Proxy-Based Work Flow for a Priori Evaluation of Data-Acquisition Programs

Abstract

Data-acquisition programs, such as surveillance and pilot, play an important role in reservoir management, and are crucial for minimizing subsurface risks and improving decision quality. Optimal design of the data-acquisition plan requires predicting the performance (e.g., in terms of the expected amount of uncertainty reduction in an objective function) of a given design before it is implemented. Because the data from the acquisition program are uncertain at the time of the analysis, multiple history-matching runs are required for different plausible realizations of the observed data to evaluate the expected effectiveness of the program in reducing uncertainty. As such, the computational cost may be prohibitive because the number of reservoir simulations needed for the multiple history-matching runs would be substantial. This paper proposes a framework on the basis of proxies and rejection sampling (filtering) to perform the multiple historymatching runs with a manageable number of reservoir simulations. The work flow proposed does not depend on the linear Gaussian assumption that is a common, yet questionable, assumption in existing methods. The work flow also enables both qualitative and quantitative analysis of a surveillance plan. Qualitatively, heavy-hitter alignment analysis for the objective function and the observed data provides actionable measures for screening different surveillance designs. Quantitatively, the evaluation of expected uncertainty reduction from different surveillance plans allows for optimal design and selection of surveillance plans.

Estimating the chance of success of information acquisition for the Norne benchmark case

“…Abellan and Noetinger (2010) proposed an approach that allows quantifying the information contained in measurements in a geoscience context. Recently, the VoI concept is being used in history matching contexts, involving data gathered at different times (Barros et al, 2015(Barros et al, , 2016He et al, 2016;Chen et al, 2017;Hong et al, 2018). Hong et al (2018) discussed the variability of VoI over ensembles, including Bayesian inference with ensemble Kalman filter (EnKF) (Evensen, 1994).…”

Clustering Techniques for Value-of-information Assessment in Closed-loop Reservoir Management

“…The development of more practical ways of a-priori assessing the value of future measurements has been a topic of several studies recently. Some of these have focused on the use of proxy models to reduce the number of high-fidelity reservoir simulations required for the VOI analysis (He et al, 2016). Others have proposed a more approximate definition of VOI which simplifies their procedure Reynolds, 2014a and2014b).…”