Combined Petroleum System Modeling and Comprehensive Two

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Combined petroleum system modeling and GCxGC to improve understanding of the crude oil chemistry in the Llanos Basin, Colombia Attila Bartha, Nelly De Nicolais, Vinod Sharma, S.K. Roy, Rajiv Srivastava, Andrew E Pomerantz, Milton Sanclemente, Wilmar Perez, Robert K. Nelson, Christopher M. Reddy, Jonas Gros, J. Samuel Arey, Jaron Lelijveld, Sharad Dubey, Diego Tortella, Thomas Hantschel, Kenneth E. Peters, and Oliver C. Mullins Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.5b00529 • Publication Date (Web): 15 Jun 2015 Downloaded from http://pubs.acs.org on June 16, 2015

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Combined petroleum system modeling and GCxGC to improve

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understanding of the crude oil chemistry in the Llanos Basin, Colombia

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Attila Bartha , Nelly De Nicolais , Vinod Sharma , S.K. Roy , Rajiv Srivastava ,

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Andrew E. Pomerantz , Milton Sanclemente , Wilmar Perez , Robert K. Nelson ,

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Christopher M. Reddy , Jonas Gros , J. Samuel Arey , Jaron Lelijveld , Sharad

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Dubey , Diego Tortella , Thomas Hantschel , Kenneth E. Peters , Oliver C.

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Mullins*,4

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1. Schlumberger, Mexico City, Mexico, Distrito Federal 11570

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2. Schlumberger, Colombia, Bogota, D.C., Colombia,

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3. ONGC Videsh, Colombia, Bogota, D.C., Colombia

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4. Schlumberger-Doll Research, Boston, MA, USA, 02139

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5. Woods Hole Oceanographic Institution, Woods Hole, MA, 02543

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6. Swiss Federal Institute of Technology at Lausanne, (EPFL), Lausanne,

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Switzerland

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7. Schlumberger, IES, Aachen, Germany

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8. Schlumberger, SIS, CA, USA, 94941

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Abstract

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Important properties of a crude oil such as viscosity are determined by the crude oil

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chemical constituents. In particular, viscosity is highly dependent on the asphaltene

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content. Various processes that act on reservoir crude oils can alter chemical

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composition such as aspahltene content and therefore impact important crude oil

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properties. In many basins, such as the Llanos basin, Colombia, processes such as

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biodegradation, water washing and multiple charging can contribute to asphaltene

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and viscosity variations. The considerable complexity of the problem requires a

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multidisciplinary workflow to understand the main factors that influence the

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quality of reservoir crude oils and their gradients. Here, we perform comprehensive

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two-dimensional gas chromatography with flame ionization detection (GCxGC-FID)

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and with mass spectrometry (GCxGC-MS) performed on samples of known

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provenance combined with petroleum system modeling to develop an understanding

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of the primary factors controlling asphaltene content and viscosity in a reservoir.

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The crude oils in our study show the impact of biodegradation, water washing and

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multiple charging. Some variation of composition is observed laterally in the

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subsurface formation. These observations help constrain the petroleum system

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model (PSM); the timing of paleo-pasteurization appears to be key in establishing

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the quality of the oils.

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1. Introduction

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Renewed interest in the characterization of reservoir fluids is being driven by

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decreasing risk tolerance in important markets, such as deepwater (Elshahawi et

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al., 2014). In addition, the technology of downhole fluid analysis (DFA) in wireline

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has facilitated acquisition of representative samples of formation crude oil with 2 ACS Paragon Plus Environment

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known provenance (Mullins, 2008a). Moreover, DFA has enabled the accurate

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measurement of reservoir fluid properties and gradients to become routine (Mullins,

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2008a). For example, within a well, all systematic errors of the DFA measurement

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of fluid properties cancel allowing accurate determination of fluid gradients

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(Mullins, 2008a). These DFA measurements are being coupled with the cubic

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equation of state (EoS) to evaluate gas-liquid equilibria (Peng and Robinson, 1976)

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and the Flory-Huggins-Zuo (FHZ) EoS, to evaluate liquid-solid (asphaltene)

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equilibria (Freed et al., 2010; Zuo et al., 2013; Freed et al., 2014). The FHZ EoS is

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the first EoS to model asphaltene gradients and, because the gravity term and other

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terms depend on asphaltene size, the FHZ EoS relies on the resolution of the

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asphaltene molecular and colloidal sizes as given in the Yen-Mullins model (Mullins

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et al, 2007, Mullins, 2010). With the asphaltene size resolved, the gravity and other

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terms are resolved enabling the thermodynamic treatment of asphaltene gradients

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in reservoir crude oils.

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The combination of the DFA measurement technology and new asphaltene science

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has led to many new applications. For example, reservoir connectivity has been

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successfully predicted when the asphaltenes are determined to be equilibrated

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(Betancourt et al, 2009; Mullins et al, 2008b; Dong et al, 2014). Nevertheless, one

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study showed that in adjacent compartmentalized reservoirs, there was no chemical

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difference in either the liquid phase as shown by GCxGC-FID (Mullins, et al, 2008b)

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nor in heavy end analysis as determined by high resolution mass spectrometry


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(Mullins et al, 2006). In these adjacent compartmentalized reservoirs, the

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concentration of asphaltenes varied between compartments, but not the chemical

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identity of the liquid components nor the heavy ends. Along similar lines, many

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asphaltenes samples were analyzed from heavy oils around a 100 kilometer long

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rim of an anticline reservoir. The heavy oils were shown to be in thermodynamic

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equilibrium as asphaltene clusters (Mullins et al, 2013). The sulfur chemistry

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(Pomerantz et al, 2013) and the asphaltene molecular and nanoaggregate weights

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(Wu et al, 2014) were shown to be uniform along this heavy oil rim.

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In one case study, the vertical connectivity of a sand was indicated by the

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equilibration of asphaltenes using the FHZ EoS with nanoaggregates over the

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interval of concern in a single well (Pomerantz et al, 2010). In addition, the liquid

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phase components and the heavy end components were also largely homogeneous

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over this same vertical interval (Pomerantz et al, 2010) in spite of the complexities

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of biodegradation and multiple charging (Peters et al, 2005; Larter et al, 2006).

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Nevertheless, in that study, comprehensive two-dimensional, comprehensive gas

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chromatography (GCxGCxFID) was used to delineate the occurrence of

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biodegradation and multiple reservoir charging (Pomerantz et al, 2010).

