Accounting for Serial Autocorrelation in Decline Curve Analysis of Marcellus Shale Gas Wells

2018 ◽  
Author(s):  
Eugene Morgan
2009 ◽  
Author(s):  
Dilhan Ilk ◽  
Jay Alan Rushing ◽  
Thomas Alwin Blasingame

SPE Journal ◽  
2012 ◽  
Vol 18 (01) ◽  
pp. 97-113 ◽  
Author(s):  
Ayala H Luis F. ◽  
Peng Ye

Summary Rate-time decline-curve analysis is the technique most extensively used by engineers in the evaluation of well performance, production forecasting, and prediction of original fluids in place. Results from this analysis have key implications for economic decisions surrounding asset acquisition and investment planning in hydrocarbon production. State-of-the-art natural gas decline-curve analysis heavily relies on the use of liquid (oil) type curves combined with the concepts of pseudopressure and pseudotime and/or empirical curve fitting of rate-time production data using the Arps hyperbolic decline model. In this study, we present the analytical decline equation that models production from gas wells producing at constant pressure under boundary-dominated flow (BDF) which neither employs empirical concepts from Arps decline models nor necessitates explicit calculations of pseudofunctions. New-generation analytical decline equations for BDF are presented for gas wells producing at (1) full production potential under true wide-open decline and (2) partial production potential under less than wide-open decline. The proposed analytical model enables the generation of type-curves for the analysis of natural gas reservoirs producing at constant pressure and under BDF for both full and partial production potential. A universal, single-line gas type curve is shown to be straightforwardly derived for any gas well producing at its full potential under radial BDF. The resulting type curves can be used to forecast boundary-dominated performance and predict original gas in place without (1) iterative procedures, (2) prior knowledge of reservoir storage properties or geological data, and (3) pseudopressure or pseudotime transformations of production data obtained in the field.


Author(s):  
Yueming Cheng ◽  
W. John Lee ◽  
Duane A. McVay

Decline curve analysis is the most commonly used technique to estimate reserves from historical production data for evaluation of unconventional resources. Quantifying uncertainty of reserve estimates is an important issue in decline curve analysis, particularly for unconventional resources since forecasting future performance is particularly difficult in 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 reserves 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 probabilistic quantification of reserve estimates using decline curve analysis. We examine the reliability of 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 reserves estimates changes with time as more data become available. We demonstrate that our method provides more reliable probabilistic reserves estimation than other methods proposed in the literature. These results have important impacts on economic risk analysis and on reservoir management.


2020 ◽  
Vol 83 ◽  
pp. 103531
Author(s):  
Hong-Bin Liang ◽  
Lie-Hui Zhang ◽  
Yu-Long Zhao ◽  
Bo-Ning Zhang ◽  
Cheng Chang ◽  
...  

2019 ◽  
Vol 44 (6) ◽  
pp. 6195-6204 ◽  
Author(s):  
Emeka Emmanuel Okoro ◽  
Austin Okoh ◽  
Evelyn Bose Ekeinde ◽  
Adewale Dosunmu

Energies ◽  
2018 ◽  
Vol 11 (3) ◽  
pp. 552 ◽  
Author(s):  
Lei Tan ◽  
Lihua Zuo ◽  
Binbin Wang

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