Methodological Evaluation of Process-Control Systems for Yield Improvement in South African Petroleum Operations
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2007
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| _version_ | 1866901910524526592 |
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| author | Cele, Nonhlanhla Nthatho, Siyabonga |
| author_facet | Cele, Nonhlanhla Nthatho, Siyabonga |
| contents | <p>The oil and gas sector in South Africa faces challenges in optimising production yields due to variability in geological formations and operational conditions. A time-series forecasting approach was employed, incorporating autoregressive integrated moving average (ARIMA) models to analyse and predict yield trends. Robust standard errors were used to quantify the uncertainty in these predictions. The ARIMA model demonstrated a correlation coefficient of $R^2 = 0.85$ for predicting yield improvements over a five-year period, indicating a significant reduction in forecast error. This study validates the use of ARIMA models as a reliable tool for predicting and improving petroleum yields in South African operations. The findings suggest that implementing these predictive models could lead to more efficient resource management and increased operational efficiency. process-control systems, yield improvement, time-series forecasting, autoregressive integrated moving average (ARIMA), South Africa</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18850523 |
| institution | Zenodo |
| language | eng |
| publishDate | 2007 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Methodological Evaluation of Process-Control Systems for Yield Improvement in South African Petroleum Operations Cele, Nonhlanhla Nthatho, Siyabonga Sub-Saharan Africa Networked Systems Statistical Metricology <p>The oil and gas sector in South Africa faces challenges in optimising production yields due to variability in geological formations and operational conditions. A time-series forecasting approach was employed, incorporating autoregressive integrated moving average (ARIMA) models to analyse and predict yield trends. Robust standard errors were used to quantify the uncertainty in these predictions. The ARIMA model demonstrated a correlation coefficient of $R^2 = 0.85$ for predicting yield improvements over a five-year period, indicating a significant reduction in forecast error. This study validates the use of ARIMA models as a reliable tool for predicting and improving petroleum yields in South African operations. The findings suggest that implementing these predictive models could lead to more efficient resource management and increased operational efficiency. process-control systems, yield improvement, time-series forecasting, autoregressive integrated moving average (ARIMA), South Africa</p> |
| title | Methodological Evaluation of Process-Control Systems for Yield Improvement in South African Petroleum Operations |
| topic | Sub-Saharan Africa Networked Systems Statistical Metricology |
| url | https://doi.org/10.5281/zenodo.18850523 |