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 semisynthetic 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 (LHS) to generate training runs from which a proxy model was created using polynomial regression. A particle swarm optimization (PSO) algorithm was employed to optimize the NPV for the polymer flood. The same approach was used to optimize a standard waterflood for comparison. Optimizations of the polymer flood and waterflood were performed for the history-matched model ensemble and the original 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 are predicted to be higher 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 in which the oil in place is shifted upward, resulting in a higher NPV. Optimizing a history-matched ensemble leads to a narrow range in absolute NPV compared to optimizing the original ensemble. This difference is because the uncertainties associated with polymer degradation are not captured during history matching. The result of cross comparison, in which an optimal polymer design strategy for one ensemble member is deployed to the other ensemble members, predicted a decline in NPV but surprisingly still showed that the overall NPV is higher than for an optimized waterflood, even for suboptimal polymer injection strategies. This observation indicates that a polymer flood could be beneficial compared to a waterflood, even if geological uncertainties are not captured properly.

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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 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. An ensemble of reservoir models is available for the Watt Field, and history matching was carried out for the entire ensemble using both techniques. 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 a Latin Hypercube to generate training runs from which a proxy model was created. The proxy model was constructed using polynomial regression and validated using further full-physics simulations. A particle swarm optimisation algorithm was then used to optimize the NPV for the polymer flood. The same approach was used to optimise a standard water flood for comparison. Optimisations of the polymer flood and water flood were performed for the history matched model ensemble and the original ensemble. The sensitivity studies showed that polymer concentration, location of polymer injection wells and time to commence polymer injection are key to optimizing the polymer flood. The optimal strategy to deploy the polymer flood and maximize NPV varies based on the history matching technique. The average NPV is predicted to be higher in the stochastic history matching compared to the adjoint technique. The variance in NPV is also higher for the stochastic history matching technique. This is due to the ability of the stochastic algorithm to explore the parameter space more broadly, which created situations where the oil in place is shifted upwards, resulting in higher NPV. Optimizing a history matched ensemble leads to a narrow variance in absolute NPV compared to history matching the original ensemble. This is because the uncertainties associated with polymer degradation 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. This indicates that a polymer flood could be beneficial compared to a water flood, even if geological uncertainties are not captured properly.

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Polymer flooding offers the potential to recover more oil from reservoirs but requires significant investmentswhich necessitate a robust analysis of economic upsides and downsides. Key uncertainties in designinga polymer flood are often reservoir geology and polymer degradation. The objective of this study is tounderstand the impact of geological uncertainties and history matching techniques on designing the optimalstrategy 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 matchdata from a prolonged waterflood in the Watt Field, a semi-synthetic reservoir that contains a wide rangeof geological and interpretational uncertainties. An ensemble of reservoir models is available for the WattField, and history matching was carried out for the entire ensemble using both techniques. Next, sensitivitystudies were carried out to identify first-order parameters that impact the Net Present Value (NPV). Theseparameters were then deployed in an experimental design study using a Latin Hypercube to generate trainingruns from which a proxy model was created. The proxy model was constructed using polynomial regressionand validated using further full-physics simulations. A particle swarm optimisation algorithm was then usedto optimize the NPV for the polymer flood. The same approach was used to optimise a standard water floodfor comparison. Optimisations of the polymer flood and water flood were performed for the history matchedmodel ensemble and the original ensemble. The sensitivity studies showed that polymer concentration, location of polymer injection wells and timeto commence polymer injection are key to optimizing the polymer flood. The optimal strategy to deploythe polymer flood and maximize NPV varies based on the history matching technique. The average NPVis predicted to be higher in the stochastic history matching compared to the adjoint technique. The variancein NPV is also higher for the stochastic history matching technique. This is due to the ability of thestochastic algorithm to explore the parameter space more broadly, which created situations where the oilin place is shifted upwards, resulting in higher NPV. Optimizing a history matched ensemble leads to anarrow variance in absolute NPV compared to history matching the original ensemble. This is because theuncertainties associated with polymer degradation are not captured during history matching. The result ofcross 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 ishigher than for an optimized water food. This indicates that a polymer flood could be beneficial comparedto a water flood, even if geological uncertainties are not captured properly.

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Geothermal energy has a high potential to contribute to a more sustainable energy system if the associated economic risks can be overcome in the design process. The development planning of deep geothermal reservoirs (over 1000 m depth) relies on computer models to forecast and then optimize system design. Optimization is easy where all the objective's (e.g. NPV) optimization parameters and, most importantly, the geology are considered as known, but this is almost always not the case. Where the complex engineering design (e.g. well placement) meets significant geological uncertainty every development option should be tested using an expensive simulation against the range of geological possibilities. The impracticality of simulating so many models results in a limited exploration of geological uncertainties and development options. Consequently, the risk of improper system design cannot be properly assessed. This paper presents an approach to understand the trade-offs in maximizing heat extraction while minimizing energy usage in re-injection for a new geothermal reservoir development while considering the uncertainty from 18 different geological models. Our approach is computationally feasible because we apply multi-objective particle swarm optimization (MOPSO), to an ensemble of response surface models, built using Gaussian process regression (GPR), for each and every geological scenario. MOPSO explores the trade-off surface for the competing objectives using the mean reservoir responses (covering the geological uncertainty). Our results highlight the impact of geological uncertainty on the optimal well placement and show the need to consider geological uncertainties adequately in optimization. The work demonstrates the shortcomings of using only one geological model of a geothermal reservoir and/or a single objective in optimization. We additionally demonstrate the practicalities of using response surface models in this way for geothermal systems. We anticipate that our work raises awareness for the scope of optimization of geothermal reservoir design under geological uncertainty.@en

