Prediction of asphaltene onset pressure gradients downhole

A method for predicting asphaltene onset pressure in a reservoir is provided. In one embodiment, the method includes performing downhole fluid analysis of formation fluid via a downhole tool at a measurement station at a first depth in a wellbore and determining an asphaltene gradient for the formation fluid at the measurement station. Asphaltene onset pressure for a second depth in the wellbore may then be predicted based on the downhole fluid analysis and the determined asphaltene gradient. Additional methods, systems, and devices are also disclosed.

BACKGROUND

Wells are generally drilled into subsurface rocks to access fluids, such as hydrocarbons, stored in subterranean formations. The formations penetrated by a well can be evaluated for various purposes, including for identifying hydrocarbon reservoirs within the formations. Flow connectivity of a reservoir is one parameter that impacts the hydrocarbon production efficiency. Asphaltenes are generally the heaviest fraction and the most polar component in a petroleum mixture. They can be precipitated as solid particles under certain pressure and temperature conditions in some crude oils. As reservoir pressure decreases, the pressure (at a given test temperature) at which asphaltene precipitation begins is referred to as the asphaltene onset pressure (AOP).

Formation evaluation may involve drawing fluid from a formation into a downhole tool. In some instances, downhole fluid analysis (DFA) is used to test the fluid while it remains in the well. Such analysis can be used to provide information on certain fluid properties in real time without the delay associated with returning fluid samples to the surface. Information obtained through downhole fluid analysis can be used as inputs to various modeling and simulation techniques to estimate the properties or behavior of petroleum fluid in a reservoir. These techniques can employ an equation of state (EOS) model that represents the phase behavior of the petroleum fluid within the reservoir, which can be used to determine various other fluid or reservoir characteristics.

SUMMARY

Certain aspects of some embodiments disclosed herein are set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of certain forms the invention might take and that these aspects are not intended to limit the scope of the invention. Indeed, the invention may encompass a variety of aspects that may not be set forth below.

In one embodiment of the present disclosure, a method includes performing downhole fluid analysis of formation fluid via a downhole tool at a measurement station at a first depth in a wellbore. The method also includes determining an asphaltene gradient for the formation fluid at the measurement station. Further, the method includes predicting asphaltene onset pressure for a second depth in the wellbore based on results of the downhole fluid analysis and the determined asphaltene gradient.

In another embodiment, a method includes obtaining characteristics of samples of live oil drawn from a formation at multiple depths within a wellbore through downhole fluid analysis and determining asphaltene gradients for the samples. Additionally, the method includes predicting asphaltene instability for additional depths within the wellbore based on the obtained characteristics and the determined asphaltene gradients.

In a further embodiment, an apparatus includes a downhole sampling tool and a controller. The downhole sampling tool includes a downhole fluid analysis module for determining parameters of sampled fluids. Further, the controller can be used to predict asphaltene onset pressure at a depth in a well based on parameters determined by downhole fluid analysis for a fluid sampled from a formation by the downhole sampling tool at another depth in the well.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

It is to be understood that the present disclosure provides many different embodiments, or examples, for implementing different features of various embodiments. Specific examples of components and arrangements are described below for purposes of explanation and to simplify the present disclosure. These are, of course, merely examples and are not intended to be limiting.

When introducing elements of various embodiments, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Moreover, any use of “top,” “bottom,” “above,” “below,” other directional terms, and variations of these terms is made for convenience, but does not mandate any particular orientation of the components.

The present disclosure relates to determining asphaltene instability or asphaltene onset pressure in hydrocarbon reservoirs. More particularly, in some embodiments asphaltene onset pressure is predicted by integrating DFA measurements and asphaltene concentration gradients analyses using the Flory-Huggins-Zuo equation of state model (FHZ EOS) in real time. The determined asphaltene onset pressure can also be used to analyze reservoir connectivity, asphaltene phase instability, and tar mat formation in oil columns.

