bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relative Permeability Modeling for CO2 Storage Using Physically Constrained Artificial Neural Networks
Authors: Mukherjee S. , Johns R. T. 
Published: April 2024

Abstract
Injection of CO2 into saline formations is regarded as a promising tool to reduce atmospheric emissions. Although often overlooked, one of the key parameters that governs the flow and transport of the plumes is relative permeability. Relative permeability curves are generally measured sparingly, assumed fixed once measured, and/or altered significantly as a history match parameter to fit injection and production data even though relative permeability has a physical significance and is dynamic within a reservoir.
We use limited field data to develop an artificial neural network (ANN) model to generate contours of relative permeability in saturation-connectivity space and predict relative permeability for both CO2 and brine phases as paths are traversed in that space. Experimental data (only one drainage and imbibition curve) is used to tune the ANN model and determine the contours and hysteresis paths, so that relative permeability and trapped saturations can vary automatically depending on the path taken. The new relative permeability model is based on the idea of state and path functions, so that there is a unique value of relative permeability for a given set of input physical parameters, avoiding relative permeability discontinuities. Further, relative permeability in the ANN model is physically constrained to lie between zero and one at the appropriate limits. We show how to fit experimental data and test the predictive capability of the model on a gas-water relative permeability dataset with multiple hysteresis scans not used in the tuning process.
Results show that the developed model fits the limited data (relative permeability and residual or trapped saturations) better than classical Corey curves and has the significant advantage that it can also predict well other hysteresis curves and residual saturations not used in tuning. To our knowledge, we are the first to show the predictive power of physically constrained ANN with such a limited field dataset. The mean squared error for the predicted relative permeability for the hysteresis scans in the gas-water relative permeability dataset is on the order of 10-5. The approach easily handles any hysteresis path in saturation-connectivity space including current problematic ones where water is completely vaporized by CO2 or when CO2 is completely dissolved in brine. With this approach, relative permeability can vary spatially, continuously, and physically according to dynamic saturation history facilitating a faster and more accurate simulation.
A CO2-modified Surfactant for Chemical Flooding and Reducing Carbon Emission
Authors: Chen Shaohua , Huang Tianping , Wang Jinxun , AlOtaibi Mohammed B 
Published: April 2024

Abstract
A CO2-modified alkyl polyglycoside (C-APG) surfactant was synthesized from a commercial APG product under mild reaction conditions including a low temperature at 50 °C and a low pressure of 2.5 bar. With this reaction, CO2 was successfully incorporated into APG molecules for efficient reduction of carbon footprint. The performance of applying C-APG as chemical flooding for improved oil production application in carbonate reservoirs with heavy crude oil was investigated and compared with its precursor - APG surfactant. The synthesized C-APG was characterized by FTIR and the physicochemical properties of both APG and C-APG including compatibility, surface tension, interfacial tension (IFT) were evaluated. Both surfactants were compatible with the high-salinity water (HSW) and had a similar critical micelle concentration (CMC). However, C-APG exhibited a much pronounced effect in reducing IFT between oil and water. The IFT of C-APG was 10-fold lower than original APG surfactant, suggesting great potential in chemical flooding for oil displacement. Micromodel test showed that the produced heavy oil by injection of APG or C-APG solution was further enhanced after water flooding. C-APG shows higher effectiveness on oil displacement due to lower IFT and stronger emulsification. Core-flooding oil displacement test indicated that C-APG injection resulted in a lower differential pressure and a higher cumulative oil production comparing APG chemical flooding. The produced fluids from C-APG flooding and extended water flooding exhibited very strong emulsification comparing the fluids produced by APG flooding. The incomplete phase separation resulted in an underestimated oil displacement efficiency by C-APG flooding. This study shows a great potential of C-APG surfactant in terms of CO2 reduction and improving oil production.
Aqueous Nanobubble Dispersion of CO2 for Enhanced Oil Recovery – Coreflooding and Huff-n-Puff Experiments
Authors: Lawal Tesleem , Wang Hao , Mirzaei-Paiaman Abouzar , Okuno Ryosuke 
Published: April 2024

