Enhanced Oil Recovery Projects Research Articles

Polymer Flooding Reduces Emissions and Energy Consumption in the North Sea

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 218231, How Polymer Flooding Reduces CO2 Emissions and Energy Consumption at the Captain Field: An Exergy-Return-on-Exergy-Investment Case Study,” by Tormod Skauge, SPE, and Arne Skauge, SPE, Energy Research Norway, and Nancy Lugo, Ithaca Energy UK, et al. The paper has not been peer reviewed. _ Insights into the energy efficiency and environmental performance of the extraction of heavy oil from the Captain field in the UK sector of the North Sea are gained using life-cycle assessment (LCA), which incorporates the concept of exergy-return-on-exergy-investment (ERoEI). The LCA allows for separation of the process into material and work streams, assessing the effect of each separately. In the complete paper, the authors compare waterflooding (WF) to polymer flooding (PF) as primary extraction strategies. Introduction The Captain field is approximately 145 km northeast of Aberdeen, Scotland. The reservoirs lie at shallow depths of approximately 900 m true vertical depth with correspondingly low temperatures of 31°C and low pressures of 87 bar. A primary challenge is the relatively viscous oil of 40–140 cp. The field was initially developed with long horizontal wells and downhole pumps. Viscous oil implies mobility challenges, however, and water injection could lead to unstable displacement, early water breakthrough, and possible coning issues. After a successful pilot PF at Captain during 2010–2017, a staged enhanced oil recovery (EOR) development approach was adopted in 2016 aiming to extend the pilot polymer-solution-injection scheme to the full field through a phased development. Following the pilot study, the Captain EOR project was split into Stage 1 and Stage 2. The first stage involved drilling of five producers and two EOR injectors by 2022. The second stage included some brownfield modifications across the three Captain installations (two installations and a floating production, storage, and offloading vessel) and a significant expansion in the subsea area. The complete paper concentrates on the expansion in the subsea areas, Areas B and C. The methodology of the study, including the development of simulation cases, LCA, exergy analysis, system boundaries, material and work streams, and CO2 emissions, is detailed in the complete paper. Production Profiles The Stage 2 EOR development evaluates PF for Areas B and C. The baseline is WF with fully produced water reinjection. WF was scheduled to begin in January 2024 and to end in December 2099. The PF case also was slated to begin in January 2024 and to end in 2035 and 2034 for Area B and Area C, respectively. The field is operated on pressure maintenance, and the voidage replacement ratio is kept close to 1.0. This means that the PF injects more liquid than the WF case during the first 5 years of injection when the oil production is higher for PF. After polymer injection ends, the PF case injects less liquid than the WF case. In Area B, water cut is high from the start for the WF case. Because of the high mobility of the water, it fingers through the oil. In contrast, when polymer is injected, the water cut falls to approximately 80% and does not reach the starting value of 94.1% before 7 years, more than 2.5 years after having stopped the polymer injection in this area. The low water cut is the result of production of a large oil bank generated by the improved sweep and mobility reduction of the injected polymer.

Preliminary experimental study of low salinity effect on organic polar compounds desorption in Hassi Messaoud field-Algeria

Low salinity water (LSW) injection is a well-recognized enhanced oil recovery method that has proved to increase oil recovery. Despite its simple implementation and low cost, no experimental investigations have been reporting its application in Algeria’s oil fields. The present work investigates the impact of LSW injection and identifies the role of organic polar compounds on oil trapping in Hassi Messaoud oil field. The study involves adsorption/desorption experiments using acid/amine organic polar compounds dissolved in non-polar refined oil while employing rock drilling cuttings. Subsequent validation of desorption data is performed by employing crude oil in both Amott tests and micro-model displacement tests using various water salinity ranges. It was evident that at water salinities ranging from 1000 to 2000 ppm, the interfacial tension decreased to 10−2 mN/m, leading to favorable rock wetting behavior and increased oil recovery of 9.18% and 20.07% for amine and acid compounds, respectively. The wettability alteration mechanism was proposed as the main contributor since the actual interfacial tension of natural surfactant might not significantly impact oil recovery. The implementation of LSW injection in Algerian fields presents fresh prospects for future enhanced oil recovery projects.

