Digital construction of geophysical well logging curves using the LSTM deep-learning network

Abstract

A complete well logging suite is needed frequently, but it is either unavailable or has missing parts. The mudstone section is prone to wellbore collapse, which often causes distortion in well logs. In many cases, well logging curves are never measured, yet are needed for petrophysical or other analyses. Re-logging is expensive and difficult to achieve, while manual construction of the missing well logging curves is costly and low in accuracy. The rapid technical evolution of deep-learning algorithms makes it possible to realize the digital construction of missing well logging curves with high precision in an automated fashion. In this article, a workflow is proposed for the digital construction of well logging curves based on the long short-term memory (LSTM) network. The LSTM network is chosen because it has the advantage of avoiding the vanishing gradient problem that exists in traditional recurrent neural networks (RNNs). Additionally, it can process sequential data. When it is used in the construction of missing well logging curves, it not only considers the relationship between each logging curve but also the influence of the data from a previous depth on data at the following depth. This influence is validated by exercises constructing acoustic, neutron porosity, and resistivity logging curves using the LSTM network, which effectively achieves high-precision construction of these missing curves. These exercises show that the LSTM network is highly superior to the RNN in the digital construction of well logging curves, in terms of accuracy, efficiency, and reliability.