Abstract
This paper presents an experimental study of aqueous nanobubble dispersion of carbon dioxide (CO2-NB) for enhanced oil recovery. CO2-NB was compared with brine and slightly supersaturated carbonated water (eCW) in corefloods with Berea sandstone cores and dead oil at room temperature. CO2-NB was also compared with a mixture of CO2 and brine with no NB in high-pressure high-temperature huff-n-puff experiments with live oil-saturated tight Kentucky sandstone cores using three different fracture-matrix configurations.
Coreflooding results showed that the displacement of oil by CO2-NB yielded a delayed breakthrough, in which the oil recovery factor at breakthrough (RFBT) was 9.1% original-oil-in-place (OOIP) greater than RFBT in the brine case. The displacement of oil by eCW did not show such an increase in RFBT, but showed a long-term gradual increase in oil recovery after the breakthrough. The increased RFBT by CO2-NB can be attributed to the nucleation of a CO2-rich phase near the displacement fronts, where the metastable aqueous phase releases CO2 to the oleic phase while the nanobubbles of CO2 with capillary pressure release CO2 to the surrounding aqueous phase. That is, the partially miscible displacement of dead oil by the in-situ generated CO2-rich phase could have enhanced RFBT by CO2-NB in comparison to the immiscible oil displacement by water in the brine case. Huff-n-puff experimental results showed a systematic increase in oil recovery by using CO2-NB in comparison to CO2-brine (with no NB). In the CO2-NB case, the annular, artificial, and combination fractures had oil recoveries of 34%, 36%, and 39% OOIP, respectively. In the CO2-brine case, they were 245, 22%, and 28% OOIP, respectively.
Chemical Tracer Application to Compare Napthalene Sulfonic Acid and Sodium Fluorobenzoic Acid as Part of Polymer Field Trial Project
Authors: Tumewu Putranda Yeremia , Silalahi Ester Tio Minar E. 
Published: April 2024

Abstract
Tracer testing is the effective method to characterize subsurface, especially to investigate well connectivity or fluid pathways, estimate traveling velocity of injected fluid, assess the breakthrough times of the tracers between injectors and producers, and evaluate swept pore volume and sweep efficiency particularly in Enhanced Oil Recovery (EOR) operation. Fluoro-benzoic acid (FBA) remains the primary and most common liquid phase tracer in oil industry, but recent laboratory and geothermal field test using Naphthalene Sulfonic Acid (NSA) also looks very promising. The NSAs are thermally stable at reservoir conditions, cost about a tenth of the cost of FBAs, and have a much lower detection limit when tested using High Performance Liquid Chromatograph (HPLC). The drawback is that they have not been widely tested in oil reservoir but have proven themselves in the laboratory tests and in geothermal reservoirs.
This paper describes performance test of NSA as chemical tracer in oil reservoir in direct comparison with proven conservative tracer, FBA by pairing an NSA tracer with a proven FBA tracer in the same injector well as part of IOR polymer pilot project. Six different types of NSAs were injected at the six different injection wells. In addition, six different types of FBAs were also injected at the same wells to make comparative data with NSA at producer wells. The tracer test was performed for 170 days with relatively constant injection and producing rate to maintain steady state condition in the reservoir. The injection and production rates were balanced for the test duration. Bottom-hole temperature is about 85°C.
Low-Tension-Gas Flood in a High-Temperature and Heterogeneous Sandstone Reservoir
Authors: AlAbdullah Mohammad B. , Delshad Mojdeh , Niu Fangya , Mohanty Kishore , Sepehrnoori Kamy , Li Jianshen , Zhou Nancy C. , Lu Meng , Miao Jijun 
Published: April 2024

