An Accurate Parametric Method for Assessing Hydrocarbon Volumetrics: Revisiting the Volumetric Equation

Author:

Ma Y. Z.1

Affiliation:

1. Schlumberger

Abstract

Summary One of the most-important bases for field-development planning is the estimate of hydrocarbon initially in place (HIIP), which has been traditionally estimated either deterministically or by Monte Carlo simulation. The classical volumetric calculation is the most-common deterministic method, and it requires the use of the averages of the reservoir variables and thus does not model the correlations of the input variables. It is well-known that ignoring the correlations among the reservoir variables can lead to incorrect estimations of hydrocarbon volumetrics. The Monte Carlo method uses the input means in the volumetric equation for random simulation of hydrocarbon volumes, yet allows modeling of the correlation between the input parameters. However, using the means and modeling the correlation of the properties are theoretically conflicting. This paper presents new parametric equations for volumetric calculation using mathematical correlation. Unlike the classical volumetric calculation, these equations are the exact expressions of the rigorous hydrocarbon volumetric equation. We discuss how these new equations enable the quantification of inaccuracy in hydrocarbon volumetric estimation by the classical method. Our examples will further show that the magnitude of inaccuracy of the traditional volumetrics depends on the reservoir characteristics; the inaccuracy is generally more significant for heterogeneous, low-quality, and tight reservoirs than for relatively homogeneous high-quality reservoirs.

Publisher

Society of Petroleum Engineers (SPE)

Subject

Geotechnical Engineering and Engineering Geology,Energy Engineering and Power Technology

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2. A practical integrated forecast method for estimated ultimate recovery (EUR) and well production performance after water breakthrough during waterflooding in naturally fractured reservoirs (NFRs);Journal of Petroleum Science and Engineering;2021-09

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4. Introduction to Geological and Reservoir Modeling;Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling;2019

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