Electrical Submersible Pump Operation Optimization with Time Series Production Data Analysis

摘要

Electrical Submersible Pump (ESP) operation faces new challenges with the advent of unconventional completions. Quick production decline means that ESP operators need proactive methods to deploy equipment for applicable flowrate ranges. The benefit for production forecasting and optimization is not only maximized accumulated oil production, but also improved ESP run life. This paper demonstrates the production forecasting capability of a time series data analysis method called Singular Spectrum Analysis (SSA). Applying SSA to customer-provided raw, daily production data results in production data historical matching and future production forecasting. The strength of SSA stems from the ability to make a decomposition of the original series into a summation of the principal independent and interpretable components such as slowly varying trends, cycling components and random noise. [1] The trending component can be used for future production forecasting if it is the only principle component among all decomposed components. Research proves that SSA can be utilized to forecast daily production rates based on a raw production dataset without any preprocessing or transformation of the original series. The trending component revealed by SSA for production prediction matches the forecasting capability of traditional reservoir production decline curve analysis (DC A), and is a considerable time-saving method. Unlike DC A, SSA is a nonparametric, modeless time series analysis method so no assumption for a certain model is needed to be setup before analysis.