The performance of Enhanced Oil Recovery (EOR) processes is adversely affected by the heterogeneous distribution of flow properties of the rock. The effects of heterogeneity are further highlighted when the mobility ratio between the displacing and the displaced fluids is unfavorable. Polymer flooding aims to mitigate this by controlling the mobility ratio resulting in an increase in the volumetric swept efficiency. However, the design of the polymer injection process has to take into account the uncertainty due to a limited knowledge of the heterogeneous properties of the reservoir. Numerical reservoir models equipped with the most updated, yet uncertain information about the reservoir should be employed to optimize the operational settings. Consequently, the optimal settings are uncertain and should be revised as the model is updated. In this report, a feedback-control scheme is proposed with a model updating step that conditions prior reservoir models to newly obtained dynamic data, and this followed by an optimization step that adjusts well control settings to maximize (or minimize) an objective function. An illustration of the implementation of the proposed closed-loop scheme is presented through an example where the rate settings of a well affected by water coning are adjusted as the reservoir models are updated. The revised control settings yield an increase in the final value of the objective function. Finally, a fast analog of a polymer flooding displacement that traces the movement of random particles from injectors to producers following probability rules that reflect the physics of the actual displacement is presented. The algorithm was calibrated against the full-physics simulation results from UTCHEM, the compositional chemical flow simulator developed at The University of Texas at Austin. This algorithm can be used for a rapid estimation of basic responses such as breakthrough time or recovery factor and to provide a simplified characterization the reservoir heterogeneity. This report is presented to fulfill the requirements to obtain the degree of Master of Science in Engineering under fast track option. It summarizes the research proposal presented for my doctorate studies that are currently ongoing. / text

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 the 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 a closed-loop production optimization scheme is implemented. However, the formulation of an optimal production strategy is based on uncertain production forecasts resulting from uncertainty in spatial representation of reservoir heterogeneity, geologic scenarios, inaccurate modeling, scaling, just to cite a few factors. Assessing the uncertainty in reservoir modeling and transferring it to uncertainty in production forecasts is crucial for efficiently controlling the process. This dissertation 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 spatial variations of reservoir properties (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 is used to refine the uncertainty map 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 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 two examples of polymer flooding where the economic performance was maximized. / text

The San Andres Formation (Permian, Guadalupian) is the most prolific oil reservoir in the Permian basin. However, despite more than 60 years of production, an estimated 70% of the original oil in place remains. Recovery of this huge resource requires a better understanding of facies and reservoir framework, which, in turn, must be accomplished using a rock-based reservoir characterization process. This high resolution correlation method is essential for understanding the complex heterogeneities found in shallow water platform carbonates. Steps in the construction of a rock-based reservoir model in the Fullerton San Andres Unit (FSAU) included (1) defining depositional facies and primary facies groups; (2) creating an outcrop depositional model; (3) integrating facies descriptions with gamma-ray and porosity log data; (3) defining field-wide high frequency sequences based on wireline logs and cycle stacking patterns; (4) developing a sequence-based reservoir framework and 3-dimensional reservoir architecture; (5) defining porosity and permeability relationships for facies groups based on rock fabric characteristics. In Fullerton Field, the San Andres Formation comprises high frequency cycles of upward shoaling shallow-marine carbonates. Studies of nine cores (1730 ft) in FSAU reveal four peritidal and five shallow subtidal depositional facies based on texture, fossil assemblages, and sedimentary structures. Peritidal facies are dominantly laminated carbonate mudstones, interpreted as deposited on an intermittently exposed tidal flat. Shallow subtidal facies are peloid and mollusk dominated wackestones and packstones, interpreted as deposited in a shallow protected lagoon. Cycle stacking patterns indicate four complete upward shallowing high frequency sequences. Comparison of high frequency sequences between cored wells shows a high degree of similarity in the overall generalized vertical sequence, especially in the proportions of peritidal and subtidal components within each sequence. Three-dimensional reservoir characterization, using 132 gamma ray and porosity logs, reveals that depositional sequences are largely flat-lying with local topographic variation identified as the fundamental influence on lateral facies distribution within the reservoir section. Integration of core and petrophysical data from surrounding fields places FSAU in the larger sequence stratigraphic framework of the Central Basin Platform. The regional depositional sequence formed a series of depositional environments ranging from intermittently exposed to open marine. San Andres facies developed during south-easterly progradation of shallow water tidal flat and sabkha sediments over a deeper open marine shelf. / text

This thesis presents a methodology to incorporate reservoir uncertainties and estimate the loss in project value when facility planning decisions are based on erroneous estimates of input variables. We propose a tank model along with integrated asset development model to simulate the concept selection process. The model endogenizes drilling decisions and includes an option to expand. Key decision variables included in the model are number of pre-drill wells, initial facility capacity and number of well slots. Comparison is made between project value derived under erroneous estimates for reserve size and under an alternate hypothesis. The results suggest loss in project value of up to 40% when reservoir estimates are erroneous. Moreover, both optimistic and pessimistic reserve estimates results in a loss in project value. However, loss in project value is bigger when reserve size is underestimated than when it is overestimated. / text