A rigorous model to predict the amount of Dissolved Calcium Carbonate Concentration throughout oil field brines: Side effect of pressure and temperature. (1st January 2015)
- Record Type:
- Journal Article
- Title:
- A rigorous model to predict the amount of Dissolved Calcium Carbonate Concentration throughout oil field brines: Side effect of pressure and temperature. (1st January 2015)
- Main Title:
- A rigorous model to predict the amount of Dissolved Calcium Carbonate Concentration throughout oil field brines: Side effect of pressure and temperature
- Authors:
- Ahmadi, Mohammad-Ali
Bahadori, Alireza
Shadizadeh, Seyed Reza - Abstract:
- Highlights: Proposing straightforward method to forecast the amount of dissolved CaCO3 concentration in oil field brines. Comparing robustness of the conventional approaches against suggested GA-LSSVM method. Handling accurate and extensive oil brine data by GA-LSSVM approach. Abstract: Scale deposits impair production reservoirs and foul down hole, surface, and injection equipment. The effects can be rapid and dramatic. One of the most frequent scale-forming compounds in water is calcium carbonate. In a reservoir the production of brine can result in the lowering of the pressure and/or temperature. A pressure drop will decrease the solubility of CaCO3 and thus increase the saturation ratio for CaCO3, whereas a temperature drop will have the opposite effect. The net outcome of a change in pressure and temperature may therefore be a decrease or an increase in the saturation ratio of CaCO3 as specified by the change of temperature relative to the change of pressure. Accordingly, applying robust predictive tools in this research is of high interest in petroleum production systems. The current study plays emphasis on applying the predictive model based on least square support vector machine (LSSVM) to estimate amount of Dissolved Calcium Carbonate Concentration in oil field brines. Genetic algorithm (GA) was used to optimize hyper parameters ( γ and σ 2 ) which are embedded in LSSVM model. Using this method is simple and accurate to determine the amount of Dissolved CalciumHighlights: Proposing straightforward method to forecast the amount of dissolved CaCO3 concentration in oil field brines. Comparing robustness of the conventional approaches against suggested GA-LSSVM method. Handling accurate and extensive oil brine data by GA-LSSVM approach. Abstract: Scale deposits impair production reservoirs and foul down hole, surface, and injection equipment. The effects can be rapid and dramatic. One of the most frequent scale-forming compounds in water is calcium carbonate. In a reservoir the production of brine can result in the lowering of the pressure and/or temperature. A pressure drop will decrease the solubility of CaCO3 and thus increase the saturation ratio for CaCO3, whereas a temperature drop will have the opposite effect. The net outcome of a change in pressure and temperature may therefore be a decrease or an increase in the saturation ratio of CaCO3 as specified by the change of temperature relative to the change of pressure. Accordingly, applying robust predictive tools in this research is of high interest in petroleum production systems. The current study plays emphasis on applying the predictive model based on least square support vector machine (LSSVM) to estimate amount of Dissolved Calcium Carbonate Concentration in oil field brines. Genetic algorithm (GA) was used to optimize hyper parameters ( γ and σ 2 ) which are embedded in LSSVM model. Using this method is simple and accurate to determine the amount of Dissolved Calcium Carbonate Concentration in oil field brines with minimum uncertainty. … (more)
- Is Part Of:
- Fuel. Volume 139(2015)
- Journal:
- Fuel
- Issue:
- Volume 139(2015)
- Issue Display:
- Volume 139, Issue 2015 (2015)
- Year:
- 2015
- Volume:
- 139
- Issue:
- 2015
- Issue Sort Value:
- 2015-0139-2015-0000
- Page Start:
- 154
- Page End:
- 159
- Publication Date:
- 2015-01-01
- Subjects:
- Oil field brine -- Salinity -- Calcium carbonate -- Modeling -- Least squares support vector machine
Fuel -- Periodicals
Coal -- Periodicals
Coal
Fuel
Periodicals
662.6 - Journal URLs:
- http://www.sciencedirect.com/science/journal/latest/00162361 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.fuel.2014.08.044 ↗
- Languages:
- English
- ISSNs:
- 0016-2361
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4048.000000
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- 7241.xml