Quantifying Expected Uncertainty Reduction and Value of Information Using Ensemble-Variance Analysis
- Jincong He (Chevron Energy Technology Company) | Pallav Sarma (Chevron Energy Technology Company (now with Tachyus Corporation)) | Eric Bhark (Chevron Energy Technology Company (now with Chevron Asia Pacific E&P Company)) | Shusei Tanaka (Chevron Energy Technology Company) | Bailian Chen (Chevron Energy Technology Company (now with Los Alamos National Laboratory)) | Xian-Huan Wen (Chevron Energy Technology Company) | Jairam Kamath (Chevron Energy Technology Company)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- April 2018
- Document Type
- Journal Paper
- 428 - 448
- 2018.Society of Petroleum Engineers
- Data Acquisition, Pilot, Surveillance, Value of Information, Uncertainty Reduction
- 5 in the last 30 days
- 296 since 2007
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Data-acquisition programs, such as surveillance and pilots, play an important role in minimizing subsurface risks and improving decision quality for reservoir management. For design optimization and investment justification of these programs, it is crucial to be able to quantify the expected uncertainty reduction and the value of information (VOI) attainable from a given design. This problem is challenging because the data from the acquisition program are uncertain at the time of the analysis. In this paper, a method called ensemble-variance analysis (EVA) is proposed. Derived from a multivariate Gaussian assumption between the observation data and the objective function, the EVA method quantifies the expected uncertainty reduction from covariance information that is estimated from an ensemble of simulations. The result of EVA can then be used with a decision tree to quantify the VOI of a given data-acquisition program.
The proposed method has several novel features compared with existing methods. First, the EVA method directly considers the data/objective-function relationship. Therefore, it can handle nonlinear forward models and an arbitrary number of parameters. Second, for cases when the multivariate Gaussian assumption between the data and objective function does not hold, the EVA method still provides a lower bound on expected uncertainty reduction, which can be useful in providing a conservative estimate of the surveillance/pilot performance. Finally, EVA also provides an estimate of the shift in the mean of the objective-function distribution, which is crucial for VOI calculation. In this paper, the EVA work flow for expected-uncertainty-reduction quantification is described. The result from EVA is benchmarked with recently proposed rigorous sampling methods, and the capacity of the method for VOI quantification is demonstrated for a pilot-analysis problem using a field-scale reservoir model.
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