Flooding Process Research Articles

Processes and controls of regional floods over eastern China

Abstract. Mounting evidence points to elevated regional flood hazards in a changing climate, but existing knowledge about their processes and controls is limited. This is partially attributed to inadequate characterizations of the spatial extent and potential drivers of these floods. Here we develop a machine-learning-based framework (mainly including the Density Based Spatial Clustering Applications with Noise (DBSCAN) clustering algorithm and a conditional random forest model) to examine the processes and controls of regional floods over eastern China. Our empirical analyses are based on a dense network of stream gauging stations with continuous observations of annual maximum flood peaks (i.e. magnitude and timing) during the period 1980–2017. A comprehensive catalogue of 318 regional floods is developed. We reveal a pronounced clustering of regional floods in both space and time over eastern China. This is dictated by cyclonic precipitating systems and/or their interactions with topography. We highlight contrasting behaviours of regional floods in terms of their spatial extents and intensities. These contrasts are determined by fine-scale structures of flood-producing storms and anomalous soil moisture. While land surface properties might play a role in basin-scale flood processes, it is more critical to capture spatial–temporal rainfall variabilities and soil moisture anomalies for reliable large-scale flood hazard modelling and impact assessments. Our analyses contribute to flood science by better characterizing the spatial dimension of flood hazards and can serve as a basis for collaborative flood risk management in a changing climate.

Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning.

Determining the optimal timing for subsequent water flooding in Alkali-Surfactant-Polymer (ASP) flooding is essential to maximizing both the technical and economic outcomes of oilfield blocks. This study identified eight critical parameters that influence the benefits of ASP flooding and established parameter ranges based on data from completed blocks and actual field measurements. The optimal timing for subsequent water flooding was determined by evaluating cumulative net profit variations throughout the ASP flooding lifecycle. Given the complexity and high-dimensional nature of evaluating multiple parameters across diverse blocks, a machine learning-driven optimization model was developed. This model enhances work efficiency by automating complex analyses. However, predictive uncertainties and limitations remain due to the variability in oilfield development and the potential for unpredictable changes in reservoir conditions, external market factors and so on, which may affect the model's results. The model was applied to six blocks in the Daqing oilfield currently in the chemical flooding phase, where injection schemes, such as extending the polymer slug, were adjusted according to the model's optimized results. These adjustments yielded an increase in cumulative net profit of 224.9million CNY compared to the original scheme, with a potential total increase of 752.1million CNY by the end of the flooding process.

Numerical simulation and validation of heavy rainfall flood inundation in small watersheds in undocumented mountainous areas

ABSTRACT With global warming and the increase in extreme precipitation events, floods are becoming more frequent in mountainous areas, and the safety of lives and property of people is seriously threatened. However, understanding of the flooding process in uninformative mountainous areas is limited due to the lack of high-quality hydrometeorological data. Hence, this study adopts the MIKE21 model to simulate flood inundation in the Shadai River basin in the Qilian Mountain region of the northern Tibetan Plateau as an example. This validates the model-simulated flow and inundation extent using the flow data obtained from the calculation of the flood trace points, extent of inundation, and high-resolution remote sensing images. The results show that the flash flood inundation mainly occurs at 12:00–01:00 AM on 18 August 2022, and the simulated and actual maximum inundation areas are 7.9 and 9.5 km2, respectively. The fitted F-statistic value is 0.81, and the relative error between the calculated flow rate of the flood trace point and the model-simulated flow rate is 8%, indicating good consistency. Furthermore, an in-depth exploration of the model parameter sensitivity reveals that the use of distributed Manning's roughness coefficient value has higher simulation accuracy.

Intense alteration on early Mars revealed by high-aluminum rocks at Jezero crater

The NASA Perseverance rover discovered light-toned float rocks scattered across the surface of Jezero crater that are particularly rich in alumina ( ~ 35 wt% Al2O3) and depleted in other major elements (except silica). These unique float rocks have heterogeneous mineralogy ranging from kaolinite/halloysite-bearing in hydrated samples, to spinel-bearing in dehydrated samples also containing a dehydrated Al-rich phase. Here we describe SuperCam and Mastcam-Z observations of the float rocks, including the first in situ identification of kaolinite or halloysite on another planet, and dehydrated phases including spinel and apparent partially dehydroxylated kaolinite. The presence of spinel in these samples is likely detrital in origin, surviving kaolinitization, pointing to an ultramafic origin. However, the association of low hydration with increased Al2O3 abundances suggests heating-induced dehydration which could have occurred during the lithification or impact excavation of these rocks. Given the orbital context of kaolinite-bearing megabreccia in the Jezero crater rim, we propose an origin for these rocks involving intense aqueous alteration of the parent material, followed by dehydration/lithification potentially through impact processes, and dispersion into Jezero crater through flood or impact-related processes.