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In a recent study, the compositional variation vertically and laterally in an oil

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reservoir was shown to be controlled by diffusion of alkane and other components to

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the oil-water contact followed by relatively rapid removal of these components by

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biodegradation (Zuo et al, 2015). In this case, the large compositional variation is

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linked to ongoing diffusion and biodegradation that has been going on for 50 million

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years as corroborated by petroleum system modeling (Zuo et al, 2015). Moreover,

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this process gave rise to large gradients in asphaltene concentration and viscosity.

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In a more general sense, it is desirable to predict or forward model reservoir fluid

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distributions at early stages of reservoir evaluation. Basin modeling is a well-

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developed discipline. Understanding in-reservoir fluid processes has recently been

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improved due to 1) improvements of thermodynamic understanding of crude oils

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embodied in the FHZ EoS and the Yen-Mullins model of asphaltenes and 2) DFA

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reservoir case studies. These two developments have revealed the existence of

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various chemistry and physics processes of reservoir fluids. For example, a recent

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paper has confirmed the formation of heavy oil and tar mats by asphaltene-enriched

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convective currents within the oil column.(Forsythe, et al, 2015) These various

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processes are being subsumed under the title, ‘reservoir fluid geodynamics’. Recent

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forward modeling within precepts of reservoir fluid geodynamics includes an

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examination of thermodynamic equilibration of reservoir fluids.(Mullins, et al,

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2015) Specifically, different rates of equilibration of different components of crude 5 ACS Paragon Plus Environment

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oil are shown. Forward modeling will become increasingly important with improved

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understanding of these in-reservoir fluid dynamics.

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For complex reservoir fluids subjected to various processes that change the chemical

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composition of crude oil, it is highly desirable to perform chemical separation that

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resolves individual chemical constituents. Some studies rely on ultra-high

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resolution mass spectroscopy for this purpose (Pomerantz et al, 2010, Vaz et al,

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2014, see also Marshall and Rodgers, 2008). GCxGCxFID has proven to be very

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useful for the purpose of chemical differentiation of liquid phase components (Reddy

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et al., 2007; Dong et al., 2014; Pomerantz et al., 2010; Ventura et al., 2010).

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GCxGCxFID uses two GC columns to perform sequential separations. The first

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column can separate chemical constituents of a complex mixture based on volatility.

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The second column can perform separation based on polarizability by virtue of a

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different stationary phase. In this manner, many individual chemical constituents

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can be resolved that would normally overlap in traditional single-dimension GC.

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Coupling GCxGCxFID with mass spectroscopy (GCxGC-MS) often yields

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unambiguous peak assignments in the chromatogram. Characterization of the

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individual chemical constituents of a crude oil and understanding of the

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relationships between structure and function is the vision embodied in the new field

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petroleomics (Marshall and Rodgers, 2008; Mullins et al., 2007).


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GCxGCxFID can differentiate reservoir crude oil from oil-based mud drilling fluids

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that have an olefin base (so-called synthetic base muds), a task that can be difficult

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with conventional GC (Reddy et al., 2007). GCxGCxFID provides excellent

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resolution in the biomarker region, enabling robust and accurate determinations of

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the extent of biodegradation and determination of the biodegradation rank on the

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Peters and Moldowan scale (Peters and Moldowan, 1993). In addition, GCxGCxFID

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can readily separate light alkanes from light aromatics. Since loss of the light

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aromatics occurs during water washing, GCxGCxFID is well suited to examine this

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effect of water on reservoir crude oil.

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In addition, during normal subsidence with source rock heating, reservoirs can

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receive petroleum even at temperatures less than 80oC where biodegradation can

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take place. Further subsidence can lead to reservoir sterilization (pasteurization)

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and with further charging, a fraction of the crude oil may not be biodegraded.

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GCxGCxFID is well suited to address multiple charge scenarios (Pomerantz et al.,

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2010) as well as the extent of biodegradation and water washing in the oil. All of

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these factors can impact oil viscosity and viscosity gradients in the reservoir and are

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thus a significant production concerns. For example, biodegradation can result in

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the reduction of crude oil volume by 50% to 70% (Head et al., 2003), thereby

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concentrating the asphaltenes and increasing the viscosity. With this extent of

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biodegradation, the asphaltene concentration has been observed to triple (Zuo et al,

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2015).


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Determination of the factors that influence crude oil physical properties is very

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important. Understanding the petroleum system context of these parameters

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enables a much more fundamental view of the reservoir and basin. A petroleum

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system model includes the source rock and all of the geological elements and

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processes for oil and gas to accumulate (Maggon and Dow, 1994). The essential

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elements are source rock, migration path, reservoir, seal and overburden for

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requisite burial. Computerized petroleum system modeling attempts to account for

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the dynamic processes that control the type and distribution of petroleum fluids.

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Based on this perspective, the petroleum system model can be matched with

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detailed chemical analysis of the reservoir crude oils to understand the primary

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factors that control crude oil properties.

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The combination of GCxGCxFID on samples of known provenance (using wireline

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formation sampling), and petroleum system modeling is particularly well suited to

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address complexities in the Llanos basin. The Llanos basin is in the eastern

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forelands of the Andes Mountains in Colombia, located between the Eastern

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Cordillera and the Guyana Precambrian shield. The growth of the Eastern

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Cordillera of the Andes in the Late Miocene gave rise to the Amazon River (Hoorn

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et al., 1995). Thus, the drainage and paleogeography of northern South America in

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the Miocene were strongly controlled by tectonic movements in the northeastern

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Andes. Figure 1 depicts the Llanos basin; note the significant subsidence of the


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basin in the west associated with the overburden produced by erosion from the

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Andes (Person et al., 2012).

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Figure 1. Llanos basin, Colombia in the eastern forelands of the Andes. Note the

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significant subsidence in the west associated with deposition from overburden

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eroding from the Andes (Person et al., 2012). The formations as indicated in Persons

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et al, 2012, are listed in the figure; the Gacheta is the main source rock.


