Research Article
Modelling and Simulation of a 3-D Reservoir for Enhanced Oil Recovery of ‘X’ Field in the Niger Delta Using Eclipse Software
Ihekoronye Kingsley Kelechi*
,
Milton Roy Zwalatha,
Caleb Abdullahi Ibrahim,
Mohammed Hussein
Issue:
Volume 9, Issue 3, September 2024
Pages:
54-67
Received:
8 January 2024
Accepted:
24 January 2024
Published:
30 August 2024
DOI:
10.11648/j.ajmcm.20240903.11
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Abstract: Surfactant-polymer flooding is a tertiary enhanced oil recovery method used to recover oil that remained in the reservoir after the primary and secondary oil recovery mechanisms. Predicting the pressure in the reservoir is important for oil production as pressure changes with time. A suitable approach to achieve this task is to derive fluid flow equation based on the reservoir characteristics and solve them numerically which provide the solution to the mathematical fluid flow model (diffusivity equation). In this study, 3-D reservoir was modelled using Eclipse software. The fluid flow equations in a porous media were derived based on the simulated model and the reservoir conditions. Numerical solution using implicit formulation to solve the mathematical fluid flow model (diffusivity equation) was investigated by developing Python codes using Jupyter library to ascertain the pressure distribution for the reservoir and imported into Eclipse simulator. Simulation was carried out using surfactant-polymer and reservoir properties to determine the oil recovery. The results of the study showed that pressure increases with time as oil production continued, and water saturation decreased for the grid-cells of the reservoir. Waterflooding had oil recovery of 38.0% and water-cut of 59.0%, while surfactant flooding had oil recoveries of 42.0%, 46.5%, 49.0% and water-cut of 57.0%, 51.0%, 46.3%. In addition, polymer flooding had oil recoveries of 44.3%, 48.4%, 54.0% and water-cut of 50.0%, 45.0% and 33.0% respectively at different concentrations of 0.3%wt. 0.4%wt. and 0.5%wt.
Abstract: Surfactant-polymer flooding is a tertiary enhanced oil recovery method used to recover oil that remained in the reservoir after the primary and secondary oil recovery mechanisms. Predicting the pressure in the reservoir is important for oil production as pressure changes with time. A suitable approach to achieve this task is to derive fluid flow eq...
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Research Article
Propensity Score Matching: An Application on Observational Data
Issue:
Volume 9, Issue 3, September 2024
Pages:
68-77
Received:
26 October 2024
Accepted:
12 November 2024
Published:
3 December 2024
Abstract: The study aimed to determine the survival rate of first-class passengers using the Titanic dataset from Kaggle. Descriptive statistics revealed that first class passengers had way more chance to survive as compared to other classes, which underscores the role of socioeconomic status in determining chances of survival. Evaluation metrics, which assess model performance independently for male and female cohorts, shed light on gender specific projected accuracy. The analysis of propensity scores matching data for male and female passengers separately ensured that each gender category had control groups and treatments that were equally distributed. It was discovered that women had higher survival rates compared to men and these findings also identified disparities in the levels of surviving among genders. Improvements in covariate balance were indicated by post-matching statistics for both the male and female cohorts, indicating that the matching process was successful for both genders. The treatment effect estimates for male and female passengers were computed independently, and the findings showed that a number of characteristics significantly improved the survival rates for each gender group. The overall results of the study emphasized how important it is to include gender when analyzing survival outcomes using the Titanic dataset. In addition, age was suggested as an important factor whereby young people had higher chances of being saved.
Abstract: The study aimed to determine the survival rate of first-class passengers using the Titanic dataset from Kaggle. Descriptive statistics revealed that first class passengers had way more chance to survive as compared to other classes, which underscores the role of socioeconomic status in determining chances of survival. Evaluation metrics, which asse...
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