Abstract
This paper will describe a new method to facilitate optimization of hydraulically fractured horizontal Bakken completions. A key component of this method is a predictive data-driven model developed from a detailed evaluation of over 50 horizontal Bakken completions. Using this model, production predictions and economic optimization can be quickly performed using only data obtained during lateral drilling operations. This type of modeling and optimization of new horizontal completions fits well with a factory mode (rapid pace of well completion and hydraulic fracturing) of resource development.
The need to create large amounts of fracture area and the associated costs create an opportunity for economy of scale. Unfortunately, the time and expense required performing logging runs to gather data, and estimate reservoir and rock properties to facilitate completion design and optimization reduces operational efficiency and increases well cost. A data-driven approach to optimizing hydraulically fractured horizontal completions does not reduce operational efficiencies since it can be developed from common data and information obtained during drilling operations.
The data-driven model shows that reservoir quality, as one would expect, has the dominate effect on Bakken production. In addition hydraulic fracture design/methodology, fracture spacing, proppant selection, frac staging methods, frac compartment isolation, drillout/cleanup procedures, wellbore length and orientation have a significant effect on well production and economics. Several case histories will be included in which data-driven modeling was used to evaluate various completion/frac scenarios, predict production and facilitate completion/frac optimization.
The challenge of optimizing horizontal Bakken completion and frac design (30 or more hydraulic fracturing treatments per well) in a fast paced factory mode of resource development is problematic. The methodology described in this paper has been helpful to identify production drivers and facilitate well planning while also supporting a factory mode of resource development.
