Probabilistic production forecasting and reserves estimation: Benchmarking Gaussian decline curve analysis against the traditional Arps method (Wolfcamp shale case study)

Muhammad Andiva Pratama, Omar Al Qoroni, Idham Kholid Rahmatullah, Mohammed Farhan Jameel, Ruud Weijermars*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This study provides novel insights into how a relatively new, Gaussian DCA method may be used to forecast well rates and estimate resource volumes produced from unconventional reservoirs. Production data of two wells in the Wolfcamp Shale Formation (Midland Basin, West Texas) were history-matched using both the Arps and Gaussian DCA method. Production forecasts were constructed based on the history-matching of historical production data, and the estimated ultimate recovery (EUR) was determined from the cumulative production at the end of the economic well-life (assumed here to be 40 years). Comparing the results of the conventional Arps and new Gaussian DCA method, we found the Gaussian DCA technique compared favorably to the conventional Arps method, the former being faster and having less error in the history-matching process. The traditional Arps history-matching technique is always initiated with very high initial well rates. In contrast, the very first spike in the actual production rates can be accurately captured by the Gaussian DCA method. The hydraulic diffusivity parameter that was obtained from the history-matching in the Gaussian DCA method was also compared with a calculated diffusivity using primary values obtained from laboratory and well log data. The hydraulic diffusivity parameter obtained from the Gaussian history-match of field data is at the lower side of the probabilistic values calculated based on the laboratory and well log data. A probabilistic regression analysis was applied and the estimated values' distribution was then adjusted to match the values from the history matches as a basis for the final probabilistic EUR estimations for the study wells. Separately, a bootstrapping method can be used to produce probabilistic EUR estimates based on single well data.

Original languageEnglish
Article number212373
JournalGeoenergy Science and Engineering
StatePublished - Jan 2024

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.


  • Bootstrapping
  • Decline curve analysis
  • Gaussian DCA
  • Production forecasting
  • Reserves estimation
  • Shale oil

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Geotechnical Engineering and Engineering Geology


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