Summary This study developed an analytical tool for the detection and characterization of scale buildup based on well history data using deep learning methods. The developed method allows for a sensitive detection of the initiation of scaling as well as an accurate prediction of the magnitude of the scale buildup. The model is driven by two series of labeled, synthetic data comprising different scenarios generated using well simulation software, each series containing more than 20 sets of well data. Both single- and multioutput, deep neural networks were built with the objective of predicting the presence and the magnitude of scale deposition, with one corresponding to the full-depth scale deposition and the other to partial-depth scale deposition. For the full-depth scale deposition problem, we built a point-wise neural network model combining two blocks, each focusing on either relatively smaller or larger tubing internal diameter (ID) changes, corresponding to more or less scale deposition. Detection of full-depth scale deposition could obtain an accuracy of more than 95% while the metric R of more than 90% is assured when predicting the magnitude of full-depth scale deposition. To characterize the segmented scale deposition, which can be transformed to a 3D problem, we simplified this problem to capture both the tubing ID changes and the segment length. Then, we used the multioutput model to predict the tubing ID and volume changes, which combines two deep neural network models with a sharing part. Tubing ID changes were extracted accurately with metric R more than 90%, while the length of the scale deposit could be classified into two classes (high scaling or low scaling) with good accuracy. Though existing physical and chemical methods can be used to analyze scale deposition, the methods are often applied after considerable production decrease has already occurred. By using deep learning algorithms, our study came up with a new way to predict the scaling problem in advance with high sensitivity and at low cost.