Aeolian deposits are typically considered to act as homogeneous “tanks” of sand, which do not contain significant heterogeneities that impact the production of hydrocarbons. However, a succession of deeply buried aeolian gas reservoirs from the Permian Rotliegend exhibit a characteristic production decline profile that is typified by high initial flow rates, followed by a rapid decline in bottomhole pressure and decline in flow rate, subsequently followed by stabilization at low flow rates for an extended period (over several decades). This effect has been termed here as the “slow-gas effect,” and this production phenomenon has previously been attributed to structural compartmentalization. This paper presents an alternative, sedimentological hypothesis for the cause of the slow-gas effect based upon facies-controlled permeability differences within aeolian dune trough architectures. To test this, three interwell (km) scale models from well-studied aeolian analogs from Utah and Arizona were modeled with standard geostatistical reservoir techniques and populated with petrophysical properties from producing Rotliegend reservoirs in Germany. These models were subsequently dynamically simulated to analyze production behavior and test whether a similar “slow-gas” production profile could be reproduced. This study finds that the slow-gas effect primarily results from heterogeneities created by the complex interaction of deposition, accumulation, and erosion within aeolian strata, as opposed to the structural compartmentalization of homogeneous tanks of sand as previously thought. Structural compartmentalization and baffling through faulting where present will have an impact on fluid flow; however, it is not considered here to be the primary cause of the slow-gas effect. Results of this work demonstrate the necessity of accurately characterizing and reproducing low permeability heterogeneity in aeolian systems. These heterogeneities can either be modeled explicitly through the use of geostatistical reservoir modeling techniques as done here, or implicitly through the use of characteristic length and transmissibility multipliers. These results have significant implications on our understanding of how tight aeolian systems produce; namely, after depletion of the near-wellbore volume, production from the surrounding reservoir is baffled by a hierarchy of low permeability bounding surfaces and associated transmissibility barriers. Application for enhancing reservoir depletion strategies include optimizing well trajectories to maximize the number of dune penetrations and percentage of net reservoir facies in communication to the well; maximizing the size of the primary reservoir compartment. Neighboring wells should be placed in separate compartments to maximize the amount of fast-flowing gas production during the early production stage. Pressure management can be used to cyclically produce, deplete, and recharge the primary reservoir compartment to manage and optimize recovery during the decline phase and production tail.
Bibliographical noteThe authors would like to thank members of the FORCE consortium (AkerBP, BP, ConocoPhillips, ENGIE, ENI Norge, Equinor, INEOS, Lundin Norway, Point Resources, Repsol, Spirit Energy, Suncor Energy, Total and VNG Norge, Wintershall DEA), the Norwegian Petroleum Directorate (NPD) and the Research Council of Norway’s Petromaks 2 project (project number 234111/E30) for funding through the SAFARI Phase 3 programme. The first author would like to thank Wintershall DEA Gmbh for permission to publish this paper. Special thanks to Magda Chmielewska for the processing of the virtual outcrop models and to Dr. Stephen Phillips, SPE Executive Editor Roberto Aguilera, and two unknown reviewers for their stimulating discussions and improvements to the manuscript. Schlumberger is gratefully acknowledged for providing academic software licenses.
- reservoir characterization
- well logging
- asset and portfolio management
- machine learning
- log analysis
- Artificial Intelligence
- production monitoring