The flood

@misc{macdonald1988the1,
    author = "MacDonald, D",
    title = "The Flood",
    year = "1988",
    howpublished = "Mesopotamiam Archaeological Evidence: Creation/ Evolution, v. 23, p. 14-20",
    note = "talkorigins\_source = {true}; raw\_reference = {MacDonald, D., 1988, The Flood: Mesopotamiam Archaeological Evidence: Creation/ Evolution, v. 23, p. 14-20.}"
}

Alkaline-surfactant-polymer (ASP) flooding is an effective technique to improve oil recovery. It has been applied typically after a water flood. Recently, there has been a successful field test where an ASP flood was conducted after a polymer flood. Is the ASP flood after a polymer flood more effective than an ASP flood after a water flood? It is difficult to conduct this experiment in exactly the same location in a field. The goal of this study is to answer this question in a laboratory heterogeneous quarter 5-spot model. A heterogeneous quarter 5-spot sand pack of size 10″ × 10″ × 1″ was constructed. Two sands with a permeability contrast of 10:1 were packed into a 2D square steel cell. An alkali-surfactant formulation was identified that produced ultra-low interfacial tension with the reservoir oil (27 cp). In one experiment (WF-ASP), waterflood was conducted first followed by the ASP flood. In a second experiment (PF-ASP), polymer flood was conducted first followed by the ASP flood. The ASP formulation and slug size were kept the same. Secondary water flood of the heterogeneous quarter 5-spot recovered 22% OOIP. Post-waterflood ASP flood recovered 32% OOIP additional oil with a cumulative (WF-ASP) oil recovery of 54%. Secondary polymer flood of the same heterogeneous quarter 5-spot yielded 50% OOIP. Post-polymerflood ASP flood recovered 32% OOIP additional oil with a cumulative (PF-ASP) oil recovery of 82% OOIP. The water flood and the subsequent ASP flood swept a large part of the high permeability region and a small part of the low permeability region. The polymer flood swept all of the high permeability region and most of the low permeability region. The subsequent ASP flood swept the polymer-swept regions. These experiments demonstrate that the polymer flood - ASP flood combination is more effective than the water flood - ASP flood combination.

@inproceedings{aitkulov2018asp,
    author = "Aitkulov, Almas and Dao, Eric and Mohanty, Kishore K.",
    title = "ASP Flood After a Polymer Flood vs. ASP Flood After a Water Flood",
    year = "2018",
    booktitle = "SPE Improved Oil Recovery Conference",
    abstract = "Alkaline-surfactant-polymer (ASP) flooding is an effective technique to improve oil recovery. It has been applied typically after a water flood. Recently, there has been a successful field test where an ASP flood was conducted after a polymer flood. Is the ASP flood after a polymer flood more effective than an ASP flood after a water flood? It is difficult to conduct this experiment in exactly the same location in a field. The goal of this study is to answer this question in a laboratory heterogeneous quarter 5-spot model. A heterogeneous quarter 5-spot sand pack of size 10″ × 10″ × 1″ was constructed. Two sands with a permeability contrast of 10:1 were packed into a 2D square steel cell. An alkali-surfactant formulation was identified that produced ultra-low interfacial tension with the reservoir oil (27 cp). In one experiment (WF-ASP), waterflood was conducted first followed by the ASP flood. In a second experiment (PF-ASP), polymer flood was conducted first followed by the ASP flood. The ASP formulation and slug size were kept the same. Secondary water flood of the heterogeneous quarter 5-spot recovered 22\% OOIP. Post-waterflood ASP flood recovered 32\% OOIP additional oil with a cumulative (WF-ASP) oil recovery of 54\%. Secondary polymer flood of the same heterogeneous quarter 5-spot yielded 50\% OOIP. Post-polymerflood ASP flood recovered 32\% OOIP additional oil with a cumulative (PF-ASP) oil recovery of 82\% OOIP. The water flood and the subsequent ASP flood swept a large part of the high permeability region and a small part of the low permeability region. The polymer flood swept all of the high permeability region and most of the low permeability region. The subsequent ASP flood swept the polymer-swept regions. These experiments demonstrate that the polymer flood - ASP flood combination is more effective than the water flood - ASP flood combination.",
    url = "https://doi.org/10.2118/190271-ms",
    doi = "10.2118/190271-ms"
}

Starting from the increasingly frequent consequences of floods in local communities in Serbia, thispaper aims to examine the level and impact of selected demographic, socio-economic and psychological factorson the level of social resilience to flood disasters. The research was conducted using a provided questionnaire andthen collected online for 261 respondents during August 2021. The research results show a statistically significantinfluence of gender, previous experience, social ties, trust in public institutions and involvement in riskcommunication processes on the level of social resilience to flooding disasters. In addition, research has foundthat strengthening social resilience requires strengthening social networking, trust and solidarity among membersof the local community. The obtained research results can significantly help all stakeholders formulate strategies,plans, and initiatives to improve social resilience to the consequences of flood disasters and create preconditionsfor building a safe and sustainable environment.

