Articles

Gas Compressibility Factor Prediction Using Machining Learning Algorithmic Protocol for Niger – Delta Gas Reservoir

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The gas compressibility factor also known as Z-factor plays an important role in obtaining thermodynamic properties of natural gas reservoir fluid property. Typically, empirical correlations and complex equation of state have been applied to determine this parameter in absence of laboratory measurements. However, high cost of running experimental measurement, poor performance and some limitations associated with these existing correlations have made the researchers to use intelligent models instead. Therefore, this study aimed at adopting support vector machine algorithm to forecast gas compressibility factor and to validate its performance with predictions from Artificial Neural network (ANN) and some existing correlations using statistical and performance plot analysis. A total of 519 data sets from Niger Delta was used in developing the model, out of it, 70 percent was used for training, 20 percent for testing and 10 percent for validation using MATLAB tool. From the statistical analysis result, it was observed that the new developed model did better than other existing methods with numerical value of 0.1997 rank, 0.0009 mean absolute error and 0.98 of coefficient of correlation using the test data. The cross plot of the support vector machine model gave the tightest cloud along 45o reference line. The residual (error) associated with the performance was impressive which was done to observe the distribution and the interval at which the error is minimal.

Determination of Physico-Chemical Parameters of St. Nicholas River, Bayelsa State, Niger Delta, Nigeria

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The Physicochemical parameters of St. Nicholas River were determined from October, 2020 to September, 2021. Three sampling stations were identified and used throughout the duration of the studies. They were Meinmokiri, Ebierewo-bugo and Egeinkiri sampling stations. The physicochemical parameters were measured in situ with Mercury in Glass Thermometer for Temperature, Hand held Digital Salinometer for  (Model AR8012) for Salinity, pH meter (Model PH-009(1)) for pH, Digital conductivity tester (Model AR8011) for Conductivity, TDS meter (Model AR8012) for Total Dissolved Solid, Digital Water Velocity meter (Model PF LV550) for Water Velocity, Secchi Disk for Transparency, Digital Depth Founder (Model SD-5) for Water Depth and Dissolved Oxygen Meter (Model OM-51-10) for Dissolved Oxygen. The mean values of the Physicochemical parameters of water at the three sampling stations are temperature; 29.98±0.550C, Dissolved Oxygen DO; 11.89±0.33mg/L, Biochemical Oxygen Demand BOD; 2.06±0.26 mg/L, Salinity; 16.80±2.06 PPT, Conductivity; 27.54±3.03µS/cm, Total Dissolved Solids TDS; 9.44±0.50mg/L, pH; 7.41±0.14, Water Depth; 0.58±0.06m, Transparency; 27.27±0.03cm and Water Velocity; 54.79±5.71m3/s. These physicochemical parameters values are comparable with other water bodies in the Niger Delta indicating contaminated waters. The contamination was caused by anthropogenic activities in terms of parameters assessed, therefore mitigation should be put in place for biodiversity conservation and sustainability of the ecosystem.