Oil And Gas Flow Rate Prediction for African Oilfields: An Overview of Existing Models, Data and Uncertainties

Abstract

An essential task that connects hydrocarbon estimation and operational decision-making especially for oilfields in Africa is the forecast of oil and gas flow rates. The reason for the focus on African oilfields is partly due to the rising concerns of poor metering of oil wells in the continent and partly due to the difficulties oil producers in the region face as a result of the lack of a universal model and scarcity of data. The methods, data sources, and difficulties of estimating oil and gas flow rates in this situation are discussed in this paper. To estimate flow rates based on reservoir features and production factors, numerous prediction models, including empirical, analytical, and numerical techniques, have been developed. To improve prediction accuracy, these models frequently include geological, geophysical, and engineering data. The prediction process, however, is rife with uncertainties brought on by complex geology, heterogeneity in the reservoir, fluid behaviour, and technical restrictions. Improving the accuracy of flow rate projections and the ensuing reservoir management techniques requires addressing these uncertainties. To improve prediction accuracy and decision-making, advanced data analytics, machine learning, and uncertainty quantification methodologies can be integrated. This paper emphasizes the value of high-quality data integration and data collecting to improve model correctness. Additionally, it underlines the significance of including uncertainty in projections and provides guidance on how decision-makers can make informed choices while taking into account probable changes in expected flow rates. Stakeholders in the oil and gas sector may improve production methods, reduce operational risks, and support the sustainable development of energy resources across the African continent by addressing these crucial factors.

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