Integration of Thermodynamics in Ensemble Modeling for Gas Development Project

2021 ◽  
Author(s):  
Rovshan Mollayev ◽  
Aghamehdi Aliyev
Keyword(s):  
Pressure Loss ◽  
Dynamic Modelling ◽  
Actual Data ◽  
Flow Assurance ◽  
Ensemble Model ◽  
Reservoir Model ◽  
Analysis Software ◽  
Data Set ◽  
Nodal Analysis ◽  
Enthalpy Balance

Abstract Study was conducted to evaluate development of gas-bearing formations in the Azerbaijan sector of the Caspian Sea. Study considered subsea wellheads tied into subsea manifold, and that manifold tied to offshore facility. Flow Assurance required the calculation of subsea Flowing Wellhead Temperature (FWHT) and Pressures (FWHP). 242 subsurface scenarios were conducted with reservoir model. To accommodate all subsurface scenarios in flow assurance assessments, it was required to carry out FWHT/P calculations for all. Reservoir model was equipped with vertical lift performance curves for pressure loss calculations in tubing and logic for pressure loss estimation in subsea system. If correctly calculated, [FWHP >= dP(subsea) + Pseparator] logic should have been satisfied. As the reservoir model was not set for FWHT calculations, an external tool was required to cope with that task. Both nodal analysis software and dynamic flow modeling were considered as appropriate tools. However, as nodal modelling allowed much more automation, it was decided to use nodal analysis over dynamic modelling. To improve FWHP calculations: the logic was built into the reservoir model to: ○  estimate dP(subsea) from gas rate vs pressure drop curves ○  confirm validity of [minFWHP(wells 1, 2…n) >= dP(subsea) + Pseparator] statement: step was re-iterated until the statement was satisfied To improve FWHT calculations: Enthalpy Balance method was tested for gas wells with 1-2% error against actual data Then, nodal analysis models with the same method were built for the project wells Code was developed to calculate FWHT as part of the ensemble model predictions in following steps: ○  Well properties of each prediction step were transferred to nodal analysis software. ○  kH was varied until nodal analysis software calculated gas rate matched to ensemble model output within 1mmscf/d error Summary: Described methods allowed to significantly increase accuracy in FWHT and FWHP calculations and accommodate all possible subsurface scenarios in Flow Assurance evaluation Integration of subsea and topside hydraulics in subsurface modelling is important to develop flow assured design for development Enthalpy Balance temperature prediction method provides good match to actual data Use of coding provides huge opportunities to automate data analysis Paper will present different approach to calculation of FWHT and FWHP in subsurface modelling, integration of subsea and topside hydraulics in subsurface modelling via alternatives ways, use enthalpy balance temperature modelling, integration between nodal analysis and subsurface modelling and coding can prove analysis of large subsurface data set.

scholarly journals An Optimized Stacking Ensemble Model for Phishing Websites Detection

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1285
Author(s):  
Mohammed Al-Sarem ◽  
Faisal Saeed ◽  
Zeyad Ghaleb Al-Mekhlafi ◽  
Badiea Abdulkarem Mohammed ◽  
Tawfik Al-Hadhrami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Ensemble Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Ensemble Model ◽  
Security Attacks ◽  
Data Set ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

scholarly journals FLOW ASSURANCE IN KUMUJE WET-GAS PIPELINE: ANALYSIS OF PIGGING SOLUTION TO LIQUID ACCUMULATION

2018 ◽  
Vol 9 (9) ◽  
pp. 380-386
Author(s):  
Sarah Akintola ◽  
Emmanuel Folorunsho ◽  
Oluwakunle Ogunsakin
Keyword(s):  
Pressure Loss ◽  
Gas Pipeline ◽  
Gas Condensate ◽  
Flow Assurance ◽  
Gas Pipelines ◽  
Wet Gas ◽  
Highly Sensitive ◽  
Temperature And Pressure ◽  
Pipeline Corrosion ◽  
Assurance Problem

Liquid condensation in gas-condensate pipelines in a pronounced phenomenon in long transporting lines because of the composition of the gas which is highly sensitive to variations in temperature and pressure along the length of the pipeline. Hence, there is a resultant liquid accumulation in onshore wet-gas pipelines because of the pipeline profile. This accumulation which is a flow assurance problem can result to pressure loss, slugging and accelerated pipeline corrosion if not properly handled.

