The Impact of Reservoir Fluid Compositional Variation and Valid Sample Acquisition on Flow Assurance Evaluation

Mullins, Oliver C.; Elshahawi, Hani; Flannery, Matthew; O'Keefe, Michael; Vannuffelen, Stephane

doi:10.4043/otc-20204-ms

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…This solution has been codified in the Yen-Mullins Model shown in Fig. [10,11] With the resolution of the size of asphaltene species in crude oil, a 1 st -principles EoS for asphaltene gradients can be developed. [7] The Yen-Mullins model establishes that at very low concentrations heavy ends should be molecularly dispersed in crude oil; the size of the predominant molecular structure of asphaltenes is 1.5nm in long dimension (Fig.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Tar Mat Formation Due to Asphaltenes Accumulation Under Gas Charge in Reservoirs

et al. 2015

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Tar mats in reservoirs can cause severe issues in oil production such as preventing aquifer pressure support and water sweep. Therefore, it's important to understand the fluid geodynamics that occur in geologic time that give rise to heavy oil gradients and to tar mats. A common event in reservoirs especially in deepwater is a late gas charge into an oil-filled reservoir. In this case, the gas can quickly migrate to the top of the reservoir through fault planes without mixing (except locally) with the existing reservoir fluid. This newly charge gas can then diffuse down into the oil column thereby significantly altering solution gas and dissolved asphaltene content and their gradients. In particular, an increase in solution gas causes a reduction of asphaltene solubility. This process can result in significant variations in asphaltene concentration throughout the column. Moreover, a density inversion can be created due to the combined effects of methane diffusion and diffusion and expulsion of asphaltenes. This results in convective flow of asphaltene-rich fluids which can yield large asphaltene concentrations and tar mats at the base of the oil column. In addition, rapid gas charge can overwhelm the ability of asphaltenes to migrate thereby leading to upstructure tar deposition. Both scenarios can be associated with Flow Assurance concerns over part or all of the field. In this paper, we discuss case studies that exhibit such potentially problematic fluid columns. Simulated cases are also modeled to provide guidance for understanding the fluid geodynamics. The ability to model density inversion and asphaltenes gravity currents is seen to help significantly in understanding of reservoir fluids dynamics.

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Section: Introductionmentioning

confidence: 99%

Dynamics of Tar Mat Formation Due to Asphaltenes Accumulation Under Gas Charge in Reservoirs

et al. 2015

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“…The basic outputs from DFA measurements are weight percentages of CO 2 , CH 4 , C 2 H 6 , C 3 H 8 , C 5 H 12 , and C 6 H 14 ?, in addition to live fluid density. Mullins et al (2009) showed that in the case of large fluid conditional variations and compartmentalization, DFA can be used as a tool to help in delineating these variations in a cost-effective manner. They introduced the example shown in Fig.…”

Section: Down-hole Fluid Analysismentioning

confidence: 99%

“…Fingerprinting in fluid characterization became an important topic receiving wide attention with direct application on improving the quality of fluid samples. Many tools (e.g., OFA, LFA, CFA, and IFA) capable of detecting in situ variation of different fluids were developed over the years (1991/2001/2003/2007), (Mullins et al 2009;Elshahawi et al 2007;Xian et al 2006). The development of these tools was to address several production problems (e.g., sizing of facilities, well placement, completions equipment, and production prediction).…”

Section: Down-hole Fluid Analysismentioning

confidence: 99%

A regression model for estimation of dew point pressure from down-hole fluid analyzer data

Alzahabi

El-Banbi

Trindade

et al. 2017

J Petrol Explor Prod Technol

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Accurate knowledge of dew point pressure is important in understanding and managing gas condensate reservoirs. Without a correct assessment of dew point pressure, an accurate description of phase changes and phase behavior cannot be achieved. Numerous models for predicting gas condensate dew point pressure from surface fluid data have been proposed in the literature. Some of these require knowledge of the full composition of the reservoir fluid (based on laboratory experiments), while others only need field parameters that are relatively easy to obtain. This paper presents a new model for predicting the dew point pressure from down-hole fluid analyzer data. Such data are now measured (usually in real time) while obtaining down-hole fluid samples. The new model predictions give a quick estimation of dew point pressure for wet gas and gas condensate reservoirs. Since it relies only on down-hole measured data, the model provides an estimate of dew point pressure without the need for laboratory analyses. During down-hole fluid sampling, the model can be used to ensure whether the sample is still in single phase, or whether the dew point was crossed during the sampling operation. An early estimate of dew point pressure is also valuable in designing further tests for gas condensate wells. The new model, constructed using a fluid database of nearly 700 gas condensate samples, was devised using sophisticated statistical/machine learning methods, and attained a mean absolute relative error value of 2% for predicting the logarithm of pressure. In comparison with other dew point estimation models (that use surface fluid data), the chosen model was found to attain a similar level of accuracy when tested on samples not used in the model building phase.

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The Impact of Reservoir Fluid Compositional Variation and Valid Sample Acquisition on Flow Assurance Evaluation

Cited by 2 publications

References 15 publications

Dynamics of Tar Mat Formation Due to Asphaltenes Accumulation Under Gas Charge in Reservoirs

Dynamics of Tar Mat Formation Due to Asphaltenes Accumulation Under Gas Charge in Reservoirs

A regression model for estimation of dew point pressure from down-hole fluid analyzer data

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