Reservoir Development Research Articles

Dynamic prediction of fracture propagation in horizontal well hydraulic fracturing: A data-driven approach for geo-energy exploitation

Accurate and efficient prediction of hydraulic fracturing fracture propagation is of utmost importance for unconventional reservoir development. Traditional numerical simulation methods require specialized domain knowledge and technical expertise, and machine learning approach can be an alternative predictive tool for fracture propagation. Additionally, existing neural networks cannot be directly applied to hydraulic fracturing scenarios influenced by multiple coupled factors. Thus, this study presents an innovative approach, “AE-ATT-ConvLSTM”, that integrates an additional Convolutional Layer, Autoencoder, and an Attention Mechanism Feature Fusion Layer into the architecture of a Convolutional Long Short-Term Memory (ConvLSTM) sequential image prediction network. This approach aims to predict fracture propagations in different fracturing stages of horizontal wells. The proposed model is trained on a sample dataset consisting of 5000 instances of hydraulic fracturing in horizontal wells. The dataset includes crucial data such as fracture propagation images, wellbore perforation images, natural fracture images, pumping schedules, and reservoir properties. The model achieves remarkable results, with an MSE less than 15 × 10−5, a maximum SSIM of 0.93, and an average FMAE less than 48. The research findings demonstrate that this method significantly enhances prediction efficiency and provides new insights for predicting fracture morphology and optimizing fracturing design.

Influence of CO2 on asphaltene adsorption dynamics at the Oil-Gas Interface: New insights into precipitation and aggregation processes

To investigate the effect of CO2 on the precipitation of asphaltene in oil, and verify the advantages of CO2 flooding, this work designs interfacial tension experiments and PVT system experiments to create the experimental environment under different temperature and pressure conditions that make CO2 contact with oil. The results indicate that CO2 can affect the oil morphology, and the trend of oil volume increase and decrease depends on the dynamic balance between the expansion effect and the component extraction effect of CO2 on oil. Under low pressure, the oil expansion caused by dissolved CO2 dominates the experiments, increasing the oil volume. Under high pressure, sufficient CO2 in the oil activates its component extraction effect, and the asphaltene in the oil precipitates and accumulates at the interface between the oil and CO2 due to the disruption of the component equilibrium of the system, exerting a surface activity effect, leading to a decrease in oil volume. Due to the movement of asphaltene towards the system interface and the formation of an asphaltene film during this stage, which hinders the entry of CO2 into the oil, the interfacial tension shows three linear decreasing trends with increasing pressure. This mechanism also changes the variation trend of the oil volume in CO2 during the pressure increase. Moreover, the components of the oil extracted by CO2 will be transferred to CO2, making its properties similar to oil. With the reduction of interfacial tension, this process promotes the miscibility between the two. These effects verify that CO2 can improve the fluidity and quality of oil during reservoir development, and reduce or even eliminate the adhesion work required for the working fluid to displace the oil. These conditions are conducive to improving reservoir development efficiency, reflecting the great application value of CO2 flooding.

Depositional architecture of early rift non-marine systems and implications for reservoir development: Oligocene to Miocene Al Wajh Formation, Midyan Basin of Red Sea, Saudi Arabia