scholarly journals The Correlation Between Distance Travelled And Spending

2015 ◽  
Vol 1 (1) ◽  
pp. 10-14
Author(s):  
Remington Free ◽  
Raphael Goldman-Pham ◽  
Maxim Vorobyov ◽  
Devin Vyas
Keyword(s):  
Statistical Analysis ◽  
Extended Period ◽  
Grocery Store ◽  
Analysis Software ◽  
Microsoft Excel ◽  
Data Set ◽  
School Competition ◽  
Initial Hypothesis ◽  
Approximate Distance ◽  
Statistical Analysis Software

The research was done for a high school competition where teams were given a data set of grocery store transactions and asked to pull interesting information out of the data. Our approach was to determine whether the approximate distance between the customer's home and the store had any impact on their spending, the hypothesis being that the greater the distance, the more the customer would spend over an extended period of time (three years). The reasoning behind this was that customers who travelled greater distances to reach a specific store would likely be motivated to buy more in order to justify the greater journey. The primary tools we used to test our hypothesis were Statistical Analysis Software (SAS) and Microsoft Excel. Our results obtained contradicted the initial hypothesis; it was found that shoppers who live closer to the stores spend much more than those who live further away.

scholarly journals Dynamic Modelling of Sharia-Based Corporate, Islamic Index and Exchange Rate: VAR Model Application

2020 ◽  
Vol 6 (2) ◽  
pp. 195
Author(s):  
Hasrun Afandi Umpusinga ◽  
Atika Riasari ◽  
Fajrin Satria Dwi Kesumah
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Dynamic Modelling ◽  
Series Data ◽  
Var Model ◽  
Suitable Model ◽  
Data Set ◽  
Dynamic Relationship

Indonesia is one of largest users of sharia-based compliant recently which bring into many concerns how the sharia stocks listing in the most valuable sharia stocks index in Indonesia perform and correlate with other variables, particularly exchange rates. The study aims to analysis the causal relationship and to forecast the performances of sharia-based stocks and its Islamic index in Indonesia along with the volatility of exchange rate. Vector Autoregressive (VAR) model is applied as the method to analyse the multivariate time series as it is believed as the suitable model in predicting such time-series data in the scope of multivariate variables. The finding suggests VAR(1) model is the fitted model as such to both analyse its dynamic relationship and forecast the data set for the next 24 weeks. While the prediction shows the JII has an increasing data, both ANTM and EXR are predicted to have a stable volatility. In addition, granger causality defines variables to have effect in its respective variables, and IRF describes the shocks in one variable cause another variable is relatively difficult in reaching its zero condition in short-term period.

An ensemble-based model for prediction of academic performance of students in undergrad professional course

2019 ◽  
Vol 17 (4) ◽  
pp. 769-781 ◽  
Author(s):  
Preet Kamal ◽  
Sachin Ahuja
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Family Income ◽  
Peer Pressure ◽  
Influential Factors ◽  
Ensemble Model ◽  
Data Set ◽  
Content Type ◽  
Factors Affecting ◽  
Behavioural Factors

Purpose The purpose of this paper is to develop a prediction model to study the factors affecting the academic performance of students pursuing an undergraduate professional course (BCA). For this purpose, the ensemble model of decision tree, gradient boost algorithm and Naïve Bayes techniques is created to achieve best and accurate results. Monitoring the academic performance of students has emerged as an essential field as it plays a vital role in the accurate development and growth of students’ critical and cognitive thinking. If the academic performance of students during the initial years of the graduation can be predicted, different stakeholders, i.e. government, policymakers, academicians, can be helped to make significant remedial strategies. This comprehensible practice can go a long way in shaping the ideologies of young minds, enhancing pedagogical practices and reframing of curriculum. This study aims to develop positive steps that can be taken to enhance future endeavours in the field of education. Design/methodology/approach A questionnaire was prepared specifically to find out influential factors affecting the academic performance of the students. Its specific area of investigation was demographic, social, academic and behavioural factors that influence the performance of the students. Then, an ensemble model was built using three techniques based on accuracy rate. A 10-fold cross-validation technique was applied to access the fitness of results obtained from proposed ensemble model. Findings The result obtained from ensemble model provides efficient and accurate prediction of student performance and helps identify the students that are at risk of failing or being a drop-out. The effect of previous semester’s academic performance shows a significant impact on current academic performance along with other factors (such as number of siblings and distance of university from residence). Any major mishap during past one year also affects the academic performance along with habit-based behavioural factors such as consumption of alcohol and tobacco. Research limitations/implications Though the existing model considers aspects related to a student’s family income and academic indicators, it tends to ignore major factors such as influence of peer pressure, self-study habits and time devoted to study after college hours. An attempt is made in this paper to examine the above cited factors in predicting the academic performance of the students. The need of the hour is to develop innovative models to assess and make advancements in the present educational set-up. The ensemble model is best suited to study all factors needed to accomplish a robust and reliable model. Originality\value The present model is developed using classification and regression algorithms. The model is able to achieve 99 per cent accuracy with the existing data set and is able to identify the influential factors affecting the academic performance. As early detection of at-risk students is possible with the proposed model, preventive and corrective measures can be proposed for improving the overall academic performance of the students.

