| Popis: |
Use of effective sand-control practices has sustained oil and gas production from wells that would otherwise have shut-in. However, a large number of gravel-packs fail early after installation necessitating expensive well intervention. A basic prerequisite to effective control among others is a good gravel-pack design and execution1. This includes obtaining a representative sample of the formation sand, analysing the grain-size distribution and selecting an optimum gravel-size. Gravel-size selection is carried out in relation to formation grain-size to control formation sand movement and using the optimum screen slot to retain the gravel. However, core samples are generally not taken in all wells in a field. In the absence of specific well information, it has become accepted practice to use offset-well data for designs creating a potential for ineffective gravel-packs. This paper discusses the application of neural networks to obtain real-time, well specific, grain-size distributions and how this input could be used to improve gravel-pack design and achieve optimum sand control. Neural networks have been applied with success to predict grain-size distributions from well logs. The availability of a continuous grain-size profile across an entire reservoir has facilitated comparison between the size of gravel on existing gravel-packs where offset-well information was used and gravel-size obtained based on neural network estimates. The results indicate significant differences. They further demonstrate that the use of estimates of grain-size distribution across the entire reservoir rather than offset well grain-size can lead to improved gravel pack performance and thus significant savings on life-cycle costs. |