An Experimental Investigation of Surfactant-Stabilized CO2 Foam Flooding in Carbonate Cores in Reservoir Conditions

Carbon dioxide (CO2) injection for enhanced oil recovery (EOR) has attracted great attention due to its potential to increase ultimate recovery from mature oil reservoirs. Despite the reported efficiency of CO2 in enhancing oil recovery, the high mobility of CO2 in porous media is one of the major issues faced during CO2 EOR projects. Foam injection is a proven approach to overcome CO2 mobility problems such as early gas breakthrough and low sweep efficiency. In this experimental study, we investigated the foam performance of a commercial anionic surfactant, alpha olefin sulfonate (AOS), in carbonate core samples for gas mobility control and oil recovery. Bulk foam screening tests demonstrated that varying surfactant concentrations above a threshold value had an insignificant effect on foam volume and half-life. Moreover, foam stability and capacity decreased with increasing temperature, while variations in salinity over the tested range had a negligible influence on foam properties. The pressure drop across a brine-saturated core sample increased with an increasing concentration of surfactant in the injected brine during foam flooding experiments. Co-injection of CO2 and AOS solution at an optimum concentration and gas fractional flow enhanced oil recovery by 6–10% of the 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.

Możliwe zastosowanie energii słonecznej do zasilania operacji wydobycia węglowodorów i metod wspomagania wydobycia ropy naftowej na dużych szerokościach geograficznych

As the world enters the renewable revolution, it is crucial for the petroleum industry to remain relevant in the public eye and become environmentally awareness. One potential path to achieve these goals in a progressive and beneficial way is to integrate renewable energy sources into various nodes of the petroleum industrial operations. Different ways of achieving such integration are described in the review section of the article. These include utilizing solar energy to power upstream operations and a variety of enhanced oil recovery projects, such as microbial enhanced oil recovery and others, or using solar energy downstream to supply power to pumps, gas lift apparatus, etc. The article also proposes a novel potential application of solar energy to power operations and provide long-term sustainable energy source to petroleum operations in high latitudes. The potential applicability of solar power in petroleum engineering operations far North has long been neglected due to potential unprofitability and difficulty of construction of solar power grid in such harsh climactic conditions. However, now, as the world transitions to a new reality of co-existence of fossil fuels with alternative energy sources in light of growing concerns over climate change, it is a strategic time to implement the technology in profitable and environmentally conscious way in countries that have a trustworthy environmental record and strong, robust environmental policies, like Canada, as well as in less environmentally achieving countries with milder emission reduction targets such as Russian Federation.

Global enhanced oil recovery assessment: A comprehensive data-driven analysis using self-organizing maps

This study presents a comprehensive analysis of Enhanced Oil Recovery (EOR) methods' application in the global oil industry, identifying trends and main factors influencing their adoption. A wealth of unstructured and scattered data from public documents covering traditional EOR methods and newer technologies was processed to create a reliable global database, encompassing 1237 EOR projects in reservoirs around the world. Data science techniques and self-organizing maps (SOM) were employed for data analysis, dimensionality reduction, and visualization of complex problems, proving suitable for high-dimensionality and incomplete data. The research integrates various data modalities and data science visualization techniques, enabling a high-level representation of EOR projects' self-contained knowledge, and providing a comprehensive view of EOR application worldwide. The study reveals implicit interrelationships between multiple variables and EOR methods, identifying trends, research and development directions, and better-informed decisions. In-depth analysis showed that factors impacting the choice, application, and success of EOR methods include technical properties of reservoirs, local, commercial, and temporal aspects. Despite the diffusion of secondary and tertiary recovery technologies, a concentration of EOR methods exists in specific regions. Factors influencing EOR projects' implementation, maintenance, and expansion range from technical reasons, deselected reserves, and infrastructure to incentive policies and regulatory aspects of each country, business structure, and global economic scenario. Both petrophysical and non-petrophysical variables influence the application of EOR methods. Availability and costs of purchasing and transporting injection fluids greatly impact decision-making processes. The analysis contributes to understanding the complex network of factors influencing EOR, and this work can aid in making more informed decisions in the industry and research for EOR applications.

