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Proppant is one of the key materials for hydraulic fracturing. For special situations, such as middle-deep reservoirs and closure pressures ranging from 40 MPa to 60 MPa, using a single proppant cannot solve the contradiction between performance, which means crushing rate and fracture conductivity, and cost. However, using combined proppants is an economically effective method for hydraulic fracturing of such special reservoirs. Firstly, for different types, particle sizes, and proportions of combined proppants, various contact relationships between proppant particles are considered. The random phenomenon of proppant particle arrangement is described using the Monte Carlo method, and the deterministic phenomenon of proppant particles is processed using an optimization model, achieving computer simulation of the microscopic arrangement of proppant particles. Secondly, a mathematical model for the force analysis of combined proppant particles is established, and an improved singular value decomposition method is used for numerical solution. A computational model for the crushing rate and fracture conductivity of combined proppants is proposed. Thirdly, the numerical calculation results are compared and discussed with the test values, verifying the accuracy of the computational model. Finally, the application of combined proppants is discussed, and a model for optimizing the proportion of combined proppants is proposed. The onsite construction technology is introduced, and the cost and economic benefits of combined proppants are compared with those of all ceramic particles and excessive all-quartz sand. It is proved that combined proppants can balance performance and price, and are an economically effective method for hydraulic fracturing of special reservoirs. The research results can select the optimal proppant material and optimize the combination of different proppant types, which can help achieve cost reduction and efficiency increase in oil and gas development. [ABSTRACT FROM AUTHOR]

The low average daily gas production per well and the poor economic benefit of exploration and development have become the main problems restricting the exploration and development of coalbed methane in China. Combining multiple coal seam geological parameters to predict the high-yield area of the block can not only provide guidance for the exploitation of coal-bed methane, but also bring enormous economic benefits. Aiming at the difficulty of coalbed methane dessert discrimination and production prediction, a method of coal-bed methane production prediction based on BP neural network is proposed in this paper. Starting from the average daily production of coalbed methane single well, we use the method of grey correlation degree to get the main controlling factors of coalbed methane production. For the main control factors, we use BP neural network with high fitting accuracy and get a good prediction result.

For higher resolution, next-generation space telescopes would be equipped with 10–20 m scale membrane diffractive primary lenses, and would have 100 m scale focal length. The large and flexible structure makes high-accuracy and high-stability control a great challenge. Specifically, both high-frequency and low-frequency disturbances must be attenuated, and the relative motion between the primary lens and the receiver (composed of the correcting optics and the imaging sensor) must be controlled. This paper presents a novel integrated control scheme to achieve the strict control goals. The dynamic model of a membrane diffractive space telescope is presented, where both high-frequency and low-frequency disturbances are considered. Nonlinear deformation of the flexible structure is also taken into account. The integrated control scheme consists of 3 parts: (1) an Agile Stable Precision platform (ASP), which can not only reduce the high-frequency vibrations for the receiver but also act as the actuator in the receiver control system; (2) a neural network controller for the spacecraft bus, which control the attitude of the spacecraft bus under uncertain low-frequency disturbances; (3) a finite-time neural network controller for the receiver to make the relative position and attitude of the receiver track on the expected state as fast as possible. Numerical simulations were carried out to verify the superiority of the integrated control scheme. Compared with traditional single stage spacecraft control (i.e., without the ASP), the accuracy and stability of the relative position and attitude are improved by at least one order of magnitude, and the settling time is greatly reduced. [ABSTRACT FROM AUTHOR]

Crushing ratio and fracture conductivity are two key indicators for proppant evaluation. The most common way to evaluate these two indicators is physical testing, but physical testing is costly and time‐consuming, and the results are unstable. Therefore, a method for calculating proppant‐crushing ratio and fracture conductivity is proposed to replace repetitive physical experiments. However, in past attempts, only mathematical models using the same particle size have been established, while the particle‐size distribution in actual proppant arrangement is within a certain numerical range. Therefore, in order to establish a mathematical model more consistent with the actual situation of proppant arrangement, firstly, the Monte Carlo method was used to simulate proppant particles arranged with different particle sizes, and then a mathematical model was established describing the force of proppant particles. Secondly, according to the sparse, ill‐conditioned, and asymmetric characteristics of the coefficient matrix, an improved singular value decomposition method is proposed to improve the accuracy and efficiency of calculation. Finally, the crushing ratio and fracture conductivity of proppant were calculated through the new model in this paper. It was found that the results calculated by the new model in this paper are closer to actual test results. [ABSTRACT FROM AUTHOR]

The authors studied models of inversing fracture parameters using early-time production data from fractured wells. Inverse results help evaluate the performance of fracturing, improve fracturing design, and predict the long-term production dynamics of fractured wells. First, polynomials were used to match variable flows. A new analytical model describing the transient-pressure behaviour of variable flow production was developed. This model is significantly superior to existing superposition analysis models in terms of calculation speed, accuracy, and stability. Then, in order to establish the best match between the calculated bottomhole pressure and the actual measured bottomhole pressure, the wellbore storage coefficient, fracture conductivity, fracture half-length, and fracture skin factor were selected as inverse fracture parameters. An automatic matching model was established, and a Levenberg-Marquardt algorithm based on a stochastic initial value and maximum probability was developed. This algorithm (1) is easy to implement, (2) can search local optimal solutions as much as possible, and (3) to improves the multisolution of inverse problems. Finally, the sensitivity of fracture parameters was analysed. Some existing automatic matching methods were compared and validated. A set of accurate, high-precision data acquisition and calculation devices was identified to promote application of the results. [ABSTRACT FROM AUTHOR]

Introduction: In contemporary society, people spend long periods under high stress, and tourism activities have gradually been internalized as a new means of stress release and self-recovery. Studies have found that the high-quality natural environment of rural tourism destinations has a higher restorative effect than other places, and the rural natural environment can provide psychological recovery to visitors on top of offering visual beauty and other experiences., Methods: This paper starts with the relationship between rural place perception and restorative experience evaluation. Based on theories such as the restorative environments theory, we investigates whether rural natural environmental factors have a restorative effect on recreationists through collecting 300 questionnaires and using SPSS 26.0 structural equation modeling for analysis., Results: The study found that there is a positive correlation between rural natural perception, place dependence, and restorative experience, forming a positive feedback loop dynamic system. The analysis suggests that enhancing the perception of the rural natural environment and place attachment can improve the restorative experience of recreationists in rural settings., Discussion: This research establishes a systematic research framework for the relationship between rural natural perception, place attachment, and restorative experience, to deeply understand the dynamic interaction between them. It reveals the relationship between rural natural perception and restorative experience, suggesting that enriching the perceptual elements in rural natural spaces can meet the diverse needs of recreationists, enhance the sense of dependence and identification with rural spaces, and thus promote the psychological well-being and restorative experience of recreationists. The study also finds that place dependence plays a mediating role between rural natural perception and restorative experience. Place attachment and place identity, as mediating variables, act as bridges and catalysts in the process of rural natural perception affecting restorative experience., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tang, Zhao and Guo.)