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Microseismic waveform and shear‐wave splitting analysis with model data
Geophysical Prospecting ( IF 2.6 ) Pub Date : 2021-03-19 , DOI: 10.1111/1365-2478.13089
Isabel White 1 , Matthew Bray 1 , James Simmons 1
Affiliation  

Accurate and precise estimation of microseismic event location in anisotropic reservoirs is challenging with borehole acquisition, due to limited azimuthal and inclination coverage. Inclusion of shear‐wave splitting information from microseismic data can improve estimates of event directivity, source location and the degree of reservoir anisotropy compared with using P‐waves alone. Additionally, S‐waves typically have a higher signal‐to‐noise ratio than P‐waves, due to higher amplitudes, decreasing the overall azimuthal and depth uncertainty in comparison with P‐wave only location methodologies. We present a joint P‐ and S‐wave hodogram workflow to reduce azimuthal uncertainty and estimate shear‐wave splitting. In this paper, finite‐difference waveform synthetics verify the workflow and illustrate interference effects associated with reflections and head‐waves. Multiple shear‐wave splitting techniques are applied to the synthetic data set in order to understand the limitations between splitting methodologies. Combining the P‐wave hodogram calculation of azimuth and inclination with the shear‐wave splitting analysis lowers the azimuthal uncertainty and improves waveform rotation results. From the comparison of three splitting methods, the minimum second eigenvalue is the most optimum for the data set. Post‐splitting analysis was key as the results are affected by the complexities in the waveforms and arrivals. Using receiver‐by‐receiver splitting quality analysis with shot and receiver clustering improves the identification of quality splitting results.

中文翻译:

利用模型数据进行微震波形和剪切波分裂分析

由于有限的方位角和倾角覆盖范围,对于井眼采集来说,准确,准确地估计各向异性储层中的微地震事件位置是一项挑战。与仅使用P波相比,从微地震数据中包含剪切波分裂信息可以改善事件方向性,震源位置和储层各向异性程度的估计。此外,与仅使用P波的定位方法相比,由于S波的幅度更高,通常具有比P波更高的信噪比,从而降低了总体方位角和深度不确定性。我们提出了一个联合的P波和S波直方图工作流程,以减少方位角不确定性并估计切变波分裂。在本文中,有限差分波形合成可以验证工作流程,并说明与反射和头波相关的干扰效应。多种剪切波分裂技术被应用于合成数据集,以了解分裂方法之间的局限性。结合方位角和倾斜度的P波直方图计算和剪切波分裂分析可降低方位角不确定性并改善波形旋转结果。通过三种分割方法的比较,最小第二特征值对于数据集是最佳的。分离后分析是关键,因为结果受波形和到达信号复杂性的影响。将发射器和接收器群集进行逐个接收器拆分质量分析,可以改善对质量拆分结果的识别。多种剪切波分裂技术被应用于合成数据集,以了解分裂方法之间的局限性。结合方位角和倾斜度的P波直方图计算和剪切波分裂分析可降低方位角不确定性并改善波形旋转结果。通过三种分割方法的比较,最小第二特征值对于数据集是最佳的。分离后分析是关键,因为结果受波形和到达信号复杂性的影响。将发射器和接收器群集进行逐个接收器拆分质量分析,可以改善对质量拆分结果的识别。多种剪切波分裂技术被应用于合成数据集,以了解分裂方法之间的局限性。结合方位角和倾斜度的P波直方图计算和剪切波分裂分析可降低方位角不确定性并改善波形旋转结果。通过三种分割方法的比较,最小第二特征值对于数据集是最佳的。分离后分析是关键,因为结果受波形和到达信号复杂性的影响。将发射器和接收器群集进行逐个接收器拆分质量分析,可以改善对质量拆分结果的识别。结合方位角和倾斜度的P波直方图计算和剪切波分裂分析可降低方位角不确定性并改善波形旋转结果。通过三种分割方法的比较,最小第二特征值对于数据集是最佳的。分离后分析是关键,因为结果受波形和到达信号复杂性的影响。将发射器和接收器群集进行逐个接收器拆分质量分析,可以改善对质量拆分结果的识别。结合方位角和倾斜度的P波直方图计算和剪切波分裂分析可降低方位角不确定性并改善波形旋转结果。通过三种分割方法的比较,最小第二特征值对于数据集是最佳的。分离后分析是关键,因为结果受波形和到达信号复杂性的影响。将发射器和接收器群集进行逐个接收器拆分质量分析,可以改善对质量拆分结果的识别。分离后分析是关键,因为结果受波形和到达信号复杂性的影响。将发射器和接收器群集进行逐个接收器拆分质量分析,可以改善对质量拆分结果的识别。分离后分析是关键,因为结果受波形和到达信号复杂性的影响。将发射器和接收器群集进行逐个接收器拆分质量分析,可以改善对质量拆分结果的识别。
更新日期:2021-05-17
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