Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.     

The use of GIS tools to support decision-making in the expansion of chain stores

This article deals with a network competitive localization problem in which a firm seeks to determine the location of a new facility, which competes with all the facilities operating in the market, both belonging to the same firm and to the competing firms. In this context, two frequently conflicting objectives are involved: maximization of the total market share captured by the firm and minimization of market share losses for its existing facilities due to being captured by the new facility (cannibalization). We formulate the location problem as a multi-objective optimization model. Some GIS tools that provide some maps representing the market share and the cannibalization effect are proposed. This allows for the visualization of the effects produced by the opening of a new facility in the market and the trade-offs between the objectives. Finally, the advantages of using these tools have been shown by means of an application to a real data case.     

Uncertainty and sensitivity analysis: tools for GIS-based model implementation

A novel procedure to analyse the uncertainty associated to the output of GIS-based models is presented. The procedure can handle models of any degree of complexity that accept any kind of input data. Two important aspects of spatial modelling are addressed: the propagation of uncertainty from model inputs and model parameters up to the model output (uncertainty analysis); and the assessment of the relative importance of the sources of uncertainty in the output uncertainty (sensitivity analysis). Two main applications are proposed. The procedure allows implementation of a GIS-based model whose output can reliably support the decision process with an optimized allocation of resources for spatial data acquisition. This is possible in low cost strategy, based on numerical simulations on a small prototype of the GIS-based model. Furthermore, the procedure provides an effective model building tool to choose, from a group of alternative models, the best one in terms of cost-benefit analysis. A comprehensive case study is described. It concerns the implementation of a new GIS-based hydrologic model, whose goal is providing near real-time flood forecasting.     

加速遗传算法在马斯京根洪水演算模型参数估计中的应用

为提高马斯京根洪水演算模型参数估计的准确性、稳定性和工作效率,根据马斯京根洪水演算模型的基本假定把模型参数估计问题转换为相应的优化问题,并提出用加速遗传算法(AGA)同时优化模型参数。实例计算的结果说明了用AGA进行参数估计的有效性和较高的演算精度,实现了参数估计的优化和简化,在洪水灾害管理中具有推广应用价值。     

Curricula,Course Outlines and Laboratory Exercises

Many modern hydrological models require data inputs provided by automated digital terrain analysis functions incorporated into GIS. These inputs include fields representing surface flow directions, up-slope contributing areas, and sub-catchment partitions. Existing raster-based terrain analysis tools, including both those in off-the-shelf GIS packages and those in the recent literature, were designed to work with digital elevation data in mountainous topography. For highly variable topography, which may include large flood plains, lakes, wetlands, and other relatively flat areas, existing tools cannot accommodate the variable signal-to-noise in the source elevation data without significant human intervention to handle special cases. A general model for calculating flow directions, up-slope contributing areas, and sub-catchment partitions that automatically adapts to the variable information content of grid-based elevation data sets is presented here. The model uses a combination of breadth-first search and global optimization to extract the maximum amount of signal from any location within the data. The model is demonstrated to work well in handling topography dominated by large flood plains, lakes and other flat areas without the need for a large number of empirical rules. An important contribution of the approach is the handling of explicit hydrologic features, which makes the spatial representation closely related to hydrological processes. The results have important implications for developing hydrological models that are tractable in large, heterogeneous watersheds using moderate resolution data.     

高度城市化区域水库水源地环境安全管理信息系统开发——以深圳某水库为例

中国高度城市化区域水源地环境安全问题较突出,同时环境安全管理技术手段落后,无法满足管理需求。为了解决这一问题,文章以深圳某水库为例探讨了高度城市化区域水库水源地环境安全管理信息系统的功能需求和开发方案,并基于GIS技术开发了具有多种功能的城区水库水源地环境安全管理信息系统。经实际应用,识别出深圳某水库水源地主要风险源为库尾截污闸、沙湾路水库段、大望桥段、丹沙路桥段;水源地敏感带除在一级水源保护区内有分布外,主要是分布在二级水源保护区内,甚至在距水库较远的二级水源保护区内的一些区域也被识别为敏感地带,该系统可弥补饮用水水源保护区划分方法的不足,用于辅助水源保护区的监管决策;主要风险源发生污染事故时,污染物最快约20 h可到达坝前取水口处;利用实测小时降雨量实现了库尾截污闸处洪峰和开闸泄洪过程,以及泄洪污染物在水库中的迁移扩散过程预报;通过现场视频监控和水质在线监测实现了水源地环境的实时监控。应用结果表明,该系统实用性强,可为高度城市化区域水库水源地环境安全管理提供有效的决策支持。     

