In this paper, we present a methodology to predict the performance of horizontal gas wells in unconventional reservoirs using publicly available completion data. Our process combines public domain data with statistical analysis and probabilistic simulation methods to forecast well performance without a detailed reservoir characterization. We have tested our methodology using a 425-well dataset from the unconventional Montney resource play in British Columbia, Canada. We believe this workflow can be applied to other resource plays with similar data.
In our SPE Paper 167154 , we determined the sensitivity of production performance to completion parameters using multivariate regression analysis on the same 425-well dataset from the Montney formation. We found that the number of fracture stages and the number of perforation clusters per stage were the most influential predictors of well performance.
In this paper, we discuss how we combined the regression analysis results with probabilistic methods to predict well performance. The model converts the deterministic regression coefficients into probabilistic distributions to account for parameters not considered in the original regression analysis, including reservoir properties. The results of our study show that by using this model, we can match the range of actual well performance outcomes with a 95% confidence.
Considering the importance of shale gas resources to the North American energy supply and the difficulty of characterizing shale gas reservoirs, this methodology offers a distinct advantage by providing a predictive model for well performance without the need for a detailed reservoir characterization. This also could be a beneficial tool to use in scoping studies where high-level, rapid evaluation is required.