基于岩心图像和测井曲线的非均质油藏多重岩石物性回归机器学习

T. Lin, M. Mezghani, Chicheng Xu, Weichang Li
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引用次数: 0

摘要

储层表征需要准确预测多种岩石物性,如体积密度(或声阻抗)、孔隙度和渗透率。然而,由于溶蚀、白云化、胶结和压裂等成岩作用的影响,在非均质储层中,这仍然是一个巨大的挑战。在非均质地层中,大多数测井资料都缺乏获得岩石详细性质的分辨率。因此,将核心图像集成到预测工作流程中是有针对性的。该研究提出了一种新的方法,通过结合机器学习(ML)算法和计算机视觉(CV)技术来解决获得高分辨率多种岩石物性的问题。该方法可以使用最少的桥塞实现岩心数据分析过程的自动化,从而减少人力和成本,提高准确性。工作流程包括:调整和提取岩心图像的特征,将测井和岩心分析与这些特征相关联,建立机器学习模型,并将模型应用于新岩心,进行岩石物理性质预测。利用颜色模型和纹理识别对核心图像进行预处理和分析,提取图像特征和核心纹理。然后将图像特征聚合到深度剖面中,重新采样,并与测井曲线和岩心分析对齐。ML回归模型,包括分类与回归树(CART)和深度神经网络(DNN),通过过滤后的相关特征和目标岩石物性的训练样本进行训练和验证。然后在盲测数据集上对模型进行测试,以评估预测性能,预测目标岩石物性,如颗粒密度、孔隙度和渗透率。计算各目标属性的直方图轮廓,分析数据的分布。从岩心图像和伽马射线测井曲线的CV分析中提取特征向量。CART模型生成每个特征对单个目标的重要性,可用于降低未来模型构建的模型复杂性。在每个目标上对模型的性能进行了评价和比较。我们在模型上取得了较好的相关性和准确性,孔隙度R2=49.7%, RMSE=2.4 p.u,对数渗透率R2=57.8%, RMSE=0.53。现场实例表明,岩心图像属性的加入可以改善非均质储层的岩石物性回归。它可以扩展到多井设置,以生成岩石物性的垂直分布,并将其集成到储层建模和表征中。机器学习算法可以帮助自动化工作流程,并且可以灵活地调整以接受各种预测输入。
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Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir
Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow. This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions. The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53. The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.
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