Abstract

There is a need to integrate the advancements in various technologies for more efficient field applications related to real-time monitoring, minimizing failures and safety issues. Use of artificial intelligent (AI) in various applications with multiple variables are becoming popular. Cementing the oil wells have been used for over 200 years cementing failures during installation and other stages of operations have been clearly identified as some of the safety issues that have resulted in various types of delays in the cementing operations and oil production and also has been the cause for some of the major disasters around the world. Recently smart cement, highly sensing chemo-thermo-piezoresistive cement has been developed with a real-time monitoring system for oil well applications. The smart cement is a bulk sensor and there are no sensors buried in it.

In this study, laboratory and field test data were used to verify the Artificial Intelligent (AI) models with Vipulanandan models for smart cement applications. The performance of the smart cement in the cemented wells will be very much influenced by the hydration of the cement which is affected by the environment and ground geological conditions. Hence laboratory tests were performed to collect the data for AI model training and verification and also set the baseline for comparing it with the cement hydration in the field well. Electrical resistivity, a material property, has been selected to monitor the smart cement from the time of mixing to the entire service life. The resistivity changed by over 12.85 times (1285%) in 28 days under the room curing condition, indicating the sensitivity of resistivity for monitoring. The field well was installed using standard casing of  inches (245 mm) in diameter and was cemented using the smart cement with enhanced piezoresistive properties. The field well was designed, built, and used to demonstrate the concept of real time monitoring of the flow of smart cement and hardening of the cement in place. A new method has been developed to measure the electrical resistivity of the materials in the laboratory and field using the two probe method. Change in the resistance of hardening cement was continuously monitored since the installation of the field well for over 4.5 years (1500 days) and over 10,000 data have been collected. Also, the temperature and strain changes in the cement were measured at various depths. In addition, the pressure testing showed the piezoresistive response of the hardened smart cement.

Both laboratory and field data were used in this analyses. Total of over 1500 data were used in this study and 80% of the data were used for training the AI model and 20% of the data were used to verify the AI model predictions with Vipulanandan model prediction. Initially various AI models with multiple layers of artificial neural networks were first calibrated using the back propagation neural network (BPNN) and evaluated for the predictions of the remaining 20% of the test data. The predicted evaluation was done using the statistical parameters such as coefficient of determination (R²) and root mean square error (RMSE). The AI model didn’t predict the initial curing of the smart cement well, since resistivity reduced to a minimum value and then continuously increased. The AI models predicted the long-term laboratory smart cement curing and piezoresistive behavior and field data of resistivity changes with depth and time very well and were comparable to the Vipulanandan behavior models.