S.A. IGBINERE

Gas lift remains one of the most widely applied artificial lift methods for sustaining oil production, particularly in mature wells with declining reservoir pressure. However, its efficiency strongly depends on the accurate selection of operating parameters such as injection gas rate, valve depth, and tubing size. Conventional approaches often rely on trial-and-error procedures, which are time-consuming and may lead to suboptimal performance. This study presents an integrated approach that combines artificial neural networks (ANN) and sensitivity analysis within a nodal analysis framework to optimize gas lift performance in a Niger Delta field.
A dataset of well test and production parameters was pre-processed, normalized, and used to train ANN models for predicting oil production rates under varying gas injection conditions. The model achieved a high prediction accuracy (R² > 0.95), demonstrating its robustness in capturing the nonlinear relationship between gas injection and liquid production. Sensitivity analysis was performed to evaluate the relative influence of injection gas rate, tubing head pressure, and valve depth on production performance. Results indicated that injection gas rate was the most significant parameter, accounting for over 60% of the observed production variability, followed by tubing head pressure.
The integration of ANN with sensitivity analysis provided clear guidelines for selecting optimal gas injection strategies that maximize production while minimizing energy costs. The study concludes that data-driven approaches can significantly improve the efficiency of gas lift optimization, offering a reliable alternative to conventional empirical and simulation-based techniques. The findings contribute to extending the economic life of mature wells in the Niger Delta and demonstrate the potential of artificial intelligence in enhancing petroleum production systems. 

Engineered materials with at least one dimension between 1 and 100nanometer are known as nano-materials. The energy, cosmetic, aerospace, in addition medicinal industries, among many others, all stand to benefit from the unique features of nano-fluids in addition colloidal suspensions made up of different nanomaterials. Nanoparticles are regarded as excellent candidates for the creation of smart drilling fluids, i.e., fluids with specifically tailored rheological in addition flow characteristics, owing to their distinct physio-chemical features. The capacity of the drilling mud to sustain cuttings is represented by its rheological properties (gel- strength, viscosity, in addition yield points), which is a crucial drilling mud feature. Failure to utilize mud with the required rheological property could lead to formation damage from excessive circulation pressure that breaks gel in additionclogged pipes from poor cutting suspension. This project is aimed at defining magnesium oxide effect on nanoparticles on the gel-strength of water base drilling mud, comparison of the gel-strength property of magnesium Oxide nano-base mud to conventional water mud, comparison of the gel-strength property of magnesium oxide nano-base mud to conventional water base containing Banana peel which improves the gel- strength of drilling mud. Laboratory preparation of the various samples of drilling mud at l0sec, 5min, l0min, 15min, 20min, 25min, 30min, 40min after 600rev/min agitation produced enough gel-strength data for each samples for graphical representation in addition analysis of gel-strength of the prepared samples of the drilling mud. The prepared samples are Sample 1 (bentonite clay mud (blank)), Sample 2 (bentonite mud + 0.5g Magnesium oxide), Sample3(bentonite mud + lg Magnesium oxide), Sample4(bentonite mud + 2g Magnesium oxide), Sample5 (bentonite mud + 0.5g Magnesium oxide + 2g of banana peel), Sample6 (bentonite mud + lg Magnesium oxide + 2g of banana peel), Sample7 (bentonite mud + 2g Magnesium oxide + 2g of banana peel), Sample8 (bentonite mud + 2g of banana peel). From the laboratory data in addition gel-strength vs time plot it was observe that the The sample formed a progressive type gel that began flat at llbf/100ft3 with the gel strength at l0sec in addition l0min being the same but rapidly improved as the static time increased. Sample 2 generated a flat-type gel that began flat at 21 bf/100 ft3 with the gel strength at 0 sec in addition 0 min remaining constant, but did not increase consistently as the static time increased. Sample3 - sample8 produced either a high-flat gel or a high- progressive gel, Sample3 in addition Sample4 which produced a high-flat gel as compared to the Sample4 - Sample8 which Also produced a high-progressive mud.