Anthropogenic activities mitigate the impacts of climate extremes on high flow regimes on the Loess Plateau

The hydrological cycle is anticipated to become increasing variable due to intensified global climate extremes and frequent floods. Understanding the evolution and driving mechanisms of floods in the context of climatic extremes is essential, particularly on the Loess Plateau (LP) where anthropogenic activities are significant. Here, we examined the spatiotemporal variability of climate extremes and their impacts on flood processes on the LP during 1956–2015. We employed a paired year approach to quantitatively assess the driving role of anthropogenic activities on flood changes in 11 major basins. Our findings indicated a general decline in extreme precipitation indices, while temperature has increased across most of the LP. Both annual runoff (Q) and high flow (Q5) magnitude significantly decreased in all basins. We identified that the major climatic indices driving Q5 included erosive precipitation amount, flood season precipitation, heavy precipitation amount, and maximum 7-day precipitation amount. Importantly, anthropogenic activities have mitigated the impact of climate extremes on Q5 regimes. Compared the post-2000 with reference period (1956–1979), under similar climatic conditions, we observed a reduction in the average Q5 magnitude by 30.19–78.14%, and a decrease in the average Q5 duration by 46.48–100.00% across the basins. The contributions of climate variability and anthropogenic activities to Q5 magnitude changes among 11 basins ranged from –37.58% to 22.02% and –56.84% to –15.08%, respectively, highlighting the crucial role of ecological restoration in reducing Q5 magnitude. These results underscore the importance of ecological restoration in flood regulation and offer valuable insights for basin management in the Yellow River Basin.

Effect of water on asphaltene-precipitation behavior of CO2-crude oil mixtures during CCUS processes

Water can extensively exist in reservoirs. Since the solubility of CO2 in water is not negligible under reservoir conditions, the aqueous phase should also be considered during the phase-behavior simulations of the CO2 flooding process. With the presence of an asphaltene phase and an aqueous phase, four phases (i.e., a vapor phase, a hydrocarbon phase, an asphaltene phase, and an aqueous phase) can coexist at a given thermodynamic equilibrium. In this study, a robust and efficient four-phase vapor–liquid-liquid-aqueous equilibrium calculation algorithm is applied to conduct four-phase vapor–liquid-asphaltene-aqueous equilibrium calculations by assuming asphaltene is a dense liquid phase. The algorithm is first validated by comparing the calculated asphaltene precipitation data with the presence of water against the experimental asphaltene precipitation data documented in the literature. The validation shows that the calculation results agree well with the experimental data, indicating that such algorithm can make reliable predictions of asphaltene precipitation with the presence of water. The validated algorithm is subsequently used to construct pressure–temperature (PT) and pressure-composition (Px) phase diagrams to study the effect of water on asphaltene precipitation under different pressure/temperature conditions and under the injection of CO2. We can conclude from the calculated results that although adding water to the reservoir fluid can cause obvious changes in the PT phase diagrams, it does not have a noticeable influence on the asphaltene precipitation onsets. However, it can be seen from the calculated Px phase diagrams that the presence of water during the CO2 flooding process can make asphaltene precipitated more easily. The maximum amount of asphaltene precipitation is decreased by the presence of water due to the fact that a considerable amount of CO2 is dissolved in the aqueous phase instead of the liquid hydrocarbon phase.

Impact of modeling methods on urban flood processes at community scale

Impact of modeling methods on urban flood processes at community scale

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.

Assessing Watershed Flood Resilience Based on a Grid-Scale System Performance Curve That Considers Double Thresholds

Enhancing flood resilience has become crucial for watershed flood prevention. However, current methods for quantifying resilience often exhibit coarse spatiotemporal granularity, leading to insufficient precision in watershed resilience assessments and hindering the accurate implementation of resilience enhancement measures. This study proposes a watershed flood resilience assessment method based on a system performance curve that considers thresholds of inundation depth and duration. A nested one- and two-dimensional coupled hydrodynamic model, spanning two spatial scales, was utilized to simulate flood processes in plain river network areas with detailed and complex hydraulic connections. The proposed framework was applied to the Hangjiahu area (Taihu Basin, China). The results indicated that the overall trend of resilience curves across different underlying surfaces initially decreased and then increase, with a significant decline observed within 20–50 h. The resilience of paddy fields and forests was the highest, while that of drylands and grasslands was the lowest, but the former had less recovery ability than the latter. The resilience of urban systems sharply declined within the first 40 h and showed no signs of recovery, with the curve remaining at a low level. In some regions, the flood tolerance depth and duration for all land use types exceeded the upper threshold. The resilience of the western part of the Hangjiahu area was higher than that of other regions, whereas the resilience of the southern region was lower compared to the northern region. The terrain and tolerance thresholds of inundation depth were the main factors affecting watershed flood resilience. The findings of this study provide a basis for a deeper understanding of the spatiotemporal evolution patterns of flood resilience and for precisely guiding the implementation and management of flood resilience enhancement projects in the watershed.

