MODELADO DE PREDICCIÓN DE LA EFECTIVIDAD DE TRATAMIENTOS QUÍMICOS PARA REMOCIÓN DE ORGÁNICOS, MEDIANTE RMN 1H Y QUIMIOMETRÍA

• Main Authors: Daniel Molina Velasco, Edilson Odilio García Navas, Emiliano Ariza León, Fernando Enrique Calvete González, Mario Quintero González
• Format: Article
• Language: Spanish
• Published: Universidad Industrial de Santander 2013-06-01
• Series: Revista Fuentes El Reventón Energético
• Subjects: Reservoir damage; 1H NMR; Chemometrics; functional groups; Chemical treatments; Organics deposits; Paraffin
• Online Access: https://revistas.uis.edu.co/index.php/revistafuentes/article/view/3610/3796
• ISSN: 1657-6527; 2145-8502

One of the most common problems presented in oil production worldwide are paraffinic deposits, which occur due to changes in the thermodynamic conditions of the original oil. When this material is deposited in the formation, causes a permeability reduction of the flow channels causing a plugging in the perforations, increasing damage to the formation and decreasing the fluid flow. The difficulty in finding new reserves, has led the industry to focus on mature fields in order to increase the recovery factor. Historically Colorado Field, has presented precipitation and deposition of paraffin; this is evidenced by the drastic fall of the production and in the periodical cleaning processes and closure of wells. One of the most common method used for removing of paraffin is the chemical treatment, which requires a previous analysis and compatibility tests to determine the feasibility of the chemical being injected. This research headed to the developing a prediction model for the effectiveness results of chemical treatments for removing of paraffin from the results obtained by the 1H-NMR technique, Chemometrics and compatibility testing. The development of this research helped to define the most significant functional groups presented in the chemical treatments feasible to apply for paraffin deposits remotion of the Mugrosa formation of Colorado Field and leaves a base for development of new products; also poses a methodology for the selection of new chemical treatments using a statistical model for predicting, preventing routine testing with rock and fluids, saving time and resources, this can be replicated to other fields with similar problems.