Exhumed early rift alluvial fan and braided fluvial fan systems are rare and consequently understanding facies distribution, stratal architecture, reservoir connectivity, and controls on stratigraphic development of these systems is quite difficult. The outcrops of early Oligocene to early Miocene Al Wajh Formation in the Red Sea region of Saudi Arabia provide an opportunity to investigate the depositional architecture and reservoir potential of an early rift alluvial to fluvial system. Eleven lithofacies (F1-F11) and four facies associations (alluvial fan facies association (FA1), braided fluvial fan facies association (FA2), floodplain facies association (FA3) and delta front facies association (FA4)) are identified in four subbasins (Maqna 1–2, Sukk and Ifal), suggesting deposition in an alluvial-braided fluvial fan system. FA1 (36–100%) and FA4 (9–13%) have higher relative proportion and widely distributed in the northern subbasins (Sukk and Ifal subbasins) while the relative proportion of FA2 (50–82%) and FA3 (18%) increases towards southern subbasins (Maqna-1 and Maqna-2 subbasins). Distribution of facies associations, relative reduction in the clast diameter, and general southward paleoflow direction suggest that the northern subbasins (Sukk and Ifal) represent the proximal settings and southern subbasins represent medial (Maqna-1 subbasin) and distal settings (Maqna-2 subbasin) of a large alluvial-braided fluvial fan environment. Stratal architecture of proximal conglomeratic channel deposits highlight that despite reasonable thickness and spatial connectivity (several meters), these deposits may have marginal reservoir quality because of poor sorting and high matrix content. Sandstone beds occurring in medial settings have a higher potential to act as hydrocarbon reservoirs by displaying excellent reservoir properties on the account of their significant thickness (up to few meters), coarse grain sizes, better sorting, considerable spatial connectivity (10s of meters) and lower proportion of interbedded mudstone.

Development of ensemble learning techniques and sequential model-based optimization for enhancing the generalizability of shale wettability predictions

Quantifying the wettability of shales is important for reservoir exploration and evaluation, as well as CO2 storage. Conventional experimental measurements are time-consuming and costly, while novel machine learning (ML) algorithms are challenged to predict shale wettability with accuracy and generalizability. Therefore, this study introduces an advanced shale wettability prediction framework by fusing ensemble learning algorithm and automatic hyperparameters optimization schemes (AHOS). Specifically, we firstly collected data from various gas geo-storage conditions from the literature. Then, an ensemble learning algorithm was utilized to extract complex nonlinear relationships among influential variables and shale wettability. During the training process, multiple optimization schemes, including random search (RS), Bayesian optimization (BO), and sequential model-based optimization (SMBO), were used and compared to automatically find the optimal hyperparameters of a wettability prediction model. The results show that AHOS can automatically and efficiently obtain the optimal hyperparameters, thus improves model's generalization. In addition, the prediction model based on AHOS process can accurately predict shale wettability. Through sensitivity analysis, we also revealed that the TOC and gas type are the top two most important influencing factors, which provides insights for CO2 sequestration. Moreover, we validated that the CatBoost model outperforms other tree-based models in predicting shale wettability and the SMBO method can provide a more effective result. In summary, the proposed shale wettability prediction framework is promising and efficient, which contributes to the exploration, evaluation, and development of shale oil reservoirs, further benefit to CO2 storage.

Research on Geological-Engineering Integration Numerical Simulation Based on EUR Maximization Objective

Shale gas reservoirs, as representative reservoirs in the Sichuan Basin, have attracted widespread attention regarding development. Using gas reservoir numerical simulation to assist development has greatly improved the work efficiency of workers. However, traditional gas reservoir numerical simulation is widely criticized for its inability to effectively integrate with geological and engineering factors. In this study, we proposed a geological engineering integration method that considers pre-fracturing parameters. We further applied it to a typical well (N03) in a certain block of the Sichuan Basin. The reliability of the method was determined through historical fitting. Based on the N03 geological model, the optimization range of fracturing construction parameters in adjacent areas was determined with the goal of maximizing EUR. Recommended values for widely distributed construction parameter combinations of Class II reservoirs were provided through orthogonal analysis. The influence order of fracturing construction parameters is (1) sand addition strength, (2) cluster spacing, (3) construction displacement, (4) fracture fluid strength, and (5) horizontal segment length. Finally, we compared the simulated data with the actual case. The results showed that an integrated numerical simulation method including geological and engineering factors can comprehensively and accurately assist in reservoir development.

Pore-Scale Numerical Simulation of Acid-Rock Reaction Processes in Carbonate Reservoirs.

