Production forecast and optimization for parent-child well pattern in unconventional reservoirs

Abstract Fracture interference has been frequently observed between the parent wells (i.e., the pre-existing wells) and the child wells (i.e., infill wells) in the unconventional tight/shale reservoirs with massive hydraulic fractures. Such interference usually decreases the expected productivity and capital efficiency of the child wells due to the overlapped drainage areas of the parent and child wells. In this study, a new procedure using machine learning (ML) methods is proposed to maximize the productivity of the child wells in Montney formation. More specifically, three types of parent-child well patterns are first defined based on production and trajectory and a comprehensive dataset is developed to include location information on the wells, fracturing parameters, and production history. Then, various strategies are employed to eliminate the redundant and nonsignificant variables and four ML methods including neural networks, random forests, gradient boosting decision trees and linear regression are introduced to predict the first-year production of child wells. Performances of the four ML methods are evaluated with anti-overfitting strategy and the random forests (RF) is found to be the most suitable one for this issue. Finally, a fine-tuned random forest model is applied to optimize the usage of proppant, the volume of fluid injected to get higher productivity of parent-child mode. In addition, the correlation among well spacing and first-year barrels of oil equivalent (BOE) of child well is also analyzed. The calculation results indicated that fracture interference decreases as the spacing increases and the recommend one is above 200 m. The results are also consistent with some actual observation data reported, which further demonstrates the feasibility and credibility of the method adopted.