Cement Sheath Fatigue Failure Prediction by Support Vector Machine Based Model

Abstract

Abstract Zonal isolation is significant for safety operation of the well. Failure to keep wellbore integrity can lead to sustained annulus pressure (SAP), and gas migration (GM), which may cause long non-productive time. Losing zonal isolation can cause severe environmental issue, which is irreversible and detrimental. However, cement sheath is exposed to temperature and pressure changes from the beginning of the drilling process to the whole life of the well. These cyclic changes can lead to fatigue failure of the cement. The objective of this study is to investigate the fatigue failure that caused by cyclic changing of temperature and pressure during life of the well. The scope of the study is based on the laboratory fatigue failure cases in previous literatures. Instead of using mechanical failure models, support vector machine (SVM) model is used to predict the fatigue failure of the cement sheath. The data is gathered from six papers of One-Petro, which includes 325 laboratory cement fatigue failure cases. The model has fourteen inputs. Seven cement related factors were selected, which include cement type, additive material, Uniaxial Confining Strength (UCS), curing temperature, curing pressure, curing age, and Young's modulus. Seven experimental related factors, which include highest inner pressure, loading increment rate, frequency of loading, experimental temperature, confining pressure, existence of outer confining part, and cycles to reach failure. The SVM model is implemented by Python. We investigated 240 combinations of input groups and selected the best performance SVM model. The classification result is zero for no fatigue failure, and one for failure. The accuracy for the SVM model is 72.7%, which shows that SVM can be an acceptable model for cement fatigue prediction. The SVM model we proposed is more applicable for real implementation. Because we used real wellbore geometry data (thick wall geometry). Although the data were based on laboratory result, the SVM model provides a helpful method in predicting cement-sheath-failure. This study provides a data based method to predict cement fatigue failure under cyclic changing pressure and temperature. The result will be instructive for the cement design and wellbore operation optimization.