Recent advances in the understanding of the molecular and colloidal structure of asphaltenes in crude oils are codified in the Yen–Mullins model of asphaltenes. The Yen–Mullins model has enabled the development of the industry’s first asphaltene equation of state for predicting asphaltene concentration gradients in oil reservoirs, the Flory–Huggins–Zuo equation of state (FHZ EOS). The FHZ EOS is built by adding gravitational forces onto the existing Flory–Huggins regular solution model that has been used widely to model the phase behavior of asphaltene precipitation in the oil and gas industry. For reservoir crude oils with a low gas/oil ratio (GOR), the FHZ EOS reduces predominantly to a simple form, the gravity term only, and for mobile heavy oil, the gravity term simply uses asphaltene clusters. The FHZ EOS has successfully been employed to estimate the concentration gradients of asphaltenes and/or heavy ends in different crude oil columns around the world, thus evaluating the reservoir connectivity, which has been confirmed by the subsequent production data. This paper reviews recent advances in applying the FHZ EOS to different crude oil reservoirs from volatile oil (condensate) to black oil to mobile heavy oil all over the world to address key reservoir issues, such as reservoir connectivity/compartmentalization, tar mat formation, non-equilibrium with a late gas charge, and asphaltene destabilization. The workflow incorporates the integration of new technology, downhole fluid analysis (DFA), coupled with the new scientific advances, the FHZ EOS and Yen–Mullins model. The combination proves a powerful new method of reservoir evaluation. Asphaltene or heavy end concentration gradients in crude oils are treated using the FHZ EOS, explicitly incorporating the size of resin molecules, asphaltene molecules, asphaltene nanoaggregates, and/or asphaltene clusters. All of the parameters in the FHZ EOS are related to DFA measurements, such as compositions, GOR, density, etc. The variations of gas and oil properties with depth are calculated by the classical cubic equation of state (EOS) based on DFA compositions and GOR using specifically developed delumping, characterizing, and oil-based drilling mud (OBM) contamination correcting techniques. Field case studies have proven the value and simplicity of this asphaltene or heavy end treatment. Heuristics can be developed from results corresponding to estimation of asphaltene gradients. Perylene-like resins with the size of ∼1 nm are dispersed as molecules in high-GOR volatile oils with high fluorescence intensity and virtually no asphaltenes (0 wt % asphaltene). Heavy asphaltene-like resins with the size of ∼1.3 nm are molecularly dissolved in volatile oil at a very low asphaltene content. Asphaltene nanoaggregates with the size of ∼2 nm are dispersed in stable crude oil at a bit higher asphaltene content. Asphaltene clusters are found in mobile heavy oil with the size of ∼5 nm at even higher asphaltene content (typically >8 wt % based on stock tank oil). Two types of t...

Formation fluid sampling early in the life of a well ensures that vital information is available for timely input to field planning decisions. Particularly in subsea wells, flow assurance is a major concern, and formation fluid samples from openhole logging help operators optimize investment in both upstream and downstream facilities. Oils have different color due to the amount of large, complex aromatic compounds they contain. Since synthetic and oil-base drilling muds contain simple aliphatic compounds, they absorb little in the shorter wavelength color channels. The Oil-Base Contamination Monitoring (OCM) technique uses optical means to monitor the buildup of color during sampling. The technique provides real-time analysis of sample contamination, as well as prediction of how long it will take to achieve an acceptably low level of contamination. As reservoir fluid replaces filtrate in the flow line, the optical density (OD) of the methane signal also increases, in proportion to the oil's methane content. Methane detection is essential for condensates and for lightly colored crude oils. For such fluids, the color buildup becomes difficult to detect; however, the high methane content of these fluids makes possible a reliable methane-based OCM algorithm. Furthermore, the methane content can be used to determine the gas/oil ratio (GOR) of the sample. When these methane-based techniques are applied for darker oils, the color absorption of the crude extends to the near infrared region and covers the methane molecular vibration peak, resulting in a higher methane signal optical magnitude. If uncorrected for, this would result in errors in the methane-based contamination prediction and GOR determination. In this paper, we show how to measure GOR downhole during sampling using the OD of the methane signal. We also describe a decolorization algorithm to remove the color effect from the methane optical channel. The algorithm is based on the exponential decay of color absorption toward the high wavelengths in the near infrared region. After decolorization, the methane channel contains only methane molecular vibration absorption, which is then used to derive an accurate crude oil contamination value and GOR. Introduction When a formation fluid sample is taken from a well drilled with Oil-Base Mud (OBM), the sample contamination by the OBM filtrate is a critical factor for the accurate measurement of the sample PVT properties. Usually when the contamination is too high, the sample may become useless. The in-situ sample OBM contamination can be predicted in real time by Live Fluid Analyzer (LFA)1,2, a module of the Modular Dynamics Tester3 MDT*, using the technique of OBM Contamination Monitoring (OCM). This technique is based on the change of color and methane concentration in the flow line as the fluid cleans up. For very dark oil, the contamination prediction from methane component becomes unreliable because of the color effect. We developed a decolorization technique to remove the color effect of heavy oils and improve the real-time contamination monitoring results.

