Effect of Compositional Grading on Reservoir Fluid Characterization: Case Study

2005 ◽  
Author(s):  
Yasser Salehirad
2008 ◽  
Vol 11 (06) ◽  
pp. 1107-1116 ◽  
Author(s):  
Chengli Dong ◽  
Michael D. O'Keefe ◽  
Hani Elshahawi ◽  
Mohamed Hashem ◽  
Stephen M. Williams ◽  
...  

Summary Downhole fluid analysis (DFA) has emerged as a key technique for characterizing the distribution of reservoir-fluid properties and determining zonal connectivity across the reservoir. Information from profiling the reservoir fluids enables sealing barriers to be proved and compositional grading to be quantified; this information cannot be obtained from conventional wireline logs. The DFA technique has been based largely on optical spectroscopy, which can provide estimates of filtrate contamination, gas/oil ratio (GOR), pH of formation water, and a hydrocarbon composition in four groups: methane (C1), ethane to pentane (C2-5), hexane and heavier hydrocarbons (C6+), and carbon dioxide (CO2). For single-phase assurance, it is possible to detect gas liberation (bubblepoint) or liquid dropout (dewpoint) while pumping reservoir fluid to the wellbore, before filling a sample bottle. In this paper, a new DFA tool is introduced that substantially increases the accuracy of these measurements. The tool uses a grating spectrometer in combination with a filter-array spectrometer. The range of compositional information is extended from four groups to five groups: C1, ethane (C2), propane to pentane (C3-5), C6+, and CO2. These spectrometers, together with improved compositional algorithms, now make possible a quantitative analysis of reservoir fluid with greater accuracy and repeatability. This accuracy enables comparison of fluid properties between wells for the first time, thus extending the application of fluid profiling from a single-well to a multiwall basis. Field-based fluid characterization is now possible. In addition, a new measurement is introduced--in-situ density of reservoir fluid. Measuring this property downhole at reservoir conditions of pressure and temperature provides important advantages over surface measurements. The density sensor is combined in a package that includes the optical spectrometers and measurements of fluid resistivity, pressure, temperature, and fluorescence that all play a vital role in determining the exact nature of the reservoir fluid. Extensive tests at a pressure/volume/temperature (PVT) laboratory are presented to illustrate sensor response in a large number of live-fluid samples. These tests of known fluid compositions were conducted under pressurized and heated conditions to simulate reservoir conditions. In addition, several field examples are presented to illustrate applicability in different environments. Introduction Reservoir-fluid samples collected at the early stage of exploration and development provide vital information for reservoir evaluation and management. Reservoir-fluid properties, such as hydrocarbon composition, GOR, CO2 content, pH, density, viscosity, and PVT behavior are key inputs for surface-facility design and optimization of production strategies. Formation-tester tools have proved to be an effective way to obtain reservoir-fluid samples for PVT analysis. Conventional reservoir-fluid analysis is conducted in a PVT laboratory, and it usually takes a long time (months) before the results become available. Also, miscible contamination of a fluid sample by drilling-mud filtrate reduces the utility of the sample for subsequent fluid analyses. However, the amount of filtrate contamination can be reduced substantially by use of focused-sampling cleanup introduced recently in the next-generation wireline formation testers (O'Keefe et al. 2008). DFA tools provide results in real time and at reservoir conditions. Current DFA techniques use absorption spectroscopy of reservoir fluids in the visible-to-near-infrared (NIR) range. The formation-fluid spectra are obtained in real time, and fluid composition is derived from the spectra on the basis of C1, C2-5, C6+, and CO2; then, GOR of the fluid is estimated from the derived composition (Betancourt et al. 2004; Fujisawa et al. 2002; Dong et al. 2006; Elshahawi et al. 2004; Fujisawa et al. 2008; Mullins et al. 2001; Smits et al. 1995). Additionally, from the differences in absorption spectrum between reservoir fluid and filtrate of oil-based mud (OBM) or water-based mud (WBM), fluid-sample contamination from the drilling fluid is estimated (Mullins et al. 2000; Fadnes et al. 2001). With the DFA technique, reservoir-fluid samples are analyzed before they are taken, and the quality of fluid samples is improved substantially. The sampling process is optimized in terms of where and when to sample and how many samples to take. Reservoir-fluid characterization from fluid-profiling methods often reveals fluid compositional grading in different zones, and it also helps to identify reservoir compartmentalization (Venkataramanan et al. 2008). A next-generation tool has been developed to improve the DFA technique. This DFA tool includes new hardware that provides more-accurate and -detailed spectra, compared to the current DFA tools, and includes new methods of deriving fluid composition and GOR from optical spectroscopy. Furthermore, the new DFA tool includes a vibrating sensor for direct measurement of fluid density and, in certain environments, viscosity. The new DFA tool provides reservoir-fluid characterization that is significantly more accurate and comprehensive compared to the current DFA technology.


2018 ◽  
Vol 65 (1) ◽  
pp. 19-36
Author(s):  
Smail Chouya ◽  
◽  
John Jong ◽  
Janice Boay ◽  
Shota Nakatsuka ◽  
...  

2019 ◽  
Author(s):  
V. Franzi ◽  
C. Robert ◽  
A. Shoeibi ◽  
R. Galimberti ◽  
E. Ndonwie Mahbou ◽  
...  

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 78 ◽  
Author(s):  
Sara Borazjani ◽  
David Kulikowski ◽  
Khalid Amrouch ◽  
Pavel Bedrikovetsky

The reliable mathematical modelling of secondary petroleum migration that incorporates structural geology and mature source rocks in the basin model, allows for prediction of the reservoir location, yielding the significant enhancement of the probability of exploration success. We investigate secondary petroleum migration with a significant composition difference between the source and oil pools. In our case study, the secondary migration period is significantly shorter than the time of the hydrocarbon pulse generation. Therefore, neither adsorption nor dispersion of components can explain the concentration difference between the source rock and the reservoir. For the first time, the present paper proposes deep bed filtration of hydrocarbons with component kinetics retention by the rock as a physics mechanism explaining compositional grading. Introduction of the component capture rate into mass balance transport equation facilitates matching the concentration difference for heavy hydrocarbons, and the tuned filtration coefficients vary in their common range. The obtained values of filtration coefficients monotonically increase with molecular weight and consequently affects the size of the oleic component, as predicted by the analytical model of deep bed filtration. The modelling shows a negligible effect of component dispersion on the compositional grading.


Sign in / Sign up

Export Citation Format

Share Document