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Many of the major producing fields in Colombia are in the Llanos basin. These

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fields are greatly impacted by the unusual hydrology of the region. Since the

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orogeny in the Miocene, the Andes Cordillera have strongly influenced weather

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patterns. The prevailing easterly winds at these latitudes in northern South

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America intersect the young and high Andes yielding heavy rainfall that feeds the

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Amazon River, by far the largest river on the planet in volumetric flow. This strong

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water influx into aquifers in the Llanos basin has replaced much salt water with

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fresh water, significantly hindering oil identification by standard electrical

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petrophysical methods. In addition, the water influx yields strong aquifer support so

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water injection into wells for pressure maintenance is not needed. Water

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breakthrough in the Llanos basin is an ever present problem for oil production.

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Moreover, the unique Llanos basin aquifers can result in hydrodynamic traps of oil.

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The Rubiales field in the Llanos basin has no structural trap; the oil is held in a

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hydrodynamic trap by the flow of water in the adjacent aquifer.(Person et al., 2012)

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Figure 2 shows the Rubiales field with its contour lines that are parallel (thus no

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structural or stratigraphic trap).


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Figure 2. The Rubiales field and its oil-water contact (OWC) in the Llanos basin

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shows nearly parallel depth contours. This oil field is in a hydrodynamic stagnation

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trap caused by flowing water at the oil-water contact.(Person et al., 2012)

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As implied in Fig. 1, there is significant influx of water into the aquifers of the

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Llanos basin leading to downward flow in the aquifers. This downward flow in the

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Rubiales field arrests the upward buoyancy of the oil, such that the oil accumulates

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on the slope in a hydrodynamic stagnation point as depicted in Fig. 2 (Person et al.,

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2012). The relative high density of the heavy oil (12oAPI) reduces the buoyancy


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force. Fig. 2 shows that this hydrodynamic situation gives rise to a difference in the

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height of the oil-water contact of 60 m across the field (Person et al., 2012). This is

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very important to know for field development, particularly to avoid well placement

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that results in water production in horizontal wells.

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Note that other descriptions of the aquifer have been argued in the literature. For

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example, Selley (1998) argued that basal reservoirs in the Llanos Basin are

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underpressured with flow back into the basin. Gonzalez et al (2014) argued that the

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Llanos Basin was a tight, low-permeability system with very little fluid flow (and

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thus no water washing). However, the explanation of Person et al, 2012, is

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consistent with a substantial tilt to the oil-water contact in the Rubiales reservoir.

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This observation is difficult to explain by any other reservoir description. In

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addition, oil reservoirs in the Llanos basin have two widely acknowledged

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properties; 1) they are low salinity (Bachu et al, 1994) and even fresh water pay, 2)

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they have very strong aquifer support, water injection is almost never performed in

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the Llanos basin, while it is much more common elsewhere. Both of these important

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field concerns that have significant implications for formation evaluation and field

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development planning are consistent with explanations as given by Person, et al

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2012. Moreover, the extent of water washing of Llanos basins crude oils can be

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tested to determine consistency with Persons et al, 2012.

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In this paper, we examine crude oils in a reservoir in the Llanos basin to

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understand the primary factors that control crude oil properties. The effect of water-

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related processes on the crude oil is emphasized. We use a combination of petroleum

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system modeling, GCxGCxFID coupled with flame ionization detector (FID) and

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GCxGC-MS along with wireline sampling optimization to understand factors that

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control crude oil composition in a reservoir. Crude oils in this reservoir are shown to

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be subject to water washing, biodegradation and multiple charging. Petroleum

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systems analysis is performed with constraints provided by the chemical analysis of

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the crude oils. Even though some detailed data that is useful for PSM was lacking,

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the analysis showed that the timing of paleo-pasteurization appears to be a key

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determinant of oil properties. This study integrates different types of data to enable

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a more robust understanding of reservoirs and their contained fluids.

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2. Methods

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2.1 Wireline Sampling Acquisition and Analysis

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In this project, samples of crude oils were acquired in two wells. In each well, two

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samples at closely spaced depths were acquired. Little variation was observed for

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samples acquired within each well. These samples were acquired using the

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openhole wireline formation sampling tool the MDT [Mullins, 2008a] Wireline

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sample acquisition in the Llanos basin can be challenging due to the viscous nature

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of the oils and the active aquifer. In addition, in open-hole sample acquisition, it is

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always necessary to pump long enough to reduce contamination from drilling fluid 13 ACS Paragon Plus Environment

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filtrate that penetrates into the oil-bearing formations. Fig. 3 depicts the wireline

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sampling tool, the MDT (Modular Formation Dynamics Tester). The MDT is a

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complex instrument package that is lowered into the well on a cable or “wireline”

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(cf. Mullins, 2008a). The MDT consists of many components or modules such as

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pumps, fluid analyzers, and sample bottles. The MDT is configured to have one or

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more probe modules which establish hydraulic communication with permeable

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formations for example using a stout, steel tube (cf. Figure 3). This tube along with

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a surrounding packer is pressed against the formation wall with great force creating

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a seal. The MDT has onboard pumps which can lower the pressure in the flow line

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enabling extraction of fluids from the (permeable) formation.

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Figure 3. Schematic diagram of a wireline sampling tool, the “MDT” which acquires

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crude oil samples from wells. Modules on the MDT perform optical spectroscopy and

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other measurements on samples for in-situ chemical analysis. This tool is used in so-

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called “open hole” just after the well is drilled and prior to placement of steel casing 14 ACS Paragon Plus Environment

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in the well. A photograph of a “probe” or stout tube is shown which is pressed firmly

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against the borehole wall along with a packer to make hydraulic communication

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with permeable zones. With the MDT pump, the pressure is reduced in the flowline,

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and fluids flow from the formation into the tool. DFA enables characterization of the

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fluid contents of the flowline; when clean formation fluids are flowing, high pressure

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sample bottles on the MDT can be opened to acquire formation fluid samples for

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subsequent study.

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The MDT tool enables acquisition of representative samples of formation fluids

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(Mullins, 2008a). The provenance of the crude oil sample in the reservoir is recorded

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with great accuracy within the well, an essential component for evaluation of the

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crude oil within a thermodynamic context where fluid gradient analysis is key.

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Downhole fluid analysis (DFA) allows minimization of contamination of the crude

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oil by filtrate of the drilling fluid that penetrates into the oil-bearing formations.

