SPE Reservoir Evaluation & Engineering
Volume 14, Number 1, February 2011, pp. 35-44

SPE-123988-PA

Using an Artificial-Neural-Network Method To Predict Carbonate Well Log Facies Successfully

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DOI  More information 10.2118/123988-PA http://dx.doi.org/10.2118/123988-PA

Citation

  • Tang, H., Toomey, N., and Meddaugh, W.S. 2011. Using an Artificial-Neural-Network Method To Predict Carbonate Well Log Facies Successfully. SPE Res Eval & Eng  14 (1): 35-44. SPE-123988-PA. doi: 10.2118/123988-PA.

Discipline Categories

  • 6.1.3 Sedimentology
  • 6.1.5 Geologic Modeling
  • 6.6.1 Well Logging

Keywords

  • Artificial neural network, Probablistic, Carbonate, Well log facies, Geologic modeling

Summary

The Maastrichtian (Upper Cretaceous) reservoir is one of five prolific oil reservoirs in the giant Wafra oil field. The Maastrichtian oil production is largely from subtidal dolomites at an average depth of 2,500 ft. Carbonate deposition occurred on a very gently dipping, shallow, arid, and restricted ramp setting that transitioned between normal marine conditions to restricted lagoonal environments. The average porosity of the reservoir interval is approximately 15%, although productive zones have porosity values up to 30-40%. The average permeability of the reservoir interval is approximately 30 md. Individual core plugs have measured permeability up to 1,200 md.

Efforts to predict sedimentary facies from well logs in carbonate reservoirs is difficult because of the complex carbonate sedimentary facies structures, strong diagenetic overprint, and challenging log analysis in part owing to the presence of vugs and fractures. In the study, a workflow including (1) core description preprocessing, (2) log- and core-data cleanup, and (3) probabilistic-neural-network (PNN) facies analysis was used to predict facies from log data accurately. After evaluation of a variety of statistical approaches, a PNN-based approach was used to predict facies from well-log data. The PNN was selected as a tool because it has the capability to delineate complex nonlinear relationships between facies and log data. The PNN method was shown to outperform multivariate statistical algorithms and, in this study, gave good prediction accuracy (above 70%). The prediction uncertainty was quantified by two probabilistic logs--discriminant ability and overall confidence. These probabilistic logs can be used to evaluate the prediction uncertainty during interpretation. Lithofacies were predicted for 15 key wells in the Wafra Maastrichtian reservoir and were effectively used to extend the understanding of the Maastrichtian stratigraphy, depositional setting, and facies distribution.

© 2011. Society of Petroleum Engineers

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History

  • Original manuscript received: 29 October 2009
  • Meeting paper published: 4 October 2009
  • Revised manuscript received: 5 July 2010
  • Manuscript approved: 11 October 2010
  • Published online: 21 February 2011
  • Version of record: 21 February 2011