Reservoir Fluid Geodynamics and Reservoir Evaluation
Schlumberger Reservoir Fluid Geodynamics Book
Oliver Mullins, Schlumberger Fellow, explores the recent emergence of reservoir fluid geodynamics as a discipline.
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. These distinct disciplines have been the primary focus of the author for the last two to three decades. 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. The author remains amazed by and grateful of this crucial fact. If instead the reservoir fluids were spatially stochastic in nature, then RFG might not be justified. 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. Moreover, the best approach for asphaltene modeling is use of
- 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)
- the known nanostructures of asphaltenes given by the Yen-Mullins model.
This approach uses very few adjustable variables, a requirement for predictive modeling of reservoir fluids. The relatively late development of RFG is due in part to the requirement that the asphaltene scientific issues had to be resolved first. The author’s three coedited books on asphaltenes help clarify the associated difficulties in resolving the asphaltene science.
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 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 auspicious development promises a bright future for RFG. 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 has been accomplished as summarized in the matrix on page ix and described in detail in Chapter 2. RFG is primarily a disci-pline to improve efficiency in oil production. Nevertheless, the deep scientific roots of RFG must be confirmed as applicable in reservoirs.
This book had to await the validation of the asphaltene thermodynamics applied to reservoirs and, of course, with data release. 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. 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. We also wanted to have equilibrated reservoirs that exhibited two of the nanostructures of the Yen-Mullins model to show there are no intermediate species missed by the Yen-Mullins model. For example, Case Study 2 shows 500 m vertical of a molecular gradient with no hint of deviation, which might indicate dimer formation, and at the base there are nanoaggregates providing excellent confirmation of our thermodynamic treatment. Case Study 11 shows a beautiful equilibrated gradient of nanoaggregates and clusters.
This book also had to await a quorum of many different RFG processes exhibited in different reservoirs, again with data release. Asphaltene equilibration applied to connectivity analysis (including Case Studies 1–7), fracturing and fault block migration (Case Studies 1, 3, 4, and 10), and reservoir baffling (Case Studies 9 and 10) is fairly straightforward (in hindsight). A common process is gas diffusion into undersat-urated crude oils with concomitant asphaltene instability with elevated asphaltene onset pressure (AOP) and formation of all manners of viscous oils, upstructure bitumen, and tar mats (including Case Studies 8, 9, 11, and 13). Case studies were needed showing the potentially dramatic effects of lateral sweep as opposed to density stacking of crude (Case Studies 9, 12, and 13). Case studies were required showing processes that have traditionally been treated within a geochemical context; nevertheless, RFG is needed for delineating the spatial varia-tion of the reservoir fluids. Biodegradation is a common theme in many RFG studies (including Case Studies 1, 11, 14, 15, 16, and 17) as well as multiple oil charges (Case Studies 10, 16, and 17). Water washing (Case Studies 15 and 16) and thermal maturity variations (includ-ing Case Studies 1, 3, 15, and 16) are also important. The huge role CO₂ charge can have on reservoir hydrocarbons had to be shown (Case Studies 2 and 18). One of the last RFG processes to be resolved during the writing of the book was gas washing of reservoir crude oil (Case Study 11) as opposed to evaporative fractionation (Case Study 2).
As big as this list is of RFG case studies, it is important to note that there are more RFG processes we have uncovered that are not (yet) released and that each new reservoir tends to fall outside of existing reservoir case studies—each reservoir is unique, like a fingerprint. It is the universal RFG workflow that is key for RFG application, not simply pattern recognition within existing RFG case studies. For a data science perspective, the combination of the relatively small number of RFG reservoir studies (perhaps 40 oil fields with 100 reservoirs) and that each new reservoir falls outside the training set indicates that an approach of standard artificial intelligence is discouraged and that of cognitive AI is encouraged.
This book includes a chapter on petroleum systems and geochemistry as applied to RFG. Geochemistry helps tie RFG to petroleum sys-tems and represents a very complementary approach. Nevertheless, asphaltenes thermo-dynamics is inextricably at the heart of RFG. This book also includes a chapter relating mass transport considerations to RFG. Most impor-tantly, the easily measured diffusive processes ongoing in several case studies (for example, Case Studies 8, 9, and 14) and treated with the simple, closed-form solution to Fick’s second law of diffusion, namely the error function, prove that these simple concepts apply over the reservoir length scale with time lines over 50 million years. This indisputable validation enables RFG to fill the void between consider-ations of petroleum systems and present-day production, enabling the complete under-standing of reservoirs from their formation to abandonment.