Probabilistic 3-D time-lapse inversion of magnetotelluric data: application to an enhanced geothermal system
Probabilistic 3-D time-lapse inversion of magnetotelluric data: application to an enhanced geothermal system
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DOI：
10.1093/gji/ggv406
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发表时间：
2015-12
影响因子：
2.8
通讯作者：
M. Rosas-Carbajal;N. Linde;J. Peacock;F. Zyserman;T. Kalscheuer;S. Thiel
中科院分区：
文献类型：
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作者：
M. Rosas-Carbajal;N. Linde;J. Peacock;F. Zyserman;T. Kalscheuer;S. Thiel
对深钻孔中注入和提取造成的质量转移进行地面监测，对于最大限度地提高石油、天然气和地热生产至关重要。感应电磁法，如大地电磁法，由于其大的穿透深度和对流体电导率和裂缝连通性变化的敏感性，对这些应用很有吸引力。在这项工作中，我们提出了一个3-D马尔可夫链蒙特卡罗反演的时间推移大地电磁数据的图像质量转移后，盐水注入。反演估计的后验概率密度函数产生的羽流，从而量化模型的不确定性。为了减少计算时间，我们的基础上减少勒让德矩分解的羽流的参数化。合成试验表明，我们的方法是有效的电阻率结构注入前是众所周知的。很好地恢复了烟羽的质心和扩散。然后，我们将我们的反演策略注入实验中增强地热系统在Paralana，南澳，并将其与3-D确定性时间推移反演。后者检索比实际注入间隔更浅的电阻率变化，而概率反演检索位于正确深度并以优先南北方向取向的羽流。为了解释时移数据，反演需要相对于基础模型的不切实际的大电阻率变化。我们认为，这是部分解释的基础模型中的时间推移的变化推断不明地下的异质性。
SUMMARY Surface-based monitoring of mass transfer caused by injections and extractions in deep boreholes is crucial to maximize oil, gas and geothermal production. Inductive electromagnetic methods, such as magnetotellurics, are appealing for these applications due to their large penetration depths and sensitivity to changes in fluid conductivity and fracture connectivity. In this work, we propose a 3-D Markov chain Monte Carlo inversion of time-lapse magnetotelluric data to image mass transfer following a saline fluid injection. The inversion estimates the posterior probability density function of the resulting plume, and thereby quantifies model uncertainty. To decrease computation times, we base the parametrization on a reduced Legendre moment decomposition of the plume. A synthetic test shows that our methodology is effective when the electrical resistivity structure prior to the injection is well known. The centre of mass and spread of the plume are well retrieved. We then apply our inversion strategy to an injection experiment in an enhanced geothermal system at Paralana, South Australia, and compare it to a 3-D deterministic time-lapse inversion. The latter retrieves resistivity changes that are more shallow than the actual injection interval, whereas the probabilistic inversion retrieves plumes that are located at the correct depths and oriented in a preferential north–south direction. To explain the time-lapse data, the inversion requires unrealistically large resistivity changes with respect to the base model. We suggest that this is partly explained by unaccounted subsurface heterogeneities in the base model from which time-lapse changes are inferred.