59 research outputs found
Predicting the Onset of Asphaltene Precipitation in Heavy Crude Oil Using Artificial Neural Network
No full textIn flow assurance issues, asphaltene precipitation in crude oil tends to block the wellbore, the production tubing, the flowlines and as well as surface facilities, thereby reducing the quantity of crude oil that could be recovered during recovery, hence there is need to predict the onset conditions under which asphaltene would precipitate. Previous models (thermodynamic/colloidal) attempt to predict the onset of asphaltene precipitation using the solubility parameter, crude oil/n-alkane mixture and the refractive index of asphaltene. However, due to the constraint in handling numerous and complex data set, this work attempts to predict the onset of asphaltene precipitation (onset solvent to bitumen/asphaltene ratio as a function of temperature and pressure) using artificial neural network (Neurosolution 6). The results obtained show that the onset solvent bitumen ratio obtained using the neural network was close to the experimental (desired) onset solvent bitumen ratio (MSE of 0, Err% 0.0553 for the training set and MSE of 0.006581, Err% of 3.343 for the testing set) with an average absolute deviation of 3.56. Artificial neural network is a robust predictive tool for predicting the onset of asphaltene precipitation in heavy crude oil. Keywords: Asphaltene, Precipitation, Structure of asphaltene, Neural Network, Onset and amoun
Evaluation of Water Injection Performance in Heterogeneous Reservoirs Using Analytical Hierarchical Processing and Fuzzy Logic
No full textPredictive Modeling of Gas Production, Utilization and Flaring in Nigeria using <i>TSRM</i> and <i>TSNN</i>: A Comparative Approach
No full textModelling the Impact of Oil Price Volatility on Investment Decision Making in Marginal Fields’ Development in Nigeria
No full textImproved Method for the Estimation of Minimum Miscibility Pressure for Pure and Impure CO2–Crude Oil Systems Using Gaussian Process Machine Learning Approach
No full textThe minimum miscibility pressure (MMP) is one of the critical parameters needed in the
successful design of a miscible gas injection for enhanced oil recovery purposes. In this
study, we explore the capability of using the Gaussian process machine learning
(GPML) approach, for accurate prediction of this vital property in both pure and impure
CO2-injection streams. We first performed a sensitivity analysis of different kernels and
then a comparative analysis with other techniques. The new GPML model, when
compared with previously published predictive models, including both correlations and
other machine learning (ML)/intelligent models, showed superior performance with the
highest correlation coefficient and the lowest error metrics
Investigating The Carrying Capacity And The Effect Of Drilling Cutting On Rheological Properties Of Jatropha Oil Based Mud
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Development of Local Demulsifier For Water - In- Oil Emulsion Treatment
No full textSeparation of water from oil before transportation or refining is very essential for economic and operational reasons. Several methods in use have suffered from drawbacks such as high costs of production and environmental concerns. The need to develop a cost effective and efficient demulsifier in treating crude oil emulsions without compromising quality and environmental safety is a major concern to the oil industry worldwide. Hence, this study aims at developing and formulating cheap and environmentally safe demulsifier from plant extracts. Single plant screening of two groups of plant samples; A, B, C (Calotropis procera: dry and fresh extract and Citrus limonum: fresh extract) and D, E, F (Jathropha curcas: dry and fresh extract and Thevetia ferifolia: fresh extract) using bottle test and centrifuge methods was conducted at 700C for 300 seconds. The effect of modifier (ether, ethylacetate, ethylene glycol, ethanol and buthanol) was determined using the same methods. The optimum concentrations in g/ml for combination of oil and water-soluble demulsifier was determined using prediction profiler plot. Model formulation was based on 23 full factorial (custom) experimental design for the two groups and the final product was compared with commercial demulsifier; product code W054 in emulsion treatments
Geochemical Fingerprinting of Oil Impacted Soil and Water Samples in Some Selected Areas within the Niger Delta
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