In the process of acidizing carbonate reservoirs, dissolution is employed for reservoir modification to enhance recovery rates. This study establishes a numerical model at the pore scale for acid-rock reaction flow based on a microscopic continuum medium model, integrating phase-field theory and component transport models. Subsequently, the results of the Darcy-Brinkman-Stokes model are compared to those of the arbitrary Lagrange-Euler method to validate the accuracy of the model. Finally, the flow behavior of the acid solution at the pore scale and the complex dissolution mechanisms in carbonate reservoirs are analyzed. The research indicates that the microscopic pore-scale dissolution in carbonate reservoirs mainly manifests as five dissolution modes: uniform dissolution, compact dissolution, conical wormholes, dominant wormhole, and ramified wormholes. Different distributions of microfractures will alter the flow state of the acid solution and the rock-acid reaction process within the pores. Once the wormhole breakthrough occurs, there is an increased probability of acid flow through the wormhole to the outlet, leading to a decrease in the effectiveness of the acidizing carbonate reservoirs. A proper understanding of pore-scale acid-rock reaction laws is of great significance for the development of carbonate oil and gas reservoirs.

Mechanisms of Crustal-Mantle Material and Energy Exchanges: Impacts on Hydrocarbon and Geothermal Resources.

The exchange of matter and energy between crust and mantle significantly influences the formation and development of oil, gas, and geothermal resources. Understanding how these exchanges impact these resources is crucial in geological science. In many oil-rich basins in China, significant accumulations of H2, CO2, geothermal energy, and other associated resources linked to deep mantle materials or geological processes have been discovered. Therefore, investigating the effects of crust-mantle material and energy exchanges on these resources is of utmost importance. This paper aims to systematically analyze the effects of mantle materials (e.g., H2, CO2, catalytic elements) and energy upwelling into basins. It synthesizes the impacts of various organic-inorganic interactions on hydrocarbon generation, evolution of organic source rocks, reservoir development, and the formation and accumulation of oil and gas. Finally, it proposes a mechanism detailing how interactions between mantle matter and energy influence specific resources. Simultaneously, this study employs numerical simulations to uncover the specific impact of magma intrusions on the geothermal field of surrounding rock formations. It demonstrates that magma chambers, continuously supplied with mantle energy, create regional thermal anomalies and facilitate the development of high-temperature geothermal resources. This underscores that mantle materials and energy not only significantly influence the generation of oil and gas but also govern the formation of geothermal resources and the evolution of thermal reservoirs. This research provides a theoretical foundation for understanding the subsequent formation of oil and gas resources under the influence of deep geological processes. Moreover, it furnishes a scientific basis for evaluating and exploring the symbiotic relationship between oil and gas resources and geothermal reservoirs.

Exploration of a brain-inspired photon reservoir computing network based on quantum-dot spin-VCSELs

Based on small-world network theory, we have developed a brain-inspired photonic reservoir computing (RC) network system utilizing quantum dot spin-vertical-cavity surface-emitting lasers (QD spin-VCSELs) and formulated a comprehensive theoretical model for it. This innovative network system comprises input layers, a reservoir network layer, and output layers. The reservoir network layer features four distinct reservoir modules that are asymmetrically coupled. Each module is represented by a QD spin-VCSEL, characterized by optical feedback and optical injection. Within these modules, four chaotic polarization components, emitted from both the ground and excited states of the QD Spin-VCSEL, form four distinct reservoirs through a process of asymmetric coupling. Moreover, these components, when emitted by the ground and excited states of a driving QD spin-VCSEL within a specific parameter space, act as targets for prediction. Delving further, we investigated the correlation between various system parameters, such as the sampling period, the interval between virtual nodes, the strengths of optical injection and feedback, frequency detuning, and the predictive accuracy of each module’s four photonic RCs concerning the four designated predictive targets. We also examined how these parameters influence the memory storage capabilities of the four photonics RCs within each module. Our findings indicate that when a module receives coupling injections from more than two other modules, and an RC within this module is also subject to coupling injections from over two other RCs, the system displays reduced predictive errors and enhanced memory storage capacities when the system parameters are fixed. Namely, the superior performance of the reservoir module in predictive accuracy and memory capacities follows from its complex interaction with multiple light injections and coupling injections, with its three various PCs benefiting from three, two, and one coupling injections respectively. Conversely, variations in optical injection and feedback strength, as well as frequency detuning, introduce only marginal fluctuations in the predictive errors across the four photonics RCs within each module and exert minimal impact on the memory storage capacity of individual photonics RCs within the modules. Our investigated results contribute to the development of photonic reservoir computing towards fast response biological neural networks.