Development and performance evaluation of nonionic surfactant-stabilized nanoemulsion for enhanced oil recovery applications in tight reservoir

Nanoemulsions have garnered great attention as a chemical additive for enhanced oil recovery (EOR) technology worldwide due to their small size and unique physicochemical properties. Herein, this work introduces a novel nonionic surfactant-stabilized nanoemulsion prepared using a low-energy method for EOR projects in the Chang 8 tight reservoir. Spontaneous imbibition experiments were conducted in oil-saturated cores, and secondary imbibition experiments were carried out in water-bearing cores. Additionally, core flooding experiments were performed to study the EOR performance of both nanoemulsion and complex surfactant. Finally, the EOR mechanisms were thoroughly investigated, specifically focusing on oil-washing capacity, reduced interfacial tension, and wettability alteration. The designed nanoemulsion, with droplets averaging 10.5 ± 0.7 nm in synthetic formation brine at ambient temperature, exhibits excellent thermal and long-term stability even under reservoir temperature conditions. The oil recovery efficiency of the nanoemulsion system in oil-saturated cores through spontaneous imbibition was 31.28%, representing a 6.42% improvement over the complex surfactant solution. In the secondary imbibition experiments, the nanoemulsion mobilized 8.53% of the residual oil, compared to 4.11% for the complex surfactant. Core flooding experiments revealed that the nanoemulsion system achieved a total oil recovery of 62.69%, outperforming the complex surfactant system by 8.68% during the chemical flooding stage and 1.82% during the subsequent brine flooding stage. The EOR mechanisms of nanoemulsion in tight reservoirs can be attributed to four aspects: reduction of oil/water interfacial tension, wettability alteration, miscibility with crude oil, and increased sweep volume. The generation of the Marangoni effect and the adsorption and diffusion characteristics are essential differences between the nanoemulsion system and the complex surfactant system. The presented findings in this paper could aid in promoting the large-scale use of nanoemulsions in Chang 8 tight reservoirs.

Assessment of World’s First Two Polymer Injectivity Tests Performed in Two Giant High-Temperature/High-Salinity Carbonate Reservoirs Using Single-Well Simulation Models and Pressure Falloff Tests Analysis