Multi-level Optimization of Reservoir Scheduling Using Multi-resolution Wavelet-Based Up-scaled Models

This paper presents a multi-level procedure for production and injection scheduling through a numerical model-based optimization of well control variables. To calculate the net present value (NPV), the objective function of optimization, this procedure uses a number of discretized systems for a reservoir model with different degrees of up-scaling prepared according to a multi-resolution wavelet technique. These up-scaled models were incorporated into optimization based on a probability function. In early optimization iterations, due to the necessity to explore the search space quickly, the coarsest grid model has a higher chance for selection than the others; however, by a selection (with a low probability) of the finest up-scaled grid model in these iterations, solutions and objective function were tuned. In the later iterations of optimization, the finest up-scaled grid model probability was the highest in order to ensure the reliability of the final solution. The optimization algorithm is an adaptive simulated annealing algorithm coupled with a polytope. This procedure was evaluated in two case studies. The first case study was a horizontal 2D oil model with water flooding. The second case study was a vertical 2D oil model with gas injection. The results show that the proposed optimization procedure provides approximately the same accuracy compared to the situation in which the fine grid model is used for all the optimization iterations. Also, the run-time for the proposed optimization procedure is comparable to the run-time of the optimization in which only the coarsest grid model is used to calculate objective function. Moreover, the superiority of the wavelet-based up-scaling over an analogous multiple grid system optimization using uniformly up-scaled models is presented.

     

山区流域暴雨洪水的数值模拟

基于中尺度天气研究预报模式WRF和流域水文模型对我国夏季山区的暴雨洪水进行模拟研究。首先,结合新安江水文模型和TOPMODEL自主研发了一个动态结合地面地下产流计算的新水文模型XXT,并选取成熟的天气研究预报模式(WRF)的模拟结果作为XXT的输入进行洪水模拟。其中WRF模拟采取3重区域嵌套模拟再现了2007和2008年夏季山东南部沂沭泗河流域的3次暴雨过程。模拟结果表明,WRF对暴雨的时空分布有较好的模拟与预测能力,模拟结果与观测一致性较好,逐时降水过程较观测更为连续,峰值有一定差异,总量略有偏大。WRF模拟的逐时站点表层土壤湿度与自动站观测的变化趋势一致,峰值偏大,空间分布与降水有着较好的响应关系。总格点径流深的计算结果同样与降水对应得较好,基本能重现出暴雨-径流过程的时空变化特征。其后,利用收集的实测降水资料率定XXT的模型参数,进而将WRF模拟的降水和潜在蒸散发输入到XXT进行流域出口洪水流量的模拟,其中2008年的模拟结果取得了0.91的效率系数,而2007年2次个例由于偏高的降水,使得洪峰流量较观测有偏大,但与观测的时间相关系数高达为0.89和0.91。研究对于山区暴雨洪水的预报预警和防汛决策具有一定的参考价值。     

Lessons learned from the 2013 Calgary flood: Assessing risk of drinking water well contamination

A devastating flood occurred in southern Alberta on June 19, 2013, from greater than normal snowfalls in the Rocky Mountains and excess precipitation during the early spring that left soils saturated and unable to absorb any additional precipitation. This flood was Canada's most costly natural disaster, with five to six billion Canadian dollars in damages. The first objective of this study was to determine if the flood caused an increase in private drinking water well contamination in the Calgary Health Zone by comparing contamination rates to previous years. The second objective was to determine which environmental factors were associated with contamination during this flood event. Test results of total coliforms (TC) and Escherichia coli (EC) of private water wells were used to determine contamination. A geographically weighted Poisson regression analysis suggested that TC contamination was not associated with this flood. The EC contamination is positively associated with floodways, flood fringe, farms, and negatively associated with intermittent water (sloughs). These results suggest that for the 2013 flood, individual well characteristics are more important than surrounding geographic features. Thus, it is recommended that homeowners who live in a high-risk area ensure their wells are properly maintained to reduce risk of water well contamination.     

Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model

This study presents a methodology for conducting sensitivity and uncertainty analysis of a GIS-based multi-criteria model used to assess flood vulnerability in a case study in Brazil. The paper explores the robustness of model outcomes against slight changes in criteria weights. One criterion was varied at-a-time, while others were fixed to their baseline values. An algorithm was developed using Python and a geospatial data abstraction library to automate the variation of weights, implement the ANP (analytic network process) tool, reclassify the raster results, compute the class switches, and generate an uncertainty surface. Results helped to identify highly vulnerable areas that are burdened by high uncertainty and to investigate which criteria contribute to this uncertainty. Overall, the criteria ‘houses with improper building material’ and ‘evacuation drills and training’ are the most sensitive ones, thus, requiring more accurate measurements. The sensitivity of these criteria is explained by their weights in the base run, their spatial distribution, and the spatial resolution. These findings can support decision makers to characterize, report, and mitigate uncertainty in vulnerability assessment. The case study results demonstrate that the developed approach is simple, flexible, transparent, and may be applied to other complex spatial problems.     

暴雨山洪灾害预警方法研究进展

暴雨山洪灾害预警是中小流域山洪灾害防控体系的薄弱环节,也是决定山洪灾害防控成败的关键。论文围绕山洪灾害预警的核心问题,从中国山洪灾害区域差异特征、山洪灾害预警技术方法、山洪灾害概率预警现状3个方面进行了综述。中国山洪灾害分布存在明显的时空差异,因此有必要根据山洪灾害的区域差异发展有针对性的预警方法。以临界雨量为指标的雨量预警是目前中国中小流域暴雨山洪灾害预警的主要技术手段,但常规方法仅给出一个(组)确定的临界雨量阈值,导致预警结果存在突出的不确定性问题。概率预警可以定量评估诸多不确定性,给出山洪灾害概率预警结果,因此具备很好的理论优势与潜在应用价值。论文展望了山洪灾害概率预警未来的研究重点与方向：(1)充分挖掘暴雨洪水样本信息,开展山洪灾害概率预警基础方法与技术集成研究;(2)加强非平稳性条件下的临界雨量阈值估算与山洪灾害概率预警研究;(3)综合考虑预警阈值发生概率及其致灾概率,优化“多级预警、多级响应”技术方法,推进山洪灾害综合预警业务系统建设与应用。     

利用GIS技术实现山洪易泛区地貌学判识的实践探讨--以云南省为例

由于受山区特殊的地貌特征控制，山洪的空间分布广泛而又相对分散，具有发灾突然、空间尺度小、分布数量多、成灾迅速等特点，要在空间上正确表达区域性的山洪泛滥可能的影响范围有相当大的难度。以云南省为例，探讨了以地貌学法为出发点，借助GIS工具提取山洪易泛区范围信息的可行性。山洪易泛范围的判识方法可为区域规划和建设规划人员和工程人员提供有价值的工具。     

基于“双评价”集成的国土空间地域功能优化分区

“双评价”（资源环境承载能力和国土空间开发适宜性评价）作为构建国土空间的基本战略格局、实施功能分区的科学基础,为主体功能区降尺度传导、国土空间结构优化、国土开发强度管制等提供了一系列重要参数。以地域功能理论为基础,从人地关系演化出发,探索“双评价”的理论内涵,建立“双评价”到地域功能优化分区的科学逻辑,指出“双评价”集成实现综合效益最大化时所形成的主体功能分区方案为地域功能优化分区的最优方案。以福建省、六盘水市为案例,将人类生产生活的合理需求转化为用地需求参数,通过降尺度的参数分解与测算,结合上位规划及政府与专家系统研判,确定总量控制、结构化控制、空间结构或战略格局控制等目标参数,并在“双评价”基础上,通过不断调整指标、参数及阈值,逐步进行格网单元地域功能优化。着重考虑相邻区域功能冲突与协调、差异化的主体功能定位、土地利用现状及规划、区域发展战略格局以及海陆统筹等区域特征,不断校核与优化。从数据的可获取性、数据集满足理论模型及评估方法的适应程度、可使用的分析方法及模型的不确定性以及尺度效应等方面,探讨功能分区方案不确定性及其解决途径,增强优化结果的鲁棒性,以此作为“三区三线”划定等国土空间规划的科学基础。     

