THE APPLICATION OF PRINCIPAL COMPONENT ANALYSIS IN PRODUCTION FORECASTING

Current methods of production forecasting, such as Decline Curve Analysis and Rate Transient Analysis, require years of production data, and their accuracy is affected by the artificial choice of model parameters. Unconventional resources, which usually lack long-term production history and have hard-to-determine model parameters, challenge traditional methods. This paper proposes a new method using principal components Analysis to estimate production with reasonable certainty. PCA is a statistical tool which unveils the hidden patterns of production by reducing high-dimension rate-time data into a linear combination of only a few principal components. This paper establishes a PCA-based predictive model which makes predictions by using information from the first few months’ production data from a well. Its efficacy has been examined with both simulation data and field data. Also, this study shows that the K-means clustering technique can enhance predictive model performance and give a reasonably certain future production range estimate based on historical data

DOBC based Fully Probability Design for Stochastic System with the Multiplicative Noise

This paper proposes a Fully Probabilistic control framework for stochastic systems with multiplicative noise and external disturbance. The proposed framework consists of two main components, the disturbance observer based compensator to reject the modelled disturbance, and the Fully Probability Design (FPD) controller to achieve the regulation objective. The disturbance observer is developed based on Bayes' theory following a probabilistic framework. Compared with the conventional FPD, the new framework in this paper is extended to deal with multiplicative noise, and at the same time improve the performance of the control system by rejecting external disturbances. The convergence analysis of the estimation and control processes is also provided. Finally, a numerical example is given to illustrate the effectiveness of the proposed control method

A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense

Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric field of attack and defense, and shuffling-based MTD has been regarded as one of the most effective ways to mitigate DDoS attacks. However, previous work does not acknowledge that frequent shuffles would significantly intensify the overhead. MTD requires a quantitative measure to compare the cost and effectiveness of available adaptations and explore the best trade-off between them. In this paper, therefore, we propose a new cost-effective shuffling method against DDoS attacks using MTD. By exploiting Multi-Objective Markov Decision Processes to model the interaction between the attacker and the defender, and designing a cost-effective shuffling algorithm, we study the best trade-off between the effectiveness and cost of shuffling in a given shuffling scenario. Finally, simulation and experimentation on an experimental software defined network (SDN) indicate that our approach imposes an acceptable shuffling overload and is effective in mitigating DDoS attacks

The Proximal Operator of the Piece-wise Exponential Function and Its Application in Compressed Sensing

This paper characterizes the proximal operator of the piece-wise exponential function $1\!-\!e^{-|x|/\sigma}$ with a given shape parameter $\sigma\!>\!0$, which is a popular nonconvex surrogate of $\ell_0$-norm in support vector machines, zero-one programming problems, and compressed sensing, etc. Although Malek-Mohammadi et al. [IEEE Transactions on Signal Processing, 64(21):5657--5671, 2016] once worked on this problem, the expressions they derived were regrettably inaccurate. In a sense, it was lacking a case. Using the Lambert W function and an extensive study of the piece-wise exponential function, we have rectified the formulation of the proximal operator of the piece-wise exponential function in light of their work. We have also undertaken a thorough analysis of this operator. Finally, as an application in compressed sensing, an iterative shrinkage and thresholding algorithm (ISTA) for the piece-wise exponential function regularization problem is developed and fully investigated. A comparative study of ISTA with nine popular non-convex penalties in compressed sensing demonstrates the advantage of the piece-wise exponential penalty

Fully Probabilistic Design for Stochastic Discrete System with Multiplicative Noise

In this paper, a novel algorithm based on fully probabilistic design (FPD) is proposed for a class of linear stochastic dynamic processes with multiplicative noise. Compared with the traditional FPD, the new procedure is presented to deal with multiplicative noise and the system parameters are estimated online using linear optimisation methods. The performance index is characterised by the Kullback-Leibler divergence (KLD) distance. The generalised probabilistic control law is obtained by solving a generalised Riccatti equation that takes the multiplicative noise into consideration. To demonstrate the effectiveness of the proposed method, a numerical example is given and the results are compared with the traditional FPD

THE APPLICATION OF PRINCIPAL COMPONENT ANALYSIS IN PRODUCTION FORECASTING

Current methods of production forecasting, such as Decline Curve Analysis and Rate Transient Analysis, require years of production data, and their accuracy is affected by the artificial choice of model parameters. Unconventional resources, which usually lack long-term production history and have hard-to-determine model parameters, challenge traditional methods. This paper proposes a new method using principal components Analysis to estimate production with reasonable certainty. PCA is a statistical tool which unveils the hidden patterns of production by reducing high-dimension rate-time data into a linear combination of only a few principal components. This paper establishes a PCA-based predictive model which makes predictions by using information from the first few months’ production data from a well. Its efficacy has been examined with both simulation data and field data. Also, this study shows that the K-means clustering technique can enhance predictive model performance and give a reasonably certain future production range estimate based on historical data

Experimental Study on the Mechanisms of Soil Water-Solute- Heat Transport and Nutrient Loss Control

The release and migration of nutrients, pesticides, and other chemicals in the runoff from agricultural lands is not only an economic loss but a threat to the quality of our surface and groundwater. In contrast to pollution from point sources, pollution from non-point sources is often low in intensity but high in volume. The development of a physically based model to simulate the transport of soil solutes would provide a better understanding of transport mechanisms and assist in the development of effective methods to control the loss of nutrients from soils and the pollution of waterways. As a result, numerous studies have been conducted in this area. But due to the soil genesis and human activity, the process is very complex, which can have a great impact on soil water movement, solute transport, as well as nutrient loss. In this study, we determined water movement and solute and heat transport through columns of disturbed soil samples. We also carried out simulated rainfall experiments on an artificial slope to study the nutrient loss

Rethink left-behind experience: new categories and its relationship with aggression

Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places. College students with left-behind experience showed higher aggression levels. To further explore the relationship between left-behind experience and aggression, the current study categorized left-behind experience using latent class analysis and explored its relationship with aggression. One thousand twenty-eight Chinese college students with left-behind experience were recruited, and their aggression levels were assessed. The results showed that there were four categories of left-behind experience: “starting from preschool, frequent contact” (35.5%), “less than 10 years in duration, limited contact” (27.0%), “starting from preschool, over 10 years in duration, limited contact” (10.9%), and “starting from school age, frequent contact” (26.6%). Overall, college students who reported frequent contact with their parents during the left-behind period showed lower levels of aggression than others did. Females were less aggressive than males in the “starting from preschool, frequent contact” left-behind situation, while males were less aggressive than females in the “starting from school age, frequent contact” situation. These findings indicate that frequent contact with leaving parents contributes to decreasing aggression of college students with left-behind experience. Meanwhile, gender is an important factor in this relationship

A tracking error–based fully probabilistic control for stochastic discrete-time systems with multiplicative noise

This article proposes the exploitation of the Kullback–Leibler divergence to characterise the uncertainty of the tracking error for general stochastic systems without constraints of certain distributions. The general solution to the fully probabilistic design of the tracking error control problem is first stated. Further development then focuses on the derivation of a randomised controller for a class of linear stochastic Gaussian systems that are affected by multiplicative noise. The derived control solution takes the multiplicative noise of the controlled system into consideration in the derivation of the randomised controller. The proposed fully probabilistic design of the tracking error of the system dynamics is a more legitimate approach than the conventional fully probabilistic design method. It directly characterises the main objective of system control. The efficiency of the proposed method is then demonstrated on a flexible beam example where the vibration quenching in flexible beams is shown to be effectively suppressed