Summary Polymer flooding is a mature enhanced oil recovery (EOR) technology that has been widely implemented around the world for more than 60 years. Polymer flooding mostly targets medium- to high-permeability sandstone reservoirs with moderate salinity, hardness, and temperatures. However, in the last few years, the envelope of polymer flooding has been expanded to harsher reservoir conditions of high-temperature and high-salinity mixed-wet to oil-wet heterogeneous carbonate reservoirs. Development of novel polymers and innovative field application concepts has allowed for the reconsideration of polymer-based EOR as a promising technology to improve sweep efficiency for these challenging reservoirs. Polymer injectivity is one of the key challenges for polymer flooding projects and requires a rigorous derisking program that includes laboratory and field testing. A comprehensive laboratory program was designed to assess and investigate polymer thermal stability, polymer rheology in porous media, adsorption, and injectivity using reservoir core samples. In addition, two polymer injectivity tests (PITs) were performed in two giant light oil (0.3 cp) carbonate reservoirs in onshore Abu Dhabi under harsh conditions of high salinity (>200 g/L), high divalent ions (>20 g/L), high temperature (>250°F), and H2S concentration of up to 40 ppm. The polymer used during the two PITs (PIT 1 2019 and PIT 2 2021) is a new generation of EOR polymer (SAV 10) with high 2-acrylamido-tertiary-butyl sulfonic acid content that was specifically developed to tolerate such harsh conditions. This paper is focused on the interpretation of the PITs and lessons learned for future polymer-based EOR projects. The detailed data acquired in both tests were used to evaluate the polymer injectivity at representative field conditions and in-depth mobility reduction. The PITs together with the extensive laboratory studies are part of a thorough derisking program for the upcoming world’s first innovative hybrid EOR multiwell pilots—simultaneous injection of miscible gas and polymer (SIMGAP) and simultaneous injection of water and polymer (SIWAP). Both PITs are composed of three stages that include a multirate waterflood baseline, polymer injection using different rates, and polymer concentrations followed by extended chase waterflooding. In addition, a sequence of multiple pressure falloff (PFO) tests was acquired during the PIT executions and analyzed to obtain the required uncertainty parameters for the history-matching exercise. Polymer preshearing was considered as part of both PIT programs with the aim to homogenize the polymer molecular weight distribution and reduce possible shear-thickening effects near the wellbore as per laboratory measurements. Two single-well 3D simulation models were built to incorporate the information from polymer laboratory studies and to interpret the large field data sets acquired during the PITs. Lessons learned from PIT 1 allowed us to optimize the PIT 2 design program and achieve better understanding of polymer characteristics. The interpretation of the pressure transient analysis (PTA) of the PFO tests and the 3D simulation models of the two PITs confirmed the generation of polymer banks and demonstrated effective propagation of the polymer into the reservoirs at target concentrations and representative rates of the future SIWAP and SIMGAP interwell pilots.

Adsorption Characteristics of CO2/CH4/H2S Mixtures in Calcite Nanopores with the Implications for CO2 Sequestration

Summary Injecting CO2 into reservoirs for storage and enhanced oil recovery (EOR) is a practical and cost-effective strategy for reducing carbon emissions. Commonly, CO2-rich industrial waste gas is used as the CO2 source, whereas contaminants such as H2S may severely impact carbon storage and EOR via competitive adsorption. Hence, the adsorption behavior of CH4, CO2, and H2S in calcite (CaCO3) micropores and the impact of H2S on CO2 sequestration and methane recovery are specifically investigated. The Grand Canonical Monte Carlo (GCMC) simulations were applied to study the adsorption characteristics of pure CO2, CH4, and H2S, and their multicomponent mixtures were also investigated in CaCO3 nanopores to reveal the impact of H2S on CO2 storage. The effects of pressure (0–20 MPa), temperature (293.15–383.15 K), pore width, buried depth, and gas mole fraction on the adsorption behaviors are simulated. Molecular dynamics (MD) simulations were performed to explore the diffusion characteristics of the three gases and their mixes. The amount of adsorbed CH4, CO2, and H2S enhances with rising pressure and declines with rising temperature. The order of adsorption quantity in CaCO3 nanopores is H2S > CO2 > CH4 based on the adsorption isotherm. At 10 MPa and 323.15 K, the interaction energies of CaCO3 with CO2, H2S, and CH4 are −2166.40 kcal/mol, −2076.93 kcal/mol, and −174.57 kcal/mol, respectively, which implies that the order of adsorption strength between the three gases and CaCO3 is CO2 > H2S > CH4. The CH4-CaCO3 and H2S-CaCO3 interaction energies are determined by van der Waals energy, whereas electrostatic energy predominates in the CO2-CaCO3 system. The adsorption loading of CH4 and CO2 are lowered by approximately 59.47% and 24.82% when the mole fraction of H2S is 20% at 323.15 K, reflecting the weakening of CH4 and CO2 adsorption by H2S due to competitive adsorption. The diffusivities of three pure gases in CaCO3 nanopore are listed in the following order: CH4 > H2S ≈ CO2. The presence of H2S in the ternary mixtures will limit diffusion and outflow of the system and each single gas, with CH4 being the gas most affected by H2S. Concerning carbon storage in CaCO3 nanopores, the CO2/CH4 binary mixture is suitable for burial in shallower formations (around 1000 m) to maximize the storage amount, while the CO2/CH4/H2S ternary mixture should be buried as deep as possible to minimize the adverse effects of H2S. The effects of H2S on CO2 sequestration and CH4 recovery in CaCO3 nanopores are clarified, which provides theoretical assistance for CO2 storage and EOR projects in carbonate formation.

