Assisted history matching using pattern recognition technology

Reservoir simulation and modeling is utilized throughout field development in different capacities. Sensitivity analysis, history matching, operations optimization and uncertainty assessment are the conventional analyses in full field model studies. Realistic modeling of the complexities of a reservoir requires a large number of grid blocks. As the complexity of a reservoir increases and consequently the number of grid blocks, so does the time required to accomplish the abovementioned tasks.;This study aims to examine the application of pattern recognition technologies to improve the time and efforts required for completing successful history matching projects. The pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM;) techniques are used to develop a Surrogate Reservoir Model (SRM) and use it as the engine to drive the history matching process. SRM is a prototype of the full field reservoir simulation model that runs in fractions of a second. SRM is built using a small number of geological realizations.;To accomplish the objectives of this work, a three step process was envisioned:;• Part one, a proof of concept study: The goal of first step was to prove that SRM is able to substitute the reservoir simulation model in a history matching project. In this part, the history match was accomplished by tuning only one property (permeability) throughout the reservoir.;• Part two, a feasibility study: This step aimed to study the feasibility of SRM as an effective tool to solve a more complicated history matching problem, particularly when the degrees of uncertainty in the reservoir increase. Therefore, the number of uncertain reservoir properties increased to three properties (permeability, porosity, and thickness). The SRM was trained, calibrated, and validated using a few geological realizations of the base reservoir model. In order to complete an automated history matching workflow, the SRM was coupled with a global optimization algorithm called Differential Evolution (DE). DE optimization method is considered as a novel and robust optimization algorithm from the class of evolutionary algorithm methods.;• Part three, a real-life challenge: The final step was to apply the lessons learned in order to achieve the history match of a real-life problem. The goal of this part was to challenge the strength of SRM in a more complicated case study. Thus, a standard test reservoir model, known as PUNQ-S3 reservoir model in the petroleum engineering literature, was selected. The PUNQ-S3 reservoir model represents a small size industrial reservoir engineering model. This model has been formulated to test the ability of various methods in the history matching and uncertainty quantification. The surrogate reservoir model was developed using ten geological realizations of the model. The uncertain properties in this model are distributions of porosity, horizontal, and vertical permeability. Similar to the second part of this study, the DE optimization method was connected to the SRM to form an automated workflow in order to perform the history matching. This automated workflow is able to produce multiple realizations of the reservoir which match the past performance. The successful matches were utilized to quantify the uncertainty in the prediction of cumulative oil production.;The results of this study prove the ability of the surrogate reservoir models, as a fast and accurate tool, to address the practical issues of reservoir simulation models in the history matching workflow. Nevertheless, the achievements of this dissertation are not only aimed at the history matching procedure, but also benefit the other time-consuming operations in the reservoir management workflow (such as sensitivity analysis, production optimization, and uncertainty assessment)

Artificial Intelligence Assisted History Matching -- Proof of Concept

History matching is the process of adjusting uncertain reservoir parameters until an acceptable match with the measured production data is obtained. Complexity and sub-optimal knowledge of reservoir characteristics makes this process time-consuming with high computational cost. In the past, computer-assisted history matching has attempted to make this process faster; however, the degree of success of these techniques continues to be a subject for debate.;In this study, the objective is to prove and examine the application of a relatively new Artificial Intelligence based technology (Surrogate Reservoir Model -- SRM) to assist the history match process. SRM is a prototype of full-field reservoir simulation model that runs in fractions of a second. The capability of generating meaningful outputs in a short time period with acceptable accuracy makes SRM a unique tool for assisted history matching.;In this project, an SRM was created for a synthetic case study of a heterogeneous and complex oil field, with 24 production wells and 30 years of production history. The history matching was performed for this field using SRM and tuning static data (permeability). The result of this study is a proof of concept and shows that SRM is able to reproduce the numerical simulator results faster and with an acceptable accuracy. These characteristics make SRM a fast and effective tool for assisted history matching

Application of Real-Time PCR method for evaluation of measles vaccine heat stability

     The Plaque Forming Unit(PFU) and Tissue Culture Infectious Dose50(TCID50) methods are used for evaluation of vaccine heat stability and effect of various stabilizers on thermal stability of vaccines. The aim of present study is using Real-Time PCRtechniquefor estimation of vaccine degradation rate and thermal stability of measles vaccines. Lyophilized measles vaccines containing three various stabilizers were reconstituted with distilled water. Three vial of each vaccine incubated at25˚C for 0, 4 and 8 hours. Titer of virus in vaccines calculated by TCID50 method. Also after RNA Extraction and cDNA synthesis, the RNA copy numbers of viruses in vaccines were estimated by absolute quantitative Real-Time PCRtesting. The data were analyzedby SPSS 19 and Sigma Plot 11 software.The result of this study showed there is a significant relationshipbetween vaccine degradation rate calculated with TCID50 and Real-Time PCR method (p<0.05). ThereforeReal-Time PCR is a good complement or appropriate replacement to traditional methods.Titration methods based on cell culture are gold tests for titration of viral vaccines and estimation of heat stability but Real-Time PCR technique can also be used for this goals. This method is faster, cheaper and easier than TCID50

