000			02154nmc a2200241 4500
005			20260520010449.0
008			260224s xxu
082			_a068.82 553.28 C62 15730
210			_aSimposio Campos Maduros
245	0	0	_aCOMBINANDO MACHINE LEARNING Y FÍSICA DE RESERVORIOS PARA LA OPTIMIZACIÓN DE YACIMIENTOS MADUROS EN LA CUENCA DEL GOLFO SAN JORGE, ARGENTINA
270			_a12/10/2023 ; 12/10/2023
300			_a16 p. ; 23-38
520			_aThe Zorro project is located inside the Cerro Dragon field which is subdivided in nine blocks due to main faults delimitating the structure and the reservoir rock distribution. The majority part of the oil production of the project is associated to secondary recovery. This work presents the results of the application of a waterflood optimization workflow powered by data physics modeling using data from the Golfo San Jorge basin. The proposed workflow combines traditional reservoir engineering equations with machine learning, data assimilation techniques and advanced optimization algorithms. The models incorporate real data, which can be updated continuously, allowing the engineers to evaluate different scenarios and identify the optimum injection plans for the asset. As the models honors physical principles, this methodology provides long-term predictions and ensure physical feasible results. The result is a sample of the application of a dynamic workflow and how the digital transformation process is providing value to the oil and gas industry. The obtained production increase shows how multidisciplinary teams are enhanced by the inclusion of technology, managing to optimize processes and production.
773			_g
942			_cCONGTP
100	1		_aGalliano, Federico _9767
100	1		_aMedda, Fernando _959482
100	1		_aGutiérrez, Fernando _959512
100	1		_aPlotno, Sebastián _961478
111	2		_aCongreso de Exploración y Desarrollo de Hidrocarburos (11er : 2022 nov. 8 - 11 : Mendoza) _961064
999			_c196175 _d196175
856			_uhttps://biblioteca.iapg.org.ar/ArchivosAdjuntos/Conexplor2022/Simp.CamposMaduros/campos-maduros02.pdf