@article{cvetković2026social,
    author = "Cvetković, Vladimir",
    title = "Social resilience to flood disasters demographic, socio-economic and psychological factors of impact",
    year = "2026",
    publisher = "Zenodo",
    abstract = "Starting from the increasingly frequent consequences of floods in local communities in Serbia, thispaper aims to examine the level and impact of selected demographic, socio-economic and psychological factorson the level of social resilience to flood disasters. The research was conducted using a provided questionnaire andthen collected online for 261 respondents during August 2021. The research results show a statistically significantinfluence of gender, previous experience, social ties, trust in public institutions and involvement in riskcommunication processes on the level of social resilience to flooding disasters. In addition, research has foundthat strengthening social resilience requires strengthening social networking, trust and solidarity among membersof the local community. The obtained research results can significantly help all stakeholders formulate strategies,plans, and initiatives to improve social resilience to the consequences of flood disasters and create preconditionsfor building a safe and sustainable environment.",
    url = "https://zenodo.org/doi/10.5281/zenodo.19975766",
    doi = "10.5281/zenodo.19975766"
}

BACKGROUND: Firefighters face high traumatic exposure during their work, increasing the risk of acute stress disorder (ASD). As a transient but critical early phase following trauma, ASD plays a pivotal role in the onset and progression of subsequent trauma-related psychological disorders. However, the symptomatic characteristics and psychological mechanisms of ASD in firefighter populations remain unclear. This study aimed to explore ASD in firefighters from a network analysis perspective, identifying potential targets to prevent further deterioration. METHODS: A total of 1085 firefighters who were actively engaged in flood rescue operations were included in this study. Two network construction methodologies, the regularized partial correlation network (RPCN) and directed acyclic graph (DAG), were employed to perform network analysis. RESULTS: For the RPCN, A3 "Things seem unreal" and A16 "Difficulty concentrating" had the highest expected influence (EI) values and served as the central symptoms. Regarding the DAG, the results indicated that A6 "Intrusive memories" had the highest probabilistic priority and was identified as the potential activation symptom of the network. Moreover, A3 "Things seem unreal" and A16 "Difficulty concentrating" were located in the second layer of the DAG, mediating the connections between the uppermost node A6 and other downstream nodes. CONCLUSION: The convergence of DAG and RPCN findings provides a holistic model of ASD in firefighter populations. Our findings offer a network-informed framework for understanding ASD psychopathology in firefighters, identifying derealization, distractibility, and intrusive memories as theoretically grounded targets for early psychological intervention.

@article{doi101016jcomppsych2026152699,
    author = "Liu, Bin and Zou, Mingxuan and Ren, Lei and Ma, Zhujing and Qin, Niping and Liu, Shuo and Yang, Qifan and Fan, Yangyan and Hou, Lihong and Yang, Zhiping and Fan, Daiming",
    title = "Deciphering acute stress disorder symptoms in firefighters: Embracing the network analysis perspective.",
    year = "2026",
    journal = "Comprehensive psychiatry",
    abstract = {BACKGROUND: Firefighters face high traumatic exposure during their work, increasing the risk of acute stress disorder (ASD). As a transient but critical early phase following trauma, ASD plays a pivotal role in the onset and progression of subsequent trauma-related psychological disorders. However, the symptomatic characteristics and psychological mechanisms of ASD in firefighter populations remain unclear. This study aimed to explore ASD in firefighters from a network analysis perspective, identifying potential targets to prevent further deterioration. METHODS: A total of 1085 firefighters who were actively engaged in flood rescue operations were included in this study. Two network construction methodologies, the regularized partial correlation network (RPCN) and directed acyclic graph (DAG), were employed to perform network analysis. RESULTS: For the RPCN, A3 "Things seem unreal" and A16 "Difficulty concentrating" had the highest expected influence (EI) values and served as the central symptoms. Regarding the DAG, the results indicated that A6 "Intrusive memories" had the highest probabilistic priority and was identified as the potential activation symptom of the network. Moreover, A3 "Things seem unreal" and A16 "Difficulty concentrating" were located in the second layer of the DAG, mediating the connections between the uppermost node A6 and other downstream nodes. CONCLUSION: The convergence of DAG and RPCN findings provides a holistic model of ASD in firefighter populations. Our findings offer a network-informed framework for understanding ASD psychopathology in firefighters, identifying derealization, distractibility, and intrusive memories as theoretically grounded targets for early psychological intervention.},
    url = "https://pubmed.ncbi.nlm.nih.gov/42061122/",
    doi = "10.1016/j.comppsych.2026.152699",
    pmid = "42061122"
}