Description of a High-Profit Combination of Low-Cost Real-Time Survey Management Practices Used to Optimize Reservoir Landing in Unfamiliar Deep Offshore Geological Environment

2021 ◽  
Author(s):  
Fabien Momot ◽  
Marie-Jocelyn Comte ◽  
Chloé Lacaze ◽  
Anas Sikal ◽  
Efficience Balou ◽  
...  
Keyword(s):  
Real Time ◽  
Management Practices ◽  
Low Cost ◽  
Vertical Displacement ◽  
Uncertainty Reduction ◽  
Vertical Position ◽  
Reservoir Model ◽  
Data Set ◽  
Wide Range ◽  
Operational Time

Abstract After a first part of the drilling campaign, including about 10 wells and branches achieved within two years, the operator started questioning the geological reservoir model and reserves implications for the field Offshore Congo. Considering the potential economic impact of this development, the decision was made to reduce wellbore positioning uncertainty relying on optimization and survey QAQC processes that could be applied without adding cost of extra equipment, operational time or personnel. With more than 10 wells drilled using recent while drilling measurement and directional tools in the same environment, a wide range of wellbore positioning information was available for analysis, post-correction, and geological/reservoir model deeper understanding. Also, investigation was done to recover existing geomagnetic data acquired during the geophysical campaign. Thanks to this extensive data set, enhanced wellbores positioning was implemented using meticulous combination of processes. The "process" overall impact is often underestimated while most of the data is already available. For lateral positioning correction, it included the processing of geomagnetic IFR data over the Moho field associated to Multi Station Correction. For vertical repositioning, BHA sag correction was applied with scrutinous assessment of residual sag uncertainty and detailed analysis of continuous survey data. This robust, cost-effective, and valuable solution was chosen to be applied by the operator in the Moho field. The process was first applied post-drilling to evaluate the level of improvement that could be brought to another well also exposed to challenging trajectory context (ERD 2 with reduced target 25 × 50 m at almost 8000m MD/RT). It confirmed that the achievable uncertainty reduction would meet well objectives without adding any risk or operational time nor jeopardizing wellbore positioning and collision avoidance. Thus, it brought up to 50 to 60% of uncertainty reduction and about 30m lateral and 3m vertical displacement. The reduction of the uncertainty and trajectory adjustment allowed to enhance geologic context understanding. The vertical position of the well was offset following this revision. This had a 5% consequence in term of oil layer thickness for this well. Then, the team designed and rolled out to the operator and contractors an execution strategy and operational workflow including remote monitoring with near real-time survey QAQC that would ensure the best correction process customized for the specific drilling challenges. This monitoring enabled reducing the ellipsoid to ~20 by 50m radius at TD = 7618m. This allowed entering in the reservoir at the exact top of the structure, behind the fault that was the optimum in term of reserves and secured 90% of potential reserves of this well. The operator's choice of valuing the available information to enhance their asset is a very interesting way to optimize the past efforts put in wellbore positioning to face the current economically constrained environment.

