Identifying productive zones of the Sarvak formation by integrating outputs of different classification methods

International audienceSarvak formation is the second major carbonate reservoir in Iran. There are several geological, petrophysical and geophysical investigations which have been carried out on this important reservoir. In this work, Sarvak is studied to find productive zones. At first, four different methods were used to identify producing intervals from well log data and well test results. Then, final zoning is generated by integrating outputs of these four methods. One of them is the conventional cutoff based method; the other three methods are based on flow equation, Bayesian and fuzzy theories. Thereafter, by considering the classification correctness rate of each classifier in each well and technique of majority voting, a unique zoning for Sarvak formation is presented. Based on the final zoning, the whole Sarvak interval is divided into seven zones. Three of them are classified as oil producing zones, two of them cannot be classified as conventionally producing zones, and the remaining two are water producing. Zone number 2 not only has the highest production rate, but also is the most homogeneous zone among the productive zones. The novelty of this research is using well test results in defining productive classes, which improves the certainty of classification in comparison with previous works that were based on core analysis and log data

Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images

Purpose: The novel coronavirus COVID-19, which spread globally in late December 2019, is a global health crisis. Chest computed tomography (CT) has played a pivotal role in providing useful information for clinicians to detect COVID-19. However, segmenting COVID-19-infected regions from chest CT results is challenging. Therefore, it is desirable to develop an efficient tool for automated segmentation of COVID-19 lesions using chest CT. Hence, we aimed to propose 2D deep-learning algorithms to automatically segment COVID-19-infected regions from chest CT slices and evaluate their performance. Material and methods: Herein, 3 known deep learning networks: U-Net, U-Net++, and Res-Unet, were trained from scratch for automated segmenting of COVID-19 lesions using chest CT images. The dataset consists of 20 labelled COVID-19 chest CT volumes. A total of 2112 images were used. The dataset was split into 80% for training and validation and 20% for testing the proposed models. Segmentation performance was assessed using Dice similarity coefficient, average symmetric surface distance (ASSD), mean absolute error (MAE), sensitivity, specificity, and precision. Results: All proposed models achieved good performance for COVID-19 lesion segmentation. Compared with Res-Unet, the U-Net and U-Net++ models provided better results, with a mean Dice value of 85.0%. Compared with all models, U-Net gained the highest segmentation performance, with 86.0% sensitivity and 2.22 mm ASSD. The U-Net model obtained 1%, 2%, and 0.66 mm improvement over the Res-Unet model in the Dice, sensitivity, and ASSD, respectively. Compared with Res-Unet, U-Net++ achieved 1%, 2%, 0.1 mm, and 0.23 mm improvement in the Dice, sensitivity, ASSD, and MAE, respectively. Conclusions: Our data indicated that the proposed models achieve an average Dice value greater than 84.0%. Two-dimensional deep learning models were able to accurately segment COVID-19 lesions from chest CT images, assisting the radiologists in faster screening and quantification of the lesion regions for further treatment. Nevertheless, further studies will be required to evaluate the clinical performance and robustness of the proposed models for COVID-19 semantic segmentation

Optimization of Multiple Bit Runs Based on ROP Models and Cost Equation: A New Methodology Applied for One of the Persian Gulf Carbonate Fields

Improving the rate of penetration (ROP) is one of the key methods to reduce drilling costs. Several ROP models have been developed and modified based on the concept where unconfined compressive strength (UCS) is inversionally proportional with the rate of penetration. These models can predict the rate of penetration of different bit types in an oil or gas field with a reasonable degree of accuracy. The ROP model studied herein relates the rate of penetration to operating conditions and bit parameters in addition to the rock strength. Also, the effects of bit hydraulics and bit wear on rate of penetration are included in the model. In this paper, the drilling performance was optimized, using the ROP models, for upcoming wells in one of the Persian Gulf carbonate fields. Based on previous drilled wells a rock strength log along the wellbore is created and modified to mach the the new well survey. The rock strength is back calculated from the ROP model which includes bit design and reported field wear in conjunction with meter by meter operating parameters, formation lithologies and pore pressure. By conducting a number of simulations a learning curve was constructed to obtain the optimum bit hydraulics, best combination of operational parameters and the most effective bit design. Based on the proposed ROP model, a simple and useful simulator was developed. This methodology can be used in pre-planning and post analysis to reduce drilling cost where previously drilled wells exist

Serological study of Helicobacter pylori infection in patients with Polycystic Ovary Syndrome

Background: Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in women of reproductive age. Chronic infections have been mentioned as one of the different etiologic factors related to PCOS. Due to the high prevalence of Helicobacter pylori infection especially in developing countries, its probable role in the pathogenesis of PCOS and the limited information available in this area, serologic study of H.Pylori infection in patients with PCOS, was performed. Methods: This research was performed as a case control study from Dec 2010 until May 2012 in 82 patients (and their spouses) with polycystic ovary syndrome (case group) and 82 non PCOS patients (control group) with an age range of 20-40 referred to Vali-e-Asr Hospital infertility clinic. Both groups and their husbands filled a questionnaire and were examined by testing their serum H.Pylori IgG and IgA antibody levels. Statistical testing and analysis was performed by t-student and λ2 tests. Results: Mean age of the women and men and also other demographic characteristics except their profession showed no significant difference (P>0.05) in the two groups (PCOS and non PCOS). H.Pylori antibody IgG serum level was positive in 78% and 76.5% and H.Pylori antibody IgA level in 30.5% and 37% of PCOS versus non PCOS patients respectively which showed no statistically significant difference (P>0.05). There was also no significant difference between the H.Pylori antibodies levels in the spouses in the two groups (P>0.05). Conclusion: This study showed no significant difference in serologic examination re-sults in PCOS versus non PCOS patients. The finding of high prevalence of H.Pylori IgG and IgA positive levels in both PCOS and non PCOS patients can be probably re-lated to the high prevalence of H.Pylori infection or exposure in Iranian population and therefore suggest an issue for further investigation