Dynamic Reservoir Research Articles

Time-lapse amplitude variation with offset difference inversion based on the reformulated Biot-Squirt model

Time-lapse AVO (Amplitude variation with offset) inversion is a significant technique for the estimation of dynamic reservoir changes. The main theoretical foundation is Zoeppritz equations in single-phase media. At present, there are studies on the inversion method of using Biot's theory. Although it has some improvements in accuracy compared with the method of using Zoeppritz equations, the squirt-flow mechanism is not considered. Furthermore, the precision of difference data equations still needs to be improved in difference inversion. In view of the above problems, the reformulated BISQ (Biot-Squirt) model is introduced into the inversion, and the iterative optimization is used to increase the precision of difference data equations continually in this paper. First, we derive the difference data equations about the reservoir-parameter changes. The reformulated BISQ model is applied, and can describe the characteristics of P-wave reflection accurately and expediently. Secondly, the objective function about the reservoir-parameter changes is constructed. The Bayesian theory and the model smoothing constraint are used, and are helpful to obtain accurate and stable solutions. Thirdly, we derive the equation for estimating the reservoir-parameter changes, and the inversion is iterative for the continuous improvement of accuracy. The difference inversion method is applied, and is helpful to enhance the data repeatability and reduce the calculation amount. Finally, synthetic and real data are applied for the inversion and testing to prove the effectiveness and feasibility of the method.

Identifying drivers of storage dynamics of lakes and reservoirs in the arid Central Asia

Abstract Knowing the storage variations in lakes and reservoirs are essential for water resources and environmental management, especially in the regions facing water scarcity. However, the quantification of the storage changes is limited by sparse in-situ observations and spatial coverage of space(air)-borne altimetric sensors that have been used conventionally in storage retrieval. This hampers the attribution analysis of lake storage changes. Here, we combined long-term optical remote sensing and multi-source terrain elevation data to derive the monthly storage time series from 1990 to 2020 for 8544 lakes and reservoirs in Central Asia, where water scarcity has been bottle-necking local socioeconomic sustainability. The regional total storage has been decreasing with a rate of -4.78±0.88 km3 year-1 mainly owing to the desiccation of the Aral Sea. For other lakes, 26% of them show decreasing while 22% show increasing trends. At a watershed-scale, the long-term changes in small to medium-sized (<5000 km2) lakes are primarily caused by the changes in surface runoff, jointly affected by precipitation and temperature changes. We also found that 29% lakes in Central Asia experienced frequent seasonal dry out in the past decades. Such seasonal dry out is mainly caused by fast evaporation losses during the summer months. For the majority (63±8%) of these lakes, their evaporation water losses are larger than the seasonal storage drawdown. Our analysis highlights the co-regulation of surface runoff and lake evaporation in the storage losses in arid and semi-arid regions.

Defective proviruses significantly impact viral transcription and immune activation in men and women with HIV-1 subtype C in rural South Africa

IntroductionThe main obstacle to achieving an HIV-1 cure is the proviral reservoir. To promote equity in HIV cure strategies, it is crucial to study the viral reservoir of the predominant HIV-1 subtype C in both women and men. Therefore, we investigated the dynamics of the (intact) viral reservoir in relation to plasma viral load (VL), CD4+ T cell count, and immune activation before and during 96 weeks of successful antiretroviral therapy (ART).MethodsEighty-two participants (62% female) newly initiating ART in a rural clinic in South Africa were included in the study. Blood samples were collected at baseline, week 48, and week 96, and CD4 count was determined. Plasma was used for VL and immune marker analyses, while isolated peripheral blood mononuclear cells (PBMCs) were used for the quantification of cellular multiple spliced HIV-1 RNA (msRNA) and the intact proviral DNA assay. For the longitudinal analyses on ART, we selected only those participants who durably suppressed their VL to <200 copies/mL during 48 (n=65) and/or 96 (n=60) weeks of treatment.ResultsAt ART initiation, the median CD4 count was 234 cells/mm3 and VL was 68,897 copies/mL. Interestingly, at baseline the number of defective proviruses was significantly correlated with VL (p<0.0001), msRNA (p<0.0001), CD4 count (p=0.0008), CXCL10 (p=0.0003) and TNF-α (p=0.0394). During successful ART, a significant decrease of both the intact and defective proviral reservoir was observed (p<0.0001). The decrease of the intact proviral reservoir was more profound compared to the defective fraction after 96 weeks of therapy. In addition, a significant decrease in cellular msRNA and IL-6, IL-7, TNF-α, sCD14, sCD163, CCL2, CXCL10, and CRP was detected.DiscussionThis study underscores the significant relationship observed prior to therapy initiation between the number of defective proviruses, viral transcription/production and their association with immune response indicators such as CD4 count, CXCL10, and TNF-α. Furthermore, the observation of a less pronounced decrease of the defective proviral DNA highlights the importance of addressing both intact and defective proviruses in therapeutic strategies to enhance clinical outcomes for people with HIV-1. Together, these findings suggest a significant role of the defective proviruses in HIV-related disease progression.

Microseismicity-Based Modelling of Induced Fracture Networks in Unconventional Reservoirs

Microseismicity-Based Modelling of Induced Fracture Networks in Unconventional Reservoirs

Unveiling Reservoir Dynamics: Influence of Mineralogy and Rock Architecture on Petrophysical Properties in the Bahariya Formation, Gebel El Dist, Western Desert, Egypt

Unveiling Reservoir Dynamics: Influence of Mineralogy and Rock Architecture on Petrophysical Properties in the Bahariya Formation, Gebel El Dist, Western Desert, Egypt

Analysis and fully memristor-based reservoir computing for temporal data classification

Reservoir computing (RC) offers a neuromorphic framework that is particularly effective for processing spatiotemporal signals. Known for its temporal processing prowess, RC significantly lowers training costs compared to conventional recurrent neural networks. A key component in its hardware deployment is the ability to generate dynamic reservoir states. Our research introduces a novel dual-memory RC system, integrating a short-term memory via a WOx-based memristor, capable of achieving 16 distinct states encoded over 4 bits, and a long-term memory component using a TiOx-based memristor within the readout layer. We thoroughly examine both memristor types and leverage the RC system to process temporal data sets. The performance of the proposed RC system is validated through two benchmark tasks: isolated spoken digit recognition and with only a fraction of complete samples forecasting the Mackey-Glass (MG) time series prediction. The system delivered an impressive 98.84% accuracy in speech digit recognition and sustained a low normalized root mean square error (NRMSE) of 0.036 in the time series prediction task, underscoring its capability. This study illuminates the adeptness of memristor-based RC systems in managing intricate temporal challenges, laying the groundwork for further innovations in neuromorphic computing.

Influence of vegetative cover on snowpack mercury speciation and stocks in the greening Canadian subarctic region

A notable greening and warming of the Arctic and Subarctic due to climate change has uncertain implications for the global cycling of mercury (Hg). Snowpacks are dynamic reservoirs for Hg susceptible to solar radiation and wind pumping, with vegetative cover potentially altering Hg photochemistry. However, the impact of northern greening on the transformation of major Hg species and on Hg stocks remain poorly understood. Temporal surface snow and snowpit sampling was conducted under tree canopies and open tundra sites at the boreal-tundra ecotone in Nunavik, Canada. Maximum (mean) concentrations of 69.1 ng/L (8.8 ng/L) total mercury (HgT) and 46.9 ng/L (5.5 ng/L) reactive mercury (HgR) were measured in forest surface snow, with maximums attributed to rapid atmospheric oxidation events. Significant post-depositional reductions were recorded in the bay, tundra, and forest (67–99% HgR) and suggested greater Hg sequestration may occur under tree canopies. Increasing methylmercury (MeHg), HgT, and dissolved organic carbon (DOC) concentrations were detected across a vegetation gradient shifting towards humic-like organic matter. Notably, springtime depth profiles presented an approximate 12-fold greater accumulation of HgT under tree canopies compared to open tundra (p < 0.01), with up to 16-times higher stocks (HgT, MeHg, DOC) at elevated vegetation density (p < 0.05). In the North, increasing vegetation cover and surface warming may favor Hg accumulation and methylation in snowpacks, facilitated by interactions with organic matter, and further enriched by the reduced wind and solar exposure experienced under forest canopies.

