Understanding Water Injection From a Control Engineering Approach Using the ERR-OLS Algorithm

Abstract

AbstractUsing existing production/injection rates is convenient for analysing a mature field as the access to relevant historical records does not cause any extra cost and a large amount of significant information can be extracted from the dataset by means of good analysis methods.In the proposed work, each of the production wells is considered as an output variable while injection rates from other wells, which have an effect on the outputs, are treated as input variables. Control and System Engineering Approaches motivate such considerations.Since injection and production are time dependent variables, time delays were considered in the analysis. This consideration allows recognition of how long it takes for the injection on a certain well to affect production.The Error Reduction Ratio (ERR) algorithm, along with the Orthogonal Least Squares (OLS) method is used to determine how each of the injection wells affects production; this is achieved by sequentially orthogonalising input variables according to the order of their contribution to production.The algorithm can be customised to explain a certain variance threshold in production resulting from injection wells. For the present case study, a 19-year monthly dataset from Beatrice, an operating field in the North Sea is studied.It is observed that the historical datasets contain missing values. In order to address this problem, missing values are predicted by using an autoregressive linear model.