论文标题
使用自动学习和不确定性定量的光电子因素预测
Photoelectric Factor Prediction Using Automated Learning and Uncertainty Quantification
论文作者
论文摘要
光电子因子（PEF）是区分不同类型的储层岩石的重要井记录工具，因为PEF测量对高原子数的元素敏感。此外，可以通过将PEF日志与其他井对数结合来确定岩石矿物的比率。但是，在某些情况下，PEF日志可能会丢失，例如在旧的井木和井中用少量的泥浆钻孔。因此，在这种情况下，开发用于估计缺失PEF日志的模型至关重要。在这项工作中，我们开发了各种机器学习模型，以使用以下井日志作为输入来预测PEF值：散装密度（RHOB），中子孔隙率（NPHI），伽马射线（GR），压缩和剪切速度。 使用自适应网络模糊推理系统（ANFI）和人工神经网络（ANN）模型对PEF值的预测分别在测试数据集中的误差约为16％和14％的平均绝对百分比误差（AAPE）。因此，提出了一种基于自动化机器学习概念的不同方法。它通过自动搜索最佳模型类型并优化正在研究的数据集的超参数来起作用。该方法选择了高斯过程回归（GPR）模型以准确估计PEF值。开发的GPR模型将测试数据集中预测的PEF值的AAPE降低至约10％AAPE。通过使用GPR模型对测量中的潜在噪声进行建模，可以进一步降低到约2％。
The photoelectric factor (PEF) is an important well logging tool to distinguish between different types of reservoir rocks because PEF measurement is sensitive to elements with high atomic number. Furthermore, the ratio of rock minerals could be determined by combining PEF log with other well logs. However, PEF log could be missing in some cases such as in old well logs and wells drilled with barite-based mud. Therefore, developing models for estimating missing PEF log is essential in those circumstances. In this work, we developed various machine learning models to predict PEF values using the following well logs as inputs: bulk density (RHOB), neutron porosity (NPHI), gamma ray (GR), compressional and shear velocity. The predictions of PEF values using adaptive-network-fuzzy inference system (ANFIS) and artificial neural network (ANN) models have errors of about 16% and 14% average absolute percentage error (AAPE) in the testing dataset, respectively. Thus, a different approach was proposed that is based on the concept of automated machine learning. It works by automatically searching for the optimal model type and optimizes its hyperparameters for the dataset under investigation. This approach selected a Gaussian process regression (GPR) model for accurate estimation of PEF values. The developed GPR model decreases the AAPE of the predicted PEF values in the testing dataset to about 10% AAPE. This error could be further decreased to about 2% by modeling the potential noise in the measurements using the GPR model.