A novel integrated modeling approach for filter diagnosis in gas turbine air intake system

2021 ◽  
pp. 095765092110443
Author(s):  
Yunfeng Jin ◽  
Chao Liu ◽  
Xin Tian ◽  
Haizhou Huang ◽  
Gaofeng Deng ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Pressure Loss ◽  
Real Data ◽  
Health Condition ◽  
Health Index ◽  
Data Set ◽  
Intake System ◽  
Control Factors ◽  
Path Component ◽  
Air Intake

Due to the complex and harsh environmental factors, the useful life of the filter in the gas turbine air intake system is usually less than its design life. When the filter is seriously degraded, the power and thermal efficiency of the gas turbine will decrease obviously due to the increase of inlet pressure loss. For evaluating the health condition of filters in the air intake system, this work forms a filter pressure loss model with the defined health index for the filter and five external environmental and control factors. By integrating the gas path component model, the combined model is applied in a real data set and the results show that (i) the proposed health index is efficient in representing the degradation state of the filter, (ii) the influencing factors on the pressure loss are successfully decoupled and their contributions on the pressure are quantitatively estimated, and (iii) the integrated model of filter pressure loss and gas path component can be used to better estimate the deterioration states of the filter as well as the gas turbine performance.

Investigation of Fan Blade off Events Using a Bayesian Framework

2017 ◽  
Author(s):  
B. Profir ◽  
M. H. Eres ◽  
J. P. Scanlan ◽  
R. Bates
Keyword(s):  
Bayesian Inference ◽  
Probabilistic Method ◽  
Dynamic Modelling ◽  
Vibrational Analysis ◽  
Posterior Distributions ◽  
Time Data ◽  
Data Set ◽  
Engineering Team ◽  
Fan Blade ◽  
Release Speed

This paper illustrates a probabilistic method of studying Fan Blade Off (FBO) events which is based upon Bayesian inference. Investigating this case study is of great interest from the point of view of the engineering team responsible with the dynamic modelling of the fan. The reason is because subsequent to an FBO event, the fan loses its axisymmetry and as a result of that, severe impacting can occur between the blades and the inner casing of the engine. The mechanical modelling (which is not the scope of this paper) involves studying the oscillation modes of the fan at various release speeds (defined as the speed at which an FBO event occurs) and at various amounts of damage (defined as the percentage of blade which gets released during an FBO event). However, it is virtually infeasible to perform the vibrational analysis for all combinations of release speed and damage. Consequently, the Bayesian updating which forms the foundation of the framework presented in the paper is used to identify the most likely combinations prone to occur after an FBO event which are then going to be used further for the mechanical analysis. The Bayesian inference engine presented here makes use of expert judgements which are updated using in-service data (which for the purposes of this paper are fictitious). The resulting inputs are then passed through 1,000,000 Monte Carlo iterations (which from a physical standpoint represent the number of FBO events simulated) in order to check which are the most common combinations of release speed and blade damage so as to report back to the mechanical engineering team. Therefore, the scope of the project outlined in this paper is to create a flexible model which changes every time data becomes available in order to reflect both the original expert judgements it was based on as well as the real data itself. The features of interest of the posterior distributions which can be seen in the Results section are the peaks of the probability distributions. The reason for this has already been outlined: only the most likely FBO events (i.e.: the peaks of the distributions) are of interest for the purposes of the dynamics analysis. Even though it may be noticed that the differences between prior and posterior distributions are not pronounced, it should be recalled that this is due to the particular data set used for the update; using another data set or adding to the existing one will produce different distributions.

scholarly journals A high-resolution bedrock map for the Antarctic Peninsula

2014 ◽  
Vol 8 (4) ◽  
pp. 1261-1273 ◽  
Author(s):  
M. Huss ◽  
D. Farinotti
Keyword(s):  
High Resolution ◽  
Sea Level ◽  
Antarctic Peninsula ◽  
Dynamic Modelling ◽  
Ice Thickness ◽  
Data Set ◽  
Ice Flow ◽  
Study Region ◽  
Entire Study ◽  
The Antarctic

Abstract. Assessing and projecting the dynamic response of glaciers on the Antarctic Peninsula to changed atmospheric and oceanic forcing requires high-resolution ice thickness data as an essential geometric constraint for ice flow models. Here, we derive a complete bedrock data set for the Antarctic Peninsula north of 70° S on a 100 m grid. We calculate distributed ice thickness based on surface topography and simple ice dynamic modelling. Our approach is constrained with all available thickness measurements from Operation IceBridge and gridded ice flow speeds for the entire study region. The new data set resolves the rugged subglacial topography in great detail, indicates deeply incised troughs, and shows that 34% of the ice volume is grounded below sea level. The Antarctic Peninsula has the potential to raise global sea level by 69 ± 5 mm. In comparison to Bedmap2, covering all Antarctica on a 1 km grid, a significantly higher mean ice thickness (+48%) is found.

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