Objectives:To estimate the rate of the Pilonidal disease (PND) recurrence and to evaluate the associated predictors of the recurrence.Methods:This is a retrospective study, conducted at King Fahd Hospital of the University in Alkhobar, Kingdom of Saudi Arabia over a period of 10 years from January 2003 until December 2013.Results:A study of 366 with PND, 19 of those were identified as secondary cases and excluded. Most involved 347 patients were Saudi (82.1%), single (87%), and men (93.1%). The mean age of the sample was 23 ± 8 years, and the mean body mass index (BMI) was 29.7 ± 6 Kg/m2. Overall recurrence rate after the surgical management was found in 25 patients (7.2%). By logistic regression to the recurrence group, young age group, prolong sitting and BMI may increase the likelihood of the disease recurrence.Conclusion:Pilonidal disease still has challenges in its management. Treatment should depend on the extent and severity of the disease. The recurrence rate in this study is approximately 7.2%. The most predominate factors associated with recurrence were prolong sitting job, young age group, and high BMI.

The purpose of this paper is to demonstrate and explain the factors that can produce notable differences between permeability measured in a laboratory from core analysis and permeability estimated from pressure transient analysis (PTA) of build-up and fall-off tests. The paper presents several synthetic cases to show the variations in obtained permeability using each method, and the contributing factors affecting the outcomes. For calculating effective permeability from PTA, errors in input data such as static, dynamic or pressure/volume/temperature (PVT) fluid data can significantly increase discrepancies. Heterogeneity in reservoir permeability distribution such as vertical reservoir layering (variation in permeability vertically), large scale lateral variation or fractures will also add to the discrepancy between methods. Comparison between the permeability from both sources for the same subject wells will be used to illustrate these factors by interpretation of the data and characterization of reservoir. The primary factor contributing to the differences in permeability is that the one measured from core analysis represents absolute permeability to air or nitrogen in the lab and corresponds to only a small sample (few inches) of the reservoir. However, the calculated permeability from a PTA represents the average effective permeability of the reservoir fluid type within the radius of investigation for that particular test. Hence, each method provides a permeability based on different fluids and volumes of investigation. A methodology that can be utilized to estimate the effective permeability for uncored wells, which usually make out most of the wells in any field, using average effective permeability from PTA will be discussed. Such a method is beneficial for simulation studies that require sufficient knowledge of permeability in each area under study. This method should help in extrapolating the permeability all over the field to get more reliable predictions. Permeability plays a very important role in reservoir characterization as well as simulation. Knowing the permeability leads to the success in the placement of wells and therefore wells performance and ultimate hydrocarbon recovery. Thus, the most accurate determination of the permeability is of extreme importance because it affects the economy of the field development. The paper discusses the factors that can produce differences between measured permeability in a laboratory from core analysis and permeability estimated from PTA and the best practices for correcting and correlating between the permeability to achieve more reliable predictions for reservoir characterization and simulation studies which results in better field development.