| dc.description.abstract | Completion optimization is a process of identifying completion parameters (e.g., lateral length, number of entry points and sand intensity) that maximize economics. Completion optimization is difficult to accomplish due to large heterogeneity in unconventional rock properties and the high cost of trying new completion practices. On the other hand, optimal development of these unconventional resources has significant economic potential. Therefore, it is critical to develop methodologies to identify optimum completion practices. This research consists of two objectives: first, to develop methodologies to determine the economic optimum completion within and beyond the current industry practices in a low-permeability heterogeneous formation that requires horizontal wells and hydraulic fracturing to flow at commercial rates, and, second, to apply those methodologies to the Town field in the Town field in Montney formation in British Columbia, Canada. To achieve these objectives, I developed multivariable regression models for the entire Montney along with three subset fields (Town, Altares and Parkland). Then, I built a physics-based reservoir simulation model, calibrated it against the production type curve, and used it to predict well performance. Third, I defined 300 different completion designs and performed economic analysis on production forecasts generated with both the regression and simulation models. Finally, I identified the completion design that yields the maximum rate of return (ROR) and present value at 10% discount rate (PV10) as the optimum completion. For Altares and Parkland fields, there was no meaningful difference between the full and reduced models; however, for the Town field, the reduced dataset generated a better predictive model. I then focused on the 44-well Town field for the remainder of my study. Both multivariable regression and simulation models in the Town field do a poor job in predicting individual well performance. For the completion parameters that are well inside the boundary of current practices and toward the average designs, the regression model has less uncertainty and is more suitable. However, for completion parameters that are closer to the upper boundary of current practices, the simulation model does a better job in predicting well performance. The results show that within current completion practices in the industry, the two models suggest the same optimal completion design of 6,560 ft of lateral length, 942 lb/ft of sand intensity and 50 entry points, which is at the upper limit of current practices for all completion parameters. Beyond current completion designs, the multivariable regression and simulation models generate different optimal completion designs. In both methodologies, lateral length and number of entry points of the optimum completion designs (by ROR and PV10) are greater than the upper limits of values currently used in the Town field. The optimized sand intensities (ROR and PV10) using the regression method are considerably higher than the upper limit of sand intensities currently used in the Town field. In the simulation method, optimized sand intensities for ROR are within observed field practices and optimized sand intensities for PV10 are at the upper end and beyond current practices used in the Town field, depending on commodity prices. In summary, these results suggest that the overall optimal completion is a more aggressive completion than current industry practices in the Town field. | en |