Experimental Study on Factors Affecting Oil Recovery in Sandstone-Buried Hill Superimposed Reservoirs.

Sandstone-buried hill superimposed reservoirs, characterized by the coexistence of the sandstone matrix and the fracture pore matrix, present unique challenges in integrated development due to their connectivity and physical properties. Currently, research on the factors affecting the recovery of sandstone-buried hill superimposed reservoirs with both porous and fractured media is limited. In this study, a more realistic method for preparing fractured cores and calculating fracture permeability was proposed, and the effect of confining pressure on fracture permeability was investigated. Additionally, a three-tube experimental apparatus was designed to study the effects of connectivity, different permeability contrasts between sandstone and buried hills, and various development strategies on recovery. The results indicate that fracture permeability is significantly affected by the confining pressure. When sandstone and buried hills are not connected, the total recovery is the highest. As connectivity increases, the recovery from sandstone gradually decreases, while the recovery from the buried hill gradually increases, leading to a decrease in the overall recovery. The permeability contrast between sandstone and buried hill has a minimal impact on the overall recovery; however, a higher permeability contrast leads to increased sandstone recovery and decreased buried hill recovery. The highest overall recovery was achieved with injection solely into the sandstone, followed by general injection, with the lowest recovery from injection solely into the buried hill. This study provides new insights into the integrated development of sandstone-buried hill reservoirs.

Numerical simulation and optimization of biological nanocomposite system for enhanced oil recovery

Abstract Nanofluid flooding is a novel technology with potential for enhanced oil recovery. In this study, a biological nanocomposite system was formed by mixing hexamethyldisilazane-modified hydrophobic nano-SiO2 with a biosurfactant produced by Bacillus. The stability of the system, its influence on rock wettability, and fluid interfacial tension were investigated experimentally. Numerical simulation methods were employed to simulate the displacement efficiency of the biological nanocomposite system and optimize the injection parameters. Finally, the application effects of the system in the field were evaluated. Results indicated that the biological nanocomposite system could change rock wettability and significantly reduce the interfacial tension to 1.8 mN/m at low concentrations. The core flooding results showed that the maximum oil recovery factor of the system reached 47.07%. Numerical simulations optimized the optimal injection concentration to be 7,000 ppm and the volume of injection to be 1.75 × 10–2 pore volumes, resulting in an oil increment exceeding 10,000 m3 in field application. This study provides a solution for the green development of oil reservoirs and provides effective technical support for the numerical simulation and process scheme optimization of biological nanocomposite systems.

Accurate identification of low-resistivity gas layer in tight sandstone gas reservoirs based on optimizable neural networks

In tight sandstone reservoirs, low resistivity gas reservoirs have a certain gas production capacity, but their reservoir resistivity value is less than 20 Ω m, or their ratio to water layer resistivity is less than 2. Therefore, the identification of low resistivity gas reservoirs is a prominent problem that needs to be resolved. In this paper, on the basis of traditional experimental analysis, NMR experiments and wettability experiments were conducted. The high bound water saturation and pore fractal dimension have a significant impact on reservoir resistivity. After various experimental analysis results were preprocessed by the image pool and data pool, an optimized neural network (ONN) classification model was established based on deep learning theory. The confusion matrix result (training datasets) was 87.13%, and the ROC area of low resistivity gas layers was close to 1, indicating good recognition performance for low resistivity gas layers. The model was used to identify the properties of the J4 well reservoir section. The identification result was consistent with the actual logging resistivity values (low resistivity) and testing conclusions (gas layer). This model can accurately identify low resistivity gas reservoirs in the research area, and has important guiding significance for identifying the reservoir properties of tight sandstone gas reservoirs in the Sichuan Basin. It can provide reference value for the exploration and development of similar gas reservoirs in other regions.

