Modeling a Successful Pilot Application of Novel Fine Stabilizer Technology to Predict Oil Well Productivity: A Benchmark for Field Replication

Abstract

A novel developed fines stabilizer (FS) chemical was used to successfully treat an oil well by reducing the decline rate from 17% per month (pre-treatment) to 1.5%/month (post-treatment for a 6-month monitoring period). Following this success story, more qualified candidates are flocking to replicate it. As a result, the challenge now is to predict each candidate's post-treatment performance. The paper focuses on the development of numerical modelling of a fine stabilizer chemical using a chemical reaction approach to understand the mechanistic process and will serve as a benchmark for future technology replication performance prediction. The mechanisms of the novel developed FS chemical were observed in the laboratory through fine particle coagulation and flocculation. The methodology of this study is to translate the mechanisms and key features associated with observed laboratory data into a scripted chemical reaction program that was coupled with a numerical reservoir simulator. Prior to well modelling, laboratory coreflooding data were validated. To properly represent the mechanistic process with a chemical reaction approach and capture the near wellbore effect, a single well model with local grid refinement was developed. The chemical reaction feature provides a versatile toolbox for modeling complex processes involving chemical and physical interactions. The actual production data history matching process with was carried out to investigate key important parameters. The FS chemical reaction was divided into two stages: damage and treatment. The damage was defined as fines deposited in the pore throat plugging and reducing permeability near the wellbore. Fines migration frequently indicates a build-up of fines in the near-wellbore region over time. As a result, the damage caused by these deposited fines reduces permeability. The novel FS chemical will remove deposited fines through micro-flocs and coagulating fines as solid. The history matching process was completed for core data and the mechanistic model with production and pressure data, and acceptable matches were obtained using rate control. The mechanistic model was tested with constrain at bottomhole pressure for few months historical data. It can predict the production performance with good accuracy compared to actual welltest data. Key parameters of FS injection were observed because of this research and can be used as a benchmark in the future to demonstrate the concept of extending oil well productivity and predicting field replication to recurrence the success story.