Summary
Perforating-laboratory experiments can be a useful element of
field-perforating-job design. In some instances, the goal is to qualitatively
compare multiple candidate perforating techniques. In others, the goal is to
obtain quantitative insight into likely flow performance in the field. Although
the laboratory will never perfectly replicate the downhole environment, it can
yield useful results, which--if properly interpreted--can enable informed
prediction of downhole-flow performance.
A traditional flow-laboratory experiment (API RP 19B 2006) yields numerous
key results, four of which are required inputs to downhole-inflow simulators.
These are perforation-tunnel length and diameter, crushed-zone thickness, and
permeability. In the case of natural perforated completions as opposed to
stimulated completions (e.g., hydraulically fractured or sand control), these
parameters (in addition to other system and wellbore parameters) dictate the
skin and ultimate flow performance of the completion.
Crushed-zone permeability is typically inferred from core-flow efficiency
(CFE) and an assumed crushed-zone thickness. Traditionally applied, this
technique can yield values that are inaccurate, and can produce misleading
predictions of downhole performance more significantly.
To address this, we have developed new methods for both measuring and
interpreting CFE. The new measurement technique yields CFE values that we show
to be more meaningful and relevant. The new interpretation technique provides a
consistent method of translating CFE to crushed-zone permeability, and is
capable of accounting for the effect of partially plugged tunnels. This work
clarifies and improves the link between laboratory and field performance of
perforators, with the ultimate goal of increasing the value of
downhole-inflow-performance predictions.
While other work is ongoing to challenge the framework of the conventional
skin models, the present paper accepts these models as a premise. This work
simply presents a coherent methodology of interpreting laboratory data, with
the intent of generating the required inputs for skin models as they currently
exist. Furthermore, it is recommended that this workflow be considered for
inclusion in any revisions to the API Section 4 (API RP 19B 2006) testing
protocol.
© 2012. Society of Petroleum Engineers
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History
- Original manuscript received:
9 August 2011
- Meeting paper published:
8 June 2011
- Revised manuscript received:
1 February 2012
- Manuscript approved:
1 March 2012
- Published online:
17 May 2012
- Version of record:
11 June 2012
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