The struggle to define an optimized completion strategy remains a significant challenge in unconventional multistage horizontal wells. Stage and cluster spacing is a design decision that often requires significant experimentation to determine what is the optimized spacing of propped fractures that efficiently drains the reservoir yet can be reliably pumped to completion. This paper illustrates the importance of bottomhole (BH) gauge data for evaluation of stage and cluster spacing and general completion quality. The knowledge derived through careful analysis of this valuable data source, can significantly reduce the amount of experimentation typically required in other completion techniques. Consequently optimized field scale completion strategies can be refined with efficiency and reduced overall costs.

Coiled tubing (CT) assisted hydraulic fracturing (HF) is a good alternative to the standard plug and perf completion in multistage horizontal well stimulation. During a CT HF operation, fracturing is initiated via pre-installed frac sleeves or sand-jetted perforations. One of the advantages of CT fracturing is improved target fracturing, where a single-entry fracture initiation point can be placed at the desired depth to target the best rock for stimulation. Another benefit of CT fracturing is the ability to gather BH treating pressure via BH gauges (BHG) mounted on the CT bottomhole assembly (BHA). Bottomhole pressure and temperature gauges, installed above and below the isolation packer, provide valuable information about fracture communication between stages, cement integrity and stress shadowing.

This paper will review dozens of CT frac jobs performed across various basins in North America. Information obtained from BHG is merged with surface treating pressure and other diagnostic data sources and subsequently analyzed. One key area of analysis is to examine stage communication data for stress shadowing effects. The primary takeaways from this paper are to illustrate the importance of BHG data, how it compares to other diagnostic data, and how it can be analyzed to effectively drive the stimulation design process.

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