OOIP Estimates as a Function of Cumulative Production: A Case Study from the Simulation History Match of the N'Sano Pinda Reservoir, Block 0, Angola

Matthew C. Jones
Southern Africa SBU, Block 0 RM, Chevron Africa and Latin America E&P, Houston, TX

Estimation of Original Oil-In-Place (OOIP) has traditionally been done using deterministic methods. The estimates improve as dynamic data is gathered through pressure and production and incorporated via material balance or simulation models until field abandonment when little uncertainty remains. Probabilistic methods that use static subsurface uncertainties to provide an OOIP distribution have increased in use in recent years. This paper compares a static OOIP distribution to a set of dynamic OOIP distributions taken from a simulation model history match (HM). The static OOIP distribution is narrowed as a function of percent of hydrocarbon pore volumes (%HCPV) processed. Implications of this work can be used to predict the cumulative volumes (or time, depending on reservoir processing rate) required to significantly reduce a static OOIP distribution.

A technique is shown where reservoir simulation is used to probabilistically HM field data. Static and dynamic uncertainties are used in an experimental design (ED) assisted HM process. The process uses the folded Plackett-Burman (FPB) ED to generate proxies that represent the quality of the HM. The proxies are used in conjunction with Monte-Carlo (MC) simulation and filters to improve the overall HM and consequently narrow uncertainties. The outcome is a newly defined distribution of static and dynamic subsurface uncertainties. By using these new distributions in a subsequent ED assisted HM, the process can be conducted in cycles; each successive cycle narrowing or shifting the uncertainty distributions for the next.

AAPG International Conference and Exhibition, Cape Town, South Africa 2008 © AAPG Search and Discovery