The rate of penetration (ROP) refers to the speed at which a drill bit breaks through rock and deepens the drill hole. ROP is of great significance for drilling optimization and drilling cost savings. In real-world settings, the ROP data available for learning and training in a new oil field are scarce or even completely missing. In this paper, we propose a novel unsupervised multisource domain adaptation (MSDA) regression method for ROP that considers transferring the knowledge learned from a well-labelled source domain to the target domain with few labeled ROP data. First, we build a multisource unsupervised domain adaptation framework based on adversarial learning (WD-MUDA) which uses a weighted combination of multiple source domains to realize the fine-grained alignment of different data distributions. Specifically, we define a new similarity metric for different domains based on the Wasserstein distance. Furthermore, considering the uneven distribution of real drilling data samples, a novel regression loss is introduced to minimize the gradient discrepancy between multisource and target samples and improve the prediction accuracy of target samples. Extensive experiments on real drilling data sets reveal that the proposed method is effective and outperforms the state-of-the-art domain adaptation methods for ROP prediction.

You can access this article if you purchase or spend a download.