Reservoir characterization

Assisted-History-Matching Benchmarking: Design-of-Experiments-Based Techniques

As the role of reservoir-flow simulation increasingly affects existing operations and field-development decisions, it follows that rigor, fitness, and consistency should be imposed on the calibration of reservoir-flow models to dynamic data through history matching.

jpt-2015-04-fig1assistedhistory.jpg
Fig. 1—DOE-based work flow for history matching and discrete-model selection.

As the role of reservoir-flow simulation increasingly affects existing operations and field-development decisions, it follows that rigor, fitness, and consistency should be imposed on the calibration of reservoir-flow models to dynamic data through history matching. To evaluate the applicability of the diverse techniques available, a study was performed to benchmark common assisted-history-matching (AHM) techniques. To benchmark the techniques consistently, a set of standards was defined against which each was evaluated. Of the techniques evaluated, the design-of-experiments (DOE) -based approach uniquely satisfied all requirements.

Introduction

In order to understand the practical utility of the wealth of history-matching techniques reported, a study was performed to benchmark four AHM techniques that have been applied in the oil and gas industry for asset-management applications—DOE-based methods, genetic algorithm, ensemble Kalman filter and smoother, and streamline-based generalized travel-time inversion.

×
SPE_logo_CMYK_trans_sm.png
Continue Reading with SPE Membership
SPE Members: Please sign in at the top of the page for access to this member-exclusive content. If you are not a member and you find JPT content valuable, we encourage you to become a part of the SPE member community to gain full access.