Application of the closed loop industrial internet of things (IIoT)‐based control system in enhancing the oil recovery factor and the oil production

Abstract

Abstract A non‐linear large scale stochastic optimisation model for enhancing the oil production and the recovery factor of the offshore oil reservoirs is proposed. The model aims at minimising the miss‐match between mathematical model and the actual dynamic behaviour of the reservoir and the exploitation time, while maximising the oil production and the recovery factor. The model involves the three dimension (3D) oil reservoirs equipped with a few vertical injection and production wells. The limited number of wells is one of the major features of the common oil reservoirs in the middle‐east region. The proposed model consists of the primarily mathematical model of the 3D reservoir, a model update algorithm and a large scale constrained non‐linear optimisation algorithm. The input to this model is the daily production rate of the oil, natural gas and water produced from the oil reservoir and the output is the optimal injection rate to be injected to the injection wells in order to maximise the oil production and the recovery factor. In order to evaluate the performance of this model, the authors apply this model on part of one of the Iran's offshore oil reservoirs and study the performance improvement due to the proposed model and compare its performance with the performance of the available Improved Oil Recovery (IOR) technique. It is illustrated that the proposed model can increase the oil production from the reservoir up to 47.96% and reduce the exploitation period up to 66.66% compared with those of the available technique.

Keywords

• Kalman filters
• predictive control