Water Flooding Research Articles

Enhanced efficiency of water purification plant by combined riverbank filtration

Water shortages occur due to several factors, with drought being one of the biggest drivers. Another major environmental issue related to the contamination of freshwater systems worldwide is thousands of micropollutants, although they generally occur at low concentration levels. The provision of safe drinking water to the population in rural developing nations remains a problem, in particular when surface water and shallow wells or non-watertight headworks wells serve as sources of drinking water. Dramatically changing raw water qualities, floods and high rainfall events anthropogenic pollution, lack of electricity supply in developing regions demand new and adapted solutions for treatment and rendering water safe for distribution. Our study aimes to find another source of water supply using riverbank filtration (RBF). The RBF is a water treatment method that removes water from rivers by pumping wells into a nearby alluvial aquifer. Several physical, chemical, and biological processes that occur underground improve the quality of surface water and eliminate the need for traditional potable water treatment. Additional treatment techniques in this process include biological degradation, sorption, and filtration. Physical, chemical, and microbiological variables were used to assess the effectiveness of the RBF system in Upper Egypt. Our study proposes a workable water treatment strategy that replaces RBF treatment or pretreatment technique for high-quality Nile water to eliminate or reduce surface water pollutants without the use of chlorine.

Hydro-informer: a deep learning model for accurate water level and flood predictions

AbstractThis study aims to develop an advanced deep learning model, Hydro-Informer, for accurate water level and flood predictions, emphasizing extreme event forecasting. Utilizing a comprehensive dataset from the Slovak Hydrometeorological Institute SHMI (2008–2020), which includes precipitation, water level, and discharge data, the model was trained using a ladder technique with a custom loss function to enhance focus on extreme values. The architecture integrates Recurrent and Convolutional Neural Networks (RNN, CNN), and Multi-Head Attention layers. Hydro-Informer achieved significant performance, with a Coefficient of Determination (R2) of 0.88, effectively predicting extreme water levels 12 h in advance in a river environment free from human regulation and structures. The model’s strong performance in identifying extreme events highlights its potential for enhancing flood management and disaster preparedness. By integrating with diverse data sources, the model can be used to develop a well-functioning warning system to mitigate flood impacts. This work proposes a novel architecture suitable for locations without water regulation structures.

Study of the effect of pore properties of microporous layer on water transport process in proton exchange membrane fuel cell

Aiming at the “water flooding” phenomenon inside the Gas Diffusion Layer (GDL) of the Proton Exchange Membrane Fuel Cell (PEMFC), this paper visualizes the effects of the pore properties and gradient structure of the microporous layer (MPL) on water transport. Specifically, we visualize the impact of pore properties and gradient structure on water transport. For this purpose, a liquid water capillary finger advancement visualization test platform is constructed to analyze the evolution of liquid water in the porous medium and reveal the effects of single porosity, single pore diameter, gradient porosity, and gradient pore diameter of the MPL on the liquid water transport. Moreover, the time of the non-wetting liquid touching the top, the maximum spreading length, and the residual volume in the MPL are analyzed to evaluate the drainage capacity. The results highlight that the microporous layer's smaller porosity and pore size could limit the interface's extended width between the microporous and gas diffusion layers. Additionally, the residual volume of non-wetting liquid is 21.6 % less in the case of φ = 0.3 than for φ = 0.45, and the residual volume of non-wetting liquid is 11.8 % less in the case of d = 0.82 mm compared to d = 2.45 mm. Furthermore, the residual volume of the microporous layer in the Catalyst layer (CL) side of the microporous layer is 11.8 % less than that in the case of d = 2.45 mm. Finally, we conclude that when the pore gradient law is opposite, it is unfavorable for liquid water discharge.

A prediction method of oil recovery for hot water chemical flooding in heavy oil reservoirs: Semi-analytical stream tube model

Abstract Chemical agents (polymer and surfactant) assisted hot water flooding is an effective means of enhancing oil recovery in heterogeneous and heavy oil reservoirs. The rapid prediction of oil recovery through hot water chemical flooding is very important. The stream tube model is a fast analytical method for predicting water flooding recovery, but it is not suitable for hot water chemical flooding. In this paper, a permeability distribution model for multi-stream tubes is established based on permeability variation coefficient using normal distribution function for reservoir heterogeneity, Using GOMPERTZ growth function model to characterize the changes in stream tube temperature, polymer viscosity, and surfactant concentration with injection volume, Using Brooks-Corey model to describe the influence of interfacial tension and temperature on the relative permeability curve. Finally, an analytical stream tube model for hot water chemical flooding of heavy oil reservoirs was established. The time discrete method is used to solve the model, then the graphs of the relationship between oil recovery and water cut are obtained. Compared with numerical simulation methods, the prediction error of oil recovery is less than 2%, and the calculation time is reduced by 89%. This model has been successfully applied to X oilfield. Based on historical fitting, it is predicted that hot water chemical flooding can enhance oil recovery by 6.3% compared to water flooding. This paper provides a fast calculation method for predicting and evaluating the effectiveness of hot water chemical flooding in heavy oil reservoirs.