Optimization of SAGD Well Elevation

The application of steam-assisted gravity drainage (SAGD) to recover heavy oil sands is becoming increasingly important in the northern Alberta McMurray Formation because of the vast resources/reserves accessible with this mechanism. Selecting the stratigraphic elevations of SAGD well pairs is a vital decision for reservoir evaluation and planning. The inherent uncertainty in the distribution of geological variables significantly influences this decision. Geostatistical simulation is used to capture geological uncertainty, which is used can be transformed into a distribution of the best possible well pair elevations. A simple exhaustive calculation scheme is used to determine the optimum stratigraphic location of a SAGD well pair where the recovery R is maximized. There are three basic steps to the methodology: (1) model the uncertainty in the top continuous bitumen (TCB) and bottom continuous bitumen (BCB) surfaces, (2) calculate the recovery at all possible elevation increments within the TCB and BCB interval, and (3) identify the elevation that maximizes R. This is repeated for multiple TCB/BCB pairs of surfaces to assess uncertainty. The methodology is described and implemented on a subset of data from the Athabasca Oilsands in Fort McMurray, Alberta.     

SCE-UA算法在流溪河模型参数优选中的应用

流溪河模型是一个主要用于流域洪水预报的分布式物理水文模型,目前采用手工试错法优选模型参数,虽然该方法在过去的研究中取得了较好的效果,但参数优选过程较为繁琐,需时较长.文中以广州市流溪水库流域为例,采用SCE-UA算法对流溪河模型优选参数,并使用优选结果进行模拟检验,取得了较好的效果.研究表明,SCE-UA算法能够快速、有效地进行流溪河模型参数的优选,相比手工试错法具有简单、方便、高效的特点,可应用于分布式流域水文模型的参数自动化优选工作.     

丹江口水库向白洋淀补水生态调度方案研究

白洋淀是华北平原最大的淡水湖泊。近年来,入淀水量和降水量持续减少导致淀水位持续下降。尽管国家实施了多次的生态补水调度,但由于缺乏长效补水机制,白洋淀生态环境恶化趋势仍没有得到有效控制。南水北调中线一期工程的建设,使利用丹江口水库向白洋淀补充生态环境用水成为可能。本文通过研究丹江口水库不同运行调度方案,探讨了利用水文和气象预测、预报信息来指导水库生态调度的可行性。在分析生态预报调度方式内涵的基础上,结合丹江口水库相机补充白洋淀生态用水实例来说明该方法的实用性和可行性。研究表明,基于预报信息的生态优化调度方案,可使白洋淀多年平均补水量增加48%以上。     

Spatially-explicit sensitivity analysis of an agent-based model of land use change

The complexity of land use and land cover (LULC) change models is often attributed to spatial heterogeneity of the phenomena they try to emulate. The associated outcome uncertainty stems from a combination of model unknowns. Contrarily to the widely shared consensus on the importance of evaluating outcome uncertainty, little attention has been given to the role a well-structured spatially explicit sensitivity analysis (SSA) of LULC models can play in corroborating model results. In this article, I propose a methodology for SSA that employs sensitivity indices (SIs), which decompose outcome uncertainty and allocate it to various combinations of inputs. Using an agent-based model of residential development, I explore the utility of the methodology in explaining the uncertainty of simulated land use change. Model sensitivity is analyzed using two approaches. The first is spatially inexplicit in that it applies SI to scalar outputs, where outcome land use maps are lumped into spatial statistics. The second approach, which is spatially explicit, employs the maps directly in SI calculations. It generates sensitivity maps that allow for identifying regions of factor influence, that is, areas where a particular input contributes most to the clusters of residential development uncertainty. I demonstrate that these two approaches are complementary, but at the same time can lead to different decisions regarding input factor prioritization.     

