Geostatistical modeling and spatial distribution analysis of porosity and permeability in the Shurijeh-B reservoir of Khangiran gas field in Iran

The main objectives of this study are analysis of spatial behavior of the porosity and permeability, presenting direction of anisotropy for each variable and describing variation of these parameters in Shurijeh B gas reservoir in Khangiran gas field. Porosity well log data of 32 wells are available for performing this geostatistical analysis. A univariate statistical analysis is done on both porosity and permeability to provide a framework for geostatistical analysis and modeling. For spatial analysis of these parameters, the experimental semivariogram of each variable in different direction as well as their variogram map plotted to find out the direction of anisotropy and their geostatistical parameters such as range, sill, and nugget effect for later geostatistical work and finally for geostatistical modeling, two approaches kriging and Sequential Gaussian Simulation are used to get porosity and permeability maps through the entire reservoir. All of statistical and geostatistical analysis has been done using GSLIB and PETREL software. Maximum and minimum direction of continuity are found to be N75W and N15E, respectively. Geostatistical parameters of calculated semivariogram in this direction like range of 7000 m and nugget of 0.2 are used for modeling. Both kriging and SGS method used for modeling but kriging tends to smooth out estimates but on the other hand SGS method tends to show up details. Cross-validation also used to validate the generated modeling

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability