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.