A new approach to assessing the water footprint of hydroelectric power based on allocation of water footprints among reservoir ecosystem services

Hydroelectric power is an important energy source to meet the growing demand for energy, and large amounts of water are consumed to generate this energy. Previous studies often assumed that the water footprint of hydroelectric power equaled the reservoir’s water footprint, but failed to allocate the reservoir water footprint among the many beneficiaries; dealing with this allocation remains a challenge. In this study, we developed a new approach to quantify the water footprint of hydroelectric power (WF_h) by separating it from the reservoir water footprint (WF) using an allocation coefficient (η_h) based on the ratio of the benefits from hydroelectric power to the total ecosystem service benefits. We used this approach in a case study of the Three Gorges Reservoir, the world’s largest reservoir, which provides multiple ecosystem services. We found large differences between the WF_h and the water footprint of per unit of hydroelectric production (PWF_h) calculated using η_h and those calculated without this factor. From 2003 to 2012, η_h decreased sharply (from 0.76 in 2005 to 0.41 in 2012), which was due to the fact that large increases in the value of non-energy ecosystem services, and particularly flood control. In 2009, flood control replaced hydroelectricity as the largest ecosystem service of water from the Three Gorges Reservoir. Using our approach, WF_h and PWF_h averaged 331.0 × 10⁶ m³ and 1.5 m³ GJ⁻¹, respectively. However, these values would almost double without allocating water footprints among different reservoir ecosystem services. Thus, previous studies have overestimated the WF_h and PWF_h of reservoirs, especially for reservoirs that serve multiple purposes. Thus, the allocation coefficient should not be ignored when calculating the WF of a product or service.     

Modeling uncertainties of reservoir flushing simulations

Every dam or barrage construction affects the watercourse and the retention of sediment that previously was carried by the river, which can lead to siltation of the reservoir and obstruction of water intakes over time, reducing their capacities. However, the information available regarding the effect of sediment and drawdown parameters, sediment management at reservoirs, as well as different equational approaches, is scarce. The current research aims to evaluate the effect of parameters associated with numerical modeling of sediment management in reservoirs considering scenarios with different drawdowns, transport equations, sediment size distributions, and thickness of the initial sediment layer. The case study of the Aimorés Hydropower Plant (HPP) is used, applying the Delft3D-FLOW model for two-dimensional modeling. All parameters influenced the volume of mobilized sediment, among which the initial layer thickness was the parameter that resulted in the greatest changes in simulated results. In general, the results show that the uncertainties in the input parameters outweigh the uncertainties between the techniques, which found large variations in results when evaluating the use of different transport equations. These results indicate the importance of proper estimation of model parameters for predicting effects with accuracy and the need for such studies before planning and management operations are evaluated to avoid environmental harm and energy waste.     

Spatial constrained inverse rock physics modelling

Predicting reservoir parameters, such as porosity, lithology, and saturations, from geophysical parameters is a problem with non‐unique solutions. The variance in solutions can be extensive, especially for saturation and lithology. However, the reservoir parameters will typically vary smoothly within certain zones—in vertical and horizontal directions. In this work, we integrate spatial correlations in the predicted parameters to constrain the range of predicted solutions from a particular type of inverse rock physics modelling method. Our analysis is based on well‐log data from the Glitne field, where vertical correlations with depth are expected. It was found that the reservoir parameters with the shortest depth correlation (lithology and saturation) provided the strongest constraint to the set of solutions. In addition, due to the interdependence between the reservoir parameters, constraining the predictions by the spatial correlation of one parameter also reduced the number of predictions of the other two parameters. Moreover, the use of additional constraints such as measured log data at specific depth locations can further narrow the range of solutions.     

Statistical facies classification from multiple seismic attributes: comparison between Bayesian classification and expectation–maximization method and application in petrophysical inversion

We present here a comparison between two statistical methods for facies classifications: Bayesian classification and expectation–maximization method. The classification can be performed using multiple seismic attributes and can be extended from well logs to three‐dimensional volumes. In this work, we propose, for both methods, a sensitivity study to investigate the impact of the choice of seismic attributes used to condition the classification. In the second part, we integrate the facies classification in a Bayesian inversion setting for the estimation of continuous rock properties, such as porosity and lithological fractions, from the same set of seismic attributes. The advantage of the expectation–maximization method is that this algorithm does not require a training dataset, which is instead required in a traditional Bayesian classifier and still provides similar results. We show the application, comparison, and analysis of these methods in a real case study in the North Sea, where eight sedimentological facies have been defined. The facies classification is computed at the well location and compared with the sedimentological profile and then extended to the 3D reservoir model using up to 14 seismic attributes.     

2017年6月16日湖北秭归Ms4.3地震成因初探

北京时间2017年6月16日19时48分, 湖北省秭归县发生M_S4.3地震(震中位置31.06°N, 110.48°E)。 此次地震导致当地160多间建筑物出现不同程度损坏, 100多人生活受到影响, 震区中危岩体出现一定程度破坏。 分析此次地震的震源参数将有助于了解其震源特征和发震机制。 本文首先获取了震中距在3°以内的40个宽频带固定台站的三分量数据, 然后利用FK(Frequency wavenumber)方法计算了在不同深度下Crust2.0模型和改进的1-D模型的格林函数, 分别使用Cut And Paste(CAP)方法反演得到本次地震的震源机制解和深度; 并采用基阶Rayleigh波振幅谱进一步约束了质心深度。 结果表明此次地震的最佳震源机制解的节面Ⅰ: 68°/59°/163°; 节面Ⅱ: 166°/75°/32°, 最佳矩震级为M_W4.3, 最佳深度约为5 km。 对比2013年巴东地震、 2014年秭归地震以及2011年湖北阳新—江西瑞昌地震的震源参数和余震信息, 此次地震有可能是水库诱发地震, 但要判断成因还需进一步研究。     