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Phase transitions, such as gas evolution, that would destroy the validity of the

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crude oil sample, can be avoided by measurement of flow line contents. In addition,

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DFA allows determination of the crude oil and water fractions in the flow line, so

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that optimal pumping times for sample acquisition are employed. The fluorescence

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measurement of the Insitu Fluid Analysis (IFA) is sensitive to crude oil early in the

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pumping period when the MDT flow line contains mostly water from both mud

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filtrate and the formation. The near infra-red measurements of the IFA enable

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quantification of the oil, water and gas fractions in the MDT flow line (Mullins, 15 ACS Paragon Plus Environment

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2008a). With trace oil detected, pumping continues to “clean up” or reduce the

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filtrate contamination in the flow line. Even trace oil is of interest to understand the

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formation fluids. The data is shown in Fig. 4 corresponding to the acquisition of oil

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samples used herein; the IFA fluorescence signal identifies trace oil in the presence

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of mostly water indicating the pumping should continue to obtain a crude oil.

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Figure 4. Fluorescence measurement on the IFA tool is used to identify trace amounts

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of oil in the MDT. A sample recovered at surface confirmed trace amounts of oil.

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Pumping then continues to obtain bulk crude oil.

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In order to maximize the oil content in MDT samples of oil-water flows, downhole

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segregation is used as depicted in Fig. 5. The oil-water mixed phase flow is pumped

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into a sample chamber and allowed to separate for approximately one hour. The

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sample is then pumped out of the segregation bottle into a sample acquisition

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bottle. The timing of bottle opening is adjusted to acquire a pure oil slug. Optical

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light scattering in the oil establishes that this segregation procedure separates free


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water from the oil, but tends not to break oil-water emulsions. This segregation

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method was used for all samples used herein.

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Figure 5. Oil and water segregate in the pumpout module while pumping (left). To

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aid in sampling, the oil and water are placed in a sample bottle for one hour

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downhole. The oil and free water segregate (right), allowing collection of oil without

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free water.

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2.2 GC×GC-FID and GC×GC-MS.

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Gas chromatography (GC) is a powerful tool that facilitates the separation of

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complex molecular mixtures for compound identification and quantitation. GC uses

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a single capillary column coated with a stationary phase. Compounds can be

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separated based on a variety of factors including molecular weight, volatility,

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polarity, and polarizability, all of which affect the partitioning of each component

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between the stationary and mobile phases. GC has been particularly useful for

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analyzing the molecular composition of crude oils. Using two GC columns in

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sequence (thus, GCxGCxFID), where the separations in the first and second column 17 ACS Paragon Plus Environment

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emphasize different chemical attributes, vastly enhances the number of compounds

320

that can be individually observed and identified (Nelson et al., 2006; Gaines et al.,

321

2006; Reddy et al., 2007). The first column often is configured to perform separation

322

primarily based on volatility. The second dimension column is typically configured

323

to separate compounds eluting from the first column by the polarity and

324

polarizability of the compounds. The GC×GC system links the first and second-

325

dimension columns by temporally freezing the effluent as it is leaving the first

326

dimension column and then re-injects the effluent into the second dimension

327

column using a thermal modulator. The modulator separates the mixture into small

328

packets of analyte that are sequentially injected into the second dimension column.

329

Using a short second dimension column, thus with fast elution, sharpens peaks and

330

increases the signal-to-noise ratio by a factor of 10 to 100 compared to traditional

331

gas chromatography. Coupling the GC×GC to a flame ionization detector (GC×GC-

332

FID), one can approximate that all hydrocarbons have the same response factor

333

(Tong et al., 1984) in order to estimate the concentration of each hydrocarbon in an

334

oil sample. When using GC×GC-MS (time-of-flight mass spectroscopy), the

335

increased chromatographic resolution allows for little or no background and much

336

cleaner mass spectra of unknown compounds, which, in turn, can be used to identify

337

specific chemical structures. Within the second dimension of a GC×GC-FID

338

chromatogram, saturated hydrocarbons elute earliest and multi-ring aromatic

339

hydrocarbons elute the latest. The added separation capacity provides three

340

powerful tools for geochemical analysis of petroleum hydrocarbons. First, saturated 18 ACS Paragon Plus Environment

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341

and aromatic hydrocarbons can be simultaneously analyzed without an initial liquid

342

chromatographic separation. This simplification not only reduces processing time

343

and cost, but also ensures more accurate analysis of hydrocarbon mixtures under

344

identical GC conditions. Second, such separations allow the calculation of the

345

saturate to aromatic ratio for the whole sample or as a function of an n-alkane

346

window (such as nC16 to nC17). Third, compounds in the same class will have a

347

similar elution pattern, which results in a predictable elution order for a compound

348

class. This provides a powerful predictive tool to identify new compounds and to

349

determine whether multiple compounds belong to one or more compound classes.

350 351

Each extract was analyzed by GC×GC-FID that employed a loop-jet modulator

352

(Gaines et al, 2004), from the Zoex Corporation (Lincoln, NE). The complete system

353

included an Agilent 6890 gas chromatograph configured with a 7683 series

354

split/splitless auto-sampler, two capillary gas chromatography columns and a flame

355

ionization detector. Each extract was injected in split-less mode and the purge vent

356

was opened at 0.5 min. The inlet temperature was 295oC. The first dimension

357

column and the loop jet modulator reside in the main. The second dimension column

358

is housed in a separate oven installed within the main GC oven. With this

359

configuration, the temperature profiles of the first dimension column, thermal

360

modulator and the second dimension column can be independently programmed.

361

The FID signal was sampled at 100 Hz. The carrier gas was H2 at a constant flow

362

rate of 0.7 ml/min. Peaks were identified with commercially available standards 19 ACS Paragon Plus Environment

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363

from Aldrich, US National Institute of Standards and Technology (NIST), and

364

Chiron (Trondheim, Norway). The first dimension column was a nonpolar 100%

365

dimethylpolysiloxane phase (Restek Rtx-1 Crossbond, 7.5 m length, 0.10 mm inner

366

diameter, 0.1 micron film thickness) that was programmed to remain isothermal at

367

33 C for 5 min and then ramped from 33 to 285 C at 1.5 C/min. The modulation loop

368

was deactivated fused silica (1.5 m length, 0.10 mm inner diameter). The modulator

369

cold jet gas was dry N2, chilled with liquid Ar, with a constant flow rate of 2.2

370

liter/min. The modulator hot jet air was heated to 105oC above the temperature of

371

the first oven. The hot jet was pulsed for 350 ms every 10 s (0.10 Hz). The

372

modulation period, therefore, was 10 s. Second dimension separations were

373

performed on a 50% phenyl polysilphenylenesiloxane column (SGE BPX50, 2.0 m

374

length, 0.10 mm inner diameter, 0.1 micron film thickness) that was programmed to

375

remain isothermal at 46oC for 5 min and then ramped from 46o to 298oC at 1.5o

376

C/min.

o

o

o

o

377

378

Samples were also analyzed by GCxGC-MS using a Hewlett-Packard Agilent 6890

379

Series gas chromatograph with a cooled injection system (CIS) and interfaced to

380

both a Hewlett-Packard 5973 quadrupole mass spectrometer and flame ionization

381

detector. A 1 microliter sample was injected into the CIS, which was temperature

382

programmed from 50 oC (0.1 min hold) to 350 oC at 720 oC/min (8 min hold).

