Assessment of Net-To-Gross Uncertainty at Reservoir Appraisal Stage: Application to a Turbidite Reservoir Offshore West Africa

Abstract : The appraisal stage net-to-gross (NTG) uncertainty of a deepwater turbidite reservoir is assessed using a comprehensive workflow that accounts for uncertainty due to different geological scenarios, incorporates historical information and company expertise through prior probability distributions, and integrates seismic data to correct for the potential bias in NTG estimate due to preferentially drilled wells. Results show that the impact of the geological scenario on NTG uncertainty is larger when the facies geometries are very different. Both the range and shape of the prior distribution impact the posterior NTG probability distributions; the range of the posterior distribution is smaller than that of the prior distribution because of the underlying Bayesian framework. With each additional well, the posterior distributions becomes narrower, indicating that more data reduces uncertainty about the global NTG value. INTRODUCTION The quest for new hydrocarbon reservoirs and technical advances take the industry deeper offshore, opening new domains for exploration and appraisal. In such a context, due to the high cost of reservoir development, a reliable uncertainty assessment of the original hydrocarbon in place (OHIP) is a must to decide on the economic viability of a field. Due to high cost of drilling in offshore environments, wells tend to be sparse and preferentially located in high pay zones. Assessing the uncertainty from such sparse data requires identifying and ranking the major parameters that control the OHIP uncertainty, and defining a sound method to make these parameters variable. Geostatistical simulation methods (Goovaerts, 1997;Chiì es and Delfiner,
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Amisha Maharaja, Andre Journel, Guillaume Caumon, Sebastien Strebelle. Assessment of Net-To-Gross Uncertainty at Reservoir Appraisal Stage: Application to a Turbidite Reservoir Offshore West Africa. Eighth Geostatistical Geostatistics Congress, 2008, Santiago, Chile, Chile. pp.707-716. ⟨hal-01844414⟩

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