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首页> 外文期刊>Stochastic environmental research and risk assessment >Stochastic inverse method for estimation of geostatistical representation of hydrogeologic stratigraphy using borehole logs and pressure observations
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Stochastic inverse method for estimation of geostatistical representation of hydrogeologic stratigraphy using borehole logs and pressure observations

机译：利用钻孔测井和压力观测资料估计水文地质地层地统计学表示的随机反演方法

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摘要

An approach is presented for identifying statistical characteristics of stratigraphies from borehole and hydraulic data. The approach employs a Markov-chain based geostatistical framework in a stochastic inversion. Borehole data provide information on the stratigraphy while pressure and flux data provide information on the hydraulic performance of the medium. The use of Markov-chain geostatistics as opposed to covariance-based geostatistics can provide a more easily interpreted model geologically and geometrically. The approach hinges on the use of mean facies lengths (negative inverse auto-transition rates) and mean transition lengths (inverse cross-transition rates) as adjustable parameters in the stochastic inversion. Along with an unconstrained Markov-chain model, simplifying constraints to the Markov-chain model, including (1) proportionally-random and (2) symmetric spatial correlations, are evaluated in the stochastic inversion. Sensitivity analyses indicate that the simplifying constraints can facilitate the inversion at the cost of spatial correlation model generality. Inverse analyses demonstrate the feasibility of this approach, indicating that despite some low parameter sensitivities, all adjustable parameters do converge for a sufficient number of ensemble realizations towards their "true" values. This paper extends the approach presented in Harp et al. (doi:10.1029/2008GL033585,2008)rnto (1) statistically characterize the hydraulic response of a geostatistical model, thereby incorporating an uncertainty analysis directly in the inverse method, (2) demonstrate that a gradient-based optimization strategy is sufficient, thereby providing relative computational efficiency compared to global optimization strategies, (3) demonstrate that the approach can be extended to a 3-D analysis, and (4) introduce the use of mean facies lengths and mean transition lengths as adjustable parameters in a geostatistical inversion, thereby allowing the approach to be extended to greater than two category Markov-chain models.
机译：提出了一种从井眼和水力数据中识别地层统计特征的方法。该方法在随机反演中采用了基于马尔可夫链的地统计框架。钻孔数据提供有关地层的信息，而压力和通量数据则提供有关介质的水力性能的信息。与基于协方差的地统计学相比，使用马尔可夫链地统计学可以提供更容易解释的地质和几何模型。该方法依赖于在随机反演中使用平均相长（负自反逆转换速率）和平均过渡长度（逆交叉逆转换速率）作为可调参数。与无约束的马尔可夫链模型一起，在随机反演中评估了简化的马尔可夫链模型的约束，包括（1）比例随机和（2）对称空间相关。敏感性分析表明，简化约束可以以空间相关模型的普遍性为代价，促进反演。逆向分析证明了该方法的可行性，表明尽管参数敏感性较低，但所有可调整参数的确会收敛到足够数量的集合实现朝向其“真实”值。本文扩展了Harp等人提出的方法。 （doi：10.1029 / 2008GL033585,2008）rnto（1）在统计学上表征地统计模型的水力响应，从而将不确定性分析直接纳入逆方法中，（2）证明基于梯度的优化策略已足够，从而提供了与全局优化策略相比的相对计算效率，（3）证明该方法可以扩展到3-D分析，并且（4）引入平均相长度和平均过渡长度作为地统计反演中的可调参数，从而允许将方法扩展到两个以上的马尔可夫链模型。

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