Impact of fluid characterization on compositional gradient in a volatile oil reservoir

Kiani, Mahboobeh; Osfouri, Shahriar; Azin, Reza; Dehghani, Seyed Ali Mousavi

doi:10.1007/s13202-015-0218-2

Cited by 8 publications

(2 citation statements)

References 42 publications

(32 reference statements)

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“…Contrary to the general view that compositional grading depends on fluid samples and their characterization, Kiani et al [9] reported that the augmentation of fluid characterization by splitting plus fractions of fluid composition did not have a significant impact on the compositional gradings of both hydrocarbons and non-hydrocarbons in a given system. However, it is understood that GOC prediction from CGS is sensitive to the quality and representativeness of the underlying PVT dataset.…”

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confidence: 88%

A semi-empirical method for predicting GOC in oil reservoirs with limited PVT dataset

Okoh¹,

Lawal²,

Yadua³

et al. 2020

SN Appl. Sci.

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Depending on the type, quality and quantity of available data, different methods exist to determine the hydrocarbonhydrocarbon contacts in reservoirs. These methods are either direct or indirect. While the direct method involves consciously drilling a well to penetrate potential fluid contacts, the indirect methods include pressure measurements across at least two different fluid types, and/or, the acquisition and subsequent characterization of downhole fluid samples. Recognizing that most petroleum reservoirs are compositionally graded to varying degrees, compositional grading simulation (CGS) is an inexpensive indirect method of evaluating potential gas-oil contact (GOC) in petroleum reservoirs. However, this typically requires detailed pressure-volume-temperature (PVT) data of the hydrocarbon fluids. But, in many cases, PVT data may be limited. To address the challenges in such cases, this paper proposes a new predictive model and practical workflow, hence extending the applicability of CGS. Employing several PVT datasets and rigorous CGS for known black and volatile oil reservoirs, a new semiempirical function is developed for estimating saturation pressure gradient from gas oil ratio (GOR), which is a readily measurable fluid property. Complementing this function, is the identification of a robust empirical model to predict saturation pressure. The associated workflow provides a systematic integration of the predictive saturation gradient and saturation pressure models to estimate the degree of undersaturation, and hence, potential GOC depth in a reservoir characterized by limited PVT dataset. For validation tests, some cases of saturated and undersaturated reservoirs in the Niger Delta are presented. The results confirm the applicability of the new method in the Niger Delta and elsewhere.

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Section: List Of Symbols Amentioning

confidence: 88%

A semi-empirical method for predicting GOC in oil reservoirs with limited PVT dataset

Okoh¹,

Lawal²,

Yadua³

et al. 2020

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Many other scientists have studied many other factors that lead to the composition of petroleum fluids in subsurface circumstances including: thermal diffusion by Dougherty and Drickamer in 1955 [6], asphaltene precipitation by Riemens et al in 1988 [48], capillary forces by Lee in 1989 in [27] and Wheaton in 1991 [54], biodegradation by Temeng et al in 1998 [53], genesis by Smalley and England in 1992 [51], natural convection by Ghorayeb and Firoozabadi in [12], unsteady flux of a water aquifer partly contacting a reservoir by Hoier and Whitson in 2001 [16], reservoir compartmentalization by Elshahawi et al in 2005 [7], and incomplete hydrocarbon migration by Gibson et al in 2006 [14]. Enormous research in this area is still active (see [5,18,26,30,38,39,41,42,52] and references therein).…”

Section: Introductionmentioning

confidence: 99%