The average global recovery factor of a typical oil and gas field is approximately 40% at after secondary recovery processes such as gas lifting and water flooding. The low recovery factor is often a result of by passing a considerable amount of oil in the reservoir due to unknown reservoir heterogeneity and incomplete understanding of the geology. Enhanced oil recovery (EOR) methods will play a key role in increasing recovery factors from existing reservoirs. Heterogeneous reservoir sands often show permeability contrast between layers and therefore lead to early water breakthrough when water flooding. In such situations, polymer flooding can potentially be a suitable EOR technique that helps to lower water cut and increase recovery. The addition of polymers to the injected water lowers the mobility ratio, thereby reducing viscous fingering and delaying water breakthrough. This study investigates how a polymer flood design can be optimized while considering geological uncertainty in the reservoir models as well as modelling decisions. We applied an adjoint based technique to match data from a prolonged waterflood in the Watt Field, a synthetic but realistic clastic reservoir that is based on real data and captures a wide range of geological heterogeneities and uncertainties through a range of different model scenarios and model realizations. We apply Latin hypercube experimental designs with the Particle Swarm Optimization algorithm in CMOST. This was used to build a proxy model employing polynomial regression for the optimization of the engineering parameters to maximize NPV. The optimization were performed for both history-matched models (constrained optimization) and the original, non-history-matched models (unconstrained optimization). The aim of this work is to analyse how geological uncertainties inherent to a heterogeneous clastic reservoir as well as modelling decisions impact the design and performance a polymer flood. We further investigate how the different optimization methods impact the predicted reservoir performance and optimal design of the polymer flood. Our findings show that both, geological and engineering uncertainties, impact polymer flooding and that designing the right well controls is essential for successful polymer flooding. Shale cut-offs are identified as a key petrophysical uncertainty when optimizing a polymer flood in a heterogeneous clastic reservoir. Furthermore, forecasts using constrained optimization yielded a much narrower range of incremental oil recovery and NPV during polymer flooding and may underestimate both, risk and opportunities for polymer flooding because the history matching of the water flood emphasizes different geological features compared to the way geology interacts with a more viscous polymer solution.

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This study investigates how a polymer flood design can be optimized while considering geological uncertainty in the reservoir models. Polymer flooding can increase oil recovery, reduce water cut, and improve sweep efficiency by diverting flow to low permeable zones. We applied two different history matching approaches (manual and gradient-based) to match data from a prolonged waterflood in the Watt field, a synthetic but realistic clastic reservoir that is based on real data and captures a wide range of geological heterogeneities and uncertainties through a range of different model scenarios and model realizations. The subsequent polymer flood is then optimized using history-matched models (constrained optimization) and the original, non-history-matched models (unconstrained optimizations). The aim is to study how geological uncertainties innate in clastic reservoir affect polymer flooding, and how different history matching approaches impact the predicted reservoir performance and optimal polymer flood design. A key observation is that shale cut-offs were a major uncertainty when optimizing the polymer flood for this field. In addition, the constrained optimization gave a much narrower forecast for incremental oil recovery during polymer flooding, possibly underestimating both risk and economic opportunities.@en

Benchmark problems have been generated to test a number of issues related to predicting reservoir behaviour (e.g. Floris et al., 2001, Christie and Blunt, 2001, Peters et al., 2010). However, such cases are usually focused on a particular aspect of the reservoir model (e.g. upscaling, property distribution, history matching, uncertainty prediction, etc.) and the other decisions in constructing the model are fixed by log values that are related to the distribution of cell properties away from the wells, fixed grids and structural features and fixed fluid properties. This is because all these features require an element of interpretation, from indirect measurements of the reservoir, noisy and incomplete data and judgments based on domain knowledge.Therefore, there is a need for a case study that would consider interpretational uncertainty integrated throughout the reservoir modelling workflow. In this benchmark study we require the modeller to make interpretational choices as well as to select the techniques applied to the case study, namely the geomodelling approach, history matching algorithm and/or uncertainty quantification technique. The interpretational choices will be around the following areas: (1)Top structure interpretation from seismic and well picks.(2)Fault location, dimensions and the connectivity of the network uncertainty.(3)Facies modelling approach.(4)Facies interpretations from well logs cutoffs.(5)Petrophysical property prediction from the available well data.(6)Grid resolution-choice between number of iterations and model resolution to capture the reservoir features adequately.A semi-synthetic study is based on real field data provided: production data, seismic sections to interpret the faults and top structures, wireline logs to identify facies correlations and saturation profile and porosity and permeability data and a host of other data. To make this problem useable in a manageable time period multiple hierarchically related gridded models were produced for a range of different interpretational choices.

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