383

Compounds were separated on a fused silica capillary column (J&W DB-5 ms, 60 m


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384

length, 0.32 mm inner diameter, 0.25 micron film thickness) with He as the carrier

385

gas at a constant flow of 1.5 ml/min. The GC oven temperature initially was held at

386

50 C for 1 min, then ramped at 20 C/min to 115 C (10 min hold), and then ramped

387

5 C/min to 320 C (10 min hold). A commercially available glass-lined fixed outlet

388

splitter (SGE) was used to split the column effluent to the MS and FID (flame

389

ionization detector) at a ratio of approximately 2:1. The MS was run in full scan

390

mode from 50 to 800 amu. The MS transfer line was held at 325 C. The FID was

391

held at 325 oC and sampled at 10 Hz.

392

2.3 Petroleum System Modeling with PetroMod®

393

Scope of the Project. Many of the producing fields in the Sub-Andean foreland

394

basin located between the Eastern Cordillera and the Guyana Precambrian Shield

395

experienced changes in the accumulated hydrocarbon quality due to the impact of

396

the unique aquifer system, which developed mainly after extensive uplift that

397

occurred in Miocene. The purpose of this basin and petroleum systems modeling

398

study was to model the impact of water influx and the effect of biodegradation on

399

the quality of the accumulated hydrocarbons using the tools and options available

400

in the latest version of PetroMod, version v2014.1 alpha. Classical petroleum

401

system analysis performed for this basin indicates Cretaceous Source rock and

402

principally Oligocene reservoirs. The main reservoir is the Mirador Formation,

403

when it exists, but reserves have been also reported in Une (Upper Cretaceous),

404

Barco (Paleocene) and Carbonera formations (Oligocene) (Moretti et al., 2009a,

o

o

o

o

o

o


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405

Moretti et al, 2009b). Several processes were considered and modeled in the study:

406

biodegradation, paleopasteurization (when reservoir heating halted further

407

biodegradation), water washing (diffusion and dissolution of methane in formation

408

water as a proxy for dissolution for light aromatics), and the transport and cooling

409

effect of aquifers. The intent of this modeling is to provide guidance and

410

understanding of the primary influences on crude oil quality especially as revealed

411

by detailed chemical analysis of these oils. The modeling is not meant to be highly

412

detailed for the large Llanos Basin.

413

2.4 Model Input. The area of the study is shown schematically in Fig. 6. The model

414

geometry and part of the input and calibration data were provided by ONGC Videsh

415

company active in the Llanos basin. The additional information for model building

416

was taken from published articles as discussed below.

417


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418 419

Figure 6. The PetroMod model employed for the Llanos basin. The model includes 10

420

layers, 15 events and 3 unconformities.

421

422

The model includes 10 layers, 15 events and 3 unconformities (Fig. 6). The timing

423

and duration of erosion intervals and the amounts of eroded sediments were

424

changed during the simulation runs to analyze the impact of erosion on the source

425

rock maturation, hydrocarbon generation and charge, and biodegradation. The

426

model incorporates only uniform facies (mixtures of sandstone, shale and siltstone)


Energy & Fuels

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427

to keep the scenarios as simple as possible. The Carbonera Formation was divided

428

into 8 sub-layers according to Bachu et al. (1995).

429

The upper boundary condition (sediment-water interface temperature) was defined

430

using the automated tool in PetroMod (Wygrala, 1989). Heat flow values at the base

431

of sediment pile were assigned based on the heat flow history recommended by

432

Bachu et al. (1995).

433

The Gacheta Formation was considered to be the main effective source rock, and the

434

Mirador Formation was the main reservoir. The Une Formation was treated as

435

reservoir as well, but the study focused mainly on Mirador Formation. Pressure and

436

temperature information from well data was used to calibrate the model to analyze

437

the impact of biodegradation, water washing, and variations in temperature due to

438

active aquifers in different scenarios. The total organic carbon (TOC) and hydrogen

439

index (HI) values defining the richness of the Gacheta Formation were based on

440

published data (Moretti et al., 2009).

441

442

A standard 14-component kinetic model (IES_TII_Brown_Limestone) was modified

443

to better reflect biodegradation and water washing. Its default methane component

444

was replaced with user defined methane prepared for diffusion and dissolution. The

445

fractions of biodegradable components were defined to get more realistic API

446

gravity values near 23°API. The crude oils from the Valdivia field in the general

447

area have API gravity values of roughly 23°. 24 ACS Paragon Plus Environment

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448

449

The calculated temperature and pressure trends were well calibrated to the

450

measured temperature and pore pressure data in the Well 1 and Well 2 wells. The

451

present day maturity and the maturation history of the effective source rock are in

452

good agreement with the published data (Moretti et al., 2009) in the study area. In

453

the hydrocarbon balance of the 3D model only the hydrocarbons generated within

454

the study area were taken into account. The hydrocarbons generated outside of the

455

area of interest and thus the long distance migration were not considered in this

456

model. The effective source rock entered in oil window around 10 mybp (million

457

years before present) and now it is in the early and peak oil window in the modelled

458

area.

459

460

461

Sensitivity analysis was performed on several parameters in the simulation runs:

462

timing/duration of unconformities, eroded amounts, threshold and paleo-

463

pasteurization temperatures related to biodegradation, fractions of biodegradable

464

components, the diffusion coefficients of the user defined methane component, and

465

the additional or excess pressure values added to hydrostatic to treat the main

466

reservoir as an aquifer. The PSM results presented are the most consistent with

467

literature data and fluid data presented herein. The parameters assigned to this

468

model are as follows: 25 ACS Paragon Plus Environment

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469

•

Eroded amount assigned to “unconformity 16.5 Ma”: 1,250 m

470

•

Eocene unconformity defined at the end of the deposition of the Mirador Formation.

