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Description:
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Analysts increasingly have used probabilistic approaches to evaluate the uncertainty in
reserves estimates based on a 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 , in 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 the 80 % confidence interval 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 a
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 .
This thesis presents an improved methodology for probabilistic quantification of reserves
estimates using a decline curve analysis and practical application of the methodology to
actual individual well decline curves . The 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 than previously published methods . Thus , the new methodology provides more reliable probabilistic reserves estimation , which has
important impacts on economic risk analysis and reservoir management . |