Under CopyrightKnauer, HenningHenningKnauerJamali, ShahinShahinJamaliWittig, VolkerVolkerWittigBracke, RolfRolfBracke2023-03-272023-03-272022https://publica.fraunhofer.de/handle/publica/439193https://doi.org/10.24406/publica-111910.24406/publica-1119The following research work deals with the topic of predicting and optimizing the rate of penetration (ROP) using artificial intelligence methods. Within the drilling process the ROP fundamentally describes the speed at which the drill bit penetrates and travels through the formation and can be used as a direct indicator to quantify the progress of the operation. Since there is a very high interest in the prediction and optimization of ROP within the drilling industry, various research works have been conducted in this field. The first section of this work gives an overview over the publications and research work conducted on this topic. The subsequent section focuses on the topic of deep drilling data availability and the creation of an extensive drilling database as a basis for future developments. The last section gives an insight into the development process of an artificial intelligence based ROP prediction model based on convolutional neural networks (CNN) and presents the preliminary results obtained.endrillingartificial intelligenceconference paper