--> Abstract: Impact of Carbonate Reservoir Heterogeneity on Reservoir Forecasts: Why Are Production Forecasts Too Optimistic and Can Anything Really Be Done to Eliminate Forecast Bias?, by William Meddaugh, W. Terry Osterloh, and Nicole Champenoy; #90124 (2011)

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AAPG ANNUAL CONFERENCE AND EXHIBITION
Making the Next Giant Leap in Geosciences
April 10-13, 2011, Houston, Texas, USA

Impact of Carbonate Reservoir Heterogeneity on Reservoir Forecasts: Why Are Production Forecasts Too Optimistic and Can Anything Really Be Done to Eliminate Forecast Bias?

William Meddaugh1; W. Terry Osterloh1; Nicole Champenoy1

(1) Chevron, Houston, TX.

The oil and gas industry regularly uses static and dynamic reservoir models to generate production forecasts. Generally, industry look-backs have shown that forecasts are optimistic for both "Greenfield" projects with limited data and "Brownfield" projects with abundant data. One of the main sources of optimistic forecasts is biased estimates of the original or net/targeted in place hydrocarbon volume. Some bias is due to sampling, particularly for Greenfield developments, and this bias can be reduced statistically or by use of appropriate uncertainty-based workflows together with a reasonable uncertainty assessment that includes the available data and an appropriate suite of analogs. An underappreciated additional source of significant bias related to in place volumes is the use of a stochastic reservoir property model to locate wells. The use of a stochastic earth model combined with well placement optimization workflows is likely to yield significantly optimistic forecasts. It is suggested that well placement and optimization be based on property distributions derived via an estimation methods such as kriging.

Reservoir models are generated using sophisticated software. Very elegant geological models can be generated without an adequate understanding of the limitations imposed by the available data, associated uncertainty, or the underlying stochastic algorithms and their input requirements (e.g. the semivariogram; a measure of heterogeneity). Forecasts based on models generated using different semivariogram ranges (all other input parameters held constant) show that the recovery factor for waterflooding may be impacted and that using a too large semivariogram range produces optimistic forecasts. Recent studies using an extensively sampled portion of the Wafra Field (Partitioned Zone; Saudi Arabia and Kuwait) First Eocene heavy oil carbonate reservoir (60 wells; 5 cored wells in 40-acres) have shown that grid size, which has minimal effect on primary recovery forecasts, will impact forecasts for displacement processes. Generally, if there are fewer than 10 cells between injectors and producers models will not capture sufficient reservoir heterogeneity and forecasts will likely be optimistic. It is suggested that the impact of heterogeneity on both static and dynamic model parameter choices be evaluated as part of any comprehensive reservoir study to better assess the impact of the parameter choices on forecast bias.