This study developed an analytical tool for the detection and characterization of scale buildup from well 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 existing scale buildup. Scale deposition causes tubing ID decreases and therefore results in production declines, so a sensitive approach to detect the scale deposition is valuable to reduce the damage and losses due to this problem. The underlying deep learning methods are both single- and multioutput, deep neural networks that consist of a combination of convolutional, long short-term memory (LSTM) and fully connected layers. We trained the networks on more than 30 sets of well data, with the objective of predicting the presence and the magnitude of scale. We started with cases of full scale deposition over the whole wellbore depth, and then extended our study to partial depth scale deposition. We built up a point-wise neural network model combining two blocks, which each contain several fully-connected layers followed by an LSTM layer specifically focusing on relatively smaller or larger tubing ID changes, corresponding to more or less scale deposition. To characterize the segmented scale deposition, we transformed to a three-dimensional problem, which can be solved by extracting the tubing ID changes and the scale deposition segment length. The multioutput model was able to predict the tubing ID and volume changes at the same time, using a combination of convolutional and LSTM layers with residual network blocks and updated using a loss function that we defined. Tubing ID changes were extracted accurately with metric R-square 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.