Study on the influence law of flooding phenomenon in the killing operation of empty wells

Flooding in the process of well killing in empty wells results in a waste of kill fluid and increases the time of well killing, which is not conducive to its smooth operation. To address this problem, a large-size vertical visualization experimental device was designed and constructed, falling experiments were performed on kill fluid under different apparent gas velocities, apparent liquid velocities, and kill-fluid viscosities, and the gas–liquid two-phase migration state at the injection point during the process of flooding was analyzed in this study. Commonly used critical gas velocity models of flooding were compared, considering the influence of fluid properties and rheological parameters on the critical gas velocity. A combination of dimensionless numbers was used to fit the experimental data, and a model of the critical gas velocity of flooding was established. The comparison results showed that the prediction accuracy of the relationship in this study was higher than that of the traditional relationship, with an average error of 9.89%. The influence of different parameters on the critical gas velocity of flooding was analyzed, and the key parameters for well killing in empty wells were optimized, providing a theoretical basis for empty well killing operations.

Detection of flood trends and drivers in the Taihu Basin, China

Study regionTaihu Basin, China Study focusFloods threaten humans, the environment, economic activity, and infrastructure. In this study, a new trend test and flood-frequency methods were adopted to detect extreme floods and their distributions based on flood-event identification. To fully understand the phased process of the influence of human activities on extreme hydrological processes, 12 copula functions were employed creatively in combined static and dynamic time-varying correlation aspects between extreme precipitation and floods. New hydrological insights for the regionAlthough both significant and insignificant increasing trends of the annual maximum water level in all three hydrological districts were examined, the periodic oscillations of all the stations were similar. Thus, it was significant to fully detect the periodical variation of floods. Extreme floods occurred mainly in the 1990s, as measured by frequency estimates. Generally, the nonstationary response relationship between heavy rain and an extreme water level was gradually strengthened; that is, a certain magnitude precipitation seemed to induce a greater-intensity flood event as time passed. Through the identification of historical flood events and the analysis of the rise and fall processes of floods, we found that the main reason for variation in the response relationship was the increase in the water level before the rising stage, rather than the water level rising in the Taihu Basin. Our study findings further existing knowledge on the regional flood-control design standard and can ensure the coexistence of humans and water systems in the future.

A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes

Abstract Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP, and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA. Subsequently, the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City. The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP, with the mean absolute error concentrated within 5 mm, and the prediction time of CNN-WCA was only 0.8% that of LISFLOOD-FP. The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding, with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97. Furthermore, the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN. The CNN-WCA model with additional physical constraints exhibits a reduction of around 34% in instances of physical discontinuity compared to CNN. Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.

Using a multiphysics coupling-oriented flood modelling approach to assess urban flooding under various regulation scenarios combined with rainstorms and tidal effects

Climate change and rapid urbanisation have intensified urban flooding. Flood models constructed using hydrological and hydrodynamic approaches are crucial tools for simulating and regulating urban flooding processes. Urban flooding arises from the interplay of various physical processes in urban water systems, influenced by heterogeneous urban surfaces, underground structures, and intricate river networks influencing surface flow, which must be considered in flood models. For coastal cities with intricate drainage systems, the disasters of combined rainstorms and tidal flooding can be mitigated through river scheduling and real-time control of hydraulic structures. However, a lack of research on the effectiveness of flood regulations limits the application of flood models. This study proposes a flood modelling approach oriented towards the coupling of multiphysics processes, achieving a comprehensive simulation and regulation of the flood process. A surface flood control model was created by coupling hydrological and hydrodynamic processes using the Storm Water Management Model and the orthogonal storage cell model, enabled by data exchange synchronisation technology. Subsequently, the model’s accuracy was validated using observed rainstorm events, identifying drainage system overflows and land surface inundation. Finally, the developed model was applied to analyse the enhancing effect of tides on rainstorm-induced flooding and the impact of various control scenarios. This study offers theoretical direction for large-scale urban flood modelling and model development, offering significant references for revisiting urban planning and formulating flood prevention strategies under the joint impact of rainstorms and tides.

