Capacitance resistance modeling for improved characterization in waterflooding and thermal recovery projects

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2016-12

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Abstract

Rates are typically one of the most measured in an oil recovery project. The abundance of these types of data is explained partly by their relative ease of collection. Additionally, their collection and reporting is often required for logistical as well as financial purposes. Numerous researchers have shown the potency of using these data for characterization and management of oil reservoirs under primary or secondary recovery. Reduced-order models typically use these measurements as input to characterize reservoirs. The capacitance resistance model (CRM) is one such reduced order modeling method. This model uses well rates (and bottomhole pressure data, if available) to characterize a reservoir in a cheap and fast way. In characterizing an oil reservoir, the CRM and its linear counterpart (the Integrated Capacitance Resistance Model or ICRM) use historical data available at the wells to infer connectivity and flow paths between these wells through a set of model parameters. This use of readily available data, enabled by the speed of these models, creates a powerful tool that can be used as an alternative or as a complement to more expensive and time consuming traditional reservoir management tools. The CRM was initially developed for secondary recovery (i.e., water-flooding) but has been shown to work very well for primary recovery and many enhanced oil recovery (EOR) processes. The increasing industry acceptance of this modeling method is because of the work researchers who have contributed in expanding the capabilities of this modeling approach. However, key questions such as the impact of noise of CRM and ICRM performance remain. Additionally, a rigorous way of designing injection rates (a key input into the CRM model) such that parameter estimation is optimal has not been addressed. Finally, ideas about the applicability of the CRM modeling method to thermal EOR processes has not been explored. This research aims to address these questions. By addressing these questions, this work aims to contribute towards deepening current under-standing of the CRM modeling method and to opening new avenues for its application and research.

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