471

472

•

Biodegradation parameters: threshold temperature=55°C, paleo-pasteurization temperature=95°C.

473

474

•

Degradation fractions: default values scaled by 0.3

475

•

Diffusion coefficient and activation energy of methane: D0: 4.64x10-11m2/s, EA: 220 kcal/mole

476

477

•

Hydrocarbon solubility table: PetroMod default values

478

479

The heat and pore pressure calculations, the migration modeling and the impact of

480

water influx on the reservoir quality were performed in full 3D. In the thermal

481

evolution of the basin the heat transport caused by water flow (convection) was also

482

considered (Person and Graven, 1992; Person et al., 1995). Water velocities were

483

calculated during the pressure–compaction equations, and the convection term of

484

the three dimensional heat flow equation coupled the heat and fluid flow

485

calculations. Topographically driven aquifer flows could have higher flow velocities

486

in the Llanos basin and therefore they were considered in the simulation runs as

487

well (Hantschel and Kauerauf, 2009). During migration modeling and accumulation

488

analysis the PetroMod’s proprietary hybrid fluid flow approach was used. This


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Energy & Fuels

489

method uses domain decomposition to solve the Darcy flow equations in areas with

490

low permeabilities, and flowpath methods in areas with high permeabilities

491

(Hantschel and Kauerauf, 2009). In the simulation runs both the dissolution and

492

diffusion of the methane in the pore water were taken into account. The transport

493

via diffusion was considered as a two-step process including the dissolution of

494

methane in water and diffusion within the water phase. Once the methane was

495

dissolved in water, the transport via water flow also became important, especially

496

when high rates of aquifer flow existed (Hantschel and Kauerauf, 2009; Grathwohl,

497

1998). The saturation of the dissolved methane in water (in percentages) and the

498

bulk hydrocarbon mass (in megatons) were calculated as overlays through geologic

499

time to represent the dissolution, water washing and fluid flow in the model.

500

501

2.5 Biodegradation. Biodegradation can be modeled with PetroMod incorporating

502

all effects of biodegradation into the petroleum systems model. It can be used with

503

the hybrid and flowpath migration methods. Biodegradation occurs mainly at the

504

oil-water contact (OWC) and depends on the size of the OWC area, temperature,

505

composition of the oil, filling history and supply of nutrients for the microbes.

506

The crude oil component groups affected in the model are grouped into five

507

components, PK_P10, PK_P20, PK_P30, PK_P40, and PK_P50. In order to achieve

508

temperatures suitable for biodegradation in the reservoirs of the Mirador

509

Formation, the paleo-pasteurization temperature was set to 95°C which is similar to 27 ACS Paragon Plus Environment

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510

upper limits of 90oC recently discussed by Head, et al, 2014. The threshold

511

temperature where biodegradation efficiency first starts to diminish was set to

512

55°C. The default degradation fractions were scaled by 0.3 to get an acceptable API

513

gravity during the events prior to present day. Many crude oils from the Llanos

514

basin exhibit biodegradation (Vaz et al, 2014). Characterization of organic acids by

515

ultra-high resolution mass spectrometry is a valuable means of characterizing

516

biodegradation (Vaz et al, 2014). Evaluating the extent of biodegradation is critical

517

to understanding these crude oils. In this study, we used the Peters and Moldowan

518

(1993) rank of biodegradation, which is shown schematically in Fig. 7. The n-

519

alkanes are preferentially consumed by the microbes while appreciable consumption

520

of the hopanes yielding in part 25-norhopanes occurs only after extensive

521

biodegradation of other chemical components.

522


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523 524

Figure 7. The Peters and Moldowan scale of biodegradation (Peters and Moldowan,

525

1993). The reservoir microbes preferentially consume specific chemical classes

526

sequentially. For example, the n-alkanes are the first chemical class to be consumed

527

in the biodegradation process.

528

529

530

2.6 Aquifer. In our model, the additional pressure of 0.02 MPa was defined in the

531

Mirador Formation to mimic water flow from east to west between 4.2 and 0.0 Ma.

532

The velocities in the defined aquifer increase with pressure differences. Higher

533

pressure differences initiate higher velocities which cool the reservoir more rapidly.


Energy & Fuels

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534

In the case of the best scenario, the pressure differences were defined to achieve a

535

realistic temperature difference of ∼1°C in the analyzed well locations.

536 537

3.0 Results and Discussion

538 539

GCxGCxFID data for one of the crude oil samples are shown in Fig. 8. As expected,

540

there is excellent separation of different chemical groups with some separation of

541

individual chemical components. In particular, the three regions of interest are well

542

separated: 1) the n-alkanes, 2) the biomarker region (hopanes, 25-norhopanes, etc.),

543

and 3) light aromatics. These regions are particularly useful to analyze water

544

washing, biodegradation, and multiple charging.

545 546


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Energy & Fuels

547 548 549

Figure 8. GCxGCxFID chromatogram of a Llanos basin crude oil clearly

550

distinguishes n-alkanes, 25-norhopanes, and light aromatic compounds. These

551

chemical classes are key to unraveling complexities of the oil reservoirs in the Llanos

552

basin.

553 554 555

The n-alkanes are evident and very pronounced. This ease of detection, coupled

556

with the fact that the n-alkanes are the first chemical class to be removed by 31 ACS Paragon Plus Environment

Energy & Fuels

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557

biodegradation makes this class particularly useful for indicating whether any

558

unbiodegraded oil remains in the reservoir. The presence of the n-alkanes indicates

559

biodegradation rank of 0 to 1 on the Peters and Moldowan scale. The current

560

reservoir temperature is over 100oC; too hot to support biodegradation. Figure 9

561

shows an expanded scale of the biomarker region, which is particularly useful for

562

analysis of biodegradation.

563

564 565 566

Figure 9. Biomarker region of the chromatogram in Fig. 8. The 25-norhopane series,

567

which results in part from biodegradation of the hopanes, is much more abundant

568

than the hopanes, indicating extensive biodegradation.

569 570 32 ACS Paragon Plus Environment

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Energy & Fuels

571 572

Fig. 7 shows that hopanes are consumed at the rank of approximately 6 on the

573

Peters and Moldowan scale. It is important to verify the assignments given in Figs.

