The Application of Flow Diagnostics for Reservoir Management

Abstract

Flow diagnostics, as referred to herein, are computational tools derived from controlled numerical flow experiments that yield quantitative information regarding the flow behavior of a reservoir model in settings much simpler than would be encountered in the actual field. In contrast to output from traditional reservoir simulators, flow-diagnostic measures can be obtained within seconds. The methodology can be used to evaluate, rank, and/or compare realizations or strategies, and the computational speed makes it ideal for interactive visualization output. We also consider application of flow diagnostics as proxies in optimization of reservoirmanagement work flows. In particular, by use of finite-volume discretizations for pressure, time of flight (TOF), and stationary tracers, we efficiently compute general Lorenz coefficients (and variants) that are shown to correlate well with simulated recovery. For efficient optimization, we develop an adjoint code for gradient computations of the considered flow-diagnostic measures. We present several numerical examples, including optimization of rates, well placements, and drilling sequences for two-and threephase synthetic and real field models. Overall, optimizing the diagnostic measures implies substantial improvement in simulation-based objectives. IntroductionComputational tools for reservoir modeling play a critical role in the development of strategies for optimal recovery of hydrocarbon resources. These tools can be simply viewed as a means of forecasting recovery given a set of data, assumptions, and operating constraints; (e.g., to validate alternative hypotheses about the reservoir or systematically explore different strategies for optimal recovery).By nature, reservoir modeling is an interdisciplinary exercise, and reservoir-modeling tools must be well-integrated to promote collaboration between scientists and engineers with different backgrounds. New work flows are emerging because of recent advances in static reservoir characterization and dynamic flow simulation. Modern numerical flow simulators have evolved to include more-general grids, complex fluid descriptions, flow physics, well controls, and couplings to surface facilities. These generalizations have helped to more realistically describe fluid flow in the reservoir on the time scales associated with reservoir management. Meanwhile, modern reservoir-characterization techniques have shifted away from traditional variogram-based models toward object-and feature-based models that more accurately describe real geologic structures. To quantify uncertainty in the characterization, it is necessary to generate an ensemble of reservoir models that may include one thousand or more individual realizations. Reservoir simulation is computationally demanding, and a single simulation on a full reservoir model may require from some minutes to hours or even days. Direct evaluation of multiple production scenarios on large ensembles of Earth models is therefore impractical with full-featured flow simulators, and the comp...

Investigating the effect of heterogeneity on infill wells

“…They proposed application of flow-capacity/storage-capacity F-; diagram, Lorenz coefficient. Moyner et al (2014) used flow diagnostics for reservoir management. They used Lorenz coefficient as the popular measure of heterogeneity in the context of streamline.…”

Well control optimization using a two-step surrogate treatment

“…In this step we assume unit mobility ratio, which results in a linear flow equation. We note that other simulation-based approaches such as flow diagnostics [31,30] could also be used in this step, though it is not clear that such approaches would provide problems amenable to global optimization.…”

“…Flow diagnostics tools [29,30], typically constructed based on reduced-physics simulations, rep-resent another effective set of surrogate treatments that can be used for optimization. For example, Møyner et al [31] performed well control optimization for systems involving two-and three-phase flow. Lie et al [30] used quantities derived from TOF and multiscale methods as fast proxies for well control optimization under waterflood.…”

Optimal Well Positioning under Geological Uncertainty by Equalizing the Arrival Time

“…Therefore, the search direction is not limited to the eight directions of an initial well position guessed. Møyner et al (2014) applied flow diagnostics to an existing well positioning method, so that both production rates and well position could be optimized simultaneously.…”