Chemical flooding in carbonates is controlled by coupled interfacial, transport, and geochemical processes, yet these mechanisms are still rarely interpreted within a single framework under harsh reservoir conditions. In this study, two oil-aged Indiana limestone cores were first brought to residual-oil state by seawater imbibition and were then exposed to stepwise chemical-flooding sequence at 90°C. Each experiment included seawater-based preflush, chemical slug, and postflush, followed by same sequence prepared in ten-times diluted seawater (10DSW). Two corefloods were performed to compare chemical effects under this sequential design: surfactant flood in one core and surfactant–polymer flood in the other. Oleyl polyoxyethylene amidopropyl carboxybetaine (OPAC) was used as surfactant, and ATBS-based polymer was added in surfactant–polymer sequence.Preliminary screening showed that OPAC acted primarily as wettability modifier rather than as strong ultralow-IFT agent. Seawater-based formulations produced the strongest wettability alteration, lowering final contact angles to 58.87° for surfactant and 53.40° for surfactant–polymer, compared with 110.28° and 95.66° for the corresponding 10DSW-based formulations. On the other hand, IFT decreased to 0.248-0.320 mN/m for surfactant and 0.241-0.308 mN/m for surfactant–polymer systems, indicating substantial but not ultralow IFT reduction. In coreflooding, the seawater-based chemical stage recovered 4.9% OOIP in surfactant flooding (SF) and 9.6% OOIP in surfactant–polymer flooding (SPF). After the subsequent 10DSW-based stage, total incremental recovery reached 9.8% OOIP in SF and 19.7% OOIP in SPF, while residual oil saturation decreased to 20.0% and 13.4%, respectively. Final surfactant retention remained modest at 0.129 mg/g-rock in surfactant flood and 0.084 mg/g-rock in surfactant–polymer flood.Effluent geochemical analysis revealed that oil mobilization was governed by ongoing rock–brine–chemical re-equilibration rather than by static wettability or capillary-desaturation interpretation alone. This behavior was evident throughout the sequence and became most pronounced in the late diluted-seawater surfactant–polymer stage, where the stronger Ca2+ enrichment, a more distinct transient Mg2+ increase, and a noticeable pH increase indicated stronger low-salinity carbonate re-equilibration. Overall, the results demonstrate that sequential carbonate chemical flooding is strongly history-dependent: the first seawater chemical stage establishes the dominant wettability shift, while later ten-times diluted seawater stages mobilize additional oil through continued interfacial action, mobility control, and rock-brine geochemical re-equilibration.

Low polymer retention is essential in polymer flooding to minimize environmental impact and ensure accurate project design. Among the factors influencing retention, wettability remains critical yet insufficiently understood, particularly in carbonate formations. Most prior studies focus on sandstones and rarely assess wettability effects under static adsorption conditions. Moreover, the precision of polymer retention quantification varies across analytical techniques. This study investigates the influence of wettability on polymer retention through static and dynamic experiments using an ATBS-based polymer in carbonate lithology. Wettability alteration was induced by controlled aging at varying durations (8 h, 3 days, and 14 days at 90 °C) for both cores and rock powder. Polymer flooding was performed on five Indiana limestone cores at 167,114 ppm salinity and 50 °C, in both oil-presence and oil-free conditions. Dynamic polymer retention was quantified using effluent viscosity, UV–Vis spectroscopy, and TOC-TN analysis for inter-method comparison. The Amott wettability index to water (δw) decreased from 0.20 after 8 h of aging to 0.09 and 0.08 after 3 and 14 days, respectively, indicating a progressive reduction in water-wetness and a shift toward near-neutral/mixed-wet conditions with an increasing oil-wet tendency. Dynamic retention in the presence of oil decreased by 35–51

Mercury injection capillary pressure (MICP) tests are used to characterize capillary profiles of geological formations. For CO2 geo-storage, sealing and trapping capacity is often assessed by converting air–mercury curves into brine–CO2 curves. These tests, however, are typically performed only at sparse intervals along the well. To extend coverage, many studies have attempted to derive continuous pseudo-capillary pressure curves from NMR T2​ measurements. Conventional approaches match T2​ and MICP curves to estimate surface relaxivity as a conversion factor, but this assumes uniform relaxivity and simple pore geometry, conditions rarely valid for complex lithologies and requiring calibration by rock type. Because conversion of cumulative T2​ response into capillary curve depends on lithology, we introduce J parameter based on sample gas porosity and permeability (J​), bimodality index (BI) for quantifying the degree of peak separation in T2 spectrum, and the bin-weighted T2​ logarithmic mean (T2lmA​) to allow autonomous rock typing by the model. Using these features, a CatBoost model was trained on 30 core samples (carbonates, sandstones, tight mudstones) spanning permeabilities from 0.001 to 11,050 mD. For the training set, the MICP and NMR T2​ distributions of 30 rock cores were subsampled resulting in 3112 ​​ datapoints. Blind test subset comprised 6 additional cores kept outside the training loop. In the blind test phase, the model reproduced MICP curves across 10− 1–105 psi (7 × 10− 4–7 × 102 MPa) pressure range with mean R2 = 0.94, and SHg saturation mean absolute error of 3.6% indicating reliable estimation of air–mercury and by conversion to brine–CO2 curves.

