A Perforating Tool Kit as a Computational Paradigm

 
Authors:

Jim Wight (Halliburton) | G. G. Craddock (Halliburton) | Dennis Haggerty (Halliburton) | Fabian Rojas (Halliburton) | Jonathan Montiverdi (Halliburton) | Arvind Yadav (Halliburton) | Manuel Magdaniel (Halliburton) | Federico Rios (Halliburton) | Kevin Harive (Halliburton)

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Source:

SPE Deepwater Drilling and Completions Conference, 14-15 September, Galveston, Texas, USA

Copyright:

2016. Society of Petroleum Engineers

Abstract:

This paper discusses using a perforating toolkit as a computational paradigm to provide perforating solutions to field personnel. This toolkit provides an extensive list of charges and gun systems and generates the effects on the target rock and overburden pressure. Outputs include depth of penetration and casing hole sizes. It has an improved graphical user interface (GUI) and updated algorithms based on data from the flow laboratory.

The perforating toolkit uses up-to-date API data as the basis for the calculations; simulations are based on parameters set by the user and outputs are in a graphical format, showing the perforating performance within the reservoir. The perforating toolkit software is designed to use improved data obtained from recent laboratory testing. The most up-to-date flow laboratory testing was used to generate the algorithms for the perforating toolkit, generating more precise algorithms and producing improved perforation predictions. Testing involved a broader range of data than was previously used, allowing the software to make more accurate predictions. This new tool was benchmarked using laboratory testing, which demonstrated the quality of the paradigm and its functional use for improved downhole perforating. The software provides the field user with a tool to predict downhole shaped-charge performance quickly and in a cost-effective manner. The models also allow a number of parameters to be varied for a better comparison of different scenarios.

This paper discusses how new data across a broader range from the flow laboratory was used to improve the basic software algorithms and help provide the most accurate prediction of downhole charge performance.

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