Practical Incorporation of Multivariate Parameter Uncertainty in Geostatistical Resource Modeling

There is a need to bridge theory and practice for incorporating parameter uncertainty in geostatistical simulation modeling workflows. Simulation workflows are a standard practice in natural resource and recovery modeling, but the incorporation of multivariate parameter uncertainty into those workflows is challenging. However, the objectives can be met without considerable extra effort and programming. The sampling distributions of statistics comprise the core theoretical notion with the addition of the spatial degrees of freedom to account for the redundancy in the spatially correlated data. Prior parameter uncertainty is estimated from multivariate spatial resampling. Simulation-based transfer of prior parameter uncertainty results in posterior distributions which are updated by data conditioning and the model domain extents and configuration. The results are theoretically tractable and practical to achieve, providing realistic assessments of uncertainty by accounting for large-scale parameter uncertainty, which is often the most important component impacting a project. A simulation-based multivariate workflow demonstrates joint modeling of intrinsic shale properties and uncertainty in estimated ultimate recovery in a shale gas project. The multivariate workflow accounts for joint prior parameter uncertainty given the current well locations and results in posterior estimates on global distributions of all modeled properties. This is achieved by transferring the joint prior parameter uncertainty through conditional simulations.     

基于洪涝情景模拟的城市公共服务灾害应急响应空间可达性评价——以医疗急救为例

在气候变化和城市化背景下,日益频发的洪涝灾害业已成为当前中国城市面临的重大挑战,也是城市灾害管理和应急响应研究的热点问题之一。本文旨在构建一套适用于城市尺度的洪涝灾害应急响应能力评估范式,以上海市外环以内中心城区为研究区,采用洪涝数值模拟与GIS网络分析相结合的研究方法,评估了当前以及未来正常条件和不同重现期洪涝情景下,城市关键公共服务部门（120急救）应急响应的空间可达性。结果显示：洪涝淹没强度（范围和水深）、道路交通状况（车流速度）以及应急服务机构的数量和位置共同决定了城市洪涝灾害医疗急救的服务范围及响应时间;由于上海市中心城区洪涝影响范围主要分布在黄浦江两岸2~3 km以内区域,因此洪涝对整个中心城区应急医疗服务的影响有限,主要是位于滨江地区部分医院的应急响应范围较正常状态下明显减少,120急救车辆无法或延迟达到部分救援点。研究表明基于洪涝情景模拟的城市公共服务应急响应空间可达性定量化评估方法,具有重要的科学价值和实践意义,可为中国城市洪涝灾害应急管理提供决策依据。     

Visualisation and evaluation of flood uncertainties based on ensemble modelling

This study evaluates how users incorporate visualisation of flood uncertainty information in decision-making. An experiment was conducted where participants were given the task to decide building locations, taking into account homeowners’ preferences as well as dilemmas imposed by flood risks at the site. Two general types of visualisations for presenting uncertainties from ensemble modelling were evaluated: (1) uncertainty maps, which used aggregated ensemble results; and (2) performance bars showing all individual simulation outputs from the ensemble. Both were supplemented with either two-dimensional (2D) or three-dimensional (3D) contextual information, to give an overview of the area.

The results showed that the type of uncertainty visualisation was highly influential on users’ decisions, whereas the representation of the contextual information (2D or 3D) was not. Visualisation with performance bars was more intuitive and effective for the task performed than the uncertainty map. It clearly affected users’ decisions in avoiding certain-to-be-flooded areas. Patterns to which the distances were decided from the homeowners’ preferred positions and the uncertainties were similar, when the 2D and 3D map models were used side by side with the uncertainty map. On the other hand, contextual information affected the time to solve the task. With the 3D map, it took the participants longer time to decide the locations, compared with the other combinations using the 2D model.

Designing the visualisation so as to provide more detailed information made respondents avoid dangerous decisions. This has also led to less variation in their overall responses.