Abstract

The Q oilfield is a medium-to-high permeability heavy oil reservoir, currently in the high water cut development stage, with continuously declining production. Accurate production forecasting can provide guidance for adjusting production strategies in the oilfield. In recent years, the rise of machine learning models has offered a better alternative for predicting well production. This paper is based on data-driven daily production dynamic forecasting of oil wells, integrating data-driven models with water drive characteristic curves to enhance the accuracy of dynamic production forecasting for oil wells.
Firstly, using exclusion discriminant analysis, time, water cut, and daily oil production were selected as machine learning feature data, and the optimal water drive characteristic curve was determined. Secondly, the optimization of long short-term memory neural network structure is carried out, and the optimal results are used to carry out the recursive multi-step prediction of long short-term memory neural network. Furthermore, the optimal data-driven model was further integrated with the optimal water drive characteristic curve to establish an oil well daily oil production forecasting model that combines water drive characteristic curves and LSTM models. Finally, using data from two wells in the Q oilfield, the predictive performance of the pure data-driven model, the integrated model, and the numerical simulation model was compared and evaluated. The results showed that the integrated model had the best predictive performance and the lowest error.
This paper establishes a dynamic production forecasting model for oil wells that deeply integrates water drive characteristics with data-driven models, improving traditional daily production forecasting methods and achieving superior predictive results.

Abstract

Low-carbon energy sources like wind and solar are essential for decarbonizing the electricity sector. In addition, the cost of electricity generated from these sources has plummeted over the last decade. Therefore, these energy sources are poised to take a significant share of the total installed capacity soon. However, they are susceptible to the impacts of climate change as their generation potential depends on the weather conditions. Estimating the installed capacity requirements of solar and wind energy to decarbonize the power sector without accounting for these possible changes in generation potential could lead to missing out on the set climate goals and meeting future electricity demand. This study evaluates the effect of climate change on the generation potential of wind and solar energy systems in India for two future periods, 2050 and 2070, under two climate scenarios or Shared Socioeconomic Pathway (SSP): SSP245 and SSP585. Almost all regions show a decrease, and most regions show a significant decline (>5%) in the generation potential of solar Photovoltaic (PV) as compared to 2010 levels under both climate scenarios and future periods. The changes in the generation potential of wind energy are more significant (>10%), and the majority of regions show a decline in generation potential. Southwestern and central regions show an increase in wind generation potential for 2070 as compared to 2050 levels under the SSP245 scenario and the SSP585 scenario, respectively.

Abstract

The global energy landscape is undergoing a crucial transition towards renewable energy solutions, underlining the importance of decentralizing energy systems. This complex and long-term process has facilitated the emergence of energy communities, enabling a wide range of actors, including citizens, to actively participate in the energy transition. A key element of this transition is the role of prosumers, individuals who consume and produce energy from renewable sources. Thermal prosumers can be integrated into district heating networks through bidirectional thermal substations, allowing excess thermal energy – produced locally from renewable sources, such as solar thermal systems, or recovered from industrial processes – to be injected into the network.
In this framework, the study introduces a novel approach to adapt traditional district heating substations to bidirectional heat exchange devices, enabling prosumers to actively use thermal energy for their needs and contribute surplus energy to the network. Using an existing network in northern Italy as a case study, an optimized layout in a “supply-to-return” configuration is proposed. To evaluate the performance and potential of the proposed bidirectional device, a detailed numerical model of the substation was implemented in Dymola software for multi-domain simulation. The model was designed to analyze the system’s summer operation, with customized systems and control logics developed from standard library models to accurately reproduce the substation‘s behavior under specific conditions. The results showed that the bidirectional substation allows for the utilization of previously unused thermal potential, due to the mismatch between demand and production, and makes it available to the network, particularly on days with high solar irradiance. This innovative approach finally extends the concept of energy sharing to thermal energy flows, at the community level, opening up new opportunities for a more integrated and sustainable management of energy resources.

Abstract

In the Xinjiang Mahu tight sandstone reservoir, the phenomenon of fracturing interference between newly fractured wells and fractured horizontal wells is increasing. Fracturing interference has a significant impact on the production of the affected wells, including oil production, water production, and production oil pressure. There are two types of fracturing interference in horizontal wells in Xinjiang Mahu Oilfield: fracture communication interference and pressure wave interference. The changes in liquid production volume and production oil pressure vary for different types of fracturing interference. Based on the production data from different types of interfered wells in Mahu Oilfield, this study analyzed the changes in liquid production and oil pressure after interference, explored the effects of different types of interference on these wells, and finally provided methods to prevent fracturing interference. The results show that: (1) Production wells experience severe interference due to fracture communication; after interference, there is a significant shift in liquid production volume with an increase in water production and noticeable fluctuation in production oil pressure. (2) Production wells experience weak interference due to fracture communication; after interference, there are slight changes in liquid production volume with a small increase in water production but no obvious fluctuation in production oil pressure. (3) By adopting fracture-opening temporary plugging fracturing technology, it is possible to control the liquid volume of individual clusters and reduce the possibility of dominant fractures expanding further away. This has an evident effect on preventing fracturing interference.

