A Novel 2.5D Deep Network Inversion of Gravity Anomalies to Estimate Basement Topography

IF 2.1 4区 工程技术 Q3 ENERGY & FUELS SPE Reservoir Evaluation & Engineering Pub Date : 2023-06-01 DOI:10.2118/211800-pa
Zahra Ashena, Hojjat Kabirzadeh, J. W. Kim, Xin Wang, Mohammed Ali
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引用次数: 1

Abstract

A novel 2.5D intelligent gravity inversion technique has been developed to estimate basement topography. A deep neural network (DNN) is used to address the fundamental nonuniqueness and nonlinearity flaws of geophysical inversions. The training data set is simulated by adopting a new technique. Using parallel computing algorithms, thousands of forward models of the subsurface with their corresponding gravity anomalies are simulated in a few minutes. Each forward model randomly selects the values of its parameter from a set of predefined ranges based on the geological and structural characteristics of the target area. A DNN model is trained based on the simulated data set to conduct the nonlinear inverse mapping of gravity anomalies to basement topography in offshore Abu Dhabi, United Arab Emirates. The performance of the trained model is assessed by making predictions on noise-free and noise-contaminated gravity data. Eventually, the DNN inversion model is used to estimate the basement topography using pseudogravity anomalies. The results show the depth of the basement is between 7.4 km and 9.3 km over the Ghasha hydrocarbon reservoir. This paper is the 2.5D and improved version of the research (SPE-211800-MS) recently presented and published in the Abu Dhabi International Petroleum Exhibition & Conference (31 October–3 November 2022) proceedings.
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一种新的2.5维重力异常深度网络反演方法估算基底地形
开发了一种新的2.5维智能重力反演技术,用于估算基底地形。利用深度神经网络(DNN)来解决地球物理反演的基本非唯一性和非线性缺陷。采用一种新的技术对训练数据集进行模拟。利用并行计算算法,在几分钟内模拟了数千个地下正演模型及其对应的重力异常。每个正演模型根据目标地区的地质和构造特征,从一组预定义的范围中随机选择其参数的值。利用模拟数据集训练DNN模型,对阿联酋阿布扎比海域的重力异常与基底地形进行非线性逆映射。通过对无噪声和受噪声污染的重力数据进行预测,评估了训练模型的性能。最后,利用DNN反演模型利用伪重力异常估计基底地形。结果表明,尕沙油气储层基底深度在7.4 ~ 9.3 km之间。这篇论文是最近在阿布扎比国际石油展览会和会议(2022年10月31日至11月3日)上发表的研究(SPE-211800-MS)的2.5D和改进版本。
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来源期刊
CiteScore
5.30
自引率
0.00%
发文量
68
审稿时长
12 months
期刊介绍: Covers the application of a wide range of topics, including reservoir characterization, geology and geophysics, core analysis, well logging, well testing, reservoir management, enhanced oil recovery, fluid mechanics, performance prediction, reservoir simulation, digital energy, uncertainty/risk assessment, information management, resource and reserve evaluation, portfolio/asset management, project valuation, and petroleum economics.
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