Full text loading...
The occurrence of geomechanical events (e.g. drag, stall, kick) might hinder production and incur in losses. In this context, manual classification of daily drilling reports is a daunting task. In this work, we propose a method -grounded in the In-Context Learning paradigm - that leverages a commercial Large Language Model to classify daily drilling reports so that we enrich the prompt fed to the LLM to boost its performance. Experimental results attest the effectiveness of our approach comapred to other three variants. This work might help the oil industry in extracting valuable information from large amount of data in order to mitigate losses and to support data driven decision making.