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

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. The techniques were tested against a comprehensive suite of modeling requirements for various operations- and business-related applications.

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 170690, “Assisted-History-Matching Benchmarking: Design-of-Experiments-Based Techniques,” by Eric Bhark, SPE, and Kaveh Dehghani, SPE, Chevron Energy Technology Company, prepared for the 2014 SPE Annual Technical Conference and Exhibition, Amsterdam, 27–29 October. The paper has not been peer reviewed.
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Assisted-History-Matching Benchmarking: Design-of-Experiments-Based Techniques

01 April 2015

Volume: 67 | Issue: 4

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