Dynamic Well Conversion and Rate Optimisation Using Ensemble-based Method

Abstract

This work describes the use of ensemble-based optimisation (EnOpt) approach to maximise a pre-defined economic criterion, the Net Present Value (NPV), while considering two well parameters simultaneously, the well type and the corresponding well control rates for a given subsurface model. EnOpt has previously been used to optimise well control settings for a fixed number of producers and injectors. It relies on the ensemble of well control settings from which the sensitivities of a suitably constructed objective function are approximated. The sensitivities to the control settings are then used with a steepest descent algorithm to determine optimal well controls. Our proposed approach relies, instead, on the selection of three variables for each well: liquid production rate, injection rate, and well conversion time. Well conversion time dictates the optimal time to switch a producer to an injector, which also plays a key role in determining well type and the optimal number of injectors and producers. The superior performance of our approach is indicated by the increase in NPV, cumulative oil production and sweep efficiency within a predefined reservoir life. This work presents three case studies: only well-type optimisation, only well rate control optimisation and simultaneous well conversion and well rate optimisation. We also discuss the effect of localisation used in the EnOpt algorithm on well conversion and well control optimisation. Based on numerical results of the reservoir PUNQ-S3 model, our approach revealed potential benefits of dynamic well conversion and rate control to optimise waterflooding. Comparing the proposed approach with the traditional EnOpt workflow, using a fixed number of producers and injectors, clearly indicates improved project revenues with a comparable computational effort.

Optimisation of costs and carbon savings in relation to the economic dispatch problem as associated with power system operation

“…At the end of the EnOpt simulation, the best control settings are the optimal control variables that minimise the objective function [Chen et al, 2009, Jahangiri, 2012, Nwaozo, 2006. EnOpt has been well established as the optimisation algorithm in searching for optimal operating strategy in reservoir management [Chen et al, 2009, Jahangiri, 2012, Chen et al, 2010a, Petvipusit, 2011. EnOpt is applied in this paper in order to develop operating strategies in the ED problem that optimise both costs and carbon savings.…”