As noted above and discussed more fully below, fluid characteristics determined by downhole fluid analysis can be used in predicting asphaltene onset pressures in hydrocarbon reservoirs. Such downhole fluid analysis can be performed with downhole tools of various wellsite systems, such as drilling systems and wireline systems. Embodiments of two such systems are depicted inFIGS. 1 and 2by way of example.

More specifically, a drilling system10is depicted inFIG. 1in accordance with one embodiment. While certain elements of the drilling system10are depicted in this figure and generally discussed below, it will be appreciated that the drilling system10may include other components in addition to, or in place of, those presently illustrated and discussed. As depicted, the system10includes a drilling rig12positioned over a well14. Although depicted as an onshore drilling system10, it is noted that the drilling system could instead be an offshore drilling system. The drilling rig12supports a drill string16that includes a bottomhole assembly18having a drill bit20. The drilling rig12can rotate the drill string16(and its drill bit20) to drill the well14.

The drill string16is suspended within the well14from a hook22of the drilling rig12via a swivel24and a kelly26. Although not depicted inFIG. 1, the skilled artisan will appreciate that the hook22can be connected to a hoisting system used to raise and lower the drill string16within the well14. As one example, such a hoisting system could include a crown block and a drawworks that cooperate to raise and lower a traveling block (to which the hook22is connected) via a hoisting line. The kelly26is coupled to the drill string16, and the swivel24allows the kelly26and the drill string16to rotate with respect to the hook22. In the presently illustrated embodiment, a rotary table28on a drill floor30of the drilling rig12is constructed to grip and turn the kelly26to drive rotation of the drill string16to drill the well14. In other embodiments, however, a top drive system could instead be used to drive rotation of the drill string16.

During operation, drill cuttings or other debris may collect near the bottom of the well14. Drilling fluid32, also referred to as drilling mud, can be circulated through the well14to remove this debris. The drilling fluid32may also clean and cool the drill bit20and provide positive pressure within the well14to inhibit formation fluids from entering the wellbore. InFIG. 1, the drilling fluid32is circulated through the well14by a pump34. The drilling fluid32is pumped from a mud pit (or some other reservoir, such as a mud tank) into the drill string16through a supply conduit36, the swivel24, and the kelly26. The drilling fluid32exits near the bottom of the drill string16(e.g., at the drill bit20) and returns to the surface through the annulus38between the wellbore and the drill string16. A return conduit40transmits the returning drilling fluid32away from the well14. In some embodiments, the returning drilling fluid32is cleansed (e.g., via one or more shale shakers, desanders, or desilters) and reused in the well14.

In addition to the drill bit20, the bottomhole assembly18also includes various instruments that measure information of interest within the well14. For example, as depicted inFIG. 1, the bottomhole assembly18includes a logging-while-drilling (LWD) module44and a measurement-while-drilling (MWD) module46. Both modules include sensors, housed in drill collars, that collect data and enable the creation of measurement logs in real-time during a drilling operation. The modules could also include memory devices for storing the measured data. The LWD module44includes sensors that measure various characteristics of the rock and formation fluid properties within the well14. Data collected by the LWD module44could include measurements of gamma rays, resistivity, neutron porosity, formation density, sound waves, optical density, and the like. The MWD module46includes sensors that measure various characteristics of the bottomhole assembly18and the wellbore, such as orientation (azimuth and inclination) of the drill bit20, torque, shock and vibration, the weight on the drill bit20, and downhole temperature and pressure. The data collected by the MWD module46can be used to control drilling operations. The bottomhole assembly18can also include one or more additional modules48, which could be LWD modules, MWD modules, or some other modules. It is noted that the bottomhole assembly18is modular, and that the positions and presence of particular modules of the assembly could be changed as desired. Further, as discussed in greater detail below, one or more of the modules44,46, and48is or includes a fluid sampling tool configured to obtain a sample of a fluid from a subterranean formation and perform downhole fluid analysis to measure various properties of the sampled fluid, which can then be used to predict asphaltene onset pressure.

