Abstract

Since the early 1920s, sensors have been used on drilling rigs to enable making operations decisions. Drilling operators have always relied on human intervention whenever the data were questionable. Automation, remote operations, smart systems and digital technology are all words which describe the recent directional change for well construction. These changes can have a huge impact on the efficiency, safety and cost of operations. The drilling industry needs real time validation of data. With the volume of data being generated on today's rigs, this requires measures beyond human capacity. There are many gaps which need to be addressed if the industry is to make this standard in the future. For example, to make automation to work, the data fed into the smart systems for decision making must be cleaned, corrected and calibrated.

This paper presents how machine learning techniques from other industries can be modified and applied to the data from drilling rig sensors. There are numerous strategies employed in medical, space and government agencies where validating data has been important to success. Of these strategies, a few were selected for drilling operation applications. An auto-encoder was used to study low dimensional data representations in an unsupervised manner. This algorithm was then adjusted to allow for partial reconstruction and handling processed drilling data. Reconstruction errors were used as a metric to identify potential errors and highlight them for the domain experts. Additionally, to identify errors, artificial potential errors were injected into the system and then the system was tested to understand if the errors could be identified by our methods. The results show that we can correctly identify anomalies such as missing data, outliers and sensor drift, using reconstruction errors. The model presented in this paper is based on drilling data from thousands of wells and tested on additional data as well as simulated data.

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