Calculating Invisible Loss Time (ILT) Index Values & Predictive Analysis Using Bayesian Approach to Improve Drilling Operational Efficiency: Adopting Best Practices

Abstract Contribution in reducing Invisible Loss Time (ILT) towards operational efficiency has been a significant approach towards adopting best practices in drilling operations but predicting ILT using mathematical models accurately has been an additional challenge due to multiple factors contributing towards ILT. This paper sketches an algorithm for calculating the ILT Index value and then uses the Bayesian approach for predicting the invisible loss time (ILT) index value over other existing statistical methods. Digital Oilfield architecture processes along with data management systems have worked in synchrony for streaming data for analytics in real-time well engineering solutions. Defined KPIs of Invisible Loss Time (ILT) were evaluated in small sets of datasets with a Bayesian Optimisation approach using a probability model for the likeness of event occurrence. The performance of this model is evaluated based on a comparison of actual vs predicted ILT index values. Benchmarked (BM) values were calculated based on the best performance for the quarter, month, and week to understand the randomness of values. Real-time data generated were packeted for small sets as per KPIs defined for probability analysis. Sets of data made available for calculation were used to feed in the probability model for forecasting the values. Results from the predictive models showcased that batch drilling activities had a significant reduction in variance amongst the ILT values. Reduced ILT while operational activity due to adaptive learning can be calculated to quantify that cost component. Weighted percentages of KPIs in decreasing order of their significance were calculated.