The bottomhole assembly18can also include other modules. As depicted inFIG. 1by way of example, such other modules include a power module50, a steering module52, and a communication module54. In one embodiment, the power module50includes a generator (such as a turbine) driven by flow of drilling mud through the drill string16. In other embodiments the power module50could also or instead include other forms of power storage or generation, such as batteries or fuel cells. The steering module52may include a rotary-steerable system that facilitates directional drilling of the well14. The communication module54enables communication of data (e.g., data collected by the LWD module44and the MWD module46) between the bottomhole assembly18and the surface. In one embodiment, the communication module54communicates via mud pulse telemetry, in which the communication module54uses the drilling fluid32in the drill string as a propagation medium for a pressure wave encoding the data to be transmitted.

The drilling system10also includes a monitoring and control system56. The monitoring and control system56can include one or more computer systems that enable monitoring and control of various components of the drilling system10. The monitoring and control system56can also receive data from the bottomhole assembly18(e.g., data from the LWD module44, the MWD module46, and the additional module48) for processing and for communication to an operator, to name just two examples. While depicted on the drill floor30inFIG. 1, it is noted that the monitoring and control system56could be positioned elsewhere, and that the system56could be a distributed system with elements provided at different places near or remote from the well14.

Another example of using a downhole tool for formation testing within the well14is depicted inFIG. 2. In this embodiment, a fluid sampling tool62is suspended in the well14on a cable64. The cable64may be a wireline cable with at least one conductor that enables data transmission between the fluid sampling tool62and a monitoring and control system66. The cable64may be raised and lowered within the well14in any suitable manner. For instance, the cable64can be reeled from a drum in a service truck, which may be a logging truck having the monitoring and control system66. The monitoring and control system66controls movement of the fluid sampling tool62within the well14and receives data from the fluid sampling tool62. In a similar fashion to the monitoring and control system56ofFIG. 1, the monitoring and control system66may include one or more computer systems or devices and may be a distributed computing system. The received data can be stored, communicated to an operator, or processed, for instance. While the fluid sampling tool62is here depicted as being deployed by way of a wireline, in some embodiments the fluid sampling tool62(or at least its functionality) is incorporated into or as one or more modules of the bottomhole assembly18, such as the LWD module44or the additional module48.

The fluid sampling tool62can take various forms. While it is depicted inFIG. 2as having a body including a probe module70, a fluid analysis module72, a pump module74, a power module76, and a fluid storage module78, the fluid sampling tool62may include different modules in other embodiments. The probe module70includes a probe82that may be extended (e.g., hydraulically driven) and pressed into engagement against a wall84of the well14to draw fluid from a formation into the fluid sampling tool62through an intake86. As depicted, the probe module70also includes one or more setting pistons88that may be extended outwardly to engage the wall84and push the end face of the probe82against another portion of the wall84. In some embodiments, the probe82includes a sealing element or packer that isolates the intake86from the rest of the wellbore. In other embodiments the fluid sampling tool62could include one or more inflatable packers that can be extended from the body of the fluid sampling tool62to circumferentially engage the wall84and isolate a region of the well14near the intake86from the rest of the wellbore. In such embodiments, the extendable probe82and setting pistons88could be omitted and the intake86could be provided in the body of the fluid sampling tool62, such as in the body of a packer module housing an extendable packer.

The pump module74draws the sampled formation fluid into the intake86, through a flowline92, and then either out into the wellbore through an outlet94or into a storage container (e.g., a bottle within fluid storage module78) for transport back to the surface when the fluid sampling tool62is removed from the well14. The fluid analysis module72, which may also be referred to as the fluid analyzer72, includes one or more sensors for measuring properties of the sampled formation fluid, such as the optical density of the fluid, and the power module76provides power to electronic components of the fluid sampling tool62.

The drilling and wireline environments depicted inFIGS. 1 and 2are examples of environments in which a fluid sampling tool may be used to facilitate analysis of a downhole fluid. The presently disclosed techniques, however, could be implemented in other environments as well. For instance, the fluid sampling tool62may be deployed in other manners, such as by a slickline, coiled tubing, or a pipe string.

