Estimating pore pressure in tight sandstone gas reservoirs: A comprehensive approach integrating rock physics models and deep neural networks

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Journal of Applied Geophysics Pub Date : 2024-09-24 DOI:10.1016/j.jappgeo.2024.105526
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Abstract

Pore pressure serves as an important driving power for subsurface fluid migration and therefore has a significant impact on gas accumulation and enrichment in tight sandstone reservoirs. Tight gas is typically produced in overpressure regions, where pressure coefficients are notably elevated. Thus, it is crucial to establish an effective methodology for precise pore pressure estimation. This study introduces an approach to improve pore pressure prediction by incorporating rock physical modeling and deep neural networks (DNNs) into the classical Eaton method. Compared to conventional techniques relying on empirical correlations between pressure coefficients and elastic properties, the proposed method considers the influence of porosity, fluids, and lithology, which could enhance reliability in pore pressure prediction. Meanwhile, a prediction model is developed using logging data and DNNs to estimate mineralogical volumetric fractions based on elastic properties. This prediction model allows improved estimation of rock matrix elastic properties using seismic-inverted data, which is crucial for estimating normal compaction velocity to extend pore pressure prediction from individual boreholes to the whole study area. Real data applications demonstrate that the predicted pressure coefficients derived from seismic data using the method presented in this paper align well with the gas enrichment estimated in previous studies for the tight sandstone reservoirs. Furthermore, regions with high values of pressure coefficients correspond to high gas content. These findings validate the effectiveness of the proposed methodology, which can provide valuable insights for identifying potential tight sandstone reservoirs.
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估算致密砂岩气藏的孔隙压力:整合岩石物理模型和深度神经网络的综合方法
孔隙压力是地下流体迁移的重要驱动力,因此对致密砂岩储层中的气体积累和富集有重大影响。致密气通常产于压力系数明显升高的超压区。因此,建立精确估算孔隙压力的有效方法至关重要。本研究介绍了一种通过将岩石物理建模和深度神经网络(DNN)融入经典伊顿法来改进孔隙压力预测的方法。与依赖压力系数和弹性性质之间经验相关性的传统技术相比,所提出的方法考虑了孔隙度、流体和岩性的影响,可以提高孔隙压力预测的可靠性。同时,利用测井数据和 DNNs 开发了一个预测模型,以根据弹性特性估算矿物体积分数。该预测模型利用地震反演数据改进了对岩石基质弹性性质的估算,这对于估算正常压实速度至关重要,从而将孔隙压力预测从单个钻孔扩展到整个研究区域。实际数据应用表明,利用本文介绍的方法从地震数据中得出的预测压力系数与以往研究中对致密砂岩储层的天然气富集度估算结果非常吻合。此外,压力系数高的区域对应着高含气量。这些发现验证了所提方法的有效性,可为识别潜在致密砂岩储层提供有价值的见解。
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来源期刊
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
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