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Oil prices have fallen dramatically to the average range of USD $30 to $60 per barrel and are expected to stay in this average range through 2015 and beyond. Historically, crude oil producing countries seemed to be more concerned on the risks and uncertainties associated with fluctuations in crude oil price economic wise globally. This was evident both in times of rising and falling price, imposing serious challenges to them. Moreover, Natural gas also contributes to Nigeria’s GDP resulting from global industrialization and more swing from crude oil price. The implications of these swings significantly influence the economy of the country in question (Nigeria), shaping the narrative around it. Statistical methods are distinctive in their ability to provide insights into correlations where thresholds exist, building a predictive statistical data-driven model for analyzing the economic impact of swings in crude oil and gas prices with corresponding output has been of great interest in the oil and gas producing countries primarily because of its successful implementation in several industries. The analytics behind the predictive models are described through mathematical functions that rely on monthly and yearly averaged spot oil and gas prices and total oil and gas produced from 2015 to 2019 data range. This research work evaluates the extent to which these variations on crude oil and gas prices with respect to their production effects on Nigerian economy thereby impose a need for a well fitted analytical model for future forecasting based on statistics. The Statistical-analysis tool used to clean and examine the data sets were Multiple Linear Regression Analysis (MLRA) coupled with analysis of variance (ANOVA) where calculations took place in data analysis add-ins tool pack of Microsoft office Excel work sheets using the functions set out by the methodology. Thorough analysis of the results showed that some of the models build to be highly fitted and statistically significant, evident from R-squared values, adjusted R-Squared, standard errors, other hypothesis and statistical assumptions detailed in the results and analysis shows that the basic objective of this research work is achieved.

Gas lifted oil wells contributed substantially worldwide during the production life of the oil reservoir. The intermittent gas lift optimization process leads to an overall reduction of OPEX and increases the NPV from the asset. When the reservoir pressure is insufficient to produce the desired rate, consequently the oil well productivity declines and the percentage of water cut increases. The well level optimization is one of the practical strategies employs to optimize well parameters especially surface injection pressure of intermittent gas lift. A sensitivity study was implemented on surface injection pressure using IPM Prosper. Gas lifted oil rates were analyzed at several surface injection pressures from 1000-5400psi. The optimum surface injection pressure of 1300psi at an oil rate of 140 STB/day was achieved with 6 injection valves (4 operating valves and 2 safety valves) at a depth of total valves of 7316ft producing 80% water cut.