Additional details as to the construction and operation of the fluid sampling tool62may be better understood through reference toFIG. 3. As shown in this figure, various components for carrying out functions of the fluid sampling tool62are connected to a controller100. The various components include a hydraulic system102connected to the probe82and the setting pistons88, a spectrometer104for measuring fluid optical properties, one or more other sensors106, a pump108, and valves112for diverting sampled fluid into storage devices110rather than venting it through the outlet94.

In operation, the hydraulic system102extends the probe82and the setting pistons88to facilitate sampling of a formation fluid through the wall84of the well14. It also retracts the probe82and the setting pistons88to facilitate subsequent movement of the fluid sampling tool62within the well. The spectrometer104, which can be positioned within the fluid analyzer72, collects data about optical properties of the sampled formation fluid. Such measured optical properties can include optical densities (absorbance) of the sampled formation fluid at different wavelengths of electromagnetic radiation. Using the optical densities, the composition of a sampled fluid (e.g., volume fractions of its constituent components) can be determined. Other sensors106can be provided in the fluid sampling tool62(e.g., as part of the probe module70or the fluid analyzer72) to take additional measurements related to the sampled fluid. In various embodiments, these additional measurements could include reservoir pressure (Pres) and temperature (T), live fluid density (ρ), live fluid viscosity (μ), electrical resistivity, saturation pressure, and fluorescence, to name several examples. Other characteristics, such as gas-to-oil ratio (GOR) and asphaltene precipitation, can also be determined using the DFA measurements.

Any suitable pump108may be provided in the pump module74to enable formation fluid to be drawn into and pumped through the flowline92in the manner discussed above. Storage devices110for formation fluid samples can include any suitable vessels (e.g., bottles) for retaining and transporting desired samples within the fluid sampling tool62to the surface. Both the storage devices110and the valves112may be provided as part of the fluid storage module78.

In the embodiment depicted inFIG. 3, the controller100facilitates operation of the fluid sampling tool62by controlling various components. Specifically, the controller100directs operation (e.g., by sending command signals) of the hydraulic system102to extend and retract the probe82and the setting pistons88and of the pump108to draw formation fluid samples into and through the fluid sampling tool. The controller100also receives data from the spectrometer104and the other sensors106. This data can be stored by the controller100or communicated to another system (e.g., the monitoring and control system56or66) for analysis. In some embodiments, the controller100is itself capable of analyzing the data it receives from the spectrometer104and the other sensors106. The controller100also operates the valves112to divert sampled fluids from the flowline92into the storage devices110.

The controller100in some embodiments is a processor-based system, an example of which is provided inFIG. 4. In this depicted embodiment, the controller100includes at least one processor120connected, by a bus122, to volatile memory124(e.g., random-access memory) and non-volatile memory126(e.g., flash memory and a read-only memory (ROM)). Coded application instructions128(e.g., software that may be executed by the processor120to enable the control and analysis functionality described herein, including AOP prediction and reservoir evaluation) and data130are stored in the non-volatile memory126. For example, the application instructions128can be stored in a ROM and the data can be stored in a flash memory. The instructions128and the data130may be also be loaded into the volatile memory124(or in a local memory132of the processor) as desired, such as to reduce latency and increase operating efficiency of the controller100.

An interface134of the controller100enables communication between the processor120and various input devices136and output devices138. The interface134can include any suitable device that enables such communication, such as a modem or a serial port. In some embodiments, the input devices136include one or more sensing components of the fluid sampling tool62(e.g., the spectrometer104) and the output devices138include displays, printers, and storage devices that allow output of data received or generated by the controller100. Input devices136and output devices138may be provided as part of the controller100, although in other embodiments such devices may be separately provided.