Accurately predicting multiphase fluid flow in oil and gas reservoirs is crucial to optimizing production and minimizing costs. However, traditional numerical reservoir simulations are computationally expensive, while innovative data-driven models may not adhere to physical laws. Building on established physics-informed machine learning (PIML) formulations, we present an integrated PIML–reinforcement learning (RL) workflow that embeds the governing fluid-flow equations and uses RL to solve the inverse problem of estimating relative-permeability model parameters from average water-saturation measurements. Using the inferred parameters, the forward physics-consistent model predicts saturation dynamics and enables inference of capillary-pressure trends during unsteady-state waterflooding. The proposed model accurately predicts the average water saturation over time and estimates trends in capillary pressure across three laboratory experiments. Additionally, a sensitivity analysis is performed to understand the impact of the estimated parameters on the model predictions.

Characterization of crude oil is important for describing the phase behavior of the fluid, which aids in determining the productivity of the reservoir. Crude oil consists of numerous hydrocarbon components, with the heavier ones typically grouped under the C7+ fraction. This fraction is often characterized by a single set of critical properties for PVT calculations, which can lead to inaccuracies. To address this, it is essential to employ advanced characterization techniques that more accurately capture the properties of the heavier crude oil components. The primary objective of this study is to apply various machine learning methods to replicate traditional C7+ characterization approaches, aiming to improve accuracy and representation. Also, clustering techniques were used to group hydrocarbon components independently from the conventional lumping techniques. The results indicated that both boosting and neural network models achieved an average accuracy with R2 values exceeding 0.85 in predicting the critical pressure, critical temperature, and acentric factor for each lumped group. Consequently, these models can effectively replace traditional methods of characterizing C7+ fractions. The application of these models can substantially reduce computational time while maintaining high accuracy. On the other hand, the clustering approach also demonstrated promising results, employing techniques such as K-means and hierarchical clustering to group hydrocarbon components based on their mole fractions. Notably, both methods effectively divided the C7+ fraction into distinct groups, yielding unique compositions for each group. By evaluating various performance metrics, both clustering methods were proven to be efficient in accurately grouping hydrocarbon components.

Polymer-enhanced CO2 foam (PEF) is an emerging technology that improves gas mobility control in chemical enhanced oil recovery applications. It also has significant potential for storing CO2 in geological formations, which can help mitigate anthropogenic emissions. Traditional surfactant-based foams are often weak and unstable, particularly under the high-salinity and high-temperature conditions commonly found in Middle Eastern carbonate reservoirs. However, adding a small amount of stable polymer to these foams can enhance their stability, rendering them more suitable for harsh environments. This study focuses on designing and thoroughly evaluating a PEF formulation under supercritical CO2 conditions at 95 degrees C temperature, 1500 psi pressure, and 105,000 ppm salinity for application in carbonate rocks. It also compares this formulation with a polymer-free foam (PFF) to assess the impact of polymer addition on the properties of both foam types. Experimental investigation includes stability, rheology, and coreflooding tests. Anionic surfactant, zwitterionic surfactant, sulfonated polymer, and their combinations were utilized in this study. Extensive bulk foam studies, which examine factors such as foam half-life and texture analysis, showed that PEF exhibits greater stability and an improved bubble structure compared to individual surfactants and their combinations. Further investigations, including coreflooding tests using Indiana limestone core plugs, demonstrated that PEF significantly outperforms PFF in terms of foam rheology and the mobility reduction factor (MRF). Additionally, coreflooding tests conducted at varying pressures (ranging from 1300 to 2000 psi) illustrated the foam flow properties for both formulations in porous media at 95 degrees C. Evaluations of CO2 storage potential indicated that PEF could store over 81.3 % of CO2, while only 24.0 % can be achieved through CO2 injection. This study provides valuable insights into the role of polymers in enhancing foam performance, in situ rheology, and stability under challenging reservoir conditions.

