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This study explores alternative chemical agents to enhance oil recovery in sandstone and carbonate reservoirs, aiming to address limitations in alkali-surfactant-polymer (ASP) flooding. Existing ASP methods face technical and environmental challenges, prompting research into alternative chemical agents. However, there are limited field deployments of these alternative chemical agents due to high costs, and ternary combinations of these agents remain unexplored. The study investigates a combination of organic alkali, amino acid-based surfactant/surface-active ionic liquid, and biopolymer. Comparative analysis with conventional ASP formulations reveals promising results. Organic alkali and biopolymer combination mitigates the adverse effects of inorganic alkalis on partially hydrolyzed polyacrylamide, enhancing the oil recovery potential. A unit technical cost (UTC) calculation showed that despite higher chemical costs per incremental barrel of oil, the alternative ASP formulations demonstrate comparable costs due to reduced facility cost. Cost-effectiveness will improve with incorporation of factors such as environmental friendliness and reduced preflush requirements. Mass production of these agents could further enhance the economic feasibility. Therefore, this study reveals that careful cost-benefit analysis, the development of low-concentration formulations, and mass production of these chemical agents could facilitate the implementation of these alternatives, ensuring compliance with environmental regulations and enabling ASP flooding in challenging reservoir conditions., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Determining the solution gas-oil ratio ( R s ) below the bubble point is a vital requirement that aids in multiple production engineering and reservoir analysis issues. Currently, there are some models available for the determination of the solution gas-oil ratio under the bubble point. However, they still may prove unreliable due to the applied assumptions and their specification to operate only under a particular range of data. In this study, the neuro-fuzzy, i.e., the adaptive neuro-fuzzy inference system (ANFIS) approach, is utilized to develop an accurate and dependable model for determining the R s below the bubble point pressure. A total of 376 pressure-volume-temperature datasets from Sudanese oil fields were used to establish the proposed ANFIS model. The trend analysis was applied to affirm the proper relationships between the inputs and outputs. Furthermore, using different statistical error analyses, the developed model was benchmarked against widely used empirical methods to evaluate the proposed method's performance in predicting the R s at pressures below the bubble point. The proposed ANFIS model performs with an average absolute percent relative error of 10.60% and a correlation coefficient of 99.04%, surpassing the previously studied correlations., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

The facile and environmentally friendly synthesis of porous organic polymers with designed polar functionalities decorating the interior frameworks as an excellent adsorbent for selective carbon dioxide capture and metal ion removal is a target worth pursuing for environmental applications. In this regard, two azo-linked porous organic polymers denoted man-Azo-P1 and man-Azo-P2 were synthesized in water by the azo-linking of 4,4'-diaminobiphenyl (benzidine) and 4,4'-methylenedianiline, respectively, with 1,3,5-trihydroxybenzene. The resulting polymers showed good BET surface areas of 290 and 78 m 2 g -1 for man-Azo-P1 and man-Azo-P2, respectively. Due to the enriched core functionality of the azo (-N=N-) and hydroxyl groups along with the porous frameworks, man-Azo-P1 exhibited a good CO 2 uptake capacity of 32 cm 3 g -1 at 273 K and 1 bar, in addition to the remarkable removal of lead (Pd), chromium (Cr), arsenic (As), nickel (Ni), copper (Cu), and mercury (Hg) ions. This performance of the synthesized man-Azo-P1 and man-Azo-P2 in the dual application of CO 2 capture and heavy metal ion removal highlights the unique properties of azo-linked POPs as excellent and stable sorbent materials for the current challenging environmental applications., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Bubble point pressure ( P b ) is essential for determining petroleum production, simulation, and reservoir characterization calculations. The P b can be measured from the pressure-volume-temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. Therefore, some studies used alternative methods, namely, empirical correlations and machine learning techniques, to obtain the P b . However, the previously published methods have restrictions like accuracy, and some use specific data to build their models. In addition, most of the previously published models have not shown the proper relationships between the features and targets to indicate the correct physical behavior. Therefore, this study develops an accurate and robust correlation to obtain the P b applying the Group Method of Data Handling (GMDH). The GMDH combines neural networks and statistical methods that generate relationships among the feature and target parameters. A total of 760 global datasets were used to develop the GMDH model. The GMDH model is verified using trend analysis and indicates that the GMDH model follows all input parameters' exact physical behavior. In addition, different statistical analyses were conducted to investigate the GMDH and the published models' robustness. The GMDH model follows the correct trend for four input parameters (gas solubility, gas specific gravity, oil specific gravity, and reservoir temperature). The GMDH correlation has the lowest average percent relative error, root mean square error, and standard deviation of 8.51%, 12.70, and 0.09, respectively, and the highest correlation coefficient of 0.9883 compared to published models. The different statistical analyses indicated that the GMDH is the first rank model to accurately and robustly predict the P b ., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

The bubble point pressure ( P b ) is a crucial pressure-volume-temperature (PVT) property and a primary input needed for performing many petroleum engineering calculations, such as reservoir simulation. The industrial practice of determining P b is by direct measurement from PVT tests or prediction using empirical correlations. The main problems encountered with the published empirical correlations are their lack of accuracy and the noncomprehensive data set used to develop the model. In addition, most of the published correlations have not proven the relationships between the inputs and outputs as part of the validation process (i.e., no trend analysis was conducted). Nowadays, deep learning techniques such as long short-term memory (LSTM) networks have begun to replace the empirical correlations as they generate high accuracy. This study, therefore, presents a robust LSTM-based model for predicting P b using a global data set of 760 collected data points from different fields worldwide to build the model. The developed model was then validated by applying trend analysis to ensure that the model follows the correct relationships between the inputs and outputs and performing statistical analysis after comparing the most published correlations. The robustness and accuracy of the model have been verified by performing various statistical analyses and using additional data that was not part of the data set used to develop the model. The trend analysis results have proven that the proposed LSTM-based model follows the correct relationships, indicating the model's reliability. Furthermore, the statistical analysis results have shown that the lowest average absolute percent relative error (AAPRE) is 8.422% and the highest correlation coefficient is 0.99. These values are much better than those given by the most accurate models in the literature., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)