Direct perturbation of the Peng–Robinson attraction and covolume parameters for reservoir fluid characterization

2015 ◽  
Vol 127 ◽  
pp. 293-309 ◽  
Author(s):  
Ashutosh Kumar ◽  
Ryosuke Okuno
Keyword(s):  
Reservoir Fluid ◽  
Direct Perturbation ◽  
Fluid Characterization

Towards a Continuous Near-Real Time Reservoir Fluid Characterization by the Implementation of Advanced Mud Logging Technology

2019 ◽  
Author(s):  
V. Franzi ◽  
C. Robert ◽  
A. Shoeibi ◽  
R. Galimberti ◽  
E. Ndonwie Mahbou ◽  
...  
Keyword(s):  
Real Time ◽  
Reservoir Fluid ◽  
Fluid Characterization ◽  
Mud Logging

scholarly journals Preinjection Reservoir Fluid Characterization at a CCS Demonstration Site: Illinois Basin – Decatur Project, USA

2013 ◽  
Vol 37 ◽  
pp. 6424-6433 ◽  
Author(s):  
Randall Locke Ii ◽  
David Larssen ◽  
Walter Salden ◽  
Christopher Patterson ◽  
Jim Kirksey ◽  
...  
Keyword(s):  
Illinois Basin ◽  
Reservoir Fluid ◽  
Demonstration Site ◽  
Fluid Characterization

Gas Deviation Factor Calculation Made Easy and Accurate Using an IR 4.0 Tool

2021 ◽  
Author(s):  
Nasser M. Al-Hajri ◽  
Akram R. Barghouti ◽  
Sulaiman T. Ureiga
Keyword(s):  
Molecular Weight ◽  
Apparent Molecular Weight ◽  
Numerical Approach ◽  
Temperature Data ◽  
Reservoir Fluid ◽  
Fluid Properties ◽  
Deviation Factor ◽  
Alternative Methodology ◽  
Fluid Characterization ◽  
Gas Properties

Abstract Gas deviation factor (z-factor) and other gas reservoir fluid properties, such as formation volume factor, density, and viscosity, are normally obtained from Pressure-Volume-Temperature (PVT) experimental analysis. This process of reservoir fluid characterization usually requires collecting pressurized fluid samples from the wellbore to conduct the experimental work. The scope of this paper will provide an alternative methodology for obtaining the z-factor. An IR 4.0 tool that heavily utilizes software coding was developed. The advanced tool uses the novel apparent molecular weight profiling concept to achieve the paper objective timely and accurately. The developed tool calculates gas properties based on downhole gradient pressure and temperature data as inputs. The methodology is applicable to dry, wet or condensate gas wells. The gas equation of state is modified to solve numerically for the z-factor using the gradient survey pressure and temperature data. The numerical solution is obtained by applying an iterative computation scheme as described below:A gas apparent molecular weight value is initialized and then gas mixture specific gravity and pseudo-critical properties are calculated.Gas mixture pseudo-reduced properties are calculated from the measured pressure and temperature values at the reservoir depth.A first z-factor value is determined as a function of the pseudo-reduced gas properties.Gas pressure gradient is obtained at the reservoir depth from the survey and used to back-calculate a second z-factor value by applying the modified gas equation of state.Relative error between the two z factor values is then calculated and compared against a low predefined tolerance.The above steps are reiterated at different assumed gas apparent molecular weight values until the predefined tolerance is achieved. This numerical approach is computerized to perform the highest possible number of iterations and then select the z-factor value corresponding to the minimum error among all iterations. The proposed workflow has been applied on literature data with known reservoir gas properties, from PVT analysis, and showed an excellent prediction performance compared to laboratory analysis with less than 5% error.

Accuracy and Precision of Reservoir Fluid Characterization Tests Through Blind Round-Robin Testing

2021 ◽  
Author(s):  
Arwa Mawlod ◽  
Afzal Memon ◽  
John Nighswander
Keyword(s):  
Molar Mass ◽  
Sensitivity Study ◽  
Round Robin ◽  
Fluid Composition ◽  
Mass Measurements ◽  
Reservoir Fluid ◽  
Stock Tank ◽  
Fluid Characterization ◽  
Plus Fraction ◽  
The Impact

Abstract Objectives/Scope: Oil and gas operators use a variety of reservoir engineering workflows in addition to the reservoir, production, and surface facility simulation tools to quantify reserves and complete field development planning activities. Reservoir fluid property data and models are fundamental input to all these workflows. Thus, it is important to understand the propagation of uncertainty in these various workflows arising from laboratory fluid property measured data and corresponding model uncertainty. The first step in understanding the impact of laboratory data uncertainty was to measure it, and as result, ADNOC Onshore undertook a detailed study to assess the performance of four selected reservoir fluid laboratories. The selected laboratories were evaluated using a blind round-robin study on stock tank liquid density and molar mass measurements, reservoir fluid flashed gas and flashed liquid C30+ reservoir composition gas chromatography measurements, and Constant Mass Expansion (CME) Pressure-Volume-Temperature (PVT) measurements using a variety of selected reservoir and pure components test fluids. Upon completion of the analytical study and establishing a range of measurement uncertainty, a sensitivity analysis study was completed using an equation of state (EoS) model to study the impact of reservoir fluid composition and molecular weight measurement uncertainty on EoS model predictions. Methods, Procedures, Process: A blind round test was designed and administered to assess the performance of the four laboratories. Strict confidentiality was maintained to conceal the identity of samples through blind test protocols. The round-robin tests were also witnessed by the researchers. The EoS sensitivity study was completed using the Peng Robinson EoS and a commercially available software package. Results, Observations, Conclusions: The results of the fully blind reservoir fluid laboratory tests along with the statistical analysis of uncertainties will be presented in this paper. One of the laboratories had a systemic deviation in the measured plus fraction composition on black oil reference standard samples. The plus fraction concentration is typically the largest weight percent component in black oil systems and, along with the plus fraction molar mass, plays a crucial role in establishing the mole percent overall reservoir fluid compositions. Another laboratory had systemic issues related to chromatogram component integration errors that resulted in inconsistent carbon number concentration trends for various components. All laboratories failed to produce consistent molecular weight measurements for the reference samples. Finally, one laboratory had a relative deviation for P-V measurements that were significantly outside the acceptable range. The EoS sensitivity study demonstrates that the fluid composition and stock tank oil molar mass measurements have a significant impact on EoS model predictions and hence the reservoir/production models input when all other parameters are fixed. Novel/Additive Information: To the best of our knowledge, this is the first time such an extensive and fully blind round-robin test of commercial reservoir fluid characterization laboratories has been completed and published in the open literature. The industry should greatly benefit from this first-of-its-kind blind round-robin dataset being made available to all. The study provides the basis, protocols, expectations, and recommendations for such independent round-robin testing for fluid characterization laboratories on a broader scale.

Sign in / Sign up

Export Citation Format

Share Document