当前位置: X-MOL 学术J. Pet. Sci. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Bayesian Deep Networks for absolute permeability and porosity uncertainty prediction from image borehole logs from brazilian carbonate reservoirs
Journal of Petroleum Science and Engineering ( IF 5.168 ) Pub Date : 2021-01-16 , DOI: 10.1016/j.petrol.2021.108361
Clécio R. Bom , Manuel Blanco Valentín , Bernardo M.O. Fraga , Jorge Campos , Bernardo Coutinho , Luciana O. Dias , Elisangela L. Faria , Márcio P. de Albuquerque , Marcelo P. de Albuquerque , Maury Duarte Correia

Image wirelogs, both acoustic and resistivity, encapsulate information regarding the studied reservoir’s petrophysical properties that are used to pursue a meaningful characterization of it. More recently, some authors used those image logs to estimate porosity and permeability using a traditional image processing pipeline or using a deep learning perspective. Among others, those quantities might be critical to determine the feasibility of a specific hydrocarbon reservoir exploitation. However, the use of such estimates are limited since it is not possible to assign uncertainty on those deterministic methods. Thus, their reliability can not be assessed. In this work, we propose a novel approach using Deep Learning and Bayesian methodologies to address the estimation of uncertainty quantification for both permeability and porosity simultaneously from the image logs. The current methodology can be used to derive probability density functions (PDFs) from each prediction. As a result, the PDFs were used to estimate the robustness and uncertainties of the predicted values. The current method achieved a correlation coefficient R2 higher than 99% in both porosity and permeability regression in the blind test sample, i.e., using a different set of data that was not applied in the training procedure.



中文翻译:

贝叶斯深层网络从巴西碳酸盐岩储层的图像井眼测井预测绝对渗透率和孔隙度不确定性

影像测井记录,包括声学和电阻率,都封装了有关所研究储层岩石物理特性的信息,这些信息用于对其进行有意义的表征。最近,一些作者使用这些图像测井数据,通过传统的图像处理管道或深度学习的观点来估计孔隙度和渗透率。其中,这些数量对于确定特定油气藏开发的可行性可能至关重要。但是,由于无法为这些确定性方法分配不确定性,因此使用此类估计数受到限制。因此,无法评估其可靠性。在这项工作中 我们提出一种使用深度学习和贝叶斯方法的新颖方法,以同时解决图像测井中渗透率和孔隙度不确定性量化的估计问题。当前的方法可用于从每个预测中得出概率密度函数(PDF)。结果,PDF被用于估计预测值的鲁棒性和不确定性。当前方法获得了相关系数[R2 比......高 99 盲测试样品中的孔隙度和渗透率回归都需要进行回归,即使用在训练程序中未应用的另一组数据。

更新日期:2021-01-18
down
wechat
bug