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.

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.

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.

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.

Development of a new CO2 EOR screening approach focused on deep-depth reservoirs

Carbon dioxide (CO2) enhanced oil recovery (EOR) is an important technology with the dual benefits of increasing oil production and reducing greenhouse gas emissions. Reservoir screening is crucial for the successful application of CO2 EOR; however, determining CO2 EOR feasibility for a specific field can be a time- and resource-intensive process. Traditional screening criteria can be overly arbitrary, making it difficult to accurately assess whether a field is suitable for CO2 EOR—failing to meet a single requirement may result in an unsuitable judgment. Existing intelligent screening tools primarily focus on classification algorithms to develop predictive models for CO2 EOR feasibility. While these tools are accurate for shallow-depth reservoirs, they struggle to predict CO2 EOR in mid- and deep-depth reservoirs accurately. In this study, an EOR database was created based on 464 EOR projects from 18 countries. Reservoir data from the Williston Basin, TORIS (Tertiary Oil Recovery Information System), and Alberta Basin were also collected to further validate the feasibility of the proposed approach. A new screening criterion was developed based on the boxplot analysis results of the collected worldwide EOR projects and existing CO2 EOR screening guidelines. Weight factors for parameters were determined using the importance permutation technique and the proposed classification algorithms to minimize bias. An innovative CO2 EOR scoring approach was developed using membership functions, composite screening scores, and the six machine learning algorithms. The results showed high prediction accuracy for the worldwide EOR projects database with R-squared values ranging from 0.91 to 0.98. The proposed screening system was further employed to evaluate the prediction accuracy for the mid- and deep-depth reservoirs. The results showed prediction rates ranging from 86% to 92% compared to the analytical solutions. Among these six regression models, random forest outperformed the others with the most stable performance in both the testing phase and case studies with the R-squared value and root mean square error of 0.97 and 4.8, respectively. The proposed screening tool can be further applied to provide recommendations on the feasibility of CO2 EOR in mid- and deep-depth reservoirs.

Screening of Chemicals to Enhance Oil Recovery in a Mature Sandstone Oilfield in Kazakhstan: Overcoming Challenges of High Residual Oil

Chemical flooding, such as alkaline-surfactant (AS) or nanoparticles-surfactant (NS) flooding, is an enhanced oil recovery (EOR) technique that has been increasingly utilized to enhance the oil production rate and recovery factor while reducing chemical adsorption. The AS/NS flooding process involves the injection of a mixture of surfactant and alkali/nanoparticles solutions into an oil reservoir to reduce the interfacial tension between the oil and water phases by surfactant and lower surfactant adsorption by alkali or nanoparticles (NPs) to improve the residual oil recovery. In this study, the AS/NS flooding is evaluated for a Kazakhstani oilfield by systematically screening the chemical constituents involved. Field A in Kazakhstan, one of the oldest fields in the country, has been waterflooded for decades and has not produced even 50% of the original oil in place (OOIP). Currently, the water cut of the field is more than 90%, with a high residual oil saturation. Therefore, besides polymer flooding to control mobility, chemical EOR is proposed as a tertiary recovery method to mobilize residual oil. This study aimed to screen chemicals, including surfactant, alkali, and NPs, to design an effective AS/NS flooding program for the target field. The study focused on conducting laboratory experiments to identify the most effective surfactant and further optimize its performance by screening suitable alkaline and NPs based on their compatibility, stability, and adsorption behavior under reservoir conditions. The performance of the screened chemicals in the porous media was analyzed by a set of coreflood experiments. The findings of the study indicated that alkali agents, particularly sodium carbonate, positively affected surfactant performance by reducing its adsorption by 9–21%. The most effective surfactant combination was found, which gave Winsor type III microemulsion and the lowest interfacial tension (IFT) of 0.2 mN/m. The coreflood tests were conducted with the screened surfactant, alkali, and NPs. Both AS and NS tests demonstrated high residual oil recovery and microemulsion production. However, NS flooding performed better as the incremental oil recovery by NS flooding was 5% higher than standalone surfactant flooding and 9% higher than AS flooding. The results of this screening study helped in designing an efficient chemical formulation to improve the remaining oil recovery from Field A. The findings of this study can be used to design EOR projects for oil fields similar to Field A.