Study on shale hydrocarbon generation pressurization and its influencing factors - taking Guoyuan area of Ordos Basin as an example

The increase in shale hydrocarbon production is the main driver of crude oil accumulation, but the lack of effective theoretical and technical means limits exploration and development. In this study, the quantitative calculation method of hydrocarbon generation pressurization was improved by the hydrocarbon overburden pressure simulation experiment. The relationship between the rate of hydrocarbon conversion rate and the content of organic matter is positively correlated with the process of hydrocarbon pressurization. On the other hand, the compression coefficient of clay minerals, the formation water content and compacted overpressure have inhibitory effects on hydrocarbon generation and pressurization. The combination of various factors determines the migration and enrichment of crude oil in shale lithology. This study, taking Ordos Basin, one of the most representative basins in China, as a case study, aims to elucidate the mechanisms and parameters involved in the generation and pressurization of hydrocarbons, providing valuable insights for the exploration and development of shale reservoirs in worldwide.

Experimental Investigation on Condensate Revaporization During Gas Injection Development in Fractured Gas Condensate Reservoirs

Experimental Investigation on Condensate Revaporization During Gas Injection Development in Fractured Gas Condensate Reservoirs

A novel approach to understanding the genesis and distribution of widespread reservoirs at the basin edge: A case study of gas-bearing sandstone in the Sangonghe Formation, Junggar Basin, Western China

A 20 m thick sandstone drilled in the second member of the Sangonghe Formation in the Qianshao area of the Junggar Basin has been identified to be a high-quality gas-bearing reservoir. However, recent missions reveal an uncertainty in reservoir distribution and bring high risk to the subsequent gas development, although the gas-bearing reservoir has been encountered by several drillings and proved to be widespread. To investigate the accurate distribution and reveal the evolution of the reservoir, we reconstruct the stratigraphic framework and depositional stages by a high-revolution sequence stratigraphic method, effectively identify the gas-bearing reservoir by seismic attributes refusion and seismic stratigraphic slices, and finally map the distribution strata distribution at different stages. The results indicate that the landward onlap phenomenon in the seismic data indicates a retrogradational stratigraphic structure during the water level rise rather than the equal-thickness stratigraphic pattern under stable water conditions. According to the retrogradational stratigraphic structure, the real driver for the widespread characteristic of a gas-bearing reservoir is the backward staggered movement of multistage sand bodies during the transgressive process rather than single-stage deposition. The fusion of seismic acoustic impedance and main frequency attributes provides an effective survey to identify the accurate distribution of the reservoir, by which interpretation of a stratigraphic slice along the vertical centerline of the reservoir extracted from the multiattribute fusion survey can present the distribution and evolution of the three stages on only one map. Achieved by an appropriate interpretation on the map, two sandy debris flow bodies are identified as being deposited at the bottom successively during the first two transgressive stages. They are completely staggered in the source direction with a clear boundary between well Qs9 and Qs7. Subsequently, the subsequent rise of the water level led to the deposition of a large-scale multibranch shallow-water delta, which exhibits the characteristics of multistage, multisource, and multibranch. The results not only advance our comprehension of widespread sand bodies but also furnish fundamental insights for the development of lithologic condensate gas reservoirs in the Qianshao area.

