Molecular dynamics simulation of surfactant reducing MMP between CH4 and n-decane.

Reinjecting produced methane offers cost-efficiency and environmental benefits for enhances oil recovery. High minimum miscibility pressure (MMP) in methane-oil systems poses a challenge. To overcome this, researchers are increasingly focusing on using surfactants to reduce MMP, thus enhancing the effectiveness of methane injections for oil recovery. This study investigated the impact of pressure and temperature on the equilibrium interfacial tension of the CH4+n-decane system using molecular dynamics simulations and the vanishing interfacial tension technique. The primary goal was to assess the potential of surfactants in lowering MMP. Among four tested surfactants, ME-6 exhibited the most promise by reducing MMP by 14.10% at 373 K. Key findings include that the addition of ME-6 enriching CH4 at the interface, enhancing its solubility in n-decane, improving n-decane diffusion capacity, CH4 weakens n-decane interactions and strengthens its own interaction with n-decane. As the difference in interactions of n-decane with ME-6's ends decreases, the system trends towards a mixed phase. This research sets the stage for broader applications of mixed-phase methane injection in reservoirs, with the potential for reduced gas flaring and environmental benefits.

Machine learning-based fracturing parameter optimization for horizontal wells in Panke field shale oil.

In the process of developing tight oil and gas reservoirs, multistage fractured horizontal wells (NFHWs) can greatly increase the production rate, and the optimal design of its fracturing parameters is also an important means to further increase the production rate. Accurate production prediction is essential for the formulation of effective development strategies and development plans before and during project execution. In this study, a novel workflow incorporating machine learning (ML) and particle swarm optimization algorithms (PSO) is proposed to predict the production rate of multi-stage fractured horizontal wells in tight reservoirs and optimize the fracturing parameters. The researchers conducted 10,000 numerical simulation experiments to build a complete training and validation dataset, based on which five machine learning production prediction models were developed. As input variables for yield prediction, eight key factors affecting yield were selected. The results of the study show that among the five models, the random forest (RF) model best establishes the mapping relationship between feature variables and yield. After verifying the validity of the Random Forest-based yield prediction model, the researchers combined it with the particle swarm optimization algorithm to determine the optimal combination of fracturing parameters under the condition of maximizing the net present value. A hybrid model, called ML-PSO, is proposed to overcome the limitations of current production forecasting studies, which are difficult to maximize economic returns and optimize the fracturing scheme based on operator preferences (e.g., target NPV). The designed workflow can not only accurately and efficiently predict the production of multi-stage fractured horizontal wells in real-time, but also be used as a parameter selection tool to optimize the fracture design. This study promotes data-driven decision-making for oil and gas development, and its tight reservoir production forecasts provide the basis for accurate forecasting models for the oil and gas industry.

A new monocyclic monoterpene derivative from volva of Phallus dongsun.

A new monoterpene derivative namely dongsunol A (1) and sixteen known compounds (2-17) were isolated from the volva of Phallus dongsun. All compounds were isolated from this fungus for the first time. Their structures and absolute configurations were determined by nuclear magnetic resonance (NMR), HRESIMS spectral data, and electronic circular dichroism (ECD). The new monoterpene derivative (1) exhibited antibacterial activity with a MIC of 200 µg/mL. Other compounds have inhibitory effects on Staphylococcus aureus and Pseudomonas aeruginosa, while have displayed moderate NO inhibitory activity and antineoplastic activity on SMMC-7721 and SW480 in vitro.

Molecular dynamics-based analysis of the factors influencing the CO2 replacement of methane hydrate.

The benefits of large reserves, wide distribution, and high combustion energy density of natural gas hydrates are of great practical importance to alleviate the energy tension, enhance the existing energy system in China and reduce the greenhouse effect. The CO2 replacement method is a critical way to develop natural gas hydrate, while traditional experimental methods are difficult to reveal the microscopic mechanism of the replacement system. An MD (molecular dynamics) technique was utilized in this work to simulate the process of carbon dioxide replacement of gas hydrates. This simulation investigates the effects of temperature, pressure, and CO2 purity during the CO2 replacement process. CO2, different concentrations of CO2/H2O, and CO2/NH3 are used as the injected fluid. The simulation results show that the influence of temperature on the CO2 replacement of natural gas hydrate is more significant than that of pressure. Within the temperature and pressure range specified in the simulation, H2O inhibits the replacement of CO2, owing to the inhibitory effect increasing as the concentration of H2O increases; NH3 promotes the process of CO2 replacement under the temperature conditions of 250 K and 260 K, and the promotion effect becomes more significant as the concentration of NH3 increases. However, adding NH3 inhibits the CO2 replacement process with hydrate when the temperature lifts to 270 K. These findings provide new ideas to improve the efficiency of the CO2 replacement method and provide theoretical insight for the engineering exploitation of hydrates.