Study on increasing polymer gel strength with Janus nanoparticles and its feasibility in profile control and water plugging

In formations subjected to prolonged flooding, dominant channels are formed in high permeability layers, thereby reducing the displacement efficiency in medium- and low-permeability layers. In this study, the Janus synthesis method was employed to produce Janus Nano-TiO2 with a particle size range of 60–120 nm and superior dispersion properties. These nanoparticles were subsequently combined with polyacrylamide to formulate a high-strength polymer nano-gel for water plugging applications. The successful synthesis of the material was confirmed through FTIR and TGA. Gelation experiments indicated that the gelation strength and time augmented significantly with the increased addition of Janus-T6. Conversely, an increase in temperature or salinity resulted in a reduction of both gelation strength and time. Rheological experiments demonstrated a decrease in polymer viscosity with rising shear rates or temperatures, revealing significant shear thinning behavior in the water-plugging agent solution. System viscosity increased with more Janus-T6. Flow experiments revealed decreasing resistance coefficients at higher permeabilities, while all cores exhibited plugging rates above 85%. Additionally, the breakthrough pressure continuously rose with the increased addition of Janus-T6. Ultimately, displacement experiments indicated a 22.83% increase in the recovery rate after employing PNG plugging followed by secondary water flooding, compared to primary water flooding alone.

An Automatic Data-Driven Long-term Rainfall Prediction using Humboldt Squid Optimized Convolutional Residual Attentive Gated Circulation Model in India

<p style="text-align:justify;"><span style="font-family:"Times New Roman","serif";font-size:12pt;line-height:200%;">Precise long-term rainfall forecasting is becoming progressively significant, predominantly in periods of fluctuating weather circumstances. These prophecies are indispensable for several areas, including agronomy, water resource supervision, flood attentiveness, and effluence monitoring. This framework investigates the composite association between climatological data, with an emphasis on the precise estimating of rainfall by finding the effect of temperature disparities on rainfall outlines in diverse states of India. The developed framework undergoes two phases: pre-processing and rainfall forecasting. At first, the Improved min-max normalization (I-MMN) technique is highlighted in the preprocessing stage to eliminate unsolicited missing values from the database. Then, the convolutional residual attentive gated circulation unit (CResAtt-GCU) technique is presented to envisage the amount of rainfall that occurs in various Indian states. Finally, the Humboldt squid optimizer (HBSO) technique is presented to tune the parameters of the developed model to minimize network complications during the training process. The developed framework is processed under the Python platform and the real-time rainfall prediction database collected between 2000-2023 is utilized for the experimentation process. Various computational measures like R2, root mean square error (RMSE), Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE), mean absolute error (MAE), and computation time are evaluated and distinguished from different studies. The overall R2 of 0.993, RMSE of 0.84, KGE of 0.96, NSE of 0.992, MAE of 0.53, and overall CT of 86.66s are obtained by the developed technique against various conventional studies on forecasting the rainfall in different regions of India.</span></p>

A Review of Climate-Driven Threats to Recreational Water Users in Hawaii.

In Hawaii, impacts from climate change, such as sea-level rise and flooding, increased hurricanes and wildfires, and warmer temperatures, intersect with aging infrastructure, toxicities from the built environment, and pathogens to threaten the health of recreational ocean users via reduced water quality, severe weather and flooding, environmental degradation, and food systems impacts. An examination of climate-driven threats to water safety is a pertinent review of threats to coastal residents globally.

