使用多项式混沌进行煤层气产量预测的不确定性量化
Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos
AI总结：
本文利用多项式混沌方法构建代理模型，用于煤层气井峰值气体产量和累积开采量的估计，通过低误差验证了该方法在地质物理建模中的有效性。
摘要：
一个代理模型近似了计算成本高昂的求解器。多项式混沌是一种通过求和精心选择的多项式组合来构建代理模型的方法。这些多项式被选择以尊重不确定输入变量（参数）的概率分布；这允许进行不确定性和全局灵敏度分析。在本文中，我们应用这些技术到一个用于估计煤层气井峰值气体产量和累积气体开采量的商业求解器。多项式展开在与计算更复杂且计算速度更慢的商业求解器相比时，显示出较低的误差。我们利用先进的数值积分技术，通过相对少量的训练数据实现了这种准确性。
英文摘要：
A surrogate model approximates a computationally expensive solver. Polynomial Chaos is a method to construct surrogate models by summing combinations of carefully chosen polynomials. The polynomials are chosen to respect the probability distributions of the uncertain input variables (parameters); this allows for both uncertainty quantification and global sensitivity analysis. In this paper we apply these techniques to a commercial solver for the estimation of peak gas rate and cumulative gas extraction from a coal seam gas well. The polynomial expansion is shown to honour the underlying geophysics with low error when compared to a much more complex and computationally slower commercial solver. We make use of advanced numerical integration techniques to achieve this accuracy using relatively small amounts of training data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