Enhanced Oil Recovery by Surfactant Injection was improved during the Last 50 Years Thanks to the Multivariable HLD Formulation Equation

This report reviews the progress in Enhanced Oil Recovery (EOR) through surfactant injection over the past half century from 1974, with a specific focus on the role of the multivariable Hydrophilic-Lipophilic Deviation (HLD) formulation equation in different expressions. This equation has been instrumental in optimizing surfactant formulations for EOR, enabling the fine-tuning of surfactant types and properties to match specific reservoir conditions like brine salinity, crude characteristic, temperature and even pressure. This short assesment discusses the evolution of surfactant types, and their impact on EOR efficiency for a given reservoir specifications. It highlights the role of intramolecular and intermolecular mixing in surfactant performance, and the benefits of using a multivariable HLD equation to predict and optimize the injected formulation. Furthermore, it explores the challenges and solutions related to surfactant adsorption, aggregation, precipitation, and effect on phase behavior and interfacial tension, and how these factors have to be considered when using surfactants for EOR projects. This review aims to address the existing gaps in the literature, such as the complex effects of surfactant mixtures with insensitivity to temperature and injected composition (amphiphilic concentration and water/oil ratio) that make difficult to propose a unified approach for different petroleum reservoirs. It concludes a discussion on the state of the art and future of surfactant EOR, emphasizing the need for continued research and collaboration across academic and industrial sectors

EOR screening using optimized artificial neural network by sparrow search algorithm

Enhanced oil recovery (EOR) is a crucial aspect of reservoir engineering, and the use of machine-learning algorithms in the initial stages of screening has been widely accepted as a fast and efficient method for screening the most suitable EOR method. This study presents an artificial neural network (ANN) that recommends the most suitable EOR method based on historical reservoir data. Data from EOR projects worldwide were collected, pre-processed, and then used to build the ANN, which initially achieved a 69% accuracy. The neural network was optimized using the Sparrow Search Algorithm (SSA) and compared with the Particle Swarm Optimization (PSO) algorithm, with a focus on weight and hyperparameter optimization. Validation of the neural network’s prediction was done using recall, precision, and the F1 score. Weight optimization yielded an accuracy of 68% with SSA and 34% with PSO, which were insufficient results for EOR prediction. However, hyperparameter optimization was applied, resulting in an accuracy of 94% with SSA and 90% with PSO. The SSA approach demonstrated faster convergence and higher accuracy in both optimization paths, highlighting its potential for optimizing the neural network in predicting the appropriate EOR method for a given reservoir.

Experimental evaluation of the potential impacts of polymer breakthrough on topside operations at Yariguí-Cantagallo field conditions