Enhanced oil recovery (EOR) in carbonate reservoirs faces significant challenges, particularly under high-temperature, high-salinity (HTHS) conditions, where traditional chemical methods may lose effectiveness. Surfactant-polymer (SP) flooding has emerged as a promising method to improve oil recovery by combining the interfacial tension reduction and wettability alteration effects of surfactants with the mobility control provided by polymers. However, achieving stability and optimal performance for SP formulations in harsh reservoir conditions remains challenging, especially for conventional surfactants. This study investigates using zwitterionic surfactants combined with anionic sulfonated polymers to address these challenges. Several experiments were used to highlight the potential of this SP formulation, including stability tests at varying temperatures and salinities, contact angle, interfacial tension (IFT), zeta potential, and static adsorption. Stability tests indicated that surfactants generally retain stability up to 80°C across a range of salinities, including high salinity conditions (214,000 ppm), with no precipitation or cloudiness observed at both 0.25 wt% and 0.5 wt% concentrations over 30 days. At 90°C, stability appears to be influenced by surfactant concentration and brine composition, with selected conditions showing potential for maintaining phase consistency. Contact angle, zeta potential, and static adsorption measurements illustrated how surfactants drive considerable wettability alteration in carbonate rocks. Zeta potential analysis demonstrated that by shifting the rock surface charge to more negative, surfactants can promote a transition from oil-wet to intermediate or water-wet states, enhancing oil recovery. Contact angle studies confirmed this shift, with the effectiveness of wettability alteration impacted by surfactant concentration, salinity, and temperature. It was found that the average contact angle drop achieved by zwitterionic surfactants can reach more than 100° at specific conditions, making the rock more water wet. Interestingly, the wettability alteration effect was slightly reduced at higher surfactant concentrations (0.5 wt%) in diluted brines. The static adsorption tests showed that this is likely due to lower surfactant adsorption on rock surfaces at diluted salinities, highlighting the importance of optimizing surfactant concentration for specific reservoir conditions. IFT measurements indicated that while the zwitterionic surfactant demonstrates some reduction in interfacial tension, its primary mechanism of action is through promoting wettability alteration toward a water-wet state, as opposed to significant IFT reduction. The addition of polymers to the SP solution revealed minimal impact on surfactant stability, with solutions retaining viscosity at 70°C and experiencing only moderate viscosity reduction at 90°C, pointing to polymer resilience in thermal environments. These findings highlight the potential of SP flooding with optimized zwitterionic surfactants as a robust EOR strategy, achieving both stability and enhanced performance in challenging HTHS carbonate reservoirs.

ATBS-based polymers are now a practical choice for polymer flooding projects in the carbonate reservoirs of the Middle East, thanks to their ability to withstand extreme conditions of high temperature and salinity. As a result, it is essential to identify and evaluate other polymers with similar capabilities to perform well in these challenging environments. This paper investigates polymer in-situ rheology under harsh carbonate conditions using a novel ATBS-based polymer comprising two different molecular weights. Polymer solutions with concentrations of 1000 and 1200 ppm were used for three single-phase and one two-phase injectivity studies. Two 3-inch and one 12-inch Indiana limestone outcrops (single-phase) and a 3-inch reservoir core (two-phase) with an absolute permeability ranging from 47 to 726 mD were utilized. Experiments were carried out with synthetic formation water (243,000 ppm) at a moderate temperature of 50 °C. A multi-tap pressure configuration was used to measure the pressure along the 12-inch core. The injectivity studies consisted of four stages: brine pre-flush, polymer injection, polymer tapering, and brine post-flush. Based on the findings, polymer solutions exhibited shear-thickening behavior in porous media, with onsets occurring below 2.5 ft/day. In lower-permeability single-phase experiments (<171 mD) using low molecular weight polymers, the shear thickening onset occurred at lower velocities compared to a high-permeability (726 mD) single-phase experiment with a high molecular weight polymer. Therefore, in this study, the permeability effect dominates that of molecular weight on polymer viscoelastic behavior. Compared to single-phase studies, a clear enhancement in injectivity was observed in the presence of oil. Based on pressure stabilization data, high and low molecular weight polymers demonstrated excellent stability across all tested velocities. These findings were further supported through RRF calculations. The measured residual resistance factor (RRF) was found to be below 3 for all experiments except for the one conducted in the presence of oil. In-situ rheology tests showed different behavior across the experiments due to variations in polymer concentrations, MW, filtration, oil presence, and the permeability of the cores. Findings in the 12-inch core showed that the first two sections of the core experienced the most significant permeability reduction despite their high permeability (>1000 mD). This research investigates the in-situ rheology of a novel ATBS-based polymer encompassing two molecular weights in carbonate reservoirs under harsh conditions. The results highlight the added advantage of using longer cores and multi-tap pressure coreflooding systems for better monitoring of polymer flooding studies. This study is one of the few that explores the in-situ rheological properties of novel ATBS-based polymers with different molecular weights.