Infill Well Location Optimization Method Based on Recoverable Potential Evaluation of Remaining Oil

Infill well location optimization poses significant challenges due to its complexity and time-consuming nature. Currently, determining the scope of infill wells relies heavily on field engineers’ experience, often using single indices such as the remaining oil saturation or abundance of remaining oil reserves to evaluate the potential of remaining oil. However, this approach lacks effectiveness in guiding the precise tapping of remaining oil in ultra-high water cut reservoirs. To address this, our study comprehensively considers the factors influencing the recoverable potential of remaining oil in such reservoirs. We characterize the differences in reservoir heterogeneity, scale of recoverable remaining oil reserves, water flooding conditions, and oil–water flow capacity to construct a quantitative evaluation index system for the recoverable potential of remaining oil. Recognizing the varying degrees of influence of different indices on the recoverable potential of remaining oil, we determine the objective weight of each evaluation index by combining an accelerated genetic algorithm with the projection pursuit model. This approach enables the construction of a recoverable potential index for remaining oil and forms a quantitative evaluation method for the recoverable potential of remaining oil in ultra-high water cut reservoirs. Subsequently, we establish a mathematical model for infill well location optimization, integrating and optimizing the infill well location coordinates, well length, well inclination angle, and azimuth angle. Using the main layer sand body of an oilfield in Bohai as a case study, we conducted evaluations of the remaining oil potential and infill well location optimization. Our results demonstrate that the assessment of the remaining oil potential comprehensively characterizes the influence of the reservoir’s physical properties and oil–water diversion capacity on the remaining oil potential across different regional positions. This evaluation can effectively guide the determination of infill well location ranges based on the evaluation results. Furthermore, infill well location optimization can effectively enhance reservoir development outcomes.

A Productivity Prediction Method of Fracture-Vuggy Reservoirs Based on the PSO-BP Neural Network

Reservoir productivity prediction is a key component of oil and gas field development, and the rapid and accurate evaluation of reservoir productivity plays an important role in evaluating oil field development potential and improving oil field development efficiency. Fracture-vuggy reservoirs are characterized by strong heterogeneity, complex distribution, and irregular development, causing great difficulties in the efficient prediction of fracture-vuggy reservoirs’ productivity. Therefore, a PSO-BP fracture-vuggy reservoir productivity prediction model optimized by feature optimization was proposed in this paper. The Chatterjee correlation coefficient was used to select the appropriate combination of seismic attributes as the input of the prediction model, and we applied the PSO-BP model to predict oil wells’ production in a typical fracture-vuggy reservoir area of Tahe Oilfield, China, with the selected seismic attributes and compared the accuracy with that provided by the BP neural network, linear support vector machine, and multiple linear regression. The prediction results using the four models based on the test set showed that compared with the other three models, the MSE of the PSO-BP model increased by 23% to 62%, the RMSE increased by 12 to 38 percent, the MAE increased by 18 to 44 percent, the SSE increased by 23 to 62 percent, and the R-square value increased by 2 to 13 percent. This comparison proves that the PSO-BP neural network model proposed in this paper is suitable for the productivity prediction of fracture-vuggy reservoirs and has better performance, which is of guiding significance for the development and production of fracture-vuggy reservoirs.

A Semianalytical Model for Advanced Description of Pressure Drop Funnel during Coalbed Methane Production.

Advanced description of pressure drop funnel is crucial in coalbed methane (CBM) production because of dewatering and depressurization methods. Improving the precision of the pressure drop funnel description facilitates obtaining the actual production status and productivity potential, both pivotal for responsible development plans. The study presents a semianalytical model that integrates pressure profiles and material balance equations, incorporating inner and outer boundary conditions, and dynamic reservoir characteristics. The pressure propagation characteristics in undersaturated coal reservoirs are described during the production life of CBM wells, and the model is validated using two wells with different production characteristics. The results indicate that the effect of water saturation on the expansion of the drainage radius surpasses that of the desorption radius, demonstrating a more precise prediction of the production boundary when dynamic water saturation is considered. Additionally, a rapid drop rate of bottomhole flowing pressure triggers simultaneous propagation of the drainage and desorption radii, resulting in a smaller production boundary and fewer well-controlled resources. Conversely, an appropriate production strategy results in a larger drainage radius and lower boundary pressure before massive gas desorption, thereby facilitating efficient propagation of the pressure drop funnel. Moreover, the pressure drop funnel characterized by the model can compute the dynamic CBM resources and recovery efficiency of a single well, providing a valuable basis for assessing productivity potential. In summary, this model offers a time-saving and practical tool for describing the dynamic pressure drop funnel in various CBM production stages and promoting efficient development for undersaturated CBM reservoirs.

