Abstract
One of the crucial steps of geological object exploration is interwell correlation. The correlation matches similar parts of different wells helping to construct geological models and assess hydrocarbon reserves. Today, a detailed interwell correlation relies on manual analysis of well-logging data: a process prone to significant time consumption and subjectivity. Alternative automation attempts include rule-based, classic machine learning, and more recent deep learning methods. However, most approaches are still of limited usage and inherit cons of manual correlation. We propose a method based on a deep learning model to solve the geological profile similarity estimation. Our similarity model takes well-logging data as input, constructs well representations and uses them to provide the similarity of wells. The developed algorithm enables extracting patterns and essential characteristics of geological profiles within the wells. We follow an unsupervised paradigm that allows us to utilize large pools of logging data available in the industry and does not rely on subjective labelling. For model testing, we used two open datasets originating in New Zealand and Norway. Our data-based similarity model has decent quality. For example, the accuracy of our model is 0.926 compared to 0.787 for a widespread gradient boosting baseline. The selected representation learning approach also provides high extrapolation capabilities to work for formations other than those used during training and start to work for limited amounts of available data, the experiments show.
NSTL