The controller100can be provided as part of the monitoring and control systems56or66outside of a well14to enable downhole fluid analysis of samples obtained by the fluid sampling tool62. In such embodiments, data collected by the fluid sampling tool62can be transmitted from the well14to the surface for analysis by the controller100. In some other embodiments, the controller100is instead provided within a downhole tool in the well14, such as within the fluid sampling tool62or in another component of the bottomhole assembly18, to enable downhole fluid analysis to be performed within the well14. Further, the controller100may be a distributed system with some components located in a downhole tool and others provided elsewhere (e.g., at the surface of the wellsite). Whether provided within or outside the well14, the controller100can receive data collected by the sensors within the fluid sampling tool62and process this data to determine one or more characteristics of interest for the sampled fluid.

In accordance with the present disclosure, the systems described above can be used to predict asphaltene onset pressure over a range of formation depths based on downhole fluid analysis of formation fluid samples. In some embodiments, the Flory-Huggins-Zuo EOS model is used to identify asphaltene instability (asphaltene onset pressure prediction) along the reservoir depth. Using this model and downhole fluid analysis measurements, fluid phase information, such as the asphaltene onset pressure at different depths, can be predicted qualitatively and quantitatively downhole in real time.

By way of example, one embodiment of a process for predicting asphaltene onset pressures is generally represented by flow chart150inFIG. 5. In this embodiment, downhole fluid analysis is performed on formation fluids (block152). For instance, a fluid sampling tool of either the drilling system or wireline system described above with respect toFIGS. 1 and 2(e.g., fluid sampling tool62) can be used to sample reservoir fluid at one or more measurement stations within a wellbore (e.g., the well14) and analyze the sampled fluids downhole (e.g., at each measurement station). More specifically, a formation fluid can be drawn into the fluid sampling tool and analyzed while the tool is positioned at a first depth (or station) within the well to determine a first set of formation fluid characteristics. The tool may then be moved successively to additional stations at different depths to sample and analyze fluids at each station. Such downhole fluid analysis enables in situ determinations of numerous characteristics of the sampled fluids in real time, including density, viscosity, saturation pressure, reservoir pressure, reservoir temperature, temperature gradient, GOR, oil-based mud (OBM) contamination, optical density, mass composition, asphaltene onset pressure, and true vertical depth (of the measurement station at which the fluid was sampled).

Results of the downhole fluid analysis can be used to determine asphaltene gradients at the measurement stations (block154). These asphaltene gradients can be determined through any suitable technique. In at least some embodiments the asphaltene gradients in the reservoir are determined through the use of the Flory-Huggins-Zuo EOS (FHZ EOS) model. The FHZ EOS model employs an equation of state together with flash calculations to predict compositions (including asphaltene) as a function of depth in the reservoir. The equation of state represents the phase behavior of the compositional components of the reservoir fluid. Such equation of state can take many forms, such as any one of many known cubic EOS. The equation of state is extended to predict compositional gradients (including an asphaltene compositional gradient) with depth that take into account the impacts of gravitational forces, chemical forces, and thermal diffusion. The flash calculations solve for fugacities of components that form at equilibrium. The asphaltene compositional gradient produced by the FHZ EOS model can be used to derive a profile of asphaltene pseudocomponents (e.g., asphaltene nanoaggregates and larger asphaltene clusters) and corresponding aggregate size of asphaltenes as a function of depth in the reservoir of interest.

The FHZ EOS model governing asphaltene grading is given by:

The first term in the exponential of Equation (1) is the gravity contribution, which depends on the difference in densities between the asphaltenes and the bulk oil. The second and third terms in the exponential are the combinatorial entropy contribution, which depend on the change in volume of the bulk oil with respect to depth, accounting for the entropy of mixing. The final term in the exponential is the enthalpy (solubility) contribution that depends on the difference between the solubility parameters of the asphaltenes and the bulk oil. Equation (1) can be solved numerically to obtain asphaltene grading.

Most of the parameters of the FHZ EOS model are either constants or can be obtained via the downhole fluid analysis described above and cubic equations of state. If oil properties and the asphaltene solubility parameter at different depths are obtained, the single adjustable parameter is the size (e.g., molar volume or diameter) of asphaltenes, which is determined by matching the optical density measured by downhole fluid analysis. In some embodiments, this parameter can be tuned by comparing the obtained size with the Yen-Mullins model to check for consistency. Generally, the size of asphaltenes can be assumed to be one of three asphaltene forms in the Yen-Mullins model (asphaltene molecules at low concentrations, nanoaggregates at medium concentrations, or clusters of nanoaggregates at high concentrations). The asphaltene gradients can then be determined (e.g., predicted) by the FHZ EOS model.

