The overall aim of this research is to investigate whether Nigeria has enough gas to meet her domestic and export commitments in the next 30 or more years. This study is significant to the Department of Petroleum Resources, DPR, as custodian of oil and gas data and would help the DPR develop a policy plan that will ensure sustainable supply. In this study, Hubbert and Gaussian curve fitting models were used for predicting natural gas production while Logistic and Linear curve fitting models were used for predicting natural gas utilization. The case 2 Hubbert Logistic model predicted that a gap between production and utilization would occurs in 2027 and natural gas reserves will become exhausted by the year 2150. Result showed that Nigeria may fail to meet its gas commitment without the discovery of new reserves and the development of gas infrastructure
Most unitized Pipelines in Nigeria are Trunk lines which take crude oil from flow stations to the Terminals. Very few International Oil and Gas Companies own and operate trunk lines in Nigeria. As a result, marginal field owners, independent producers, and some JV partners share the trunk line for the sale of their crude. But because of the use of wide range of non-compliant meters by the injectors into the trunk lines a lot of line losses due to measurement errors are introduced. Another major feature is that trunk lines are exposed to leakages due to sabotage, aged pipeline and valve failures. The issue here is how does the owner of the trunk line back allocate these losses to their respective injectors. The Reverse Mass Balanced Methodology (RMBM) is currently in use having replaced Interim Methodology (IM) in 2017. In RMBM, the crude trunk line losses have been found to be unaccountable and it's proportionate rule for distribution of the losses to the producers are inequitable as the field owners expressed dissatisfaction with unfair deduction from trunk line operators. This study developed a procedure and an algorithm for estimation of crude contributions from each producer at the Terminal and equitable distribution of crude trunk line losses to the producers irrespective of the type of meters, meter factor and leakages and sporadic theft on the trunk lines. This study also identified two alternatives to the RMBM, the use of Artificial Intelligence (AI) and Flow based models. The results showed that flow-based model accounts for both individual and group losses, not accounted for in the RMBM, and allocates and corrects for leak volumes at the point of leak instead of at the terminal. This is a significant improvement from the RMBM.
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