This study develops and validates a coupled geochemical modeling framework for simulating moderate- and low-salinity polymer flooding in carbonate reservoirs. Using the MATLAB Reservoir Simulation Toolbox (MRST) integrated with IPhreeqc, the model captures the intricate interplay of polymer transport, adsorption, ion exchange, and polymer-ion complexes formation under varying salinity conditions. The primary objective is to accurately history-match experimental data and provide deeper insights into the geochemical and transport dynamics critical to optimizing polymer flooding performance. The history-matching results demonstrated agreement with experimental data for normalized polymer concentration, effluent ionic composition, and pressure drop profiles. In moderate-salinity flooding, the model accurately reproduced the delayed stabilization of polymer concentration, reflecting the significant polymer retention caused by stronger electrostatic interactions and higher adsorption on rock surfaces. Conversely, in low-salinity flooding, the reduced ionic strength promoted polymer expansion, leading to diminished retention and faster stabilization of the effluent polymer concentration. The simulations also effectively captured the differential pressure behavior across brine pre-flush, polymer injection, and post-flush stages. The observed increase in pressure drop during polymer injection was well-replicated, with a higher peak pressure noted in low-salinity flooding due to enhanced polymer in-situ viscosity. The model also accounts for the slight decline in pressure drop during the post-flush phase, reflecting polymer desorption and subsequent permeability recovery. Effluent concentrations of magnesium (Mg) and calcium (Ca) provided additional validation of the model's geochemical accuracy. Transient spikes in Mg and Ca concentrations during low-salinity polymer injection were attributed to polymer-ion complexes formation and the disruption of thermodynamic equilibrium leading to mineral dissolution. The subsequent decline in the corresponding concentrations during the post-flush phase was captured, reflecting polymer desorption and subsequent ion adsorption on exposed surface sites. This behavior highlights the dynamic interaction between surface processes and brine chemistry during polymer flooding. Furthermore, a critical analysis of the polymer mass conservation equation's dispersion term was further performed, strengthening the model's predictive capability. Optimized dispersion coefficients effectively balance sharp polymer fronts with realistic mixing effects, enhancing agreement between simulated and observed polymer propagation. The inclusion of key parameters, such as polymer-ion complexation constants and salinity-dependent retention, ensures that the model captures the detailed of polymer transport in porous media. This work establishes a comprehensive framework for understanding low-salinity polymer flooding (LSPF) mechanisms by bridging comprehensive experimental observations with advanced numerical modeling. The validated model offers reliable predictions for key operational parameters and provides actionable strategies for designing efficient polymer flooding operations in carbonate reservoirs. By addressing the combined effects of salinity, polymer chemistry, and geochemical interactions, this study helps refine LSPF strategies, rendering it a more effective approach for improving oil recovery in challenging reservoir conditions.

Surfactant-polymer (SP) flooding has emerged as a promising enhanced oil recovery (EOR) technique for carbonate reservoirs characterized by high-temperature and high-salinity (HTHS) conditions. These reservoirs, often defined by their low permeability, heterogeneity, and predominantly oil-wet nature, pose significant challenges to conventional recovery methods. This study explores the potential of combining a zwitterionic carboxybetaine surfactant with an ATBS-based polymer to improve mobility control, enhance sweep efficiency, and optimize flow dynamics within carbonate reservoirs. Comprehensive coreflooding experiments were conducted on Indiana limestone cores under controlled laboratory conditions. The influence of surfactant concentration, polymer addition, and brine salinity on pressure drop, rheology, and geochemical interactions was investigated. Resistance factor (RF) enhancements observed during SP flooding were substantial, particularly at reduced salinity, with RF values increasing from 3.46 at seawater salinity to 10.06 at 10-times diluted seawater (10DSW). Effluent analyses highlighted the critical role of geochemical interactions, such as ion exchange and mineral dissolution. Calcium and magnesium ions, released during rock-fluid interactions, actively influenced the equilibrium, promoting favorable changes in flow dynamics. Rheological evaluations revealed the ATBS polymer's thermal resilience, with the solutions retaining effective viscosities at reservoir-relevant temperatures (70°C) and showing only moderate reductions at 90°C. The presence of surfactants in SP formulations did not compromise polymer stability, ensuring robust viscosifying performance. Furthermore, low-salinity brines not only enhanced polymer viscosity but also reduced polymer retention, as evidenced by residual resistance factors (RRF) consistently below 1.35 across all injection scenarios. This highlights the SP system's ability to maintain injectivity while delivering significant mobility control benefits. This study demonstrates the potential of surfactant-polymer flooding as a transformative EOR approach tailored to carbonate reservoirs under HTHS conditions. The findings underscore the importance of salinity optimization, chemical formulation tuning, and the integration of rheological and geochemical insights to maximize recovery efficiency. Future work will focus on extending these laboratory findings to field-scale applications, ensuring cost-effectiveness and operational feasibility in diverse reservoir environments.

Polymer injectivity into porous media is one of the main issues crucial for the success of a polymer flood project, especially in low-permeability carbonates. Most studies on polymer flooding have focused on high-permeability carbonate cores, with few studies examining permeabilities below 100 md. This paper investigates the impact of filtration, mechanical predegradation, and oil presence on in-situ rheology and injectivity of an acrylamido-tertiary-butyl sulfonate (ATBS)-based polymer in 22-86 md carbonate cores. In this work, an ATBS polymer of 1,000 ppm concentration was used, and various pretreatment approaches were adopted to improve polymer injectivity, including prefiltration, preshearing, and their combination. Polymer injectivity and in-situ polymer rheology evaluations were performed in the absence and presence of oil using carbonate core samples with absolute permeabilities between 22 md and 86 md. For the two-phase studies, the cores were aged at irreducible water saturation and 120 degrees C for 14 days and then flooded with glycerol followed by brine to achieve a representative immobile residual oil saturation (S-or). The corefloods were conducted at 50 degrees C in high salinity water of 243,000 ppm. The resistance factor (RF) was calculated using water permeability at S-or to present the impact of oil presence on polymer rheology. Bulk rheological studies have confirmed that the polymer can withstand high salinity and temperature. However, achieving polymer injectivity in low permeability core samples in the absence of oil has been challenging, with a continuous increase in pressure drop. Various filtration schemes were tested in combination with shear degradation through multiple coreflooding experiments. Preshearing the polymer by 40% and subsequently filtering it through 3-mu m, 1.2-mu m, 0.8-mu m, and 0.45-mu m filter membranes improved its injectivity. With this filtration process, the polymer successfully propagated through a core plug of 64 md in the absence of oil. Experiments with oil showed improved injectivity in low-permeability core plugs. The polymer was successfully injected in samples with permeability as low as 26 md, without predegradation, using a 1.2-mu m filter. Interpreting the injectivity behavior without an internal pressure tab system was challenging. At representative reservoir flow rates, near-Newtonian behavior was observed. However, there was evidence of shear thickening behavior at higher injection rates. The permeability reduction factor determined from the successful corefloods was between 2 and 4. However, it could not be verified due to the absence of polymer retention data and an internal pressure tab system. Nevertheless, it was found that the residual resistance factor (RRF) was lower when oil was present. Different approaches are reported in the literature to evaluate and improve polymer injectivity; however, there is a lack of research that combines preshear degradation, permeability, and oil presence effects. This study is distinctive in its evaluation of the impact of preshearing and prefiltration on enhancing the injectivity of an ATBS polymer in low-permeability carbonate rock. Furthermore, this study is one of the few to demonstrate the evaluation of promising ATBS-based polymer propagation through 22-86 md carbonate core plugs in the absence and presence of oil.

Polymer retention poses a significant challenge in polymer flooding applications, emphasizing the importance of accurately determining retention levels for successful project design. In carbonate reservoirs of the Middle East, where temperatures exceed 90 degrees C, conducting adsorption tests under similar temperature conditions becomes crucial for the precise determination of adsorption values. The choice of analytical method potentially impacts the accuracy of retention measurements from effluent analysis. This study investigates the effect of temperature on the performance of a polymer, specifically its rheological behavior and retention. Rheological and polymer flooding experiments were carried out using an acrylamido tertiary butyl sulfonate (ATBS)-based polymer in formation water (167,114 ppm) at different temperatures (25 degrees C, 60 degrees C, and 90 degrees C) with required oxygen control measures. Dynamic polymer retention was conducted in both the absence of oil (single-phase tests) and the presence of oil (two-phase tests). In addition, different analytical techniques were evaluated, including viscosity measurements, ultraviolet (UV)-visible spectroscopy, and total organic carbon-total nitrogen (TOC-TN) analysis, to determine the most accurate method for measuring the polymer concentration with the least associated uncertainty. Furthermore, the study investigates the effects of these uncertainties on the final dynamic polymer retention values by applying the propagation of error theory. The effluent polymer concentration was determined using viscosity correlation, UV spectrometry, and TOC-TN analysis, all of which were reliable methods with coefficient of determination (R-2) values of similar to 0.99. The study analyzed the effects of flow through porous media and backpressure regulator on polymer degradation. The results showed that the degradation rates were around 2% for flow through porous media and 16% for mechanical degradation due to the backpressure regulator for all temperature conditions. For the effluent sample, the concentration of polymer was lower when using the viscosity method due to polymer degradation. However, the TOC-TN and UV methods were unaffected as they measured the TN and absorbance at a specific wavelength, respectively. Therefore, all viscosity results were corrected for polymer degradation effects in all tests. During the two-phase coreflooding experiment conducted at 25 degrees C, the accuracy of the UV spectrometry and viscosity measurements was affected by the presence of oil, rendering these methods unsuitable. However, the TOC-TN measurements were able to determine effluent polymer concentration and, subsequently, the retention value. Moreover, the use of glycerin preflush to inhibit oil production during polymer injection in the two- phase studies showed that all three methods were appropriate. The error range was obtained using the propagation of error theory for all the methods. Accordingly, it was noted that the temperature did not affect the dynamic retention values in both single-phase and two-phase conditions. The findings of this study highlight that when adequate oxygen control measures are implemented, the temperature does not exhibit a statistically significant impact on the retention of the ATBS-based polymer under investigation. Furthermore, TOC-TN has been identified as the optimal analytical method due to its minimal uncertainties and ease of measuring polymer concentration under varying experimental conditions.

Surfactant flooding is a proven chemical flooding technique with limited applications in carbonates, especially in the Middle East, due to prevailing conditions of high temperature, high salinity (HTHS), and mixed-to-oil wettability. Under such conditions, surfactants break down or become inefficient, limiting their effectiveness for enhanced oil recovery (EOR) applications. This study evaluates the efficiency and stability of superior chemistry carboxylate-based anionic surfactant blends for EOR applications in Middle East HTHS carbonate reservoirs. The study employed a range of tests to identify optimal surfactant blends for HTHS carbonate conditions, including thermal and aqueous stability tests and phase behavior analyses for salinity optimization. Both carboxylate and internal olefin sulfonate surfactants were utilized at elevated temperatures and salinities, up to 120°C and 170 kppm, respectively. Stable formulations were selected based on their performance under these representative field conditions. Interfacial tension was measured using a spinning drop tensiometer and verified using Huh correlation during phase behavior studies. The screening was further supported by surfactant retention to ensure efficiency and economic viability through both static and dynamic studies. Moreover, a coreflooding experiment was conducted to evaluate incremental oil recovery through this optimized surfactant formulation. The study demonstrated that specific blends of alkyl ethoxy carboxylate surfactants with internal olefin sulfonate co-surfactants are highly effective in terms of thermal and aqueous stability. These blends consistently maintained transparent, homogeneous solutions without any noticeable turbidity, even when exposed to rigorous reservoir conditions characterized by elevated temperature and salinity. However, for reservoirs with higher salinity, seawater pre-flush is recommended to condition the formation before surfactant injection. The surfactant blends also demonstrated ultra-low interfacial tensions in the order of 10−3 at their optimal salinity levels, close to seawater salinity (43 kppm). This was confirmed by spinning drop measurements and Huh correlation. In parallel, contact-angle measurements showed a shift from strongly oil-wet (~161 °) to water-wet (~69°) conditions, confirming that the formulations induce favorable wettability alteration alongside IFT reduction. Additionally, the surfactant blends exhibited low adsorption onto carbonate rock surfaces, with retention values within the generally accepted economic viability range (~0.1 to 0.2 mg/g-rock), underscoring their suitability and promise for practical EOR applications in harsh, high-temperature, and high-salinity carbonate reservoir environments. Coreflooding experiments further validated the effectiveness of the optimized surfactant blend, achieving a significant incremental oil recovery of 22.9% OOIP (≈ 76 % of Sorw) —16.5 % OOIP from the surfactant slug (≈ 55 % of Sorw) and a further 6.4 % OOIP from the salinity-gradient post-flush (≈ 21 % of Sorw). This study expands the envelope of surfactant flooding EOR applications to Middle East HTHS carbonate reservoirs. By introducing a holistic evaluation framework integrating thermal and aqueous stability, interfacial tension, phase behavior, and salinity optimization, this research highlights the potential of surfactant-based EOR for harsh reservoir conditions. Although further research is necessary to address all challenges associated with surfactant-based EOR in carbonates, the insights gained establish a foundation and pave the way for possible future field-scale applications.

Summary This paper deals with a mathematical modeling and optimization-based approach for estimating relative permeability and capillary pressure from average water saturation data collected during unsteady-state waterflooding experiments. Assuming the Lomeland-Ebeltoft-Thomas (LET) model for the variation of the relative permeability with saturation, the appropriate governing equations, boundary, and initial conditions were solved within the Pyomo framework. Using interior point optimization (IPOPT) with a least-squares objective function, the six parameters of the LET model that ensure the history matching between the measured and calculated average saturation are determined. Additionally, we inferred the capillary pressure function and performed a Sobol sensitivity analysis on the LET model parameters. The results showcase the reliability and robustness of our proposed approach, as it estimates the crucial parameters driving the variation of oil-water flow relative permeability across several cases and effectively predicts the capillary pressure trend. The proposed approach can be seen as an alternative to experimental and numerical simulation-based techniques for predicting relative permeability and capillary pressure curves.

Abstract Polymer injectivity into porous media is one of the main issues that is crucial for the success of a polymer flood project, especially in low-permeability carbonates. Most studies on polymer flooding have focused on high-permeability carbonate cores, with few studies examining permeabilities below 100 mD. This paper investigates the impact of filtration, mechanical pre-degradation, and oil presence on in-situ rheology and injectivity of an ATBS-based polymer in low-permeability carbonate cores. In this work, an ATBS polymer of 1000 ppm concentration was used, and various pre-treatment approaches were adopted to improve polymer injectivity, including pre-filtration, pre-shearing, and their combination. Polymer injectivity and in-situ polymer rheology evaluations were performed in the absence and presence of oil using carbonate core samples with absolute permeabilities between 21 and 85 mD. For the two-phase studies, the cores were aged at irreducible water saturation and 120 °C for 14 days, then flooded with glycerol followed by brine to achieve a representative immobile Sor. The corefloods were conducted at 50°C in high salinity water of 243,000 ppm. Bulk rheological studies have confirmed that the polymer can withstand high salinity and temperature. However, achieving polymer injectivity in low permeability core samples without oil has been challenging, with a continuous increase in pressure drop. Various filtration schemes were tested in combination with shear degradation through multiple coreflooding experiments. Pre-shearing the polymer by 40% and subsequently filtering it through 3, 1.2, 0.8, and 0.45 µm filter membranes improved its injectivity. With this filtration process, the polymer successfully propagated through a core plug of 63 mD in the absence of oil. Experiments with oil showed improved injectivity in low-permeability core plugs. The polymer was successfully injected in samples with permeability as low as 36 mD, without pre-degradation, using a 1.2 µm filter. In-situ rheology tests demonstrated a pronounced impact of oil presence. A near-Newtonian behavior at representative reservoir flow rates was noted. However, at higher injection rates, a shear thickening behavior was evident. The permeability reduction factor determined from the successful corefloods was between 2 and 4. Additionally, it was found that the residual resistance factor was lower when oil was present, suggesting less polymer retention and less damage to the formation. Different approaches are reported in the literature to evaluate and improve polymer injectivity; however, there is a lack of research that combines pre-shear degradation, permeability, and oil presence effects. This study is distinctive in its evaluation of the impact of pre-shearing and pre-filtration on enhancing the injectivity of an ATBS polymer in low-permeability carbonate rock. The results emphasize the importance of conducting polymer injectivity tests in the presence of oil to achieve more accurate outcomes.

Polymer retention is considered a major challenge in polymer flooding applications, especially in carbonates. This is due to the prevailing conditions of low permeability (<100 md), high temperature (>85 degrees C), and high salinity (>100,000 ppm) generally found in these formations, which limit the effectiveness of commonly used polymers such as hydrolyzed polyacrylamide (HPAM) and xanthan gum. To address these challenges, a polymer based on acrylamide tertiary butyl sulfonate (ATBS) has been used due to its tolerance to high-temperature and -salinity conditions. However, the high cost of manufacturing these polymers, combined with their anionic properties that promote adsorption onto positively charged carbonate rocks, necessitates the exploration of methods to reduce polymer retention. In this study, we aim to determine the sufficient concentration of hardness ions (Ca2+ and Mg2+) required to significantly reduce the adsorption of this polymer. The study is unique in its focus on mitigating polymer retention in carbonate formations using softened brine, as no prior research has investigated this aspect. Four different brines were investigated with a salinity of 8,000 ppm total dissolved salts (TDS) and varying ionic composition designed mainly by eliminating the hardness- causing ions, Ca2+ and Mg2+. A geochemical study was performed using the PHREEQC software to analyze the interaction between these injected brines and the rock. Furthermore, comprehensive rheological and static adsorption studies were performed at a temperature of 25 degrees C using the potential ATBS- based polymer to evaluate the polymer performance and adsorption in these brines. Later, dynamic adsorption studies were conducted in both single- phase and two-phase conditions to further quantify polymer adsorption. The geochemical study showed an anhydrite saturation index (SI) of less than 0.5 for all the brines used when interacting with the rock, indicating a very low tendency for calcium sulfate precipitation. Furthermore, the rheological studies showed that polymer viscosity significantly increased with reduced hardness, where a polymer solution viscosity of 7.5 cp was obtained in zero hardness brine, nearly 1.5 times higher than the polymer viscosity of the base makeup brine of 8,000 ppm. Moreover, it was observed that, by carefully tuning the concentrations of the divalent cations, the polymer concentration consumption for the required target viscosity was reduced by 40-50%. For the single- phase static adsorption experiments, the polymer solution in softened brines resulted in lower adsorption in the range of 37-62 mu g/g- rock as opposed to 102 mu g/g-rock for the base makeup brine. On the other hand, the single-phase dynamic adsorption results showed an even lowered polymer adsorption of 33 mu g/g- rock for the softened brine compared with 45 mu g/g- rock for the base makeup brine. Additionally, the single- phase dynamic adsorption studies showed a remarkable improvement in polymer injectivity using softened brine. The polymer retention in wettability- altered cores was further reduced. The study highlights that water softening improves the performance of polymers, specifically in terms of lowering polymer adsorption. It concludes that a threshold hardness level (Ca2+ and Mg2+) of approximately 100 ppm is sufficient to achieve a significant reduction in polymer adsorption for the tested experimental conditions. In this paper, we show that the softened water increases the polymer viscosity and reduces polymer adsorption, which leads to an overall reduction in polymer consumption. Hence, the softened makeup water has the potential to enhance the application envelope of this potential polymer for polymer flood, especially in the case of carbonate reservoirs.

Abstract Recent advances in machine learning have opened new possibilities for accurately solving and understanding complex physical phenomena by combining governing equations with data-driven models. Considering these advancements, this study aims to leverage the potential of a physics-informed machine learning, complemented by reinforcement learning, to estimate relative permeability and capillary pressure functions from unsteady-state core-flooding (waterflooding) data. The study covers the solution of an inverse problem using reinforcement learning, aiming to estimate LET model parameters governing the evolution of relative permeability to achieve the best fit with experimental data through a forward problem solution. In the forward problem, the estimated parameters are utilized to determine the water saturation and the trend of capillary pressure. The estimated curves portray the relationship between relative permeability values and saturation, demonstrating their asymptotic progression towards residual and maximum saturation points. Additionally, the estimated capillary pressure trend aligns with the existing literature, validating the accuracy of our approach. The study shows that the proposed approach offers a promising method for estimating petrophysical properties and provides valuable insights into fluid flow behaviour within a porous media.

This study is intended to compare the capabilities of three different deep learning-based convolutional neural network models in predicting reservoir rock porosity and absolute permeability from 2D carbonate rock images. We consider a comprehensive evaluation scenario to investigate the performance and training time involved in using the proposed models. These are studied and evaluated using 2D micro-CT images captured at various image resolutions from the four different core samples. The selected core samples demonstrate a wider range of absolute permeability and different levels of heterogeneity. We achieve model variability by adopting the transfer learning framework in two of the three designed models using pre-trained, VGG16, and MobileNetV2 models. Results obtained demonstrate that transfer learning improves model accuracy to predictions at the expense of computational time. With the influence of transfer learning, results show that the accuracy and computational time largely depend on the number of trained parameters being transferred. The proposed models can predict both the rock porosity and absolute permeability within a few seconds compared to numerical simulations and experiments which require larger amounts of time.

Carbon dioxide foam injection stands as a promising method for enhanced oil recovery (EOR) and carbon sequestration. However, accurately predicting its efficiency amidst varying operational conditions and reservoir parameters remains a significant challenge for conventional modeling techniques. This study explores the application of machine learning (ML) methodologies to develop a robust model for matching experimental values in CO(2 )foam flooding scenarios. Leveraging a comprehensive dataset encompassing diverse surfactants and rock types, with varied porosity and permeability, our model demonstrates accurate predictions across a wide spectrum of conditions. By focusing on key parameters such as foam apparent viscosity, interfacial tension (IFT), injected foam volume, initial oil saturation, porosity, and permeability, we unveil the pivotal role of these factors in determining CO2 foam EOR performance. Through rigorous analysis, we identify the relative importance of each input parameter, with injected foam volume, apparent viscosity, and IFT emerging as dominant factors. The most accurate model was deep neural network (DNN) (R-2 value of 0.99). Higher foam viscosity and lower IFT were found to significantly enhance oil recovery rates, though their effects plateau beyond certain thresholds (apparent viscosities above 1200 cP and IFT values below 0.2 mN/m). The findings underscore the potential of ML-driven approaches in enhancing CO2 foam EOR predictions, offering insights crucial for optimizing foam flooding performance across diverse reservoir settings.