Casing Damage Prediction Model Based on the Data-Driven Method

Abstract

Casing damage caused by sand production in unconsolidated sandstone reservoirs often results in oil wells unable to produce normally. However, due to the complex mechanism of sheath damage caused by sand production, there is no more mature technology for predicting the risk of casing damage in advance. Data-driven method can better integrate various factors and use a large amount of historical data to solve complex classification prediction problems. In this paper, XGBoost and LightGBM algorithms are used to establish casing damage prediction models, and 13 model application experiments are carried out to optimize the set of casing damage factors. These two algorithms are used to calculate the feature importance of each factor and determine the final set of factors. The evaluation results of five key metrics show that both prediction models show good performance, and the prediction accuracy is 0.99 for the XGBoost model and 0.94 for the LightGBM model. Applying the established prediction model can determine reasonable range of the maximum daily liquid production of a single layer (Qlmax) to reduce the probability of casing damage. In addition, at certain Qlmax, increasing the perforation density can significantly reduce the probability of casing damage. Therefore, increasing the perforation density can achieve high production without causing casing damage.