Time-guided convolutional neural networks for spatiotemporal urban flood modelling

Urban flood modelling is key to understand flood risks and develop effective interventions in flood management. Deep learning (DL), known for its robust and automatic feature extraction capabilities, has been applied for urban flood predictions. However, the hybrid spatiotemporal structure of conventional DL-enabled urban flood models is limited in terms of accuracy and efficiency. To address this gap, this study develops a new DL model guided by time information. This model uses a classic CNN (Convolution Neural Network) architecture, Unet, as its backbone. Time information is integrated into inputs via an extra channel to specify the desired prediction time, facilitating the simulation of the spatiotemporal flood process. Additionally, a modified loss function is formulated to tackle the sample imbalance problem between flooded and non-flooded sites. The model performance is assessed in an urban area in Dalian, China with a total of 18 rainfall events of varying return periods. The model attains average precision and recall values of 0.90 and 0.81, respectively, across different time steps during various events. Furthermore, the model exhibits transferability in ungauged regions where a high influence of surrounding environments on local flood processes is identified by Grad-CAM (Gradient-weighted Class Activation Mapping) analysis. The results show that the new Unet model has great promise in efficiently providing accurate spatiotemporal flood simulations. The time-guided Unet model can serve as practical tools for rapid flood simulation in urban areas.

Three-dimensional physical experimental study on mechanisms and influencing factors of CO2 huff-n-puff and flooding process in shale reservoirs after fracturing

Carbon dioxide (CO2) capture and geological storage technology is a promising strategy for enhancing oil and gas production and mitigating climate change in unconventional formations. In shale reservoirs, production decline is rapid under volumetric fracturing development. Currently, CO2 injection and huff-n-puff techniques are cost-effective methods to boost shale oil recovery. However, traditional huff-n-puff techniques are limited in capturing reservoir heterogeneity and intricate fracture conditions. This study employed a three-dimensional physical experimental apparatus to explore CO₂ huff-n-puff dynamics across six distinct fracture patterns. By implementing a real-time monitoring system with multiple pressure points, the study elucidated the impacts of varying fracture penetration levels and spacing on dynamic pressure wave patterns, mobilization extent, and development outcomes during CO2 huff-n-puff. The results demonstrate that fractures substantially improve CO₂ huff-n-puff efficiency in shale reservoirs compared to a seamless model, with Model 6 achieving the highest recovery rate of 42.38%. As fracture penetration increases, CO₂ dynamic coverage expands. However, full penetration leads to preferential flow through fractures, reducing matrix contact and diminishing huff-n-puff efficiency. Reduced fracture spacing enlarges the well's control domain, amplifies CO2 huff-n-puff dynamic coverage area, and increases oil production and recovery rate. Conversely, decreasing fracture spacing accelerates oil and gas displacement rates, leading to higher economic expenses. The study concludes that 2/3 fracture penetration in five fractures offers the most efficient recovery method. For a 500 m horizontal well, optimal fracture spacing is recommended at 125 m, with a fracture length of 145.8 m for a well spacing of 417 m.

Flood simulation and impact analysis of Xun River under backwater condition

Abstract The intersection of river channels is easy to cause the backside of the main flow, which greatly influences the change of hydrological conditions of river channels. To study the changing characteristics of river flood under the condition of uplift, a one-dimensional hydrodynamic model was established to simulate the evolution process of river flood under the condition of seeking river uplift. Factors such as distance, height, flood speed, inundation area, inundation depth, and flood speed were selected to analyze the changing rules of the influence of river bullying. The results show that the influence degree of the river following is closely related to the size of the flood. Under the same earthquake magnitude, the backwater distance, backwater height, inundation area, inundation depth, and inundation speed of sub-river flood increase with the increase of flood magnitude, while the flood speed decreases. When the magnitude of the flood is once in 200 years, the flood elements reach the maximum, the reflux distance is 8, 368 m, the reflux height is 3.2 m, and the velocity is reduced by 43.2%. This study can provide relevant data for the flood control work in the Sun River basin and provide a reference for the scheme design of tributary interchanges.