574

8 and 9, especially given that the current reservoir temperature precludes recent

575

biodegradation. Figure 10 shows the results of GCxGC-MS data for a 25-norhopane

576

and for a hopane and confirm the general assignments shown in Figs. 8 and 9. In

577

particular, in the mass spectrum, the methyl substitution at C-25 yields a fragment

578

at 191 amu as shown in Fig. 10b, while a 25-norhopane compound yields a

579

molecular fragment at 177 amu as shown in Fig. 10a. Thus, GCxGC-MS analysis is

580

used here to confirm the GCxGC-FID peak assignments.

581


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

582

583

Figure 10a. GCxGC-MS traces confirming the assignment of 17•(H),21•(H)-25,30-

584

bisnorhopane designated as 25-nor-NH. Likewise other assignments were confirmed

585

in a similar manner. 10b. GCxGC-MS traces confirming the assignment of

586

17•(H),21•(H)-30-norhopane designated as NH. The 25-norhopanes are clearly more

587

intense than the hopanes in this and other data sets confirming the determination of


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Energy & Fuels

588

biodegradation rank 6 on the Peters and Moldowan scale. This figure represents an

589

extracted-ion chromatogram from the GCxGC-MS.

590

591

592

The conclusion is that a fraction of the reservoir oil underwent biodegradation with

593

a Peters and Moldowan rank of 6, and that another fraction did not undergo

594

biodegradation with a Peters and Moldowan rank of 0 to 1. Given that the current

595

reservoir temperature is >100oC, it is likely that the initial charge occurred when

596

the reservoir was sufficiently cool for biodegradation and the secondary charge

597

occurred when the reservoir was too hot for biodegradation to occur. Subsidence of

598

the reservoir and source rock could account for these observations.

599 600

Confirmation of other assignments were performed for many compounds. For

601

example, peaks Ts, Tm, 25-nor-Ts, and 25-nor-TM identified in Figs. 8 and 9 were

602

confirmed by GCxGC-MS as shown in Figure 11.


Energy & Fuels

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603 604

605

Figure 11. GCxGCxMS confirmation of assignments in GCxGCxFID for the

606

compounds designated as Ts, 25-nor-TS, Tm and 25-norTm.

607 608 609

Both samples indicate that the original charge which was biodegraded and the new

610

charge which is unbiodegraded did mix to some degree. In the charging process,

611

often there is a lack of mixing that is seen, primarily associated with a relatively 36 ACS Paragon Plus Environment

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Energy & Fuels

612

small spatial extent of charge paths or charge planes.(Stainforth, 2004) In reservoir

613

diffusive mixing can cause the observed mixing here.(Zuo et al., 2015) It is not clear

614

whether convective mixing observed elsewhere (Forsythe et al., 2015) could play a

615

role here.

616 617

3.1 Water Washing from GCxGCxFID

618 619

The relatively water-soluble light aromatics separate very well in GCxGCxFID. Fig.

620

12 shows the GCxGCxFID chromatograms of two oil samples from two wells in the

621

Llanos basin, focusing on the light aromatics. The GC×GC chromatogram readily

622

separates light aromatics which in turn can be used to analyze the impact of water

623

washing (Arey et al., 2005; Arey et al., 2007; Nabi et al., 2014). Water-washing

624

systematically removes the more water soluble crude oil components, the light

625

aromatics components from the upper left corner of the GC×GC chromatogram,

626

encroaching inward toward the middle of the chromatogram. This trend is

627

consistent with the conspicuous absence of certain hydrocarbons from the samples

628

from both Well 2 and Well 1 (Figure 12). In both crude oil samples, naphthalene and

629

phenanthrene are nearly absent (almost no detectable peak), and the methyl

630

naphthalenes (C1-naphthalenes) are in low abundance compared to higher-

631

alkylated analogues. The water solubilities of these aromatics decrease with

632

increasing alkyl substitution, suggesting that the parent compounds (naphthalene

633

and phenanthrene) have been removed principally by water-washing. We do not 37 ACS Paragon Plus Environment

Energy & Fuels

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634

have an unaltered equivalent for this oil. Consequently, we roughly approximated

635

the extent of water-washing based on the ‘expected’ light aromatic ratios for such an

636

oil. Using known partition functions of the naphthalenes and phenanthrene in oil

637

versus water at 100°C, we estimate that the crude oils from Wells 1 and 2 were

638

subjected to 5,000 times to 40,000 times their volumes in water, respectively.

639

640

641

Figure 12. GCxGCxFID chromatograms for a sample from each of the two wells show

642

evidence of water washing, indicated by the relative absence of naphthalene and

643

phenanthrene and their methylated analogues compared to higher-alkyl analogues.

644


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Energy & Fuels

645

Water washing in these oil samples is quite evident in the GCxGC chromatograms.

646

This is consistent with observations and explanations of given in Person et al, 2014,

647

regarding large water influx in the Llanos basin. These explanations are also

648

consistent with low salinity pay and strong aquifer support which are both well-

649

known characteristics of llanos basin reservoirs. In addition, we presume that the

650

small aromatics migrate within the reservoir by diffusion (Zuo et al, 2015), but in

651

the water column by advection.(Zhang et al 2005a, Zhang et al, 2005b)

652

To estimate the water velocity in the aquifer, we use the data from Fig. 2 and the

653

literature (Person et al., 2012). Darcy’s law is ∆ = − 1.

654 655

where Q/A is the linear flow rate, k is permeability, ∆P is the pressure drop

656

associated with the flow, µ is viscosity and L is the length over which the flow

657

occurs. For the Rubiales field, the oil-water contact differs by 60 meters across the

658

field, and the crude oil is ~0.985 g/cc (Person et al., 2012) giving a ∆P of ~0.1 atm.

659

The viscosity of water is 1 cP (a conservative estimate), the permeability is ~400

660

mD, and L~ 20 km (cf. Fig. 2) (Person et al., 2012). To obtain the water velocity in

661

the porous medium, one divides Q/A by porosity, here ~0.2. This gives a linear

662

velocity of water of 10 km/million years. We use this velocity for the Llanos aquifer.

663

Over the estimated mean life of the oil in the reservoir (10 million years), the total

664

distance traveled by the aquifer water would be 100 km. This considerable flow rate 39 ACS Paragon Plus Environment

Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

665

and flow distance are qualitatively compatible with the large volumes of water

666

obtained above for water washing the crude oil.

