# title
Practical Application of Probabilistic Approach to Estimate Reserves Using Production-Decline Data

# abstract
<jats:title>Abstract</jats:title>                <jats:p>Analysts have increasingly used probabilistic approaches to evaluate the uncertainty in reserves estimates based on decline curve analysis. This is because the results represent statistical analysis of historical data that usually possess significant amounts of noise. Probabilistic approaches usually provide a distribution of reserves estimates with three confidence levels (P10, P50 and P90) and a corresponding 80% confidence interval. The question arises: how reliable is this 80% confidence interval? In other words, on a large set of analyses, is the true value of reserves contained within this interval 80% of the time? Our investigation indicates that it is common in practice for true values of reserves to lie outside 80% confidence intervals much more than 20% of the time using traditional statistical analyses. This indicates that uncertainty is being underestimated, often significantly. Thus, the challenge in probabilistic reserves estimation using decline curve analysis 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.</jats:p>                <jats:p>This paper presents an improved methodology for probabilistic quantification of reserves estimates using decline curve analysis and practical application of the methodology to actual individual well decline curves. Application of our proposed new method to 100 oil and gas wells demonstrates that it provides much wider 80% confidence intervals, which contain the true values approximately 80% of the time. In addition, the method yields more accurate P50 values. Thus, the new methodology provides more reliable probabilistic reserves estimation, which has important impacts on economic risk analysis and reservoir management.</jats:p>

# authorList[]
Cheng, Y., Wang, Y., McVay, D. A., & Lee, W. J.