Characterization of some crude oil samples from Niger delta area of Nigeria using infrared absorption spectrometric technique

2019 ◽  
Author(s):  
Chem Int

Crude oil obtained from different locations in the Niger Delta area of Nigeria was analyzed by Infrared Absorption Spectrometric technique using Nicolet IS5 Fourier Transform spectrometer to identify the functional groups and compounds in the samples. Results obtained revealed that the amount of surface active components in the crude was in following trend, sample E > sample A > sample C > sample D > sample B, while the level of biodegradability follows the trend; sample E > sample B > sample A > sample C > sample D. Results show that Sample E has the highest amount of surface active components as well as the highest level of biodegradability. Sample B has the least amount of surface active components, while sample D has the least level of biodegradability. The presence of functional groups such as amines, sulfates, isocyanates, hydroxyl, halo compounds, thiols and nitro compounds in the crude increases the surface active properties of the crude due to their polarity and hydrophilicity, which influences the interfacial tension of the crude and the oil recovery efficiency. The level of crude biodegradability is dependent on the amount of aliphatic saturates in the crude, the concentration of acidic components as well as sulphur and nitrogen compounds in the crude. Infrared spectroscopy identifies the functional group in crude samples, this is necessary in knowing the amount of the surface active components and the level of biodegradability of crude oil.

2020 ◽  
Vol 34 (11) ◽  
pp. 13650-13663
Author(s):  
Kion Norrman ◽  
Kristian B. Olesen ◽  
Morten S. L. Zimmermann ◽  
Rakan Fadhel ◽  
Pieter Vijn ◽  
...  

1982 ◽  
Vol 22 (02) ◽  
pp. 245-258 ◽  
Author(s):  
E.F. deZabala ◽  
J.M. Vislocky ◽  
E. Rubin ◽  
C.J. Radke

Abstract A simple equilibrium chemical model is presented for continuous, linear, alkaline waterflooding of acid oils. The unique feature of the theory is that the chemistry of the acid hydrolysis to produce surfactants is included, but only for a single acid species. The in-situ produced surfactant is presumed to alter the oil/water fractional flow curves depending on its local concentration. Alkali adsorption lag is accounted for by base ion exchange with the reservoir rock. The effect of varying acid number, mobility ratio, and injected pH is investigated for secondary and tertiary alkaline flooding. Since the surface-active agent is produced in-situ, a continuous alkaline flood behaves similar to a displacement with a surfactant pulse. This surfactant-pulse behavior strands otherwise mobile oil. It also leads to delayed and reduced enhanced oil recovery for adverse mobility ratios, especially in the tertiary mode. Caustic ion exchange significantly delays enhanced oil production at low injected pH. New, experimental tertiary caustic displacements are presented for Ranger-zone oil in Wilmington sands. Tertiary oil recovery is observed once mobility control is established. Qualitative agreement is found between the chemical displacement model and the experimental displacement results. Introduction Use of alkaline agents to enhance oil recovery has considerable economic impetus. Hence, significant effort has been directed toward understanding and applying the process. To date, however, little progress has been made toward quantifying the alkaline flooding technique with a chemical displacement model. Part of the reason why simulation models have not been forthcoming for alkali recovery schemes is the wide divergence of opinion on the governing principles. Currently, there are at least eight postulated recovery mechanisms. As classified by Johnson and Radke and Somerton, these include emulsification with entrainment, emulsification with entrapment, emulsification (i.e., spontaneous or shear induced) with coalescence, wettability reversal (i.e., oil-wet to water-wet or water-wet to oil-wet), wettability gradients, oil-phase swelling (i.e., from water-in-oil emulsions), disruption of rigid films, and low interfacial tensions. The contradictions among these mechanisms apparently reside in the chemical sensitivity of the crude oil and the reservoir rock to reaction with hydroxide. Different crude oils in different reservoir rock can lead to widely disparate behavior upon contact with alkali under varying environments such as temperature, salinity, hardness concentration, and pH. The alkaline process remains one of the most complicated and least understood. It is not surprising that there is no consensus on how to design a high-pH flood for successful oil recovery. One theme, however, does unify all present understanding. The crude oil must contain acidic components, so that a finite acid number (i.e., the milligrams of potassium hydroxide required to neutralize 1 gram of oil) is necessary. Acid species in the oil react with hydroxide to produce salts, which must be surface active. It is not alkali per se that enhances oil recovery, but rather the hydrolyzed surfactant products. Therefore, a high acid number is not a sufficient recovery criterion, because not all the hydrolyzed acid species will be interfacially active. That acid crude oils can produce surfactants upon contact with alkali is well documented. The alkali technique must be distinguished from all others by the fundamental basis that the chemicals promoting oil recovery are generated in situ by saponification. SPEJ P. 245^


2011 ◽  
Vol 33 (22) ◽  
pp. 2089-2103
Author(s):  
A. S. Abdulkareem ◽  
J. O. Odigure ◽  
M. D. O. Otaru ◽  
M. B. Kuranga ◽  
A. S. Afolabi

Author(s):  
Isemin Isemin ◽  
Akinsete Oluwatoyin ◽  
Akpabio Julius

Oil viscosity is one of the most important physical and thermodynamic property used when considering reservoir simulation, production forecasting and enhanced oil recovery. Traditional experimental procedure is expensive and time consuming while correlations are replete however they are limited in precision, hence need for a new Machine Learning (ML) models to accurately quantify oil viscosity of Niger Delta crude oil. This work presents use of ML model to predict gas-saturated and undersaturated oil viscosities. The ML used is the Support Vector Machine (SVM), it is applicable for linear and non-linear problems, the algorithm creates a hyperplane that separates data into two classes. The model was developed using data sets collected from the Niger Delta oil field. The data set was used to train, cross-validate, and test the models for reliability and accuracy. Correlation of Coefficient, Average Absolute Relative Error (AARE) and Root Mean Square Error (RMSE) were used to evaluate the developed model and compared with other correlations. Result indicated that SVM model outperformed other empirical models revealing the accuracy and advantage SVM a ML technique over expensive empirical correlations.


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