Abstract
Cementing operations consists of placing cement slurries between casing-casing and casing-formation to achieve multiple objectives including zonal isolation, corrosion resistance, isolation of water bearing formations, etc. Cement slurries are tailored in the laboratory environment simulating downhole conditions to achieve the required placement times (i.e. thickening time) for the slurry to remain in a liquid state during pumping. This study presents an artificial intelligence model developed to predict the cement slurries thickening time based on historical experimental data, along with the ability of the model to continue learning based on the flow of output results.
Collected data sets of different input parameters were gathered and studied to estimate cement thickening time. Based on statistical analysis, only five inputs (Pressure (psi), measured depth (ft),bottom hole circulating temperature (°F), retarder additive concentration (%), and slurry density (ppg)) had a positive impact on the correlation coefficients between the inputs and target properties. A model was developed using Python programming language to predict the thickening time. The data was fed into the model to train and test the input factors impacting the target property using a neural network method.
The model expressed an integrated relationship between the training and validation data which resulted in an optimum learning performance revealed by a loss function plot. As a result, the scatter plot for prediction vs actual has illustrated the prediction of the thickening time with an average accuracy of 83% confirmed by the model box plot. The model validation consisted of employing a set of data to predict the thickening time for more than 11 slurries with an average absolute error of 6%.
Model implementation indicated that over 200 hours per month were saved due to reduced number of thickening time test iterations required to be run before executing the job which positively impacting operational efficiencies associated with cost, resource allocation, and investigation assessment. The outcome results from this model and the process used herein could be scaled up to consider other properties, i.e. ultra-sonic cement analyzer UCA, fluid loss, free fluid, etc.
