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Discounting and risking in production forecasting

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Discounting and risking can help mitigate the effects of our less than perfect knowledge and model shortcomings. Discounting is a pragmatic measure reflecting that it is impossible to capture the full range of all factors contributing to forecast uncertainty.

Technical factors

As well as addressing some of the human factors, discounting can be used to reflect technical factors often inherent in modeling techniques such as:

  • Under-estimation of reservoir heterogeneity
  • Under-estimation of back-out and interference effects
  • Under-estimation of schedule uncertainty, especially in complex projects
  • Learning curve when applying new technology

Historical performance analysis

Discount factor must be determined by technical judgment and experience but primarily it must be anchored in historical performance analysis for the field in question, if available, and for analogue fields. That is to say that past under and over performance compared to predictions should guide the discount factor.


Discount factors can be applied to forecasts in two ways:

  • Discount rates or decline of the forecast itself by a constant factor
  • Determine the root cause of the risk and adjust model parameters (such as reduced availability factor, higher skins, adjusting relperms) to achieve the desired effect.

The latter is preferred as it leads to improved models for future forecasts and better and more consistent discounting with analogue forecasts.

Excessive discounting

A decision to apply technical discounting should be an integral part of the process to develop consistent Low, Best and High Cases and should not be applied as an afterthought to individual cases. Excessive discounting applied to generated forecasts should always raise a red flag.


Noteworthy papers in OnePetro

Goodwin, N. 2015. Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods. Society of Petroleum Engineers.

Landa, Jorge. 2007. “Assessment of Forecast Uncertainty in Mature Reservoirs”. SPE Distinguished Lecturer Program.

Choudhary, M. K., Yoon, S., & Ludvigsen, B. E. 2007. Application of Global Optimization Methods for History Matching and Probabilistic Forecasting - Case Studies. Society of Petroleum Engineers.

Zubarev, D. I. 2009. Pros and Cons of Applying Proxy-models as a Substitute for Full Reservoir Simulations. Society of Petroleum Engineers.

Mohaghegh, S. D. 2006. Quantifying Uncertainties Associated With Reservoir Simulation Studies Using a Surrogate Reservoir Model. Society of Petroleum Engineers.

Mohaghegh, S. D., Modavi, C. A., Hafez, H. H., Haajizadeh, M., Kenawy, M. M., & Guruswamy, S. 2006. Development of Surrogate Reservoir Models (SRM) For Fast Track Analysis of Complex Reservoirs. Society of Petroleum Engineers.

Peng, C. Y., & Gupta, R. 2003. Experimental Design in Deterministic Modelling: Assessing Significant Uncertainties. Society of Petroleum Engineers.

Schaaf, T., Coureaud, B., & Labat, N. 2008. Using Experimental Designs, Assisted History Matching Tools and Bayesian Framework to get Probabilistic Production Forecasts. Society of Petroleum Engineers.

Osterloh, W. T. 2008. Use of Multiple-Response Optimization To Assist Reservoir Simulation Probabilistic Forecasting and History Matching. Society of Petroleum Engineers.

Nandurdikar, N. S., & Wallace, L. 2011. Failure to Produce: An Investigation of Deficiencies in Production Attainment. Society of Petroleum Engineers.

Noteworthy books

Society of Petroleum Engineers (U.S.). 2011. Production forecasting. Richardson, Tex: Society of Petroleum Engineers. WorldCat or SPE Bookstore

Ringrose, P., & Bentley, M. 2014. Reservoir model design: A practitioner's guide.

External links

Production forecasts and reserves estimates in unconventional resources. Society of Petroleum Engineers.

Production Forecasts and Reserves Estimates in Unconventional Resources. Society of Petroleum Engineers.

See also