Optimum tubing size (OTS) selection was traditionally done by using nodal analysis to perform sensitivity analysis on the different tubing sizes. This approach was found to be both cumbersome and time-consuming. This study developed a userfriendly and time-efficient OTS prediction computer model that could allow Petroleum Production Engineers to select the best tubing size for any vertical oil well. The tubing size selection was based on the present operating flow rate, economic considerations and future operating flow rate as defined by the OTS selection criteria of this study. The robustness of the model was tested using tubing sizes ranging from 0.824 to 6.0 inch in a vertical well producing from both saturated and undersaturated oil reservoirs. The 2.750-inch tubing was found the OTS for both scenarios. In the validation, the results obtained from the novel OTS prediction model and Guo et al. (Petroleum production engineering: a computer-assisted approach, Gulf Professional Publishing, Cambridge) spreadsheet program using the Poetmann-Carpenter method were in excellent agreement for operating flow rate but not for operating pressure. Furthermore, the novel OTS prediction model was in excellent agreement with the same spreadsheet program based on modified Hagedorn-Brown correlation for both operating flow rate and pressure. The results showed that the model developed in this study is reliable and can be used in the field for vertical oil wells. The new model could as well inform the Production Engineer when the well would need artificial lift for economic production of the well. It was recommended that Newton-Raphson and modified Hagedorn-Brown methods be used in future study.

Sand production is major problem for the oil and gas industry and solutions to this problem is a continuous process as new challenges arise with time. Various sand control methods have been proposed for tackling the sand production challenge, and research and experience have shown that the use of mechanical sand control methods are more suitable, with gravel packing being the most effective. Gravel packs are proven to be an effective mechanical sand control technique, and a good gravel pack completion design is of great importance to exclude sand from the wellbore while enhancing well productivity. Implementation of sand control by gravel packing in the Niger Delta is usually found to require importation of commercial gravel for the purpose of sand control which is a challenge in terms of high purchasing and transportation costs with import taxes. A solution is presented in this study which involves sourcing for gravel locally and investigating its suitability for sand control by gravel packing. Locally sourced gravel was compared with commercial gravel using sieve analysis and results showed that the locally sourced gravel closely met the requirements of the commercial gravel depicted by slot widths that are close to that obtained by the commercial gravel. Based on the results of this study, favorable performance is expected when locally sourced gravel is used for sand control by gravel packing in unconsolidated formations in the Niger Delta. It is recommended to source for different gravel types from different locations and evaluate using sieve analysis and Laser Particle Size Distribution Analysis to determine suitable gravel types for these type of formations.

Application of nanofluids flooding in the oil and gas industry is recently emerging as enhanced oil recovery methods. Nanoparticles have the ability to alter the rock formation in order to recover oil trapped in the pores of the rock to improve oil recovery. In this study, core plug samples were formulated in the laboratory to investigate the effect of nanoparticles on oil recovery. The formulated core samples were saturated in low salinity brine. However, low salinity brine was used because it has the ability to alter rock wettability. After core flooding with brine for secondary recovery process, extracted oil from Irvingia gabonensis was introduced into the formation to investigate the effect of Irvingia gabonensis on oil recovery. The result of the study showed that magnesium oxide, silicon oxide, aluminum oxide and zinc oxide had oil recovery of 38.1%, 45.6%, 47.7% and 35.1%, respectively. However, when the nanofluids with Irvingia gabonensis were injected into the formation as a displacing agent, the oil recovery greatly improved to 50.3%, 52.0%, 53.2% and 52.4% for (MgO, SiO2, Al2O3 and ZnO). The result of the study showed that nanofluid flooding is a promising potential to improve oil recovery in the Niger Delta.

Lithology identification is an important aspect in reservoir characterization with one of its main purpose of well planning and drilling activities. A faster and more effective lithology identification could be obtained from an ensemble of optimized models using voting classifiers. In this study, a voting classifier machine learning model was developed to predict the lithology of different lithologies using an assembly of different classification algorithms: Support Vector Machine (SVM), Logistic Regression, Random Forest Classifier, K-Nearest Neighbor, and Multilayer Perceptron (MLP) models. The result of the comparative analysis shows that the implementation of the voting classifier model helped to increase the prediction performance by 1.50% compared to the individual models. Despite a small significance at deployment in real scenario it improves the chances of classifying the lithology.

No abstract