Medical and chemical efficacy of respiratory physiotherapy and Remdesivir in patients with COVID-19 pneumonia: A systematic review and meta-analysis

Evaluated efficacy of Respiratory Physiotherapy and Remdesivir on patients with COVID-19 pneumonia. In current systematic review and meta-analysis study, articles published January 2019 to December 1, 2021 were reviewed in the databases of PubMed, Scopus, Web of Science, and EBSCO. Risk ratio and mean differences with 95% confidence interval (CI), fixed effect model and Mantel-Haenszel or Inverse-variance formula were calculated. The Meta analysis have been evaluated with the statistical software Stata/MP v.16 (The fastest version of Stata). Mean differences of PaO2/FiO2 ratio at 6h after chest Respiratory Physiotherapy was (MD, 66 mmHg 95 % CI 64.71 mmHg, 67.28 mmHg; p=0.0007). Risk ratio of recovery rate between experimental and control group was 0.20 (RR, 0.20 95 % CI 0.15, 0.25) with high heterogeneity (I2 =78.84%; p=0.00). Risk ratio of mortality rate between experimental and control group was -0.34 (RR, -0.34 95 % CI -0.65, -0.03) with low heterogeneity (I2<0%; p=0.51). Based on the findings of meta-analysis, Respiratory Physiotherapy can play an effective role in respiratory therapy and rehabilitation of patients admitted to the ICU with COVID-19. A meta-analysis showed that treatment with Remdesivir could increase the recovery rate, especially in the early days of COVID-19; also reduces the mortality rate

Glomerular filtration rate and urine osmolality in unilateral ureteropelvic junction obstruction

Background: Renal maldevelopment, interstitial fibrosis, ischemic atrophy, decreased glomerular filtration rate (GFR), and renal blood flow (RBF) are inevitable consequences of chronic kidney obstruction that only partially improve after early intervention. There are only few studies that evaluated urine osmolality in affected kidney and its correlation with short-term outcome. Materials and Methods: Thirty patients (age<1 year) with unilateral ureteropelvic junction obstruction (UUPJO) were enrolled in this study. UUPJO was confirmed using Technetium 99 isotope scans and the patients were indicated to be operated afterward. Urine and blood samples were obtained before, 24, 48, and 72 h after the surgery. The serum level of blood urea nitrogen, creatinine, and glucose were measured. GFR, urine osmolality (measured and calculated), and urine specific gravity were determined too. Results: Cortical thickness of hydronephrotic kidney was significantly increased 6 months after the surgery. GFR was significantly increased 72-h postsurgery compared to before operation. Neither means of calculated nor of measured urine osmolalities were significantly different in various stages. The last calculated urine osmolality (72 h) had significant correlation with the last measured osmolality (72 h); r0=0.962, P=0.0001. The last GFR (72 h) had positive significant correlation with GFR before the surgery and GFRs at 24 and 48 h postsurgery. Using regression tests, only the before surgery GFR was the predictor of the last GFR(72 h). Conclusion: In UUPJO the measured and calculated urine osmolality of the affected kidney did not differ. In addition, GFR before surgery should be considered as the predictor of the GFR shortly after repair

Determining and Prioritizing Admission Criteria for Talented Students Office in Isfahan University of Medical Sciences Using Analytical Hierarchy Process Model

Introduction: Students' scores on university entrance exam (Conquer) or their average scores are not appropriate criteria to recognize them as talented students. There have been limited studies concerning the comparison analysis of factors for selecting medical students as talented. This study was done to determine and prioritize the selection criteria for admitting students to the talented students' office. Methods: A group of 5 physicians having experience in medical education was established. Renzulli model was selected as the best descriptive model, then using brain-storming, the features of a talented student were identified. Based on Analytical Hierarchy Process (AHP), the questionnaires were designed and distributed among 21 experts. Using hierarchical analysis formula and by Excel software, the weight of each criterion was calculated. To define the weights of the sub-criteria, the detailed features of each criterion were determined and then, the total score of each sub-criterion was calculated by multiplying the score achieved in the first hierarchical analysis by the score attained in the second one. Results: Creativity had the maximum score (38%), being responsible and prominent, were in second place acquiring 31% of the total weight. The highest grades belonged to registration of invention (127), cooperation in writing books and articles (104), and having high rank in Olympiads (92), respectively. The educational status which previously was the only admission criterion was in the fourth position, after having high rank in Olympiads. Conclusion: The areas of creativity, responsibility and being prominent were the main criteria for talented students' admission and there was not any significant difference between their scores. The educational status had a less important role in the prioritization system of this study. It seems that a student is required to have the three main criteria to be recognized as talented