Abstract
This study explores Low-Tension-Gas (LTG) flooding as a hybrid low-tension surfactant and nitrogen gas-enhanced oil recovery method for a high-temperature (90-100°C), heterogeneous (permeability 0.06 µD-1180 mD), sandstone reservoir. We began with history-matching two corefloods, an outcrop, and a reservoir core, employing the in-house UTCHEM simulator. Flow parameters were derived from these core floods, and fluid properties were ascertained from lab experiments. A 0.6-acre pilot-scale model in UTCHEM, based on field data and geostatistics, was used to simulate the LTG process.
The comprehensive field scale sensitivity study indicates an additional oil recovery factor (RF) between 11-18% from LTG injection, influenced primarily by residual oil saturation to waterflood, injection rate, LTG slug size, and surfactant concentration. Gravity segregation between gas and microemulsion phases, affected by reservoir vertical heterogeneity, emerged as a critical factor. A lower kv/kh ratio led to a more stable front with a moderate gas override. An optimal chemical injection design involves 1 wt% surfactant concentration within 1 pore volume of LTG slug. Higher injection rates resulted in increased oil recovery and shorter pilot duration, though constrained by field operational limits. Adjusting foam quality during LTG injection affected gravity segregation, necessitating further lab experiments for correlation with gas mobility reduction. Foam shear thinning could enhance field foam injectivity without significantly impacting cumulative oil recovery. Increasing the injection rate within allowable injection bottomhole pressure is recommended for field implementation to prevent fracture induction. This is the first study that extensively explores the impact of reservoir and design parameters for LTG injection for challenging reservoir conditions.
Streamline-Based Robust Rate Allocation Optimization for Polymer Flooding Considering Geological Uncertainty
Authors: Zhang Ruxin , Datta-Gupta Akhil 
Published: April 2024

Abstract
Optimal injection/production rate allocation is an integral part of effective reservoir management for enhanced oil recovery (EOR). Field-scale rate allocation optimization with hundreds of wells can be time consuming and complicated by the multitude of operational and well related constraints. The common practice is to either do the optimization manually or limit the simulation-based optimization to a single realization which ignores the geological uncertainty and causes suboptimal rate allocation.
This paper proposes a streamline-based robust optimization algorithm that accounts for geologic uncertainty while allocating injection/production rates to maximize sweep efficiency and hydrocarbon recovery. The algorithm follows a sequence of time intervals. For each time interval, the well rates of each realization are tuned iteratively based on well pair rate multiplier that is consolidated for all realizations to account for geological uncertainty. The robust optimization is based on an expected value of the performance index over multiple realizations, and can be tuned to include appropriate risk tolerance. The optimization process continues to the next time interval, until the end of the field life.
Power and utility of the proposed algorithm is applied to a large-scale polymer flooding field case (base model). Multiple geological realizations are generated by grid-connectivity transformation (GCT) based on the base model to account for geological uncertainty. Before optimization, multiple history-matched realizations are selected and divided into training realizations and one blind test realization. The training realizations and base model are used in their entirety to conduct robust optimization, but the base model is chosen to conduct nominal optimization for comparison purposes. The optimal rate schedules from both the robust optimization and the nominal optimization are applied to a blind test realization to examine the impact of geological uncertainty. The optimal rate schedule from the robust optimization consistently outperformed that from the nominal optimization in terms of sweep efficiency, hydrocarbon recovery, and polymer utilization.
Experimental Characterization to Support a FAWAG Project in an Offshore Pre-Salt Field
Authors: Vieira R. A. M. , dos Santos S. S. F. , do Nascimento L. P. T. , de Souza D. M. P. T. , da Silva C. N. 
Published: April 2024

Abstract
This work describes the experimental characterization of foam flow in a Pre-Salt field, carried out to support the implementation of a FAWAG pilot currently under evaluation. Foam performance was quantitatively determined as a function of surfactant concentration, water composition, gas composition, water saturation, oil saturation, and rock permeability. Surfactant adsorption on the rock and its partition in the oil phase was also measured. These results were used to estimate parameters to be entered into foam models available in commercial reservoir simulators. Other fields subject to gas injection for secondary or tertiary recovery purposes, or even CCS/CCUS projects, are potential beneficiaries of this information.
Synergistic Impact of Low Salinity and Surfactant on Wettability Alteration in Carbonates
Authors: Reinoso B. S. , Mohanty K. K. , Panda M. 
Published: April 2024

