Artificial lift systems (ALS) should be thoroughly analyzed to minimize non-productive time, increase production and reserves, reduce cost and failure, and maximize capital efficiency. This work proposes a novel systematic for ALS analysis approach that implements data analytics for historical performance tracking and benchmarking, heuristic diagnostics for suboptimal identification, and reliable model-based solutions for opportunity evaluation. The proposed methodology follows the principles of integrated reservoir management and greatly reduces the time to manage ALS systems by combining expert knowledge, data analysis with model-based calculation in pursuit of extreme efficiency and high accuracy. Our efficient workflow builds from intelligent data pre-processing capabilities, which facilitate fast and structured analysis of complex datasets. The metrics and scoreboard are intuitive, comprehensive and unique, and are scarcely documented in technical literature. These evaluations from various aspects generate different paths for asset management with diversified views.
By following a very systematic approach and incorporating industry standards and propriety analysis and metrics, this workflow leads to fast delivery of analysis/results that enable production engineers to make smarter decisions faster. Our solution makes the analysis unbiased, and integration of new datasets super easy. This could greatly benefit executive decision-making and operational practices in a field.