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Accelerating Numerical Simulations of CO2 Geological Storage in Deep Saline Aquifers via Machine-Learning-Driven Grid Block Classification

The accurate prediction of pressure and saturation distribution during the simulation of CO2 injection into saline aquifers is essential for the successful implementation of carbon sequestration projects. Traditional numerical simulations, while reliable, are computationally expensive. Machine learning (ML) has emerged as a promising tool to accelerate these simulations; however, challenges remain in effectively capturing complex reservoir dynamics, particularly in regions experiencing rapid changes in pressure and saturation. This article addresses the challenges by introducing a fully automated, data-driven ML classifier that distinguishes between regions of fast and slow variation within the reservoir. Firstly, we demonstrate the variability in pressure across different reservoir grid blocks using a simple brine injection and production scenario, highlighting the limitations of conventional acceleration approaches. Subsequently, the proposed methodology leverages ML proxies to rapidly and accurately predict the behavior of slow-varying regions in CO2 injection simulations, while traditional iterative methods are reserved for fast-varying areas. The results show that this hybrid approach significantly reduces the computational load without compromising on accuracy. This provides a more efficient and scalable solution for modeling CO2 storage in saline aquifers.

Insights Into Magma Reservoir Dynamics From a Global Comparison of Volcanic and Plutonic Zircon Trace Element Variability in Individual Hand Samples

AbstractTrace element compositional trends in zircons separated from single hand samples have been used to infer dynamic processes in magma reservoirs. Here, we compile published zircon trace element chemistry to quantify any systematic difference between the range of compositions observed in zircon from individual volcanic and plutonic hand samples and compare these results with geochemical modeling to derive implications for magma reservoir dynamics. We find that both rock types span a wide range of hand‐sample scale variability (i.e., wide range of coefficients of variation), but there is no systematic difference in the average variability between plutonic and volcanic samples (i.e., no difference in the mean coefficient of variation). This indicates that dynamic processes related to eruption are not necessarily required as a fundamental process to create hand sample‐scale compositional heterogeneity beyond what is present due to dynamic processes in the reservoir recorded in plutonic samples. Modeling of felsic systems (&gt;68.5 wt.% SiO2) indicates that the similar average variability in felsic volcanic and plutonic hand samples cannot be reproduced by closed‐system crystallization of compositionally distinct melts locally within a magma reservoir (i.e., isolated melt pockets in a crystal mush) but requires mixing of at least two felsic melt compositions at a small spatial scale. This study provides a framework for focused studies on individual volcanic‐plutonic systems exploring how plutonic and volcanic zircon compositional variability records the time and length scales of magma reservoir processes.

Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method

Based on on-site construction experience, considering the time-varying characteristics of gas well quantity, production time, effective reservoir thickness, controlled reserves, reserve abundance, formation pressure, and the energy storage coefficient, a data-driven method was used to establish a natural gas production prediction model based on differential simulation theory. The calculation results showed that the average error between the actual production and predicted production was 12.49%, and the model determination coefficient was 0.99, indicating that the model can effectively predict natural gas production. Additionally, we observed that the influence of factors such as reserve abundance, the number of wells in operation, controlled reserves, the previous year’s gas production, formation pressure, the energy storage coefficient, effective matrix thickness, and annual production time on the annual gas production increases progressively as the F-values decrease. These insights are pivotal to a more profound understanding of gas production dynamics in volcanic reservoirs and are instrumental in optimizing stimulation treatments and enhancing resource recovery in such reservoirs and other unconventional hydrocarbon formations.

Influence of reservoir water saturation on coalbed methane development: Based on multi-field coupling numerical simulation

Water in coal reservoirs serves as both the driving force of reservoir fluid and the resistance to coalbed methane (CBM) migration. However, the impact and control mechanism of water saturation on CBM development in the field have not been revealed yet. The evolution of reservoir dynamics and gas production rate under various initial water saturation conditions is simulated based on the COMSOL multiphysics numerical simulation software. The simulation results show that the reduction of initial water saturation can significantly enhance the effective permeability of the reservoir gas phase, which can be increased by up to 4764%. This increases the rate of gas production, which is the main mechanism for controlling production by water saturation in the reservoir. For high water saturation reservoirs, the positive effect of inter-well interference should be reasonably utilized, and high well network density should be used to rapidly increase gas-effective permeability. For low water saturation reservoirs, the negative effect of inter-well interference should be avoided, and low well network density should be used to increase the control of single well reserves.

