Abstract
Building empirical models to estimate formation properties from logging curves is a key task in logging formation evaluation, which can be implemented with numerous statistical or machine learning regression algorithms. The majority of existing works are limited in the paradigm of point-to-point mapping, which implicitly assumes the relationships between logging responses and formation properties can be represented by simple pointwise mapping models. However, since logging responses of a specified measuring point are comprehensive reflections of formations near it, the above assumption is not true in fact. In this study, through the introduction and combination of deep learning techniques such as end-to-end deep network, mask loss, total variation regularization, and multi-task learning, we develop an algorithm framework for more accurate core calibrated formation property prediction. Algorithms in the proposed framework take sequences of logging responses as input, and simultaneously predict sequences of multiple formation properties with the same length. This not only makes it possible to utilize the sequential dependence and morphological features of input logging response curves, but also highlights the morphological consistency and the inner correlation among predicted property sequences. The proposed framework is realized into three algorithms, namely, the multi-task fully convolutional neural network (MtFCNN), the multi-task long short term memory network (MtLSTM), and the multi-task gated recurrent unit network (MtGRUN). Advantages of these algorithms are fully confirmed by comparative experiments and real-world applications, while their abilities to utilize morphological features of logging curves, reduce the risk of overfitting, and emphasize the inner correlation among multiple formation properties are also substantiated. We believe the proposed algorithms have provided improvements for more accurate borehole formation property prediction, while the introduced techniques will also inspire future studies on machine learning assisted logging formation evaluation.
NSTL