Importance of Clay Swelling on the Efficacy of Cyclic Steam Stimulation in the East Moldabek Formation in Kazakhstan

Both steam and hot water flooding of high-viscosity oils in the presence of swelling clays are difficult methods for producing oil efficiently because of potential formation permeability reduction. This paper pertains to heavy oil recovery from the East Moldabek formation where the oil API gravity is about 22 and is inundated with swelling clays. To achieve this, we used the IntersectTM reservoir simulator to compare oil recovery economics using both hot water and steam injection as a function of steam cycle duration, temperature, and steam dryness. We also studied clay swelling in the East Moldabek formation where clay poses a significant challenge due to its impact on permeability reduction. In this research, we developed an equation based on experimental data to establish a relationship between water mineralization and permeability in the East Moldabek formation. The equation provides valuable insight on how to mitigate clay swelling which is crucial for enhancing oil recovery efficiency—especially in sandstone reservoirs. Our modeling studies provide the recovery efficiencies for salinities of the hot water EOR versus cyclic steam EOR methods in a formation containing swelling clays. Specifically, the reduction in formation permeability as a function of the distilled water fraction is the controlling parameter in hot water or steam flooding—when the formation water mixture becomes less saline, oil recovery decreases. Our research shows that clay swelling can significantly impact cyclic steam stimulation outcomes, potentially reducing its effectiveness, while hot water flooding may offer a more cost-effective and operationally feasible solution in formations where clay swelling is a concern. Economic analysis reveals the potential for achieving an optimal favorable condition for hot water injection. Therefore, this paper provides a guideline on how to conduct thermal oil recovery for heavy oils in fields with high clay content such as the East Moldabek deposit.

Potential phosphorus mobilization from riparian vegetation following freezing

Phosphorus (P) rich runoff from agricultural landscapes is a major contributor to freshwater eutrophication. Vegetated riparian zones are often employed to retain P from field runoff before it enters streams. In cold regions, vegetation has the potential to release P to runoff following freezing; however, it is unclear if this differs with winter frost severity, riparian zone local topography, vegetation type, and/or exposure to flooding/inundation. To explore the vulnerability of riparian vegetation to winter P losses, this study quantified soil and vegetation P concentrations from 8 riparian zones in Canada at different topographic positions within each riparian site (upper/field edge, lower/water edge) in both the fall and spring seasons and related this to observed surface temperatures and water levels throughout the non-growing season. This was complemented by two laboratory mesocosm experiments in which (1) plant samples were subjected to moderate (−40C) and severe (−250C) simulated winter frost treatments to quantify changes in their water extractable P (WEP) content, and (2) mesocosms were inundated with water to determine if dissolved P concentrations in flood water differed when plants were left intact, clipped but left on the soil surface, or harvested. Greater soil and vegetation P concentrations were observed at upper locations (field edge), and this remained consistent following freezing; however, vegetation WEP concentrations increased with greater simulated frost severity. In the field, temperatures were moderated by snow cover and although differences with riparian zone position were apparent between fall and spring collected samples, changes in P pools did not appear to be related to frost severity or inundation in the field. The mesocosm experiment revealed that harvesting vegetation considerably reduced dissolved P concentrations in flood water. This study shows that vegetated riparian zones can act as a source of P to streams during the winter non-growing season, and highlights the potential for riparian vegetation management in reducing P losses from riparian zones in cold climates.

Exploring residents’ willingness-to-pay for benefits provided by Sustainable Drainage Systems

ABSTRACT Sustainable Drainage Systems (SuDS) have increasingly been used in the recent years since they help adapt cities to climate change and make them more sustainable and resilient. Since they are multifunctional, these stormwater control measures provide numerous benefits. However, the question of acceptability by inhabitants remains partially open. This article seeks to assess preferences of the inhabitants living nearby with regards to SuDS implemented on public land. It relies on a discrete choice experiments whose attributes are the different benefits provided by SuDS. The study was conducted in the Eurometropolis of Strasbourg (France). Results show a strong preference for a reduction of urban heat island effects as well as flood prevention and water protection. Based on a latent class model, we also highlight the heterogeneous nature of the preferences. These results are important for the acceptance of environmental programs and a better guarantee of desirable and effective results.

The Identification and Quantification of Hidden Hazards in Small Scale Reservoir Engineering Based on Deep Learning: Intelligent Perception for Safety of Small Reservoir Projects in Jiangxi Province

This study aims to enhance the detection and assessment of safety hazards in small-scale reservoir engineering using advanced image processing and deep learning techniques. Given the critical importance of small reservoirs in flood management, water supply, and ecological balance, the effective monitoring of their structural integrity is crucial. This paper developed a fully convolutional semantic segmentation method for hidden danger images of small reservoirs using an encoding–decoding structure, utilizing a deep learning framework of convolutional neural networks (CNNs) to process and analyze high-resolution images captured by unmanned aerial vehicles (UAVs). The method incorporated data augmentation and adaptive learning techniques to improve model accuracy under diverse environmental conditions. Finally, the quantification data of hidden dangers (length, width, area, etc.) were obtained by converting the image pixels to the actual size. Results demonstrate significant improvements in detecting structural deficiencies, such as cracks and seepage areas, with increased precision and recall rates compared to conventional methods, and the HHSN-25 network (Hidden Hazard Segmentation Network with 25 layers) proposed in this paper outperforms other methods. The main evaluation indicator, mIoU of HHSN-25, is higher than other methods, reaching 87.00%, and the Unet is 85.50%, and the Unet++ is 85.55%. The proposed model achieves reliable real-time performance, allowing for early warning and effective management of potential risks. This study contributes to the development of more efficient monitoring systems for small-scale reservoirs, enhancing their safety and operational sustainability.