If the oil properties change with depth, the cubic EOS is used to describe such equilibrium or non-equilibrium phase behavior of the reservoir fluid. Therefore, the oil properties at different depths are calculated by using the cubic EOS. Subsequently, the FHZ EOS is used to calculate local asphaltene equilibration with a local fluid at each small vertical depth interval. Thus, the asphaltene gradient in the equilibrium or non-equilibrium hydrocarbon reservoir column can be obtained, which can be subsequently used for reservoir connectivity analysis.

Asphaltene onset pressure for a range of reservoir depths may then be predicted (block156) based on the results of the downhole fluid analysis and the determined asphaltene gradients. By way of example, once asphaltene gradients (asphaltene concentration or fluid composition at different depth) are obtained, phase equilibrium (such as P-T flash) calculations can be performed at each of the set of depths. To conduct this calculation, the following equilibrium criteria should be satisfied for the components at each depth
xioilγioil=xiasphγiasph(2)
where superscripts oil and asph represent the oil and asphaltene phases, x is the mole fraction, and γ is the activity coefficient. Because the equilibrium criteria are used at the same depth for both phases, the gravitational term can be canceled out in the FHZ EOS model and the Flory-Huggins regular solution model can be used in the asphaltene instability analysis.

Prior to the phase equilibrium calculation, a phase stability test can be performed to check whether the crude oil is stable in a single-phase state without asphaltene separation (i.e., whether the asphaltenes can be stably dispersed or suspended in crude oils). Generally, the system is stable if the Gibbs free energy of the system reaches the minimum. Hence, the single phase stability testing is performed based on the reduced molar Gibbs tangent plane distance (TPD) function:

ln⁢⁢γiα=ln⁡(viαvα)+1-viαvα+viαRT⁢(δi-δ)2(4)
where superscript α denotes phase oil or asph. The mixture v and δ are calculated by:

The phase equilibrium calculations noted above can be performed after the phase stability check based on the gradients calculated by the FHZ EOS model at specified depth, temperature and pressure. Notably, in some embodiments the parameters used in asphaltene gradients analysis and asphaltene instability analysis are the same, enabling a single model to work for both asphaltene grading and phase transition predictions.

Bulk fluid properties may be used to apply the FHZ EOS model for asphaltene gradients and asphaltene phase instability analyses. An equation of state approach (e.g., using the cubic EOS) can be used to calculate compositional grading without taking into consideration asphaltenes separately and specially. Fluid properties, such as component and bulk partial molar volume, compositions, density, molecular weight, and the like, can be calculated by the equation of state. Because the equation of state is typically tuned to match pressure-volume-temperature (PVT) properties of the fluids in question to obtain the fluid model, the properties calculated by the equation of state are bulk fluid properties, including the resin and asphaltene contributions. Therefore, the mixing rules of v, ρ, and δ may not be used for oil because the values estimated in this way represent bulk (maltene plus asphaltene) v, ρ, and δ On the other hand, once fluid properties of the mixture are obtained, properties of maltenes can be obtained by use of the mixing rules.

The solubility parameters can be calculated by use of either the equation of state or correlations. In one embodiment, the solubility parameter of asphaltene used in the gradients analysis is initially described by an empirical correlation. Then the value of the solubility parameter of asphaltene can be tuned (based on additional information) to match asphaltene onset pressure measured in the asphaltene instability analysis using the Flory-Huggins regular solution model. It will be further appreciated that the measurements obtained through downhole fluid analysis can be used to tune the FHZ EOS model. Such tuning can increase the accuracy of future predictions of reservoir fluid properties, and modeling via the FHZ EOS model could be repeated as desired for additional predictions of reservoir fluid properties.

