0
Reaearch Papers: Unconventional Petroleum

Quantification of Uncertainty in Reserve Estimation From Decline Curve Analysis of Production Data for Unconventional Reservoirs

[+] Author and Article Information
Yueming Cheng

Department of Petroleum and Natural Gas Engineering, West Virginia University, 345B Mineral Resources Building, Morgantown, WV 26506

W. John Lee, Duane A. McVay

Department of Petroleum Engineering, Texas A&M University, College Station, TX 77843

J. Energy Resour. Technol 130(4), 043201 (Oct 28, 2008) (6 pages) doi:10.1115/1.3000096 History: Received October 02, 2007; Revised August 24, 2008; Published October 28, 2008

Decline curve analysis is the most commonly used technique to estimate reserves from historical production data for the evaluation of unconventional resources. Quantifying the uncertainty of reserve estimates is an important issue in decline curve analysis, particularly for unconventional resources since forecasting future performance is particularly difficult in the analysis of unconventional oil or gas wells. Probabilistic approaches are sometimes used to provide a distribution of reserve estimates with three confidence levels (P10, P50, and P90) and a corresponding 80% confidence interval to quantify uncertainties. Our investigation indicates that uncertainty is commonly underestimated in practice when using traditional statistical analyses. The challenge in probabilistic reserve estimation is not only how to appropriately characterize probabilistic properties of complex production data sets, but also how to determine and then improve the reliability of the uncertainty quantifications. In this paper, we present an advanced technique for the probabilistic quantification of reserve estimates using decline curve analysis. We examine the reliability of the uncertainty quantification of reserve estimates by analyzing actual oil and gas wells that have produced to near-abandonment conditions, and also show how uncertainty in reserve estimates changes with time as more data become available. We demonstrate that our method provides a more reliable probabilistic reserve estimation than other methods proposed in the literature. These results have important impacts on economic risk analysis and on reservoir management.

FIGURES IN THIS ARTICLE
<>
Copyright © 2008 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 8

Production forecast for well no. 4 (from an unconventional tight gas reservoir) using the modified bootstrap method. The actual performance is within the 80% confidence interval.

Grahic Jump Location
Figure 7

Production forecast for well no. 3 (from an unconventional tight gas reservoir) using the modified bootstrap method. The actual performance is within the 80% confidence interval.

Grahic Jump Location
Figure 6

Production forecast for well no. 2 (from an unconventional tight gas reservoir) using the modified bootstrap method. The actual performance is within the 80% confidence interval.

Grahic Jump Location
Figure 5

Production forecast for well no. 1 (from an unconventional tight gas reservoir) using the modified bootstrap method. The actual performance is within the 80% confidence interval.

Grahic Jump Location
Figure 4

Backward 2 year scenario: 6 year production history is known but only the last 2 years of data were used for regression with DCA. The actual performance is within the 80% confidence interval.

Grahic Jump Location
Figure 3

Traditional approach: 6 year production history was used for regression with DCA. The actual performance is outside the 80% confidence interval.

Grahic Jump Location
Figure 2

Schematic illustrating multiple backward scenarios

Grahic Jump Location
Figure 1

Schematic of the modified bootstrap approach

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In