Study on Surfactant-Polymer Flooding after Polymer Flooding in High-Permeability Heterogeneous Offshore Oilfields: A Case Study of Bohai S Oilfield.

Polymer flooding is an effective development technology to enhance oil recovery, and it has been widely used all over the world. However, after long-term polymer flooding, a large number of oilfields have experienced a sharp decline in reservoir development efficiency. High water cut wells, serious dispersion of residual oil distribution and complex reservoir conditions all bring great challenges to enhance oil recovery. In this study, the method of enhancing oil recovery after polymer flooding was studied by taking the S Oilfield as an example. A surfactant-polymer system suitable for high-permeability heterogeneous oilfields was developed, comprising biogenic surfactants and polymers. Microscopic displacement experiments were conducted using cast thin sections from the S Oilfield, and nuclear magnetic resonance was employed for core displacement experiments. Numerical simulation experiments were also conducted on the S Oilfield. The results show that the enhanced oil recovery mechanism of the surfactant-polymer system is to adjust the flow direction, expand the swept volume, emulsify crude oil and reduce interfacial tension. Surfactant-polymer flooding proves to be effective in improving recovery efficiency, significantly reducing the time of flooding and further enhancing the strong swept area. The nuclear magnetic resonance results indicate a high amplitude of passive utilization of residual oil during the surfactant-polymer flooding stage, highlighting the enormous potential for an increased recovery ratio. Surfactant-polymer flooding emerges as a more suitable technique to enhance oil recovery in the post polymer-flooding stage in high-permeability heterogeneous oilfields.

The role of Anchoviella spp. as forage species in different stages of reservoir development in the upper Tocantins River, Central Brazil.

We report the spatiotemporal increase in abundance of Anchoviella spp. (Engraulidae) through the diet of fish consumers after reservoir formation in the upper Tocantins River, Central Brazil. The increase in the abundance of this planktivorous fish evidences the growing importance of autochthonous herbivory food chains as the reservoir stabilized in this novel lentic ecosystem. These changes would otherwise remain unnoticed only through fish sampling, highlighting the importance of assessing trophic interactions to understand responses to damming at the community and ecosystem levels.

Ultrafast Dynamics of Bloch Surface Wave Polaritons in Large-Area 2D Semiconductor Monolayers at Room Temperature.

The dynamics of strongly coupled polariton systems integrated with 2D transition metal dichalcogenides (TMDs) is key to enabling efficient coherent processes and achieving high-performance TMD-based polaritonic devices, such as ultralow-threshold polariton lasers and ultrafast optical switches. However, there has been a lack of a comprehensive understanding of the excited state dynamics in TMD-based polariton systems. In this work, ultrafast pump-probe optical spectroscopy is used to investigate the room temperature dynamics of the polariton systems consisting of TMD monolayer excitons strongly coupled with Bloch surface waves (BSWs) supported by all-dielectric photonic structures. The transient response is found for both above-exciton energy pumping and polariton-resonant pumping. The excited state population and ultrafast coherent coupling of the exciton reservoir and lower polariton (LP) branch are observed for resonant pumping. Moreover, it is found that the transient response of the LP first decays on a short-time scale of 0.15-0.25ps compared to the calculated intrinsic lifetime of 0.11-0.20ps, and is followed by a longer decay (>100ps) due to the dynamical evolution of the exciton reservoir. The results provide a fundamental understanding of the dynamics of TMD-based polariton systems while showing the potential for achieving efficient coherent optical processes for device applications.