
doi: 10.2118/169322-ms
Abstract In this paper we describe a perforating technology that helped deliver highly productive wells in the Tunu field, a multilayer sandstone gas reservoir in Indonesia. We describe the simulation models used to evaluate perforating designs and operational risks, and we include post-job productivity data for 9 wells, all of which ended up delivering more than 500% of the expected productivity, in large part due to highly conductive perforation tunnels. The intervention technique used in the Tunu field is based on applying Dynamic Underbalance (DUB) perforating with a nitrogen kick-off technique to perforate on balance or slightly under-balance. This technique enables perforating long intervals with deep penetrating charges at high shot density, and with very low risk of gun jumping. This technique also promotes natural well flow after perforating, without the extra cost of coiled tubing intervention to perform liquid unloading. We discuss several aspects of job design and simulation. Predictive simulations based on API RP 19B Section 4 data and rock perforating models for sandstones indicated that perforating damage clean-up with dynamic underbalance would deliver the highest well productivity. The simulation model that predicts wellbore dynamics, namely pressure waves in the wellbore, at the sand face and inside the reservoir, also predicts the gunstring dynamics with the associated gunshock loads on the conveyance. Gunshock simulations showed that the DUB technique also minimizes operational risks. All the important aspects of the DUB perforating technique were predicted in the job planning stages. Guns were custom loaded to produce a good dynamic underbalance to remove the low permeability crushed rock zone from inside and around the perforation tunnels, thus minimizing perforation skin and maximizing well productivity. In many cases well productivity turned out to be exceedingly high, more than 500% of the initially expected productivity for7 DUB perforating jobs and 2 DUB post-perforating clean-up jobs.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 3 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
