利用生成对抗网络和反向传播神经网络从突围数据预测最大水平主应力

IF 3.2 3区 工程技术 Q1 ENGINEERING, PETROLEUM SPE Journal Pub Date : 2023-10-01 DOI:10.2118/217970-pa
Bisheng Wu, Haoze Zhang, Shengshen Wu, Yuanxun Nie, Xi Zhang, Robert G. Jeffrey
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引用次数: 0

摘要

正确认识地应力的大小和方向对油气勘探具有重要意义。传统的破井方法直接利用岩石强度和井筒形状(即破井深度和宽度)信息来预测地应力,但难以准确描述破井形状与地应力的关系。本文提出了一种结合生成对抗网络(GAN)和反向传播神经网络(BPNN)的深度学习模型,用于从突破数据中预测最大水平主应力(MHPS)。首先,利用GAN生成一定数量的近似于原始数据的新训练数据,有效地提高训练数据的数量和质量。其次,使用GAN增强的训练数据来训练BPNN, BPNN根据井筒破裂几何形状预测MHPS。两个独立的模块,GAN和BPNN,分别使用训练数据来训练自己。这种双重深度学习模式确保可以找到原位应力和井筒破裂形状之间的潜在关系。为了检验该技术的可靠性,我们使用来自已发表文献的86组实验室数据来训练模型,并使用来自其他已发表文献的19组实验室数据来测试训练模型的预测性能。结果表明,该模型具有较好的预测精度,平均相对误差为4.76%。此外,这种结合GAN和BPNN的深度学习模型只需要几秒钟就可以在笔记本电脑上运行,从而为预测MHPS提供了有效和高效的工具。
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Prediction of the Maximum Horizontal Principal Stress from Breakout Data Using Generative Adversarial Networks and Backpropagation Neural Network
Summary A good understanding of the magnitude and direction of in-situ stresses is very important for oil and gas exploration. The conventional wellbore breakout method directly uses information about rock strength and wellbore shape (i.e., depth and width of breakout) to predict the in-situ stresses, but it is difficult to accurately describe the relationship between the breakout shape and the in-situ stresses. This paper presents a deep learning model, combining the generative adversarial networks (GAN) and backpropagation neural network (BPNN) to predict the maximum horizontal principal stress (MHPS) from breakout data. First, a GAN is used to effectively improve the quantity and quality of training data by generating a certain number of new training data that approximate the original data. Second, the training data enhanced by the GAN are used to train the BPNN, which predicts the MHPS based on wellbore breakout geometries. The two independent modules, the GAN and BPNN, use the training data to train themselves, respectively. This dual deep learning pattern ensures that the potential relationship between the in-situ stresses and wellbore breakout shape can be found. To examine the reliability of this technique, 86 sets of laboratory data from published literature are used to train the model, and 19 sets of laboratory data from other published literature are used to test the prediction performance of the trained model. The results show that the proposed model has good accuracy with an average relative error of 4.76% when predicting the MHPS. In addition, this deep learning model combining the GAN and BPNN requires only a few seconds to run on a laptop computer, thus providing an effective and efficient tool for predicting the MHPS.
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来源期刊
SPE Journal
SPE Journal 工程技术-工程:石油
CiteScore
7.20
自引率
11.10%
发文量
229
审稿时长
4.5 months
期刊介绍: Covers theories and emerging concepts spanning all aspects of engineering for oil and gas exploration and production, including reservoir characterization, multiphase flow, drilling dynamics, well architecture, gas well deliverability, numerical simulation, enhanced oil recovery, CO2 sequestration, and benchmarking and performance indicators.
期刊最新文献
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