Reservoir fluid geodynamics (RFG) has recently been launched as a formal technical arena that accounts for fluid redistributions and tar formation in reservoirs largely after trap filling. Elements of RFG, such as analysis of biodegradation, have long been in place; nevertheless, RFG is now strongly enabled by recent developments: 1) downhole fluid analysis (DFA) allows routine elucidation of reservoir fluid gradients, 2) the development of the first equation of state for asphaltene gradients allows identification of equilibrium vs. geodynamic processes of reservoir fluids and 3) RFG analyses of 35 oilfields systematize a multitude of RFG processes and show their direct impact on wide-ranging production concerns. Thermodynamic analyses identifying reservoir fluid geodynamic processes rely heavily on measurement of fluid gradients to avoid ambiguous interpretations. The unique role of asphaltene gradients and their integration with other data streams are the focus herein. RFG oilfield studies have repeatedly shown that analyses of asphaltene gradients are critical to proper evaluation of RFG processes. Naturally, any reservoir concern that directly involves asphaltenes such as heavy oil, viscosity gradients, asphaltene onset pressure, bitumen deposition, tar mat formation, and indirectly, GOR gradients are strongly dependent on asphaltene gradients. Moreover, as shown in numerous case studies herein, asphaltene gradients can be measured with accuracy and the corresponding thermodynamic analyses allow explicit identification of RFG processes not traditionally associated with asphaltenes, such as analysis of connectivity, fault block migration, baffling, spill-fill mechanisms and many others discussed below. In turn, these processes imply other corroborative reservoir and fluid properties that can then be confirmed. Crude oil chemical compositional data, such as ultrahigh resolution two-dimensional gas chromatography, combined with geochemical interpretation, is highly desirable for understanding RFG processes. Nevertheless, biomarkers and other fluid properties often exhibit small gradients relative to standard deviations (except with biodegradation) but often can still corroborate specific RFG processes. In general, integration of fluid gradient analysis with other data streams including petrophysics, core analysis, stratigraphy, geology and geophysics is critical; nevertheless, which integration is most needed depends on particular reservoir attributes and RFG processes that are in question. Examples of data integration are shown for ten reservoirs undergoing various fluid geodynamic processes. Asphaltene gradient analysis is relatively new, yet it is essential for characterization of RFG processes.

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fax 01-972-952-9435. AbstractDownhole 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 proven 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 (CO 2 ). For single-phase assurance it is possible to detect gas liberation (bubble point) or liquid dropout (dew point) while pumping reservoir fluid to the wellbore, before filling a sample bottle.In this paper, a new DFA tool is introduced which greatly 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: methane (C1), ethane (C2), propane to pentane (C3-5), C6+, and CO 2 . These spectrometers, together with improved compositional algorithms, now make possible a quantitative analysis of reservoir fluid with much 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 multi-well. 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, fluid resistivity, pressure, temperature, and fluorescence measurements 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 emulate reservoir conditions. In addition several field examples are presented to illustrate applicability in different environments.