This work presents the methodology of integrating hydraulic fracture modeling with RTA to understand flow regime characteristics within the Barnett Shale. Using a characteristic model from the RTA study, the "scaling" methodology is presented as a tool to assist with refracturing ("refrac") candidate selection. The intent of this methodology is to create a proxy for reservoir quality and effective fracture surface area.
Our primary objectives were:
• Introduce an integrated workflow using fracture modeling and RTA to understand diagnostic signatures (i.e., long-term linear flow) exhibited by older vintage Barnett wells.
• Develop a methodology to identify refrac candidates.
• Apply and investigate validity of applying "scaling" methodology as a tool to assist with refrac candidate selection.
This work:
• Provides justification for observed diagnostic signatures exhibited by time-rate data from wells within the Barnett using frac modeling and RTA.
• Presents the implementation of the "scaling" methodology to assist with refrac candidate selection.
• Validates "scaling" methodology through look-back analysis of previously performed refracs in the Barnett.
General conclusions and observations of this work:
• Long-term linear flow behavior exhibited by legacy wells in the Barnett can be attributed to wider cluster spacing and low matrix permeability.
• Diagnostic signatures of older vintage wells can be characterized by "relatively" higher Arps’ hyperbolic decline parameters.
• The "scaling" methodology can be applied to potential refrac candidate selection.
Long-term linear flow has been observed in older vintage Barnett time-rate data. This work used RTA coupled with fracture modeling, and theorized that older-style completion designs, coupled with low-matrix permeability, leads to the time-rate data exhibiting long term linear flow.
Within the Barnett Shale, a significant amount of acreage was developed using older relatively sub-optimal completion designs. This observation presents the opportunity for refrac operations to increase production. However, potential refrac candidates can be difficult to prioritize, given the amount of variability in original completion design and reservoir characteristics, and the time required to rank based on many candidate criteria. This work proposes a model-based approach using the "scaling" methodology to assist with potentially decoupling reservoir and completion effects to assist with refracturing candidate selection.