Study on characteristics, efficiency, and variations of water flooding in different stages for low permeability oil sandstone

Study on characteristics, efficiency, and variations of water flooding in different stages for low permeability oil sandstone

A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

STUDY ON THE INFLUENCING FACTORS OF DEMULSIFIER ON THE OIL-WATER INTERFACE VISCOELASTICITY

Water flooding is a commonly used development method in oilfields, and the water cut of the output fluid of oil wells gradually increases with the increase of injected water in water wells. Due to the presence of natural surfactants such as gum, asphaltene and organic acid in crude oil, it is easy to form a high strength viscoelastic oilwater interfacial film and a stable emulsion. To facilitate the storage and transportation of crude oil, it is necessary to demulsify the crude oil emulsion. In this paper, the CQ crude oil was taken as the research object, and the effects of demulsifier type, concentration, demulsifier temperature and oil-water properties on the viscoelasticity of oil-water interfacial film were investigated by using rheometer and then the demulsification mechanism was explored. The results show that the lower the interfacial film strength, the better the demulsifier. In addition, with the increase of demulsifier concentration and temperature, the oil-water interfacial viscoelasticity decreases and the interfacial film strength further decreases. In general, the demulsification effect will be significantly improved with the increase of demulsifier concentration and temperature, and the dehydration effect will be better. The research results can provide theoretical guidance for the field application of demulsification.

Addressing Challenges to Ensuring Justice and Sustainability in Policy and Infrastructure for Texas Water Resources in the 21st Century

Environmental justice and sustainability have both become major concerns for water resource management, particularly with recent federal emphasis on environmental justice under the Biden administration in the United States. Texas, like many U.S. states, lags behind the federal government in this emphasis. While many localities have made progress in some respects—for example, some major Texas municipalities have included equity and sustainability metrics in their recent climate action plans—others have not. This has left a patchwork of persistent water management and availability issues that are exacerbated by extreme weather and worsening impacts of climate change. We provide a review of many of Texas’s water equity and sustainability challenges, both now and in a more extreme climate future. These include water access, affordability, contamination, flooding, drought, and aging infrastructure. For example, many Texas counties rank highest in the nation for flood risk, including coastal counties with high populations of disadvantaged communities and counties containing populations that live in persistent poverty in the Lower Rio Grande Valley. Additionally, approximately 44,000 Texans, or about 0.4% of the state population, lack access to complete plumbing facilities in their homes. The costs of water infrastructure leaks (estimated at about 51 gallons of water per day statewide) are shared across customers of all income levels, though they place a disproportionate burden on low-income customers. We then assess existing statewide and local policy and planning efforts and gaps in addressing these concerns in Texas. We focus particularly on the role of efforts to incorporate community voice—the ideas, concerns, needs, and expertise of impacted community members, dismantle causes of injustice, and improve equity in spending. If communities are not intentional with future development, new water infrastructure could continue to perpetuate existing harms. Thus, we provide a research agenda and recommendations for addressing some of the policy and planning gaps and persistent environmental justice issues. We aim to help water managers and policy makers identify and dismantle sources of inequity, particularly through including community voice.

Flood algorithm: a novel metaheuristic algorithm for optimization problems

Metaheuristic algorithms are an important area of research that provides significant advances in solving complex optimization problems within acceptable time periods. Since the performances of these algorithms vary for different types of problems, many studies have been and need to be done to propose different metaheuristic algorithms. In this article, a new metaheuristic algorithm called flood algorithm (FA) is proposed for optimization problems. It is inspired by the flow of flood water on the earth’s surface. The proposed algorithm is tested both on benchmark functions and on a real-world problem of preparing an exam seating plan, and the results are compared with different metaheuristic algorithms. The comparison results show that the proposed algorithm has competitive performance with other metaheuristic algorithms used in the comparison in terms of solution accuracy and time.

