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    • 16. 发明授权
    • Proxy methods for expensive function optimization with expensive nonlinear constraints
    • 具有昂贵的非线性约束的昂贵功能优化的代理方法
    • US08670960B2
    • 2014-03-11
    • US12914270
    • 2010-10-28
    • Benoit CouetKashif RashidDavid WilkinsonSaumil AmbaniEren CetinkayaHugues Djikpesse
    • Benoit CouetKashif RashidDavid WilkinsonSaumil AmbaniEren CetinkayaHugues Djikpesse
    • G06F17/10G06F17/17
    • G06F17/11
    • A method for optimizing expensive functions with expensive nonlinear constraints. The method includes selecting sample data for evaluating an expensive function of a simulation, generating a function proxy model for the expensive function and a constraint proxy model for an expensive nonlinear constraint of the expensive function using an approximation scheme, calculating a first solution point for the simulation using the proxy models, and evaluating the expensive function at the first solution point using the sample data. When the expensive function and the proxy models do not converge at the first solution point, the method further includes adding the first solution point to the sample data for updating the proxy models. The method further includes repeating the calculation and evaluation of solution points until the expensive function and the proxy models converge and, following convergence, identifying an optimal solution of the function proxy model and the constraint proxy model.
    • 一种用昂贵的非线性约束优化昂贵功能的方法。 该方法包括选择用于评估模拟的昂贵功能的样本数据,为昂贵的函数生成功能代理模型和使用近似方案的昂贵功能的昂贵非线性约束的约束代理模型,计算第一解点 使用代理模型进行仿真,并使用样本数据评估第一个解决点处的昂贵功能。 当昂贵的功能和代理模型不会在第一个解决方案点收敛时,该方法还包括将第一个解决点添加到样本数据中以更新代理模型。 该方法还包括重复求解点的计算和评估,直到昂贵的函数和代理模型收敛,并且在收敛之后,识别功能代理模型和约束代理模型的最优解。
    • 17. 发明授权
    • Automated field development planning of well and drainage locations
    • 井场排水场自动化现场开发规划
    • US08005658B2
    • 2011-08-23
    • US11756244
    • 2007-05-31
    • Peter Gerhard TilkeWilliam J. BaileyBenoit CouetMichael PrangeMartin Crick
    • Peter Gerhard TilkeWilliam J. BaileyBenoit CouetMichael PrangeMartin Crick
    • G06F17/50G06G7/48G01V3/38G01V1/40
    • E21B43/30E21B41/00
    • A hybrid evolutionary algorithm (“HEA”) technique is described for automatically calculating well and drainage locations in a field. The technique includes planning a set of wells on a static reservoir model using an automated well planner tool that designs realistic wells that satisfy drilling and construction constraints. A subset of these locations is then selected based on dynamic flow simulation using a cost function that maximizes recovery or economic benefit. In particular, a large population of candidate targets, drain holes and trajectories is initially created using fast calculation analysis tools of cost and value, and as the workflow proceeds, the population size is reduced in each successive operation, thereby facilitating use of increasingly sophisticated calculation analysis tools for economic valuation of the reservoir while reducing overall time required to obtain the result. In the final operation, only a small number of full reservoir simulations are required for the most promising FDPs.
    • 描述了一种混合进化算法(“HEA”)技术,用于自动计算场和排水位置。 该技术包括使用自动化井计划工具在静态储层模型上规划一组井,以设计满足钻井和施工约束的现实井。 然后基于使用最大化恢复或经济效益的成本函数的动态流模拟来选择这些位置的子集。 特别是,使用成本和价值的快速计算分析工具最初创建了大量候选目标,排水孔和轨迹,随着工作流程的进行,每个连续操作中的人口规模减小,从而便于使用越来越复杂的计算 水库经济评估的分析工具,同时减少获得结果所需的总体时间。 在最后的操作中,最有希望的FDP需要少量的全油藏模拟。
    • 18. 发明申请
    • AUTOMATED FIELD DEVELOPMENT PLANNING
    • 自动化现场开发规划
    • US20100185427A1
    • 2010-07-22
    • US12356137
    • 2009-01-20
    • Peter Gerhard TilkeVijaya HalabeRaj BanerjeeTarek M. HabashyMichael ThambynayagamJeffrey SpathAndrew J. CarnegieBenoit CouetWilliam J. BaileyMichael David Prange
    • Peter Gerhard TilkeVijaya HalabeRaj BanerjeeTarek M. HabashyMichael ThambynayagamJeffrey SpathAndrew J. CarnegieBenoit CouetWilliam J. BaileyMichael David Prange
    • G06G7/48
    • E21B43/00
    • A system for automatically optimizing a Field Development Plan (FDP) for an oil or gas field uses a fast analytic reservoir simulator to dynamically model oil or gas production from the entire reservoir over time in an accurate and rapid manner. An objective function defining a Figure of Merit (FoM) for candidate FDPs is maximized, using an optimization algorithm, to determine an optimized FDP in light of physical, engineering, operational, legal and engineering constraints. The objective function for the Figure of Merit, e.g., net present value (NPV) or total production for a given period of time, relies on a production forecast from the fast analytic reservoir simulator for the entire FDP. The position, orientation and dimensions of analytical model elements for the subsurface oil or gas field, as well as the physical properties associated with these elements, correlate to connected flow volume data from a Shared Earth Model (SEM). Uncertainty in the SEM is considered via stochastic sampling. In the presence of uncertainty, the optimum Field Development Plan (FoM) is selected by maximizing an objective function defining a risk-based Figure of Merit for the entire FDP.
    • 用于自动优化油田或油田现场开发计划(FDP)的系统使用快速分析油藏模拟器,以准确和快速的方式随时间动态地模拟整个油藏的油气产量。 根据物理,工程,操作,法律和工程方面的约束,使用优化算法来定义候选FDP的优点图(FoM)的客观函数被最大化以确定优化的FDP。 品质图的目标函数,例如净现值(NPV)或给定时间段内的总产量依赖于快速分析储层模拟器对于整个FDP的生产预测。 地下油或气田分析模型元素的位置,方向和尺寸以及与这些元素相关的物理性质与共享地球模型(SEM)的连接流量数据相关。 SEM中的不确定度是通过随机抽样来考虑的。 在存在不确定性的情况下,通过最大化定义基于风险的整个FDP优点图表的目标函数来选择最佳现场开发计划(FoM)。