In toluene, asphaltenes are dispersed as molecules at low concentrations, as nanoaggregates at moderate concentrations, and as clusters of nanoaggregates at high concentrations. These three asphaltene species are codified in the Yen− Mullins model. For reservoir crude oils, equilibrated asphaltene gradients can be modeled with the Flory−Huggins−Zuo equation of state (EoS). The gravity term and other terms depend on the particle sizes of the asphaltenes which are given in the Yen−Mullins model; these different asphaltene species (molecular and two nanocolloidal species) have been identified in gravity gradients in various reservoir studies. Here, the asphaltene gradient in a large reservoir is examined and found to be consistent with a molecular dispersion of asphaltenes in the crude oil. A variety of fluid and reservoir properties are evaluated to ensure validity of the analysis, particularly of thermodynamic equilibrium of the reservoir fluid. For crude oil samples throughout the reservoir, downhole fluid analysis (DFA), gas chromatography (GC), and two-dimensional gas chromatography (GC×GC) with cubic EoS and geochemical interpretation are consistent with fluid equilibration. Pressure measurement and production results are also consistent with fluid equilibration. This analysis is applicable to other reservoirs; molecular dispersions of asphaltenes are expected for other light oil reservoirs.

Fluid distributions throughout oilfield reservoirs have been measured with increasing accuracy and coverage, both vertical and laterally, in recent years. Currently, a routine observation is that, when reservoir crude oils are in thermodynamic equilibrium, then the reservoir is a single flow unit with fluid flow communication, addressing the most important oil production risk associated with reservoir structure. The most accurate method of determining fluid equilibration is by measurement and analysis of the distribution of dissolved (or suspended) asphaltenes in the oil. This analysis employs the Flory–Huggins–Zuo equation of state (EoS) with its reliance on the Yen–Mullins model of asphaltene nanostructures. This capability has enabled the introduction of a new technical discipline, reservoir fluid geodynamics, which provides a significant advance in the understanding of oilfield reservoirs. Other reservoir fluid components are also measured to assess equilibration of reservoir fluids, including dissolved gas, liquid-phase components, various biomarkers, and methane isotopic ratios. Often, there is a single species of asphaltenes in the reservoir in accordance with the Yen–Mullins model (molecules, nanoaggregates, and clusters). However, for some reservoirs, two species of asphaltenes are evident. These reservoirs provide a stringent test to (1) discern nanostructures of asphaltenes and (2) determine whether there are other prominent aggregate species of asphaltenes in addition to those indicated in the Yen–Mullins model. This paper explores five reservoirs: three of these reservoirs exhibit one dominant species; two exhibit two species of the Yen–Mullins model; and none of the reservoirs exhibits additional species, providing validation of asphaltene nanostructures in the Yen–Mullins model and its application with the Flory–Huggins–Zuo EoS for novel characterization of reservoirs.

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.

The increasing complexities of newly discovered reservoirs coupled with the increasing cost of field development mandate significantly improved and timely work flows for reservoir evaluation. Traditional modeling workflows are typically time consuming and require well-organized cross-disciplinary integration between geoscientists. Such models and processes are not well suited to be used and updated during formation-evaluation acquisition phases of field development. In this paper, a more accessible approach is proposed and demonstrated. The existing fluids model is combined with the current geologic model to construct an accurate representation of key features of the reservoir. This model is then used to predict data for a wireline formation sampling and testing tool (WFT), with emphasis on downhole fluid analysis (DFA). In this process, current reservoir understanding is tested by direct measurement in real time. If differences are uncovered between predicted and measured log data, the WFT tool is in the well, and measurements can be made to uncover the source of the error. In this paper a workflow is demonstrated in which WFT DFA and pressure/volume/temperature (PVT) lab reports were used to build a fluid model after the first exploration well data was acquired. This model was then used to predict fluid properties and WFT DFA logs for a subsequent well intersecting nominally the same compartment. These DFA predictions presumed fluid equilibrium and flow connectivity. Real-time comparisons were made of predicted and measured pressures, fluid gradients, contacts and DFA data obtained from the WFT logging run. Agreement of predicted and measured log data indicates that fluid properties and reservoir connectivities used for the modeling are correct. If predictions disagree with measurements, the acquisition program can be altered in real time to ensure sufficient data are acquired to understand the reservoir model inaccuracies. During the WFT logging job, this predictive model enabled validation of critical WFT data. This process also allowed testing of the reservoir connectivity. It was discovered that either compartmentalization or lateral disequilibrium of the fluids in the reservoir exists. Interpretation of the DFA data suggested that a subtle lateral disequilibrium exists, and the assumption of reservoir connectivity is supported.

No abstract