Abstract
The waterflood recovery is often low in tight carbonate reservoirs, which tend to be heterogeneous and oil-wet/mixed-wet. Wettability alteration is a promising strategy for improving oil recovery in such reservoirs. This study aims to improve oil recovery in such a reservoir in West Texas. The mineralogy is determined by XRD analysis. The zeta potential is measured for the drill cuttings. Contact angles are measured on core trims, and imbibition experiments are conducted to evaluate wettability alteration. The optimum salinity was determined to be produced water diluted 40 times from zeta potential measurements. The synergy between low-salinity water and surfactants yielded more than 40% oil recovery in spontaneous imbibition experiments. The addition of sulfate ions and weak acids improved spontaneous imbibition. The results of core flood tests underscored the efficacy of surfactants in improving oil recovery through wettability alteration after waterflooding. This study highlights the potential of low-salinity and surfactant-assisted flooding to improve oil recovery in limestone reservoirs.
Foam Flood in Yates Reservoir for Improving Oil Recovery
Authors: Panda Manmath , Valdez Raul 
Published: April 2024

Abstract
The Yates reservoir is a major, multibillion-barrel legacy oil reservoir in West Texas discovered in 1926. Oil production mainly comes from the San Andres formation, which is a highly fractured dolomite rock. The fractures in the reservoir provide preferential pathways for fluid flow, leading to early breakthrough of injected fluids and reduced sweep efficiency. As a result, even though a number of secondary and tertiary injection techniques have been implemented over the field's history to improve the sweep, only a third of the Original Oil In-Place (OOIP) has been recovered thus far. The majority of the bypassed oil is believed to be in the matrix.
This paper describes a foam flood that has been implemented at Yates to sweep the bypassed oil in the matrix rock. Foam flooding in the Yates reservoir involves injecting a surfactant, along with the produced gas into the reservoir. The surfactant reduces the surface tension between the gas and liquid phases, creating a stable foam. This foam is then injected into the reservoir to improve oil recovery. In the case of the Yates reservoir, foam flooding can address several challenges specific to fractured carbonate reservoirs. Foam flooding helps mitigate poor sweep efficiency issue by reducing the mobility of the injected gas, forcing it to contact and displace more oil within the matrix.
An extensive laboratory program was implemented to select suitable surfactants that produce stable foam in Yates fluids and rock system. Several corefloods were also conducted at the reservoir conditions to evaluate the benefits of a foam flood at Yates. The core flood results indicate that an incremental oil recovery of 13-16% can be achieved by a foam flood.
A 3D compositional simulation model around the injection pattern YFU 4045 was developed to evaluate the field implementation of the foam flood at Yates. The simulation model used the foam parameters derived from the laboratory experiments. The simulation results indicated that a field pilot can be implemented to achieve improved recovery in pattern YFU 4045.
A continuous foam injection pilot was implemented for a period of six months by simultaneously injecting produced gas and surfactant solution with average concentration of 1200 ppm that resulted in an average foam quality of 50%. Several surveillance techniques were implemented to confirm foam formation, such as, setting a down hole pressure gauge and running injection profiles with and without the foam. A gas tracer was also injected to verify if gas diversion was achieved because of the foam injection.
Analysis of the pattern producer performance shows a significantly higher incremental oil recovery after the foam flood compared to the simulation model. A dimensionless scaling technique was developed using the foam flood pilot results to expand the foam flood to other areas of the field.
Full-Field Polymer Flooding Project – Principles and Challenges at the Kalamkas Oilfield
Authors: Kushekov R. M. , Sagyndikov M. S. , Ispanbetov T. K. , Pourafshary P. , Shyrakbayev D. A. 
Published: April 2024