Enhance Oil Recovery by Carbonized Water Flooding in Tight Oil Reservoirs

The low permeability of tight sandstone reservoirs limits the application of water flooding to enhance oil recovery. Due to the properties of CO2, people improve crude oil recovery by carbonizing water flooding. However, many studies have not prioritized determining the concentration of carbonated water before comparing the recovery rates of carbonated water flooding and pure water flooding after water flooding. This article takes the Chang 6 oil reservoir as the research object, and uses a combination of nuclear magnetic resonance testing and displacement experiments to conduct experiments on optimizing the concentration of carbonated water. Based on this, experiments on water flooding and carbon water flooding after water flooding to improve crude oil recovery are conducted to compare and analyze the oil enhancement potential of carbonated water flooding. The experimental results show that the physical properties, pore throat structure and seepage capacity of sandstone samples of type I, II and III decrease in turn. With the increase of carbonated water concentration, the expansion coefficient, viscosity reduction rate, contact angle reduction rate and core permeability loss rate of crude oil increase. The damage rate of 0.4mmol/cm3 carbonated water solution (17.6g CO2: 1L water) to the core permeability is only 0.98%, which is the optimal experimental concentration. During the water flooding process, crude oil in pores of different scales in Type I rock cores is utilized, and the degree of utilization in large pores is relatively high. Continuing to use carbonated water to drive oil in the rock cores after water flooding increased the recovery rate of crude oil by 15.24%; The degree of crude oil utilization in small pores of Tpye II core after carbonization and water flooding increased by 29.4%, and the final recovery rate of crude oil increased by 11.35% compared to the core after water flooding; The physical properties of Tpye III core are poor, with a small proportion of large pores, resulting in a 9.04% increase in crude oil recovery rate. Compared with pure water flooding, the recovery rate and utilization degree of large and small pores in the three types of rock cores after carbonization water flooding have been improved to varying degrees. Among them, the utilization degree of crude oil in large pores is the most significant, which increases the recovery rate of crude oil in the rock core after displacement. This study can provide a theoretical basis for improving crude oil recovery by carbonizing water flooding.

Research on Internal Flooding Analysis of Small Modular Reactors Based on Particle Method

Abstract The analysis of internal water flooding in nuclear power plants is a necessary part of safety review in the approval process of nuclear power plant construction projects. According to relevant regulations, it is necessary to conduct water flooding spread analysis on pipelines, water tanks, or pools filled with liquid in nuclear power plant buildings, analyze the spread path and water flooding height. This article analyzes and calculates the flow characteristics of water in a nuclear power plant building using a fluid calculation method based on the Lagrangian method (particle method). Taking the small modular reactor JR building as a case study, it analyzes the spread and height of four water flooding sources inside the building. According to the simulation time of 1800S, the results show that the flooding caused by the No. 2 water source is the most severe, with a flooding height of 1.367m. Drainage or preventive measures need to be taken to prevent the pipeline from breaking accidents; The flooding sources 1, 2, and 4 have a relatively mild impact, resulting in a lower flooding height and will not have a significant impact on important safety equipment, with little impact on the safety of nuclear power plants. The CFD analysis method was used to conduct water flooding analysis for nuclear power plants, which has the characteristics of strong adaptability and good visualization. It can dynamically simulate the entire process of water flooding analysis and better guide the design of water flooding protection for nuclear power plants.

Research on Mechanism of Controlling Water and Stabilizing Production in Heavy Oil Reservoirs with Edge-bottom Water

The development of edge-bottom water heavy oil reservoirs typically involves short water-free production period and a rapid increase in water cut, leading to generally low oil recovery factors. Combining the advantages of foam and viscosity reducers for composite development can effectively address the challenges in edge-bottom water reservoirs; however, the mechanism of action remains unclear. In this study, the concentrations of foaming agent and viscosity reducer were initially determined using a foam evaluator and an Anton Paar rheometer. Subsequently, a 2D large flat plate model was employed to conduct a composite group production experiment after water flooding, and then the change in oil saturation during flooding was analyzed by measuring electrical resistance. Finally, the dynamic curves of flooding were compared to analyze the mechanism of water control and oil stabilization (WCOS). The results indicate that the 2D large flat plate model and the method of inverting saturation field maps can effectively simulate the oil-water flow behavior during the composite flooding process after water flooding. The synergistic mechanism of N2 foam controlling bottom water coning and CO2 enhanced viscosity reducer to reduce viscosity in deep areas was revealed, increasing the overall recovery factor by 10.3% compared to water flooding. The mechanism of the combined oil recovery is clarified, which providing a reference for formulating WCOS plans following water flooding.