非常规气藏储层改造体积评价方法研究

为精确描述非常规气藏压裂后的复杂流动特征及定量评价储层改造体积（SRV），利用自主研发的缝网重构算法“破裂树生长法”建立压后缝网模型，并以此为基础提出了使用拟稳态流动时特定的压力等值线来确定SRV范围的定量评价方法，最后以长宁201井区为例进行了矿场实例分析。该方法根据微地震监测点的位置，重构出微裂缝网的连通关系。在复杂微裂缝网的基础上建立离散裂缝地质模型并进行生产数值模拟，根据数值模拟结果的压力分布精确划定SRV的范围。利用该方法计算出长宁201井区的SRV体积为0.052 1 km3，以该缝网模型的数值模拟产量预测结果符合实际生产规律，方法实用性较好。     

压裂水平井产能预测方法研究进展

水平井压裂技术在低渗透及非常规储层中得到了广泛应用,压裂后水平井的产能预测关系到油田开发方案的制定,因此,国内外学者对压后渗流模型的建立和求解方法做出了不懈的努力。本文详细回顾了国内外学者所建压裂水平井产能预测模型及求解方法,指出了不同模型和求解方法的优缺点,并展望了压裂水平井产能模型的发展方向。     

Moving beyond ‘more crop per drop’: insights from two decades of research on agricultural water productivity

ABSTRACT

Concern over increasing water scarcity has led to the introduction of the concept of agricultural water productivity and an emphasis on interventions to achieve ‘more crop per drop’. Yet, a strong debate continues on how the concept is to be defined and used. Drawing largely from the irrigation literature, the origins of the concept and its methodological developments are reviewed, and its use in applied work over two decades is discussed. Based on this analysis of conceptual and applied research, key insights into the concept’s contributions and limitations are presented, as well as opportunities for further refinements.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study

This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market and three other financial markets: the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GEFVD. We show that the net spillover indices (of directional connectedness), used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GEFVD. We show that, considering data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the Generalized Forecast Error Variance Decomposition is preferable to the row normalization suggested by Diebold and Yilmaz since it yields net spillovers free of sign and ranking errors.     

Policy reforms and productivity change in the judiciary system: a cost function approach applied to time series of the Dutch judiciary system between 1980 and 2016

The judiciary is constantly undergoing change in order to respond to a wide range of social developments that have brought the sector under increasing pressure. In order to deal with the constant call for enhancing budgets, different policy measures have been taken to downsize the appeal to the judiciary and to improve productivity. One of the central questions hereby is whether these measures have actually contributed to a more productive sector. This paper focuses on the development of productivity in the judiciary and how policy measures have affected productivity. In this empirical analysis, we apply a cost function model to time series data of the Dutch judiciary between 1980 and 2016. The results show a dramatic decline of productivity over the entire period (?50%), in spite of various policy measures. The year 2001 can be regarded as a turning point, as since then productivity has remained more or less stable. It seems that this is largely due to the establishment of the Council for the Judiciary and the associated increase in (financial and operational) autonomy for the judiciary. Another striking result is that technical change is biased toward using more personnel through time.     

球磨过程中介质群运动区域划分方法研究

球磨过程中介质群运动状态变化对物料的破碎效率影响极大，通过对介质群运动状态进行区域划分，探究不同球磨工况下的介质群运动区域特征更能有效揭示介质群对物料的破碎方式和有效破碎区域。针对理论划分介质群运动区域过于理想、单一，试验追踪法成本过高的问题，提出了一种介质群运动区域划分的新方法-EDEM网格划分法。首先把筒体的横截面划分为若干个微元，利用数理统计的方法得到介质群位置概率分布函数，获取介质群运动速度区域分布图和碰撞特性区域分布图。然后给出了回转运动、螺旋运动及复合颤振运动三种工况下介质群运动区域的划分实例，并与试验追踪法对比，验证了EDEM网格划分法的准确性和有效性。最后通过粉磨试验中进料的破碎速率和微细颗粒产率探讨了介质群运动区域特性对颗粒的破碎方式和有效破碎区域的影响效果。本研究为优化球磨过程的影响因素和操作参数提供了一种快捷、有效的方法。     

Backtesting portfolio value-at-risk with estimated portfolio weights

This article theoretically and empirically analyzes backtesting portfolio value-at-risk (VaR) with estimation risk in an intrinsically multi-variate framework. It particularly takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating portfolio weights and that from estimating the multi-variate dynamic model make the existing methods in a univariate framework inapplicable. It proposes a general theory to quantify estimation risk applicable to the present problem and suggests practitioners a simple but effective way to implement valid inference to overcome the effect of estimation risk in backtesting portfolio VaR. In particular, we apply our theory to the efficient mean-variance-skewness portfolio for a multi-variate generalized autoregressive conditional heteroscedasticity model with multi-variate general hyperbolic distributed innovations. Some Monte Carlo simulations and an empirical application demonstrate the merits of our method.     

Nonlinear Factor-Augmented Predictive Regression Models with Functional Coefficients

This article introduces a new class of functional-coefficient predictive regression models, where the regressors consist of auto-regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one-step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor-augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite-sample performance of the proposed model and methodology.