Definitive Petrophysical Evaluation of Thin Hydrocarbon Reservoir Sequences
- Azlan A. Majid (Gaffney, Cline & Associates) | Paul F. Worthington (Gaffney, Cline & Associates)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- October 2012
- Document Type
- Journal Paper
- 584 - 595
- 2012. Society of Petroleum Engineers
- 5.6.1 Open hole/cased hole log analysis, 2.4.3 Sand/Solids Control
- 2 in the last 30 days
- 1,093 since 2007
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The petrophysical evaluation of thinly-bedded sand/shale sequences is strongly dependent on the way in which layered and dispersed shales are taken into account. There are two underpinning issues: the recognition of thin bed geometries and the estimation of shale content from lithology logs. The first of these is managed through a scenario approach, whereby different downhole scenarios are set out according to specified ranges of bed thickness. Each range has its own interpretation protocol. The second issue relates to the inherent overestimation of clay-mineral volume fraction by shale indicators and its impact on the petrophysical evaluation of thinly-bedded sand porosity. This shortcoming is managed by applying a validated scaling factor to log-derived estimates of shale volume fraction so that these become workable estimates of clay-mineral fraction for the porosity evaluation of sand layers. Both these problems become more acute for thinner beds. These two key issues are addressed conjunctively through interpretive workflows, each of which relates to a discrete range of bed thickness and uses fit-for-purpose petrophysical data sets. In particular, the multicomponent induction log is adopted as a pivotal technology for the evaluation of sand resistivity within thinly-bedded sand/shale sequences. The objective is to achieve a sound petrophysical interpretation for each downhole situation. Therefore, groundtruthing through core data is advocated where this allows the shale problem to be managed better. The outcome is a systematic approach to thin-bed evaluation, based on prescribed data inputs that optimize project effectiveness. The benefits are illustrated through case histories that demonstrate a securable upside in projected hydrocarbon volumes.
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