Case Studies 1 and 2 show molecular gradients Case Studies 3, 4, 5, and 6 among others show nanoaggregate gradients and Case Study 7 shows a cluster gradient of considerable height around a 100-km periphery of a giant field, an extraordinarily stringent test. First and foremost, we had to have equilibrated reservoirs showing each of the three nanostructures of the Yen-Mullins model, thereby using reservoir asphaltene gradients to confirm asphaltene nanoscience, thus spanning 13 to 14 orders of magnitude in linear dimension, an uncommon feat in any discipline. This book had to await the validation of the asphaltene thermodynamics applied to reservoirs and, of course, with data release. Nevertheless, the deep scientific roots of RFG must be confirmed as applicable in reservoirs. RFG is primarily a disci-pline to improve efficiency in oil production. This has been accomplished as summarized in the matrix on page ix and described in detail in Chapter 2. Naturally, this book required the description of a large number of RFG case studies in which all the case stud-ies had to address key reservoir concerns. This auspicious development promises a bright future for RFG. The new Ora* intelligent wireline formation testing platform provides routine and efficient measurement of reservoir fluid gradients in answer product context, thereby enabling real-time RFG during wireline logging jobs. This theoretical treatment requires measure-ment of fluid gradients, which is fulfilled by DFA, as detailed in the author’s previous book on this topic. The author’s three coedited books on asphaltenes help clarify the associated difficulties in resolving the asphaltene science. The relatively late development of RFG is due in part to the requirement that the asphaltene scientific issues had to be resolved first. This approach uses very few adjustable variables, a requirement for predictive modeling of reservoir fluids. the known nanostructures of asphaltenes given by the Yen-Mullins model.a very simple polymer solution theory, the Flory-Huggins-Zuo EOS with one chemical interaction parameter for the solvent (live crude oil) with variation depending primar-ily on the gas/oil ratio and one largely fixed chemical interaction parameter for the solute (asphaltenes).Moreover, the best approach for asphaltene modeling is use of In addition, the RFG case studies prove repeatedly that only a cubic equation of state (EOS) thermodynamic treatment of the gas and liquid phase components of crude oil is not sufficient to launch RFG asphaltene thermodynamic modeling of fluid gradients is the crucial enabler for RFG. If instead the reservoir fluids were spatially stochastic in nature, then RFG might not be justified. The author remains amazed by and grateful of this crucial fact. RFG can be codified as a discipline because the reservoir fluids are sufficiently well behaved that an asphaltene thermodynamic treatment of reservoir fluids is effective. These distinct disciplines have been the primary focus of the author for the last two to three decades.
Reservoir fluid geodynamics (RFG) resulted from the confluence of downhole fluid analysis (DFA), the nanoscience and thermodynamics of asphaltenes, and many reservoir studies performed through the lens of RFG. Oliver Mullins, Schlumberger Fellow, explores the recent emergence of reservoir fluid geodynamics as a discipline.
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