Mathematical Modelling of Oil Viscosity at Bubble point Pressure and Dead Oil Viscosity of Nigerian Crude

2019 ◽  
Author(s):  
Yisa Adeeyo
Keyword(s):  
Mathematical Modelling ◽  
Oil Viscosity ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Point Pressure

Mechanism of Live Oil Viscosity Anomaly Near to Bubble Point Pressure

2017 ◽  
pp. 35-45
Author(s):  
Baghir Suleimanov ◽  
◽  
E.M. Abbasov ◽  
M.R. Sisenbayeva ◽  
◽  
...  
Keyword(s):  
Oil Viscosity ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Point Pressure ◽  
Live Oil

Developing a Model to Predict Oil Viscosity Using Specific Gravity and Formation Volume Factor as Correlating Parameters

2021 ◽  
Author(s):  
Henry Ijomanta ◽  
Olorunfemi Kawonise
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Fluid Composition ◽  
Oil Viscosity ◽  
Volume Factor ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Reservoir Fluid ◽  
Point Pressure ◽  
Oil Density

Abstract This paper presents the research work on using a machine learning algorithm to predict the viscosity of Niger Delta oil reservoirs using formation volume factor and fluid density at bubble point pressure as correlating parameters. Oil Viscosity stands out when considering the amount of oil recoverable from the reservoir hence it is an important input into the recovery factor computation, material balance analysis, reservoir simulation/history match, EOR evaluations and many other applications. Laboratory techniques of obtaining oil viscosity are quite expensive and time consuming, hence the need for various mathematical correlations developed for its estimation. Majority of the correlations make use of empirical and experimental relationships developed from analyzing oil samples to obtain a trend to predict viscosity mostly for a basin. None of these has been developed for oil viscosity for Niger Delta fluids. Viscosity has been globally defined as the resistance to shear stresses within the fluid or the resistance of the fluid molecules to deformation. For a typical reservoir fluid system, where the liquid and gas exist in dynamic equilibrium, reservoir fluid composition along with temperature and pressure has been established to determine reservoir fluid viscosity1. Hence for an isothermal system and at a defined pressure in the reservoir the viscosity will be dependent on largely the composition. The reservoir fluid composition is also represented by the reservoir fluid density and the formation volume factor; therefore it is possible to deduce the viscosity of reservoir fluids from the oil density and formation volume factor even though a direct relationship has not been established between these parameters. Therefore, a correlation that can establish a relationship between the specific gravity (density) and FVF with viscosity will have significant value in the oil and industry. The data used for this analysis includes viscosity, formation volume factor, oil density at 2800 sample bubble point pressure. The data was obtained by analyzing over 3500 PVT Analysis reports, extracting the data points using a python work program, cleaning up the data and removing erroneous data, performing preliminary analysis to establish baseline relationships between the data. Supervised learning using a classification tree model was used as the machine learning approach. Seven different machine learning algorithms were reviewed, and the Random Forest Regressor was selected as the most suitable algorithm for the prediction. The model prediction results were quiet encouraging as the model was able to predict viscosity within 10% deviation from the experimental viscosity for over 80% of the cases resulting in about 90% prediction accuracy. The analysis of the results further revealed that the model could better predict viscosity of Medium to Light oil with an R2 value of between 0.90-0.96 without adjusting some obvious erroneous data points. Future of this research work will involve further in-depth analysis which will merge the preliminary QC plots with the results to evaluate the effect of the outlier sample points on the final predictability of the model. Also explore other machine learning models to further improve predictability and be able to predict viscosity across other pressure values other than the bubble point pressure to capture viscosity along the producing life of the reservoir.

Experimental investigation of degassing properties of geothermal fluids

2021 ◽  
Author(s):  
Chris Boeije ◽  
Pacelli Zitha ◽  
Anne Pluymakers
Keyword(s):  
High Pressure ◽  
Temperature Drop ◽  
Hot Water ◽  
Visual Cell ◽  
Dissolved Gas ◽  
Bubble Point ◽  
Free Gas ◽  
Bubble Point Pressure ◽  
Geothermal Fluids ◽  
Point Pressure

<p>Geothermal energy, the extraction of hot water from the subsurface (500 m to 5 km deep), is generally considered one of the key technologies to achieve the demands of the energy transition.  One of the main problems during production of geothermal waters is degassing. Many subsurface waters contain substantial amounts of dissolved gasses. As the hot water travels up the production well, the pressure and/or temperature drop will cause dissolved gas to come out of the solution. This causes several problems, such as corrosion of the facilities (due to pH changes and/or degassing-related precipitation) and in some cases even to blocking of the reservoir as the free gas limits the water flow.  To better understand under which conditions free gas nucleates, we need confirmation of theoretical bubble point pressure and temperature, and understand what controls the evolution of the bubble front:  i.e. what are the conditions under which free gas emerges from the solution and at what rate are bubbles created?</p><p>An experimental setup was designed in which the degassing process can be observed visually. The setup consists of a high-pressure visual cell which contains water saturated with dissolved gas at high-pressure. The pressure within the cell can be reduced in a reproducible manner using a back-pressure regulator at the outlet of the system. A high-speed camera paired with a uniform LED light source is used to record the degassing process. The pressure in the cell is monitored using a pressure transducer which is synchronized with the camera. The resulting images are then analysed using a MATLAB routine, which allows for determination of the bubble point pressure and rate of bubble formation.</p><p>The first two sets of experiments at ambient temperatures (~20 <sup>o</sup>C) were carried out using two different gases, N<sub>2</sub> and CO<sub>2</sub>. Initial pressure was 70 and 30 bar for the N<sub>2</sub> and CO<sub>2</sub> experiments respectively. In these first experiments we determined the influence of the initial fluid used to pressurize the system. Using gas as the initial fluid causes a large amount of bubbles, whereas only a single bubble was observed for a system where degassed water is used as the initial fluid. An intermediate system where degassed water is pumped into a system full of air at ambient conditions and is subsequently pressurized yields a number of bubbles in between the two systems described previously. All three methods give reproducible bubble point pressures within 2 bar (i.e. pressure where the first free bubble is formed). There are clear differences in bubble point between N<sub>2</sub> and CO<sub>2</sub>.</p><p>A series of follow-up experiments is planned that will investigate specific properties at more extreme conditions: at higher pressures (up to 500 bar) and temperatures (500 <sup>o</sup>C) and using high-salinity brines (2.5 M).</p>

Measurement of CO2Solubility in Ionic Liquids: [BMP][Tf2N] and [BMP][MeSO4] by Measuring Bubble-Point Pressure

2011 ◽  
Vol 56 (4) ◽  
pp. 1197-1203 ◽  
Author(s):  
Joon-Hyuk Yim ◽  
Ha Na Song ◽  
Ki-Pung Yoo ◽  
Jong Sung Lim
Keyword(s):  
Ionic Liquids ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Point Pressure
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