The interplay among reservoir heterogeneities, structural complexities and unfavorable mobility ratios are usually responsible for premature water breakthrough in brown fields across the world. Recently, a deep conformance control technology, known as Thermally Activated Particle (TAP), has been successful in addressing this challenge. Limited intervention and lower deployment cost make it very attractive for mature waterflooded fields. Cerro Dragón is a giant field located in San Jorge Gulf basin with multi-layered channel deposits. Presence of highly conductive channels and unfavorable mobility ratios have severely impacted the sweep efficiency, resulting in low oil recovery and high water production. Building on the learnings from the previous pilots, TAP has been recently deployed at a segment scale in one of the blocks of Cerro Dragón field. This paper shares the technical details behind the screening, designing and the deployment of TAP technology. After initial screening of multiple candidates, a reservoir segment has been selected for TAP implementation. Inter well tracer data with comprehensive injection/production data analysis identified the communicating wells and thief zones volumes were then estimated around each injector. The estimated thief zones volumes were also confirmed by volumetric calculation. Thermal modeling and numerical simulation were utilized using a representative and history matched model to finalize the size, concentration and the proper activation of injected TAP molecules. Subsequently, in early 2018, nearly 360 metric tons of TAP was safely deployed into six target injectors. This campaign met all design guidelines, all planned surveillance data was acquired, and the project was executed on time and on budget. Post deployment, frequent sampling at offset producers was performed confirming no breakthrough of un-activated polymeric particles. Oil rates and WOR trends are currently being monitored, as part of the longer-term surveillance plan, to quantify incremental benefits from TAP EOR technology. Previous 2011 implementation and positive results in other segments of the field were an important input and they are also described in this paper. The results of this treatment will provide very helpful guidelines that can be used in any brown fields to improve the efficiency of waterflooding especially in highly heterogeneous reservoirs with low waterflooding performance.

This paper presents the process and results of the application of Data Physics to optimize production of a mature field in the Gulf of San Jorge Basin in Argentina. Data Physics is a novel technology that blends the reservoir physics (black oil) used in traditional numerical simulation with machine learning and advanced optimization techniques. Data Physics was described in detail in a prior paper (Sarma, et al SPE-185507-MS) as a physics-based modeling approach augmented by machine learning. In essence, historical production and injection data are assimilated using an Ensemble Kalman Filter (EnKF) to infer the petrophysical parameters and create a predictive model of the field. This model is then used with Evolutionary Algorithms (EA) to find the pareto front for multiple optimization objectives like production, injection and NPV. Ultimately, the main objective of Data Physics is to enable Closed Loop Optimization. The technology was applied on a small section of a very large field in the Gulf of San Jorge comprised of 134 wells including 83 active producers and 27 active water injectors; up to 12 mandrels per well are used to provide with selective injection, while production is carried out in a comingled manner. Production zonal allocation is calculated using an in-house process based on swabbing tests and recovery factors and is used as input to the Data Physics application, while injection allocation is based on tracer logs performed in each injection well twice a year. This paper describes the modeling and optimization phases as well as the implementation in the field and the results obtained after performing two close loop optimization cycles. The initial model was developed between October and December 2018 and initial field implementation took place between January to March 2019. A second optimization cycle was then executed in January 2020 and results observed for several months.