Predictability of well construction time with multivariate probabilistic approach

Abstract

Current univariate approach to predict the probability of well construction time has limited accuracy due to the fact that it ignores key factors affecting the time. In this study, we propose a multivariate probabilistic approach to predict the risks of well construction time. It takes advantage of an extended multi-dimensional Bernacchia–Pigolotti kernel density estimation technique and combines probability distributions by means of Monte-Carlo simulations to establish a depth-dependent probabilistic model. This method is applied to predict the durations of drilling phases of 192 wells, most of which are located in the Australia- Asia region. Despite the challenge of gappy records, our model shows an excellent statistical agreement with the observed data. Our results suggested that the total time is longer than the trouble-free time by at least 4 days, and at most 12 days within the 10%–90% confidence interval. This model allows us to derive the likelihoods of duration for each phase at a certain depth and to generate inputs for training data-driven models, facilitating evaluation and prediction of the risks of an entire drilling operation.

Keywords

• well construction time
• multivariate probabilistic modelling
• probabilistic approach
• Markov Chain Monte-Carlo