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。     

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.     

基于贝叶斯推论的致密储层水力单元预测表征技术

针对小壕兔-大16井区储层结构复杂、有利区预测困难的问题,运用贝叶斯挖掘算法,利用计算机辅助模拟手段,构建研究区精细水力单元模型,探明储层砂体发育规律。研究结果表明,基于贝叶斯推论的表征技术能高效识别储层水力单元类型,为气藏描述提供准确的地质信息;指数函数递增法在测井曲线划段过程中表现更加出色,R_t测井段间样点的聚簇规律归因于储层的高含水特性;3类水力单元具有较大的原始油气储量和潜力,亦是提高气藏采收率过程中亟待突破的对象。该研究成果对预测储层有利区具有重要参考价值。     

基于不确定性用气量的输气管网供气可靠度计算方法

由于未来用气量是一个随机变量,过去采用点预测的方式无法客观体现出其随机性的特征,由此得到的供气可靠性评价结果也难以客观反映实际情况。为了准确预测用气量,在调研输气管网供气量预测模型研究进展的基础上,提出了一种采用基于小波分解的神经网络模型来预测随机性用气量的供气可靠度计算方法,通过对比分析模型预测结果与实际用气量的误差,确定用气量所服从的分布类型及参数,结合管道的最大允许输气量,以供气可靠度最高作为目标函数,建立了优化流量分配及计算管网供气可靠度的数学模型,进而利用该模型对某一虚拟管网进行了供气可靠性评价。结果表明:(1)所建模型求解的流量分配方案优先保障权重较大/较为重要的用户,但会牺牲其他用户的供气可靠度,管网总体供气可靠度亦会下降;(2)取消权重后,管网总体供气可靠度提高,优先保障距离气源地较近的用户,若不要求完全满足用户的用气量,则在降低一定的标准后,所有用户的供气可靠度都能达标。结论认为:所提出的计算方法结合了用气量随机的特性,能够更加客观地评价管网的供气可靠度,同时由于引入了用户的权重,在计算管网供气能力的时候能优先满足重要的用户,更加符合实际情况,其评价结果可以指导输气管网的高效运行。     

New challenges in solar energy resource and forecasting in Greece

Aerosols and clouds are the most important constituents in the atmosphere that affect the incoming solar radiation, either directly through absorbing and scattering processes or indirectly by changing the optical properties and lifetime of clouds. Under clear skies, aerosols become the dominant factor that affect the intensity of solar irradiance reaching the ground. Under cloudy skies, the high temporal and spatial variability of cloudiness is the key factor for the estimation of solar irradiance. In this study, recent research activities related to the climatology and the prediction of solar energy in Greece are presented with emphasis on new challenges in the climatology of global horizontal irradiance (GHI) and direct normal irradiance (DNI), the changes of DNI due to the decreasing aerosol optical depth and the short-term (15–240 min) forecasts of solar irradiance with the collaborative use of neural networks and satellite images.     

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.     

复频电导技术在隧洞超前探水中的应用

围岩富水带是隧道超前预报最关注的问题，目前，多使用电磁方法来预报围岩的含水性，但受到场地条件限制，面临掌子面条件、金属机具干扰、三维波场定位等诸多困难，特别是掌子面前方100 m范围内围岩的含水性预报，在国内外都是一个新的高难课题。在研究了岩体电导率与电容率复频特性的基础上，结合巴基斯坦NJ-TBM引水隧洞工程，开发了复频电导探水(CFC)技术。该技术基于电磁波反射与相干原理，选用100 kHz～10 MHz频率范围，采用电偶极子发射与阵列接收方式和偏移成像技术，突破了隧道内场地条件的限制。依据1/4相干波长确定含水体的位置，相干能量确定含水量的大小。在巴基斯坦NJ-TBM隧洞超前探水的应用，证明了该方法的可行性与有效性。      

基于电力消耗的行业总产值预测

电力行业是国民经济的基础性能源产业，对其他行业的发展起着至关重要的支撑作用。电力行业本身不存在库存现象进而能够相对真实近乎实时地反映行业经济运行情况，这使得从电力消耗到行业总产值的预测成为可能。针对某省规模以上工业企业基于电力消耗的总产值预测问题展开研究，结合该省2010—2013年近38 000家规模以上工业企业的用电量和总产值数据，利用基于粒子群优化参数的支持向量机建立预测模型。以2010年1月至2013年12月的数据作为训练样本，对2013年8月至2013年12月各行业的总产值进行预测和检验，并与常规交叉验证寻优的支持向量机模型和BP（back propagation）神经网络模型进行对比。结果表明，所采用的方法较其他方法可以更准确、可靠地预测行业总产值，基于用电量的行业总产值预测方法是科学、可行的。