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Summary 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.
Abstract 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.
Ibiam, Emmanuel (Heriot-Watt University) | Geiger, Sebastian (Heriot-Watt University) | Almaqbali, Adnan (Heriot-Watt University) | Demyanov, Vasily (Heriot-Watt University) | Arnold, Dan (Heriot-Watt University)
Abstract 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.
Abstract Polymer flooding is economically successful in reservoirs where the water flood mobility ratio is high, and/or the reservoir heterogeneity is adverse, because of the improved sweep resulting from mobility-controlled oil displacement. The performance of a polymer flood can be further improved if the process is dynamically controlled using updated reservoir models and by implementing a closed-loop production optimization scheme. However, the formulation of an optimal production strategy should be based on uncertain production forecasts resulting from uncertainty in for example spatial representation of reservoir heterogeneity, geologic scenarios, inaccurate modeling, scaling, and other factors. Assessing the uncertainty in reservoir modeling and transferring it to uncertainty in production forecasts is crucial for efficiently controlling the process. This paper presents a feedback control framework that (1) assesses uncertainty in reservoir modeling and production forecasts, (2) updates the prior uncertainty in reservoir models by integrating continuously monitored production data, and (3) formulates optimal injection/production rates for the updated reservoir models. This approach focuses on assessing uncertainty in reservoir modeling and production forecasts originated mainly by uncertain geologic scenarios and heterogeneity. This uncertainty is mapped in a metric space created by comparing multiple reservoir models and measuring differences in effective heterogeneity related to well connectivity and well responses characteristic of polymer flooding. Continuously monitored production data are used to refine the prior uncertainty scores using a Bayesian inversion algorithm. In contrast to classical approach of history matching by model perturbation, a model selection problem is implemented where highly probable reservoir models are selected to represent the posterior uncertainty in production forecasts. The model selection procedure yields the posterior uncertainty associated with the reservoir model. The production optimization problem is solved using the posterior models and using a proxy model of polymer flooding to rapidly evaluate the objective function and response surfaces to represent the relationship between well controls and an economic objective function. The value of the feedback control framework is demonstrated with a synthetic example of polymer flooding where the economic performance was maximized.
Abstract History matching a field's performance to understand the reservoir behavior and characterize its static and dynamic properties has been a key activity for reservoir engineers for a long time. Recently, significant effort has been made to devise techniques that allow this process to be automated. These techniques suffer with a number of limitations and often yield History Match (HM) solution points in the uncertainty space that carry certain bias inherent to the algorithm used. Common limitations include:Techniques more often failing to yield any realization with acceptable HM quality at the well levels, Large number of iterations and simulation runs required to minimize the HM errors to acceptable values and, Significant volume of information generated during this iterative process, which becomes unmanageable to interpret and reconcile. Consequently, the assisted HM unfortunately becomes just an iterative process of minimizing the objective function, and the main goal of understanding the reservoir performance gets diluted. When these techniques are applied for Enhanced Oil Recovery (EOR) studies, the list of uncertainty parameters becomes very extensive due to the addition of parameters defining the EOR process itself. As a result, the number of scenarios increases in addition simulations tend to become slower due to incorporation of EOR module and model requirements for space and time resolution to simulate physio-chemical phenomena. Traditional HM techniques then become cumbersome which might lead to inadequate characterization of the potential upside and downside scenarios. This in turn could adversely impact business decisions involving huge capital investments for any field (re-)development opportunities. This paper will discuss a technique that evolved during the HM exercise for an EOR pilot area in the Middle East. The methodology preserves the idea of Assisted History Match (AHM) to generate multiple HM realizations, however, the solution points are found much quicker eliminating large number of iterations, thereby minimizing the computational expense. The devised methodology is based on the combination of Design of Experiment (DoE) based Stochastic Uncertainty Management (SUM) workflow with the gradient based calculation (Adjoint) approach to find multiple HM realizations. First, a complete stochastic uncertainty management workflow is applied sampling the entire uncertainty space and multiple realizations are screened ensuring enough variability in parameters and acceptable HM error. The gradient based calculations are then applied on each of these selected realizations to further minimize the HM error. The Adjoint method yields final set of improved HM realizations with different starting points in the solution space that were obtained via DoE. This helps in two ways – firstly, it covers the entire uncertainty space for better characterization of upside and downside cases and secondly, provides a focused view of the reservoir characteristics. Additionally, the paper would also offer insights gained from the HM exercise into water coning behavior in a viscous oil reservoir and illustrate the reservoir parameters and their significance that need to be addressed to adequately capture the coning phenomenon.