale experience), the exploration and development of the West Siberian tight and shale reserves was more focused on the reported best practices of the exploration of North American shale. In this study, the advance in the specific areas of shale and tight oil exploration was considered, with more emphasis placed on the progress in the exploration of West Siberian shales. According to the review literature, thermal enhanced recovery methods capable of converting organic matter into hydrocarbons were studied more than other methods of enhanced oil recovery. Aligned with global trends, there has been a growing focus on research aiming to integrate data-driven approaches and pore-scale simulations to enhance recovery from tight and shale formations. Multiple pilot studies have showcased promising prospects for implementing multistage hydraulic fracturing. Nevertheless, there are limited pilot studies dedicated to enhanced oil recovery methods for West Siberian shale.

Gas-liquid surface characterization and liquid film thinning of Non-Aqueous foam

Foam flooding is a crucial enhanced oil recovery technique for profile control during the oil displacement process. The stability of the foam is the key factor for the success of foam flooding, but typical aqueous foams generally lose their stability in the presence of hydrocarbons because of their low oil tolerance. Non-aqueous foams could possess outstanding stability in the presence of hydrocarbons as a result of their unique properties. However, few studies have been conducted on the stabilization mechanisms of non-aqueous foams in the presence of hydrocarbons. In this study, comparative experiments are performed to investigate differences in the stabilization mechanism between aqueous and non-aqueous foams. The results show that a non-aqueous foam had excellent oil tolerance in a bulk foaming test. Then, the stabilization mechanisms of foams are investigated in terms of surface dilatational viscoelasticity and liquid film thinning. For a non-aqueous foam system, the maximum viscoelastic modulus of 55 mN/m occurs at the maximum surfactant concentration used which was 5.0 wt% for this set of tests, which indicates that the liquid film of FS22 non-aqueous foam was more stable. In a foam film thinning experiment, the thinning time of an aqueous foam system is shortened but the liquid film thickness is increased by crude oil, whereas crude oil increased the thinning time of a non-aqueous foam system but decreased its liquid film thickness. In a non-aqueous foam system, the film could remain stable for hours before rupturing, which indicates that its stability in the presence of an oil phase is excellent. These results are meaningful for the understanding of the stabilization mechanisms of oil-based foams and the employment of non-aqueous foams for enhanced oil recovery.

An approach of using machine learning classification for screening of enhanced oil recovery techniques

Screening of an appropriate enhanced oil recovery (EOR) technique is important for the maximum reservoir recovery and economics of an oil exploration and production project. The conventional approaches for selecting EOR are often time-consuming and may fail to produce satisfactory results for the complex reservoirs. This paper focuses on implementing of selected five supervised machine learning (ML) techniques for accurate screening of EOR methods for complex reservoirs. Two of them are never used before for EOR screening. A global database of 358 successful EOR projects is collected of which 176 are used in the present study to train Neural Network (NN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gaussian Naive Bayes, and Random Forest (RF) classifier and tested their efficiencies. The result shows that the RF classification technique predicts the most suitable EOR with an accuracy value of 0.91 which is the highest accuracy among all other techniques. The study also ranks the reservoir and fluid parameters depending on their influences on the EOR screening. Viscosity is found to be the most impacting factor in the selection of an EOR technique with a feature importance of 34.6%.

Recovery of heavy crude oil with electrical enhanced oil recovery using lignin nanoparticles: A review

This research work addresses the problem of extraction of heavy oil efficiently with the simultaneous use of Electrical EOR (enhanced oil recovery) and environment friendly nanofluids. The economic extraction of heavy crude oil remains a major concern to resolve as it is extremely viscous and difficult to flow, making standard extraction techniques inefficient, it is attainable by reduction of oil viscosity through implementation of EOR techniques. Electrical EOR processes utilize electrical resistance heating, microwave heating, and electromagnetic induction heating. The traditional thermal methods experience substantial heat loss to the surrounding formation in thin pay zones and through the wellbore in deep wells. This results in either early fluid injection breakthrough in high permeability zones or challenges in fluid penetration into the formations in low permeability zones and reservoir heterogeneity which makes the process less efficient. By using electrical approaches in enhanced recovery methods, these difficulties can be overcome because they are all based on the emission of specific waves to raise the temperature of the reservoir, handle difficulties of reservoir heterogeneity and heat losses in thin formations. Therefore, electrical EOR is being developed as a more advanced and efficient approach than conventional EOR methods. Additionally, nanomaterials (or nanofluids) can be used with these electrical approaches, as an effective medium to ease the heavy oil extraction, as they alter rock wettability, reduce fluid viscosity, increase thermal conductivity, and change interfacial tension, further changing the molecular interaction between the different interfaces. However, the preparation of nanomaterial for EOR poses environmental concerns. So, the objective of this review is to assess the increased efficiency of the Electrical EOR by using abundantly present eco-friendly nanoparticles such as organic polymer Lignin. This work proposes to use lignin-based nanoparticles for the first time as the best ecofriendly nanoparticle to extract heavy oil efficiently using Electrical EOR.