667

3.2 Petroleum System Modeling

668

Our initial petroleum system model did not result in the observed biodegradation in

669

the reservoirs. In the model, when the source rock entered the oil window, the

670

reservoir was too hot to allow biodegradation. In order to match the observed

671

biodegradation in the Llanos basin reservoir of interest, it was necessary to adjust

672

subsidence, uplift and erosion. In this manner, the PSM could account for

673

observations. The final sequence is shown in Fig. 13.

674

675


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Energy & Fuels

676

Figure 13. The subsidence, uplift and erosional sequence used to model the Llanos

677

basin. These details were important to establish whether biodegradation can be

678

modeled in the petroleum system.

679

680

681

The burial sequence depicted Fig. 13 gave rise to the output of the PSM. The

682

reservoir was close to pasteurizing 16 million years ago in the model as shown in

683

Fig. 14a, but complete pasteurization did not occur. Consequently, a substantial

684

fraction of the reservoir crude oil was subject to biodegradation, consistent with

685

GCxGCxFID results on the crude oils.

686

A rather small modification to the burial sequence shown in Fig. 13 gave rise to the

687

scenario shown in Fig. 14b. In this scenario, the reservoir was paleo-pasteurized 16

688

million years ago, precluding biodegradation of almost the entire charge. Indeed,

689

there are blocks in the Llanos basin with much lighter oil. It is quite plausible that

690

the timing of paleo-pasteurization controls this important reservoir issue.

691


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

692

693

Figure 14a. The reservoir temperature and cumulative reservoir oil for the burial

694

sequence depicted in Fig. 13. Note that a substantial fraction of the crude oil was

695

subject to biodegradation prior to reservoir paleo-pasteurization 7 million years ago.

696

14b. The reservoir temperature and cumulative reservoir oil for a scenario somewhat 42 ACS Paragon Plus Environment

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Energy & Fuels

697

similar to the burial sequence shown in Fig. 13. With early paleo-pasteurization,

698

biodegradation was precluded for most of the reservoir crude oil yielding a reservoir

699

of much lighter crude oil.

700

701

The key parameter for the PSM is whether the reservoir was sufficiently heated 16

702

million years ago to pasteurize. A few degrees centigrade can make all the

703

difference. The details of the heat flow model as well as the burial model are

704

therefore critically important as to when paleopasteurization took place.(Bachu, et

705

al, 1994) Besides the conductive heat transfer the impact of the thermal effects of

706

water flow through geologic time were also considered in the calculation of thermal

707

histories. Scenarios with and without convective heat flow transfer were simulated

708

and then compared, and the effects of the aquifer flows on the temperature and

709

sterilization timing were analyzed.

710

For example, convective heat flow in addition to conductive heat flow can both be

711

important factors in this thermal profile. Several parameters affect this prediction,

712

including thermal flux, subsidence (with subsequent erosion), and water flux into

713

the reservoir aquifer. The objective of the PSM here is not to uniquely predict

714

reservoir and crude oil observations. Instead, what has been accomplished here is to

715

use a somewhat sparse data set about a rather large basin to explain possible key

716

parameters that influence important reservoir properties.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

717

In addition to treating biodegradation, PSM was also able to account for water

718

washing. The parameters that affect water washing include the differential of the

719

hydrostatic head pressure across the reservoir. Similar parameters for the

720

magnitude of the gradient were used as derived above for the Rubiales field. In

721

addition, the diffusion rate of the light aromatics in crude oil were estimated as

722

being slightly lower than typical methane diffusion rates in reservoir crude oil. The

723

translational diffusion constant is linear in radius, not volume, thereby limiting

724

differences in diffusion constants. By this means the PSM could produce water

725

washing in a qualitative sense.

726

4. Conclusions

727

An integrated approach using a Petroleum System Modeling coupled with

728

GCxGCxFID and GCxGC-MS data on wireline samples improves understanding of

729

the complex issues of biodegradation, multiple charging and water washing of

730

reservoir crude oils in the Llanos basin. Although only limited basin data was

731

available for construction of the petroleum system model, a self-consistent basin

732

description emerged where PSM incorporated firm conclusions from the

733

GCxGCxFID interpretation. In particular, the initial charge into the reservoirs was

734

shown to be heavily biodegraded, but with subsidence the reservoir became hotter

735

and pasteurized, so subsequent crude oil charge was not biodegraded. The timing of

736

the paleo-pasteurization is likely largely responsible for the quality of the crude oil.

737

In addition, substantial and somewhat variable water washing is indicated by the 44 ACS Paragon Plus Environment

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Energy & Fuels

738

analysis of light aromatics. It is highly desirable to check consistency with advanced

739

analytical chemistry such as GCxGCxFID and with petroleum system modeling.

740

Even in cases with limited basin information for the PSM and with few samples, a

741

general understanding of those geodynamic processes acting on reservoir fluids can

742

be delineated.

743

744

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815 48 ACS Paragon Plus Environment

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Mullins, O.C.; Ventura, G.T.; Nelson, R.L.; Betancourt, S.S.; , Raghuraman, B.;

856

Reddy, C.M.; Oil Reservoir Characterization by coupling Downhole Fluid Analysis 50 ACS Paragon Plus Environment

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Accuracy Fourier Transform Mass Spectrometry and Chemometric Analysis,

934

Energy & Fuels, 2013, 27 (3), pp 1277–1284

935

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Ventura, G.T., Raghuraman, B., Nelson, R.L., Mullins, O.C., Reddy, C.M.,

937

Chemical compound class oil fingerprinting techniques using comprehensive two-

938

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939

1026-1035

940

941

Wu, Q.; Seifert, D.J.; Pomerantz, A.E.; Mullins, O.C.; Zare, R.N.; Constant

942

Asphaltene Molecular and Nanoaggregate Mass in a Gravitationally Segregated

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Reservoir, Energy & Fuels, 2014, 28, 3010−3015

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Wygrala, B., Integrated study of an oil field in the southern Po Basin, Northern

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1989, PhD Dissertation, University of Cologne in Germany

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Zhang Y., M. M. Person, E. Merino, M., Hydrologic and geochemical controls on

950

soluble benzene migration in sedimentary basins, Geofluids, 2005a, 5, 2, 83-105

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Zhang Y., M. