Worldwide Petroleum Consulting
History Matching and Forecasting-2022

History Matching and Forecasting-2022

April 4, 2022

This year’s history matching and forecasting selections, made by reviewer Gopi Nalla of DeGolyer and MacNaughton, reflect the importance of accurate and innovative methodology in the approach toward development of unconventional or challenging plays, from tight oil to highly heterogeneous gas fields to coalbed methane.

The authors of paper URTEC 208352 evaluate and compare the performance of rate-normalization and pressure-deconvolution techniques for both synthetic and tight-oil examples. While the synopsis is devoted mostly to the authors’ work in applying these techniques to synthetic cases, much of the complete paper is devoted to tight-oil examples. Ultimately, the authors recommend the pressure-deconvolution approach generally.

In paper SPE 207933, the authors apply an integrated approach of using reservoir pressure/gas compressibility (P/Z) calculations to obtain a field gas initially in place (FGIIP) estimation that is then incorporated into an integrated asset model. The technique is applied to a giant onshore gas field. The authors conclude that the new FGIIP estimation can be applied as a reference to re‑review the static modeling legacy and to narrow static modeling uncertainties, leading to reliable forecasting and more-efficient field development.

An application of the iterative ensemble Kalman smoother to a scenario involving horizontal coalbed-methane wells for a low-permeability field in Australia is the subject of paper URTEC 208291. A forecast study was conducted to validate the history-matched ensemble, with the results showing a good match of 12 months of the new production data not used in history matching, highlighting the robust prediction capabilities of the presented approach.

SPE papers continue to a be a vital resource for industry professionals; arguably, such work is more important than ever as the industry and the world adjusts to new modes of collaboration and realities in both the office and the field. I invite you to read the full text of these papers on OnePetro and to find further recent works that advance the literature of this specialized but critical Tech Focus topic.

This Month’s Technical Papers

Techniques for History-Matching and Forecasting Tight Oil Reservoirs Compared

Material-Balance Method With Static Modeling Helps Generate Reliable Forecasting

Iterative Ensemble Kalman Smoother Applied to History-Matching Coalbed Methane Wells

Recommended Additional Reading

URTEC 208361 Effect of Relative Permeability on Modeling of Shale Oil and Gas Production by Hamid Behmanesh, University of Calgary, et al.

SPE 204835 Successful Case Study of Machine-Learning Application To Streamline and Improve History-Matching Process for Complex Gas-Condensate Reservoirs in Hai Thach Field, Offshore Vietnam by Son Hoang, Bien Dong Petroleum Operating Company, et al.

SPE 207855 Unleashing the Potential of Relative Permeability Using Artificial Intelligence by Abdur Rahman Shah, Schlumberger, et al.


Gopi Nalla, SPE, is a senior reservoir engineer with DeGolyer and MacNaughton. He has 18 years of industry experience and previously worked for 12 years with Chevron and 2 years with Idaho National Laboratory. Nalla holds an MS degree in petroleum engineering from The University of Texas at Austin and a BS degree in chemical engineering from the National Institute of Technology, India. He is a licensed professional engineer in Texas and California and serves on the JPT Editorial Review Board. Nalla also has served as a reviewer for SPE Reservoir Evaluation & Engineering. He can be reached at

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