After a successful polymer injection pilot, Ecopetrol plans to deploy at full-field scale this enhanced oil recovery (EOR) technology on the Yariguí-Cantagallo field. Although a significant increase oil production is expected, the impact of the residual polymer on the produced water cycle should not be neglected. Indeed, after the polymer breakthrough, a significant part of the EOR chemical will be present in the produced fluids, and this may negatively impact the water/oil separation and the water treatment processes. An early review of this scenario provides an accurate vision of potential issues and, above all, enables to anticipate handling difficulties with produced fluids, and identifying mitigation strategies. This paper proposes a specific and comprehensive experimental methodology to assess production risks using laboratory equipment designed to mimic the current separation processes of the Yariguí-Cantagallo field, which were as representative as possible. The objective is to highlight the impact of the residual polymer first on water/oil separation processes with bottle tests, electrostatic dehydration tests, and polymer fouling evaluation on heat exchangers and, second, on water treatment using long-term gravity separation tests, Jar test, flotation column, and walnut shell filtration unit. The assessment considered different scenarios in terms of polymer concentration, water-cut, water composition and initial oil content in the produced water. The compatibility between the polymer and some oilfield chemicals, such as demulsifiers and water clarifiers, was also investigated. The efficiency of these physical and chemical treatments was evaluated by monitoring the separation kinetics as well as by evaluating the quality of the phases with measures of water-in-oil content, oil-in-water content, or water turbidity. For this case study and at laboratory scale, the polymer does not seem to stabilize tight emulsions, rather observing an improvement in water/oil separation kinetics. However, the quality of the separated water is strongly degraded with a higher oil content and some incompatibilities with current demulsifiers which reduce their efficiency have been observed. The risk of polymer precipitation or fouling on heat surface is very low under the experimental conditions tested. The performance of the different water treatment stages is slightly affected in the presence of polymer, even if the water quality remains good. The operational risk assessment conclusions and pending recommendations draw the map of conditions where the residual polymer seems to cause problems or not. This anticipated experimental approach can provide clues and solutions to better manage the impact of the residual polymer in back produced fluids. Adjusting process parameters on existing surface facilities and working on chemical treatment optimization should ensure to produce oil, and release produced water on specifications. This challenge will be one of the keys for technical success and for upholding the expected economic performance of this EOR project.

Experimental Investigation on the Relationship between Biot’s Coefficient and Hydrostatic Stress for Enhanced Oil Recovery Projects

The majority of global conventional oil reservoirs have been dramatically depleted during the last few decades. To increase the oil production rate, enhanced oil recovery (EOR) techniques are commonly utilized. The ratio of the recovered oil volume to the rock volume change is defined as Biot’s coefficient. During the EOR operations, Biot’s coefficient continuously changes due to the fluid injection and oil production; however, so far, only porosity-dependent or constant values of Biot’s coefficient have been incorporated in the EOR calculations, which is not valid since the role of external stress changes is overlooked. In this research, the Biot’s coefficient of a sandstone formation was measured through the acoustic wave propagation technique. A stress-dependent equation of Biot’s coefficient was achieved for application in the EOR calculations. The findings illustrated that Biot’s coefficient decreases logarithmically with the hydrostatic stress. Moreover, the Biot’s coefficient varied from 0.52 to 0.60 for an applied hydrostatic stress of 3.50 MPa to 21 MPa. Furthermore, it was found that there was no anisotropy of Biot’s coefficient in the sandstone formations. The extracted empirical correlation can be utilized for EOR projects in which the recovered oil volume is of paramount importance economically.

Evaluation of enhanced oil recovery methods for mature continental heavy oil fields in China based on geology, technology and sustainability criteria

Enhanced oil recovery (EOR) methods in mature oil fields play an irreplaceable role in supporting the long-term and stable production of crude oil. This study establishes a unified and comparable evaluation index system based on geology, technology and sustainability, re-evaluates the existing EOR projects focused on heavy oil extraction by utilizing hybrid AHP-CRITIC-FCE models, and selects the most efficient, economic, and low-carbon footprint EOR technologies to facilitate the development of mature Chinese heavy oil fields. Three main criteria and nineteen subcriteria are considered in the multi-criteria decision-making system. The weights given to criteria are determined by combining the subjective AHP and objective CRITIC methods. The fuzzy comprehensive evaluation method is adopted to prioritize the performance of the EOR process. Empirical analysis is conducted on seven typical heavy oil technologies. The study finds that geological factors account for the largest proportion, and the top five indicators in terms of weight are buried depth, recovery factor, viscosity, operating cost per tonne of oil and reservoir thickness. Three technologies are recommended for potential future green footprint heavy oil EOR methods. This study combines both expert judgment and field data appraisal to help decision makers better plan the future strategic layout of EOR technology.