Abstract

Injecting CO2 into the reservoir can achieve geological storage of CO2 while improving recovery, which can effectively mitigate CO2 emissions. The balance between reservoir rocks and formation water is disrupted by the injected CO2, resulting in changes in porosity, permeability, and formation water characteristics as well as mineral corrosion and precipitation. These changes ultimately impact the safety and capacity of CO2 storage. In this paper, the CO2-Brine-Rock interaction mechanism and its effect on the reservoir are studied by using sandstone cores from the Qiuling Oilfield in China as experiment objects, through indoor core flooding experiment, nuclear magnetic resonance (NMR) experiments, and ion analysis of the reaction solution. The experimental results show that the porosity and permeability of the cores increased slightly after CO2 flooding, and the increase in porosity at the entrance is higher than that at the export; The content of Na+, Ka+, HCO3-, CO32- ions increase and the content of Mg2+, Ca2+, Ba2+ ions decrease in the final reaction solution. The results of this study provide theoretical support for further research on the CO2 capture mechanism and geological storage of CO2 in water-bearing reservoirs.

Abstract

We investigated the relationship between people flow and building energy consumption during the summer using a state-space model for an office building in Tokyo. We estimated the elasticity of people flow for the cooling heat load and the power consumption of outlets, water supply and drainage pumps, and air conditioning fans. The results provide insights that will be valuable for spatial downscaling to accurately understand the dynamics of energy consumption in individual buildings when the use of electricity data.

Abstract

The miniaturization of electronic devices generates high heat flux, so evaporators that combine miniaturization and heat transfer capabilities are needed to meet the heat transfer requirements. In this paper, the inner flow and heat transfer characteristics of a microchannel coil evaporator were investigated using numerical analysis. The effects of evaporating pressure, and heat flux on the heat transfer coefficient, outlet temperature, and outlet vapor volume fraction were analyzed. In addition, a comparison was made with a rectangular evaporator designed with an equivalent diameter of 0.4 mm. The results indicate that the microchannel coil evaporator can reach the maximum outlet temperature of 53.75 °C, and the heat transfer coefficient is 1835.6 W/m2. Compared to it, the area of the microchannel rectangular evaporator is reduced by 69% and the outlet temperature of the evaporator is decreased by 5.4% for the same amount of heat change. Meanwhile, the heat transfer coefficient has improved by 11.2%.

Abstract

We employed a techno-economic analysis to assess the feasibility and implications of a green energy transition at the city level, focusing on the potential for stranded assets. We explore the integration of rooftop photovoltaics (PV) and electric vehicles (EVs), highlighting a promising future where self-consumption can reach 100% and energy self-sufficiency can rise to 60%, alongside a 52% reduction in CO2 emissions and cost savings of up to 10%. The transition in Hanoi is estimated to result in $7.4 billion in stranded assets, while also generating 5,000 new jobs in the PV supply chain and achieving a net present value (NPV) of $4.7 billion. Despite the PV+EV system’s inability to fully meet the city’s electricity demand, our research confirms that it is a cost-effective solution, with the levelized cost of electricity (LCOE) reaching a low of 6.59 cents/kWh. However, considerations regarding stranded assets and grid integration capacities are crucial for a rapid transition.

Abstract

CO2 flooding technology is a highly efficient method of enhanced oil recovery (EOR), but it can lead to CO2 channeling, which significantly reduces oil recovery and threatens well safety. Due to the complex geological structure of reservoirs, there is a high level of uncertainty. Traditional numerical simulation and empirical methods are limited in their ability to accurately predict CO2 channeling, resulting in uncertain channeling times. In addition, there are CO2 channeling intricately influenced by a multitude of factors, including fluid properties, inter-well connectivity, injection-production mechanisms, and CO2 channeling capacity, reflecting inherently high-dimensional nature and CO2 channeling dataset has sparsity. Therefore, it is necessary to introduce a data-driven transfer learning framework to precisely predict the timing of CO2 channeling. Our proposed framework heavily relies on the Extreme Gradient Boosting (XGBoost) algorithm. Source domains were constructed by learning from various scenarios gas channeling data. The knowledge learned by the source domain model is transferred, allowing for high-precision predictions by simply adjusting parameters for the target domain model. This approach leads to a more comprehensive and accurate analysis of CO2 channeling times. The model was trained on 120 actual reservoir and 200 simulation well datasets tested on the 18 well datasets of target domain, achieving an average R2 value of 0.972 and a MSE value is 2393. Distinguished from numerical simulation and empirical formulas, this work presents a novel, swift, and precise to forecasting CO2 channeling, offering valuable insights for reservoir engineers in managing CO2 channeling prevention and mitigation strategies.

Abstract

Toyosu, a waterfront urban development on the reclaimed land of Tokyo Bay, faces issues such as a lack of walkability and high heat vulnerability. The main objective of this research is to leverage Digital Twins technology to enhance multimodal transportation system resilience, equity, and connectivity, utilizing the problem of heat on Toyosu of Tokyo as a case study. The research questions include: 1) What are the key variables (e.g., urban design, infrastructure, human) for designing a resilience hub? 2) Considering these key variables, how do we leverage emerging tools (e.g., Digital Twins systems) to inform planning and decision-making to reduce pedestrian vulnerability to heat? This study proposes a proof-of-concept Digital Twin Interface (DTI) utilizing geographic information system (GIS), agent-based modeling (ABM), and digital urban design modeling tools (Rhino/Grasshopper). Two scenarios were constructed to simulate the thermal comfort of Toyosu in 2023 and in 2030, demonstrating the potential for urban form to impact pedestrian comfort. The output of these simulations is integrated within the DTI to inform pedestrian decision making during high heat events. This study informs the design of resilience hubs broadly and is applied to the case of heat stress in Toyosu as a proof-of-concept. The variables impacting walkability, multimodal connectivity, and transportation resilience are further examined. This research extends and applies previous research efforts leveraging Digital Twin technology in transportation planning and decision making. This can aid transportation planners, engineers, and researchers who desire to enhance multimodal transportation system resilience with a focus on pedestrian comfort and safety.