Abstract
The Kalamkas oilfield is one of the largest brownfields in Kazakhstan and has a 45-year development history. With a 27% recovery factor, it still has the potential to make a valuable contribution to Kazakhstan’s oil production. Polymer flooding (PF) is supposed to be the most effective technique to reveal this potential since the first pilot projects in 80’s conducted. Nowadays, when new explorations are expensive, the application and expansion of such technologies are critical to field development. This article presents the main features of modern polymer flooding implementations. Features such as mobile polymer flooding units and their complex reservoir-oriented injections help to create cost-effective staged full-field planning. Strong selection criteria consisting of basic geological and production characteristics were developed to target the "sweet spots" of the reservoir. An analytical method for oil production forecasting was applied to evaluate the feasibility of each selected area. The consistency and duration of polymer flooding areas were identified based on the forecasting results. Moreover, this pragmatic approach is time-saving and allows fast decision-making compared to simulation studies. 26 new polymer areas involving 80 injection wells (~15% of Kalamkas injectors) and over 600 offset producers were selected for polymer flooding. New areas were ranked in terms of their predicted efficiency and considered to be covered by polymer flooding step-by-step for the next 20 years. With the availability of mobile polymer flooding units, it is possible to switch units from completed areas to new involving areas, significantly improving the project’s cost-effectiveness. Another feature of our case is that polymer flooding areas can be grouped by geographical principles and their technical reasons for using the same type of polymer. Thus, polymer flooding units handle multiple formations located at different depths even with different rock and fluid properties. It is also observed that most PF areas are expected to be economically feasible with up to 70% pore volume injection; this parameter can be increased even further if local polymer price is optimized. In conclusion, the developed full-field polymer flooding project is expected to provide 7.5% incremental oil recovery. The current experience of PF pilot projects and lessons learned provide a strong foundation for future extension strategy. Full-field polymer flooding project at the Kalamkas oilfield is expected to be one of the largest chemical EOR projects in the Commonwealth of Independent States (CIS) despite harsh water conditions (~130 g/l salinity) compared to other projects such as Daqing, Mangala, Marmul, and Pelican Lake.
Pattern Recognition for Wettability Alteration with Surfactants in Carbonate Reservoirs by Using Machine Learning
Authors: Yao Y. , Wei M. , Ali M. , Qiu Y. , Cui Y. , Leng J. 
Published: April 2024

Abstract
Surfactants are widely applied agents to interact with the adsorbates on carbonate rock surface, which could alter wettability from oil-wetness to water-wetness and thus enhance oil recovery. Surfactant huff-puff is often conducted to achieve wettability alteration and has been applied under various conditions. Currently, many investigations have been reported to apply conventional data analysis methods to analyze different sets of surfactant huff-puff projects. Yet the application of machine learning algorithms to reveal the inherent patterns is rarely reported. In this study, we integrate principal component analysis (PCA) with hierarchical clustering algorithm (HCA) to uncover the hidden patterns embedded in global surfactant huff-puff dataset. PCA is effective to transform the original data space to principal components space, where five principal components could represent the information of original nine reservoir and fluid parameters and maintain around 90% of total variance. Based on the transformed data space, HCA is implemented with optimized structure, including Euclidean distance measure, Ward’s linkage method and three clusters. It shows that HCA obtains a normalized mutual information score, V-measure score, and Fowlkes-Mallows index of 0.75, 0.75 and 0.83, which suggests the effectiveness and reliability of the newly proposed PCA/HCA procedure. The PCA/HCA is able to group similar surfactant huff-puff treatments into the same cluster. Three distinct cluster patterns are found in the surfactant huff-puff dataset. The second cluster includes most of the high temperature and high salinity carbonate rocks, while the third cluster includes chalk rocks with fairly low permeability and high porosity. The first cluster contains surfactant treatments conducted with low temperature and low divalent concentrations. Besides, the analog reasoning results show that the PCA/HCA is able to provide valuable experiences for designing surfactant treatments and predicting future performance for new candidate surfactant treatment projects. This procedure could greatly save time and cost.