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In the shale oil & gas industry, operators are looking toward big data analytics to optimize operations and reduce cost. In this paper, we mainly focus on how to assist operators in understanding the subsurface formation, thereby helping them make optimal decisions. A large number of geology reports and well logs describing the sub-surface have been accumulated over years. Issuing geology reports is more time consuming and depends more on the expertise of engineers than acquiring the well logs. To assist in issuing geology reports, we propose an encoder-decoder-based model to automatically generate rock descriptions in human-readable format from multivariate well logs. Due to the different formats of data, this task differs dramatically from image and video captioning. The challenges are how to model structured rock descriptions and leverage the information in multivariate well logs. To achieve this, we design a hierarchical structure and two forms of attention for the decoder. Extensive validations are conducted on public well data of North Dakota in the United States. We show that our model is effective in generating rock descriptions. The two forms of attention enable the provision of a better insight into relations between well-log types and rock properties with our model from a data-driven perspective.

In the shale oil & gas industry, operators are looking toward big data and new analytics tools and techniques to optimize operations and reduce cost. Formation evaluation is one of the most crucial steps before the fracturing operation. To assist engineers in understanding the subsurface and in turn make optimal operations, we focus on learning semantic relations between geology reports and well logs, which are collected during down-hole drilling. The challenges are how to represent the features of the geology reports and the well logs collected at measured depths and how to effectively embed them into a common feature space. We propose both linear and nonlinear (artificial neural network) models to achieve such an embedding. Extensive validations are conducted on public well data of North Dakota in the United States. We empirically discover that both geology reports and well logs follow a neighborhood property measured by geological distance. We show that this spatial information is highly effective in both the linear and nonlinear models and our nonlinear model with the spatial information performs the best among the state-of-the-art methods.