Numerical simulation and optimisation of polymer flooding in a heterogenous reservoir : constrained versus unconstrained optimisation

Polymer flooding offers the potential to recover more oil from reservoirs but requires significant investments which necessitate a robust analysis of economic upsides and downsides. Key uncertainties in designing a polymer flood are often reservoir geology and polymer degradation. The objective of this study is to understand the impact of geological uncertainties and history matching techniques on designing the optimal strategy for, and quantifying the economic risks of, polymer flooding in a heterogeneous clastic reservoir. We applied two different history matching techniques (adjoint-based and a stochastic algorithm) to match data from a prolonged waterflood in the Watt Field, a semi-synthetic reservoir that contains a wide range of geological and interpretational uncertainties. Next, sensitivity studies were carried out to identify first-order parameters that impact the Net Present Value (NPV). These parameters were then deployed in an experimental design study using Latin Hypercube Sampling to generate training runs from which a proxy model was created using polynomial regression. A particle swarm optimization algorithm was employed to optimize the NPV for the polymer flood. The same approach was used to optimize a standard water flood for comparison. Optimizations of the polymer flood and water flood were performed for the history matched model ensemble and the original ensemble. The Adjoint technique yielded a better quality match compared to stochastic history matching, whereas, the stochastic history matching resulted in a more diverse set of history matched ensemble. The optimal strategy to deploy the polymer flood and maximize NPV varies based on the history matching technique. The average NPV and the variance is predicted to be higher by 4% ($600 million) and 1.9% ($149 million) respectively in the stochastic history matching compared to the adjoint technique. This difference is due to the ability of the stochastic algorithm to explore the parameter space more broadly, which created situations where the oil in place was shifted upwards, resulting in higher NPV. Optimizing a history matched ensemble leads to a narrower range in absolute NPV compared to optimizing the original ensemble. This difference is because the uncertainties associated with polymer flooding are not captured during history matching. The result of cross comparison, where an optimal polymer design strategy for one ensemble member is deployed to the other ensemble members, predicted a decline in NPV but surprisingly still shows that the overall NPV is higher than for an optimized water food, even for sub-optimal polymer injection strategies. This observation indicates that a polymer flood could be beneficial compared to a water flood, even if geological uncertainties are not captured properly. This thesis reported the bias of stochastic algorithm by creating reservoir models where oil in place were shifted upwards. This can be further investigated and addressed.

Grantor dc:publisher

Heriot-Watt University

Year dc:date.issued

2021

Author dc:creator

• Ibiam, Emmanuel

Advisor dc:contributor.advisor

• Geiger, Professor Sebastian

Statement dc:rights

• All items in ROS are protected by the Creative Commons copyright license (http://creativecommons.org/licenses/by-nc-nd/2.5/scotland/), with some rights reserved.

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10399/4872

OAI identifier oai:identifier

oai:ros.hw.ac.uk:10399/4872