Abstract
In the oil and gas industrial field, fracturing construction technology is commonly used for increasing oil-gas production. A major concern in fracturing construction is whether the pressure at the wellhead will exceed safety threshold, when a large amount of sand-containing fluid is periodically injected into a well. To avoid accidents like blowout caused by extreme wellhead pressure, it is critically important to monitor pressure change in real time. In this work we propose to predict the pressure peaks during each fracturing period, for better designing future fracturing strategy. Towards this end, we present a novel non-parametric graph regression method, which is able to model the correlation of historical pressure peaks and learn the features of fracturing cycles via Laplacian-smoothness based graph learning, whereby the periodic fracturing signals, namely, fracking fluid concentration sequence, together with peaks, are able to be encoded in a latent Euclidean space by Laplacian-smoothness inspired graph learning. Then we introduce non-parametric linear regression for peak prediction based on the most similar nodes (e.g. peaks) w.r.t the node in the predicting period. Meanwhile, the current graph is updated using the predicted peak value. In the graph update, we particularly introduce a node forgetting mechanism to control the graph scale and to reduce the computational complexity, so as to achieve rapid prediction of construction operations. We conduct extensive experiments on real-world datasets from a well. The experimental results demonstrate that the proposed method can effectively predict the oil pressure peak and significantly outperform the state-of-the-art models.
NSTL