Intra-survey reservoir fluctuations – implications for quantitative 4D seismic analysis

During the time taken for seismic data to be acquired, reservoir pressure may fluctuate as a consequence of field production and operational procedures and fluid fronts may move significantly. These variations prevent accurate quantitative measurement of the reservoir change using 4D seismic data. Modelling studies on the Norne field simulation model using acquisition data from ocean-bottom seismometer and towed streamer systems indicate that the pre-stack intra-survey reservoir fluctuations are important and cannot be neglected. Similarly, the time-lapse seismic image in the post-stack domain does not represent a difference between two states of the reservoir at a unique base and monitor time, but is a mixed version of reality that depends on the sequence and timing of seismic shooting. The outcome is a lack of accuracy in the measurement of reservoir changes using the resulting processed and stacked 4D seismic data. Even for perfect spatial repeatability between surveys, a spatially variant noise floor is still anticipated to remain. For our particular North Sea acquisition data, we find that towed streamer data are more affected than the ocean-bottom seismometer data. We think that this may be typical for towed streamers due to their restricted aperture compared to ocean-bottom seismometer acquisitions, even for a favourable time sequence of shooting and spatial repeatability. Importantly, the pressure signals on the near and far offset stacks commonly used in quantitative 4D seismic inversion are found to be inconsistent due to the acquisition timestamp. Saturation changes at the boundaries of fluid fronts appear to show a similar inconsistency across sub-stacks. We recommend that 4D data are shot in a consistent manner to optimize aerial time coverage, and that additionally, the timestamp of the acquisition should be used to optimize pre-stack quantitative reservoir analysis.     

联合多点地质统计学与序贯高斯模拟的随机反演方法

岩相和储层物性参数是油藏表征的重要参数,地震反演是储层表征和油气藏勘探开发的重要手段.随机地震反演通常基于地质统计学理论,能够对不同类型的信息源进行综合,建立具有较高分辨率的储层模型,因而得到广泛关注.其中,概率扰动方法是一种高效的迭代随机反演策略,它能综合考虑多种约束信息,且只需要较少的迭代次数即可获得反演结果.在概率扰动的优化反演策略中,本文有效的联合多点地质统计学与序贯高斯模拟,并结合统计岩石物理理论实现随机反演.首先,通过多点地质统计学随机模拟,获得一系列等可能的岩相模型,扰动更新初始岩相模型后利用相控序贯高斯模拟建立多个储层物性参数模型;然后通过统计岩石物理理论,计算相应的弹性参数;最后,正演得到合成地震记录并与实际地震数据对比,通过概率扰动方法进行迭代,直到获得满足给定误差要求的反演结果.利用多点地质统计学,能够更好地表征储层空间特征.相控序贯高斯模拟的应用,能够有效反映不同岩相中储层物性参数的分布.提出的方法可在较少的迭代次数内同时获得具有较高分辨率的岩相和物性参数反演结果,模型测试和实际数据应用验证了方法的可行性和有效性.

     

陆水水库的浮游生物及营养类型

报道了１９９１￣１９９５年对面积为１６９３ｈｍ＾２的黑龙滩水库渔业利用优化模式研究的结果,主要内容有：水生生物种类组成和现存量调查、浮游植物初级生产力测定、渔获物统计和鱼产力估算;水体理化性状和水位变化对鱼产量和浮游生物群落结构的影响,网箱养殖鲤、草鱼的高产试验结果;多功能水库生态系统的渔业利用优化模式初步探讨。经三年努力,水库鱼产量由６２２ｔ上升到１２５５．１ｔ,平均单产７５０ｋｇ／ｈｍ＾２。为     

3-D internal architecture of methane hydrate-bearing turbidite channels in the eastern Nankai Trough, Japan

The reservoir architecture of methane hydrate (MH) bearing turbidite channels in the eastern Nankai Trough, offshore Japan is evaluated using a combination of 3-D seismic and well data. On the 3-D seismic section, the MH-bearing turbidite channels correspond to complex patterns of strong seismic reflectors, which show the 3-D internal architecture of the channel complex. A seismic-sequence stratigraphic analysis reveals that the channel complex can be roughly classified into three different stages of depositional sequence (upper, middle, and lower). Each depositional sequence results in a different depositional system that primarily controls the reservoir architecture of the turbidite channels. To construct a 3-D facies model, the stacking patterns of the turbidite channels are interpreted, and the reservoir heterogeneities of MH-bearing sediments are discussed. The identified channels at the upper sequence around the β1 well exhibit low-sinuosity channels consisting of various channel widths that range from tens to several hundreds of meters. Paleo-current flow directions of the turbidite channels are typically oriented along the north-northeast-to-south-southwest direction. High-amplitude patterns were identified above the channels along the north-to-south and north-northeast-to-south-southeast directions. These roughly coincide with the paleo-current flow of the turbidite channels. An interval velocity using high-density velocity analysis shows that velocity anomalies (>2000 m/s) are found on the northeastern side of the turbidite channels. The depositional stage of the northeastern side of the turbidite channels exhibits slightly older sediment stages than the depositional stages of the remaining channels. Hence, the velocity anomalies of the northeastern side of the channels are related to the different stages of sediment supply, and this may lead to the different reservoir architectures of the turbidite channels.