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The sub-optimum conditions of Nigeria conventional refineries remains a drastic setback since all other industries are intertwined with its outputs. It is noted that amidst all other glaring contributing factors as listed in the study, insufficient large capital funds on the part of private investors and vandalization of pipelines seems to be conspicuous. The stolen crude oil got from the pipelines serve as the beginning point for illegal refining. Asides the issue of quality control of products, their sub-standard operation also affects Nigerians environmentally and economically. The more the Government invest time and resources to stop their operation; the more they spring up like mushrooms. This paper reflects the authors view on achieving a win-win scenario. The upgrading of existing illegal refineries at strategic locations within the country to standard modular refineries seems to be a more feasible and friendly approach. The issue of channeling of intermediate products can be solved through the introduction of clusters, where final products of one become raw material for the other. Intermediate products can also be channeled to Government owned conventional refineries for further processing. From analysis, it is deduced that overall design capacities from clusters have optimum effect on the intermediate environment in terms of meeting demand. Extensive operational training on crude oil refining will help buttress the point while Transfer of Knowledge from official vendors to Nigerian Engineers on how to fabricate modular refining units locally will also help to reduce overall cost.

Thin oil rim are reservoirs with hydrocarbon pay zone not more than 100 ft. Production challenges mainly water, and gas coning arises due to this trait and presence of a larger gas cap and aquifer. To safely exploit from oil rim reservoirs, the right production choices and optimization of production parameters need to be implemented before the commencement of any recovery scheme. A case study reservoir of an oil rim in the Niger-delta producing region is placed on a concurrent production via 8 horizontal producer wells after history matching and optimization of production parameters using a black oil simulator (Eclipse). Six different pattern scenarios of water and gas injection at 3 rates are considered: optimized oil recovery estimates of 5.43%, 4.51%, 5.41%, 4.93%, 5.89%, and 5.82% for inverted direct line water injection, inverted staggered water injection, inverted 4 spot injection, 5 spot water injection, inverted 7 spot water injection, and inverted 9 spot water injection respectively. The incremental oil recovery over the base case (no injection) recorded in like manner are 1.43%, 0.51%, 1.41%, 0.93%, 1.89%, and 1.82%. The results show that pattern water injection at 1000 stb/day is more suitable for optimum oil recovery for oil rims under concurrent production.

Drilling fluids are the most important materials in drilling operations, therefore improving the properties of these fluids are very essential in order to meet up with the increase in demands and required standards. In this experimental study, Solanum tuberosum formulated biopolymer was used to improve the water based mud rheological properties and artificial neural network predicted data for (PV) plastic viscosity, (AP) apparent viscosity and (YP) yield point. Artificial neural network (ANN) was used to train the rheological properties of the formulated mud and the network developed predicted the rheological properties of an untrained combination of bentonite and modified biopolymer. The main target is to regenerate or predict the rheological properties of the formulated mud; (AP) apparent viscosity, (YP) yield point and (PV) plastic viscosity generated originally from experimental procedures but this time using the ANN. The mean average error target was set to around 5-10%. As a model for choosing the best ANN architecture for predicting target value, two statistical parameters, mean squared error (MSE) and correlation coefficient (R2) were utilized. A system with the lower estimations of MSE and the higher estimations of R2 gives more precise forecasts. Three different network were created and the two statistical parameters were used to determine the best number of neurons in the hidden layer. The developed neural network with best prediction has Root Mean Square Error (MSE) of 1.25221 and overall correlation coefficient (R2) of 0.99373 for the predicted plastic viscosity, yield point and apparent viscosity