Estimating Sediment Trap Efficiency of Flood Events During Flood Season in the Three Gorges Reservoir

AbstractSediment trapping significantly influences the comprehensive benefits of reservoirs and hinders the connection between sediment and nutrients in the upstream and downstream of rivers. Quantifying the role of sediment trapping by dams is important because this operation controls fluvial geomorphology, aquatic ecology, and water quality, particularly for flood processes with highly variable inflows. This study developed a new model to calculate the sediment trap efficiency (TE) of flood events during the flood season in the Three Gorges Reservoir (TGR) using a generalized theoretical approach. The TE of 55 flood events that occurred since the impoundment of the TGR were calculated and ranged from 16.0% to 99.8%, with a mean of 77.8%. Contrary to the previous understanding that large reservoirs have consistently large TE values based on long‐term data, our results show that large reservoirs can have a wide range of TE values during short‐term flood events. The proposed model estimates TE using three variables: inflow discharge, outflow discharge, and reservoir water level. Furthermore, the additional sedimentation risk to the TGR by optimized scheduling during the flood season is discussed. The results provide a reference for sedimentation management and optimal operation in the TGR.

Investigation the Impact of Different Injection Agents on Tight Oil Reservoirs of Recovery Performance

Abstract Objectives/Scope: Due to strong water sensitivity and extremely low permeability, conventional water flooding method is not suitable for the Mahu Conglomerate reservoir. In response to the complex reservoir physical properties, different injection agents such as CO2, N2, water and air huff and puff injection were carried out in order to optimize injection parameters for enhanced oil recovery in tight conglomerate reservoirs. Methods, Procedures, Process: By means of NMR, scanning electron microscopy, energy spectrum, pore fluids state in different media after different agents displacement were studied. Displacement and CT testing on different media and post fracturing state were conducted. Through experiments on on-site cores from two different layers, three different injection media (H2O, N2, CO2 and air with different O2 concentration) were tested for enhanced oil recovery. Then, the injection process was performed under different injection pressures. Finally, multiple cycles of enhanced oil recovery tests were conducted corresponding optimal injection pressures. Results, Observations, Conclusions: The remaining oil in the pores is mainly in free and bound states, with a free state of approximately 58% and a bound state of approximately 42%. Four pressures were selected for this test based on the minimum miscibility pressure of CO2 with crude oil: 15MPa, 20MPa, 23MPa, and 25MPa. The recovery factor under different pressures is 6.44%, 8.14%, 10.31%, and 10.38%, respectively. As the huff and puff pressure increases, the recovery of CO2 huff and puff gradually increases, and the increase of recovery is insignificant when the mixture pressure is reached. The gas flooding process involves oil production from large and submicron pores first. As the displacement pressure difference increases, oil production from large pores decreases, with medium pores becoming the main force of oil production, while oil production from nanoscale micropores shows an increasing trend. The increase in oxygen content results in significant development of sub-micron micron scale pores. The gas breakthrough time is long, the capillary force decreases, and air can undergo low-temperature oxidation with crude oil, resulting in an overall oil displacement efficiency of 65.81%. Novel/Additive Information: At present, there is few research on the pore fluid flow and status of oil displacement in tight conglomerate reservoirs with different injection media. This study provides a good understanding of the submicron to nanoscale pore throat structure characteristics of tight oil reservoirs, revealing the state of pore fluids in gravel after displacement by different injection media, and providing support for on-site application of CO2 enhanced oil recovery in tight conglomerate oil reservoirs.

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.

Reservoir numerical simulation method considering time-varying relative permeability during polymer flooding process

Abstract In response to the abnormal decline in liquid production capacity during the middle and later stages of chemical flooding in Chinese offshore oil fields, in-depth analysis was conducted using reservoir engineering, dynamic analysis, and reservoir simulation methods. Because of the viscoelasticity of polymer solution, the relative permeability curve of oil/water during polymer flooding is different from that during water flooding, including the minimum mobile water saturation and residual oil saturation. In this study, an interpolation coefficient ‘f’ related to polymer solution concentration was firstly proposed for simplicity. Then, reservoir numerical simulation with the self-developed simulator ‘SIMFAST’ were conducted to investigate the influence of the interpolation coefficient ‘f’ on the injector and producer performance and the applications in actual field production history matching. The results indicated that compared with the commercial simulators, the self-developed simulator with the interpolation factor could more reliably describe the characteristic of the injected polymer solution in controlling water cut rise, and more efficiently simplify the combination of parameters on relative permeability curves during polymer flooding in offshore oilfields. Finally, above reservoir simulation method considering the change of the relative permeability curves during chemical flooding, was successfully conducted in the production history matching in J oilfield in Bohai Bay China, in terms of oil rate & water cut parameters. The research results give an efficient method to investigate the fluid seepage behaviour variation and its influence on oil rate and water cut during polymer flooding in China offshore reservoirs, which offers very useful technical support for the accurate design of chemical flooding schemes.