Once parameter estimation completed, the asphaltene onset pressure at different depths can be predicted using a simulator. One example of predicted asphaltene onset pressure at different depths is depicted inFIG. 6. In this figure, the predicted asphaltene onset pressure (represented by the solid curve) is shown as varying with depth. Asphaltene onset pressures measured at four different depths are also plotted on the graph ofFIG. 6.

As described above, the FHZ EOS model is used to calculate asphaltene gradients and compare the obtained asphaltene size with the Yen-Mullins model to check for size consistency between the models. The predicted asphaltene onset pressure at each depth can be checked by solving Equations (2)-(4) to see whether they are stable or not. If they are not stable and have asphaltene nanoaggregates or clusters, a tar mat (generally a discontinuity in asphaltene content with depth) may form. In this case, various techniques could be used to provide additional insight into the discontinuity. For instance, core samples could be collected at locations where asphaltene might be destabilized to determine whether solid asphaltenes are in the core samples or oil samples could be collected for geochemistry analysis to determine if a late stage of gas charging occurred in the reservoir. Further, as a data consistency technique, if an asphaltene onset pressure is not available downhole, collected oil samples could be checked for asphaltene onset pressure to test whether the believed discontinuity is, in fact, accurate (rather than being the result of a measurement or some other error). Also, through comparison of the predicted and measured asphaltene onset pressures, a flow assurance problem can be identified, and a comprehensive flow assurance study can be performed in a laboratory for additional information. Still further, if the data implies a large viscosity increase, lab analysis or other downhole measurements can be conducted to confirm that implication.

If samples are stable and have asphaltene clusters, then heavy oil or bitumen (continuity in asphaltene content with depth) is indicated. A continuous bitumen layer (another kind of tar mat) may form at the base in such cases. This implies large asphaltene gradients, as well as large increases in viscosity and specific gravity (e.g., American Petroleum Institute (API) gravity). Because of an exponential increase in asphaltene content with depth, asphaltene viscosity increases exponentially to large values. In such cases, the asphaltene movement may be limited and they may form a bitumen layer that inhibits flow through a formation matrix.

The predicted asphaltene onset pressure gradient can be used for a variety of purposes, such as reservoir characterization. For example, in certain embodiments, a comparison of the measured and predicted asphaltene onset pressures can be effected (block158). In the embodiment represented inFIG. 5, the predicted asphaltene onset pressure can be used to evaluate reservoir connectivity. Particularly, the magnitude of the difference between the measured and predicted asphaltene onset pressures can be compared to a threshold to determine (block160) whether a reservoir is connected between two depths or is compartmentalized. For instance, based on known fluid parameters and asphaltene gradients determined at one or more depths in the well, an asphaltene onset pressure prediction can be made at an additional depth. The prediction can be based on an assumption of connectivity between the one or more depths and the additional depth. If the magnitude of the difference between the predicted and measured asphaltene onset pressures for the additional depth is small (e.g., below the threshold), the difference can likely be attributed to uncertainty in the prediction or measurement and connectivity of the reservoir between the one or more depths and the additional depth can be affirmed (block162). Conversely, compartmentalization can be identified (block164) from large differences (e.g., above the threshold magnitude) between the predicted and measured asphaltene onset pressures for the additional depth. In this case, one or more additional stations in the wellbore can be identified (block166) for additional downhole fluid analysis. At these additional stations (e.g., between two stations indicated as compartmentalized), downhole fluid analysis can be performed and the asphaltene gradient can be determined as described above. Such additional data can be used to determine the source of the previous discrepancy between the measured and predicted asphaltene onset pressure measurements.

Various processes disclosed herein, including that generally represented by flow chart150, can be carried out by any suitable devices or systems, such as the controller100in connection with a downhole tool (e.g., LWD module44or additional module48ofFIG. 1, or fluid sampling tool62ofFIG. 2). These suitable devices and systems can use algorithms, executable code, lookup tables, and the like to carry out the functionality described above. Also, in some embodiments these processes may be performed in substantially real time without removing fluid samples from the well14.