The Role of Fertilization on Soil Carbon Sequestration in Bibliometric Analysis

The soil carbon pool is the largest and most dynamic carbon reservoir in terrestrial ecosystems. Fertilization, an important component of agricultural management, is a significant factor influencing soil carbon sequestration. This study analyzed literature from the Web of Science from 2008 to 2024 using CiteSpace. The results revealed a steady increase in publications on this topic, with a significant surge in the recent four years. The analysis highlighted key collaborations among countries, institutions, and authors, and identified main journal sources and seminal works in the research on the role of fertilization in soil carbon sequestrations. Keyword analysis indicated that current research hotspots include ‘soil organic carbon dynamics and organic matter decomposition’, ‘microbial community dynamics and carbon cycling’, and ‘agricultural management practices on carbon sequestration’. In the context of climate change, future research is likely to focus on enhancing sustainable agricultural practices, promoting biochar and resource utilization, and utilizing microbial communities to optimize soil carbon sequestration. This study provides a comprehensive overview of the role of fertilization in soil carbon sequestration, providing important insights for improving soil carbon sequestration strategies.

Gas Injection Dynamics and Hydrogen Storage in Carbonate Reservoirs: Core-Flooding and Molecular Simulation Study

Gas Injection Dynamics and Hydrogen Storage in Carbonate Reservoirs: Core-Flooding and Molecular Simulation Study

Including dynamic capacity impact in an improved model for optimal flood control operation in river-type reservoirs

Flood control operations in river-type reservoirs are significantly affected by both the dynamic reservoir capacity and flood propagation within the reservoir area. However, the traditional reservoir flood control optimal operation (RFCOO) model is usually based on the static capacity method, which may bring large errors in scheduling calculations for river-type reservoirs. To address this issue, an improved model, the reservoir dynamic capacity flood control optimal operation (RDCFCOO) model, that includes the influence of the dynamic reservoir capacity and flood propagation, was developed to improve the accuracy of flood regulation calculations in river-type reservoirs. This improved model includes a 1D unsteady flow simulation and a rederived objective function based on the maximum peak clipping criterion, and expresses the state transformation equations using the de Saint-Venant equations. Furthermore, a multi-core parallel DP (PDP) algorithm that incorporates reduction of state dimensionality (RSD) was developed to effectively derive optimal operation schemes. The improved model and algorithm developed herein were validated through an application to the Xiangjiaba Reservoir, China. The results show that: (1) By adding the item (qiw) to the objective function and substituting the de Saint-Venant equations for the water balance equation, the RDCFCOO model developed in this paper can account for the impact of dynamic capacity and flood propagation. As a result, it exhibits a more accurate and detailed flood control process that was closer to the actual conditions of river-type reservoirs compared to the RFCOO model; (2) By removing the invalid discrete state points from the search space and fully utilizing multi-core processors, the proposed PDP with RSD can significantly improve the computational efficiency of the DP in solving RDCFCOO problems. The improved model and algorithm can effectively improve the calculation accuracy of flood control optimal scheduling in river-type reservoirs and provide more information for decision-makers.

Secreted nucleases reclaim extracellular DNA during biofilm development

DNA is the genetic code found inside all living cells and its molecular stability can also be utilized outside the cell. While extracellular DNA (eDNA) has been identified as a structural polymer in bacterial biofilms, whether it persists stably throughout development remains unclear. Here, we report that eDNA is temporarily invested in the biofilm matrix before being reclaimed later in development. Specifically, by imaging eDNA dynamics within undomesticated Bacillus subtilis biofilms, we found eDNA is produced during biofilm establishment before being globally degraded in a spatiotemporally coordinated pulse. We identified YhcR, a secreted Ca2+-dependent nuclease, as responsible for eDNA degradation in pellicle biofilms. YhcR cooperates with two other nucleases, NucA and NucB, to reclaim eDNA for its phosphate content in colony biofilms. Our results identify extracellular nucleases that are crucial for eDNA reclamation during biofilm development and we therefore propose a new role for eDNA as a dynamic metabolic reservoir.

