Application of a Regression-Based EOS PVT Program to Laboratory Data

Abstract

Summary An equation-of-state (EOS)-based PVT program was applied to match laboratory PVT data for three published and nine additional reservoir fluid samples. This paper includes laboratory test data for the nine samples and describes PVT program features, especially regression, that we find conducive to rapid determination of EOS parameter values needed to match data. With regression, both the parameter values needed to match data. With regression, both the Peng-Robinson (PR) and Zudkevitch-Joffe-Redlich-Kwong (ZJRK) EOS Peng-Robinson (PR) and Zudkevitch-Joffe-Redlich-Kwong (ZJRK) EOS give comparable and generally good agreement with laboratory data. Without regression or significant adjustment of EOS parameters, neither EOS adequately predicts observed reservoir fluid PVT behavior. Our EOS tuning uses a small degree of C7+ fraction splitting. The agreement of these EOS results with data compares favorably with that obtained in previously published studies that used extensive C7+ splitting. Introduction A recent trend in compositional simulation is the use of an EOS, as opposed to independent correlations, to calculate K-values and equilibrium-phase properties. An important prerequisite in meaningful use of the EOS-based prerequisite in meaningful use of the EOS-based compositional model is satisfactory agreement between EOS results and laboratory PVT test data relevant to the reservoir fluid and recovery process. A number of studies report comparisons of cubic EOS and laboratory PVT results for a wide variety of reservoir fluids and conditions. Most of these studies emphasize the C7+ characterization as the key element in attaining agreement between EOS and laboratory results. Some studies use more than 40 components that result from splitting the C7+ fraction. Some authors imply a predictive EOS capability provided one EOS parameter predictive EOS capability provided one EOS parameter is adjusted to match the reservoir fluid saturation pressure. The work reported here reflects our experience that the EOS is generally not predictive and extensive splitting of the C7+ fraction to match laboratory data is generally unnecessary. We indicate that more of the available laboratory data than were frequently used (or reported) in past studies should be used in evaluating and tuning an EOS. The reservoir fluid studies presented illustrate the capability and efficiency of multivariable, nonlinear regression in seeking agreement between EOS and observed PVT results. PVT results. We do not dismiss "proper" C7+ characterization as a necessary element in tuning an EOS. Rather, we support a philosophy of minimal splitting followed by adjustment, using regression, of the heaviest (plus) fraction's two EOS parameters, generally denoted by and . We describe regression-based PVT program features that we feel contribute to time-efficient tuning of an EOS, which is necessary before its use in field-scale simulation. Laboratory data given for six oil and three retrograde gas condensate samples include reservoir temperature expansions, surface separations, N2 reservoir fluid behavior, and one set of multiple-contact data. Results are presented for three additional fluids with data reported in the literature. Generalizations regarding the regression procedure and results, based on these 12 fluid systems and a larger number of unreported fluid studies, are stated where possible or warranted.

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溶解パラメーター推算にPeng-Robinson状態方程式を使用した簡易アルゴリズムによるアスファルテン析出挙動の予測

“…Algorithm of the Model stant composition expansion (CCE) data. The regression was performed by minimizing the obCubic equations of state (EOS) will not generally jective function: predict accurately laboratory data of oil/gas mixtures without tuning of the EOS parameters 23) . The…”