This study presents a novel hybrid methodology combining machine learning (ML) with conventional reservoir simulation to optimize waterflooding in the geologically complex Bahariya Formation, Western Desert, Egypt. The research addresses the critical need for accurate oil recovery efficiency (RF%) prediction by developing and deploying robust data-driven models. Linear regression quantified the impact of nine key parameters across three injection patterns. A pivotal finding from numerical simulation was the ranking of Ultimate Oil Recovery (UOR) for each pattern under identical conditions: Peripheral flooding achieved the highest recovery at 44.7%, followed by Staggered Line Drive (SLD) at 39.4%, and the 5-Spot pattern at 33.7%. Our ML models demonstrated exceptional predictive accuracy, with R² scores of 0.974, 0.972, and 0.953 for the respective patterns, and a correspondingly low RMSE range of 0.0057-0.0085. Permutation importance analysis quantified the dominant influence of residual oil saturation (Sor), accounting for 38-42% of predictive power. Crucially, the models revealed distinct, pattern-dependent control parameters: injection rate (WINJ) showed markedly higher sensitivity in the Peripheral pattern (23% contribution), while API gravity was the second most important feature for the 5-Spot pattern (18% contribution). The findings provide a validated, efficient framework for rapid waterflooding scenario screening and optimization. This work highlights the substantial potential of hybrid AI-numerical approaches to enhance decision making and challenges conventional assumptions about pattern selection, demonstrating that the optimal pattern is profoundly dependent on specific reservoir characteristics. Field engineers can apply these insights to optimize injection strategies during reservoir development planning, potentially increasing recovery factors while reducing reliance on time-intensive simulation runs.

@article{doi101038s41598026495615,
    author = "Gad, Ramy and Salem, Adel M and El Farouk, Omar M and El-Hoshoudy, A N",
    title = "A hybrid simulation-machine learning proxy model for waterflood design optimization in the Bahariya Formation.",
    year = "2026",
    journal = "Scientific reports",
    abstract = "This study presents a novel hybrid methodology combining machine learning (ML) with conventional reservoir simulation to optimize waterflooding in the geologically complex Bahariya Formation, Western Desert, Egypt. The research addresses the critical need for accurate oil recovery efficiency (RF\%) prediction by developing and deploying robust data-driven models. Linear regression quantified the impact of nine key parameters across three injection patterns. A pivotal finding from numerical simulation was the ranking of Ultimate Oil Recovery (UOR) for each pattern under identical conditions: Peripheral flooding achieved the highest recovery at 44.7\%, followed by Staggered Line Drive (SLD) at 39.4\%, and the 5-Spot pattern at 33.7\%. Our ML models demonstrated exceptional predictive accuracy, with R² scores of 0.974, 0.972, and 0.953 for the respective patterns, and a correspondingly low RMSE range of 0.0057-0.0085. Permutation importance analysis quantified the dominant influence of residual oil saturation (Sor), accounting for 38-42\% of predictive power. Crucially, the models revealed distinct, pattern-dependent control parameters: injection rate (WINJ) showed markedly higher sensitivity in the Peripheral pattern (23\% contribution), while API gravity was the second most important feature for the 5-Spot pattern (18\% contribution). The findings provide a validated, efficient framework for rapid waterflooding scenario screening and optimization. This work highlights the substantial potential of hybrid AI-numerical approaches to enhance decision making and challenges conventional assumptions about pattern selection, demonstrating that the optimal pattern is profoundly dependent on specific reservoir characteristics. Field engineers can apply these insights to optimize injection strategies during reservoir development planning, potentially increasing recovery factors while reducing reliance on time-intensive simulation runs.",
    url = "https://pmc.ncbi.nlm.nih.gov/articles/11219757/",
    doi = "10.1038/s41598-026-49561-5",
    pmcid = "11219757",
    pmid = "42067534"
}