Software for CO2 Storage in Natural Gas Reservoirs

The paper presents a simulation-based approach for optimizing CO2 injection into depleted gas reservoirs, with the goal of enhancing underground CO2 storage. The research employs a two-dimensional dynamic reservoir model, developed using Darcy’s law, to describe gas flow in a pressure-homogeneous porous medium, along with real gas equations. The model integrates the Du Fort–Frenkel and finite-difference methods to accurately simulate the behavior of CO2 during injection and storage. Real data from an operational gas storage facility were used to calibrate the model. CO2sim v1 software, specifically developed for this purpose, simulates CO2 injection cycles and quiescence phases, enabling the optimization of storage capacity and energy efficiency. The reservoir model, based on the engineering of the geological structure, is discretized into approximately 16,000 cells and solved using the finite-difference method, allowing for rapid simulation of CO2 injection and quiescence processes. The average computation time for a 150-day cycle is approximately 5 min. Simulation results indicate that increasing the number of injection wells and carefully controlling the injection rates significantly improves the distribution of CO2 within the reservoir, thereby enhancing storage efficiency. Additionally, appropriate well placement and prolonged quiescence periods lead to better CO2 dispersion, increasing the storage potential while reducing energy costs. The study concludes that further development of the software, along with comprehensive technical and economic assessments, is required to fully optimize CO2 storage on a commercial scale.

Research on Numerical Simulation Methods for Water Invasion Dynamics in Fractured Gas Reservoirs with Water Content

Abstract Fractured gas reservoirs with water have complex gas-water relationship, large permeability difference between matrix and fracture, complex gas-water percolation mode, rapid production decline, short stable production period and large reduction of recovery factor due to water production of gas wells, which poses great challenges to efficient development of gas fields. The fracture system is the main channel for the edge and bottom water channeling of gas reservoirs. It is the core issue to understand the water invasion dynamic characteristics of such gas reservoirs to accurately depict the fracture geometry occurrence and truly simulate the gas-water two-phase flow mechanism in the fracture-matrix dual media. Taking the Jia-2 gas reservoir in Moxi gas field as an example, a discrete fracture model construction method that explicitly characterizes the fracture system is proposed. Based on the discrete fracture model, a mathematical model of gas-water two-phase flow in the fracture-matrix dual system is constructed and numerically solved. The influence mechanism of fracture opening, length, density and occurrence on the water invasion performance of multi-layer gas reservoirs is discussed, and the main control factors of the water invasion and water channeling law of fractured water gas reservoirs are identified. The application shows that the water invasion dynamics of gas reservoirs based on the discrete fracture model are more consistent with the actual conditions of the mine, providing a theoretical basis for deepening the understanding of water invasion dynamics of this type of gas reservoirs, optimizing the gas well working system, and formulating targeted water control countermeasures..

Wyznaczanie dynamicznego ciśnienia formacji w strefie odwiertu

The article shows that the pressure drop in the near-wellbore zone decreases with increasing time that the well is left uncased due to the filtration of the drilling fluid filtrate into the formation. Under certain conditions, this does not pose a risk of drill string sticking. From the perspective of establishing the nature of changes in dynamic reservoir pressure behind the mud cake, the experimental studies by Johnson and Klotz (Stepanov, 1999), which determine the amount of filtrate entering the reservoir under both static and dynamic conditions, are particularly interesting. It should be noted that establishing the amount of filtrate experimentally takes into account in advance the nonlinearity of fluid movement along the borehole wall and through formation rocks. Reservoir pressure will increase to the depth of penetration of the drilling fluid filtrate, so the value of Rk is taken equal to the radius of a certain contour with formation pressure. The article shows that the dynamic reservoir pressure behind the mud cake initially rises intensively over time, and then, after a certain point, remains almost constant; the filtrate from the drilling fluid entering the formation partially displaces the reservoir fluid and fills the resulting pore space. During dynamic filtration, an intensive increase in pressure behind the mud cake occurs within 20–30 hours and depends little on the absolute values of fluid loss from the washing liquid, the thickness of the mud cake, and the initial pressure drop. Experience in drilling deep wells shows that sticking of the lower part of the drill string primarily occurs when opening productive horizons with low permeability (0.06–2) · 10–15 m2. Consequently, the ratio of the permeability of the rock of deep-lying productive horizons to that of the mud cake is approximately more than 1,000. Therefore, when opening productive horizons, pressure equalization practically does not occur, and leaving the drill string against these rock formations without movement for 15–20 minutes leads to its sticking due to the pressure difference.

Future Projection of Water Resources of Ruzizi River Basin: What Are the Challenges for Management Strategy?

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R2, NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources.