Multi-view and multi-scale super-resolution method of logging curves based on fractal theory

With the increasing development of unconventional reservoirs around the world, there is an increasing need to enhance the level of geological characterization. Obviously, since the well loggings are one of the most important data to obtain a fine-scale reservoir model, obtaining well loggings with sufficiently high vertical resolution has always been an important issue and challenge. Therefore, due to its low cost and less time-consuming, employing an advanced signal processing technique to enhance the vertical resolution of loggings has always been a research hotspot in the relevant literature. However, non-homogeneity of the target reservoir is not taken into account in the traditional methods such as vertical resolution matching and spline function interpolation. Furthermore, for state-of-the-art methods that employ different versions of shallow or deep machine learning models, how to adequately exploit the multi-scale shape and temporal information in the employed loggings has been a very challenging problem. To address the above problems, by combining the fractal theory with the long short-term memory network technique, a novel multi-view and multi-scale logging resolution enhancing method was proposed in this paper to make full use of the self-shape-similarity and temporal correlation information in the logging data. Experimental results show that, compared with bi-cubic linear interpolation, sparse representation, super-resolution convolutional neural network and random forest, more promising supper-resolution results can be obtained using the proposed method.