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Using learning models to capture dynamic complexity in petroleum exploration

机译:利用学习模型捕捉石油勘探中动态复杂性

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Large-scale petroleum projects requrie capital investments and operaitng decisions that stretch out over many years.Estimating hte porofitability of such vaentures is a complex task,usually requrieing significant time and effort.Firms typically produce spreadsheet-based cash-=flow models that provide extensive detail on hte costs assoicated with a project.THis paper asserts that decision-making aobut lage investments is imporved by foucsing less on such "detail complexity"- drilling down into data to achieve greater detail and precision-and more on dynmaic complexity.By dynamic complexity we mean the dynaics of how conditios will changhe over time and,equally important,how you cna manage a venture to adapt to sthose changing conditions as they emerge.
机译:大规模的石油项目Requrie Capital Investments和Operaitng决定在多年上延伸。这些令人令人华欲的HTE致密性是一个复杂的任务,通常需要大量的时间和努力。用于提供广泛的基于电子表格的Cash-=流量模型 关于HTE成本的细节与一个项目分解。这纸张声称,通过少讨论“细节复杂性” - 钻取数据以实现更详细和精度的决策制作AOBUT遗产投资,以实现更详细的细节和更精度 - 以及Dynmaic复杂性.by动态 复杂性我们的意思是Conditios如何随着时间的推移以及同样重要的,你如何管理冒险,以适应Sthose变化的条件。

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