scholarly journals Deep Learning Approach for Robust Prediction of Reservoir Bubble Point Pressure

ACS Omega ◽  
2021 ◽  
Author(s):  
Fahd Saeed Alakbari ◽  
Mysara Eissa Mohyaldinn ◽  
Mohammed Abdalla Ayoub ◽  
Ali Samer Muhsan
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Point Pressure ◽  
Robust Prediction

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

Bubble point pressure estimates from Gibbs ensemble simulations

2007 ◽  
Vol 260 (2) ◽  
pp. 195-198 ◽  
Author(s):  
A. Özgür Yazaydın ◽  
Marcus G. Martin
Keyword(s):  
Gibbs Ensemble ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Ensemble Simulations ◽  
Point Pressure
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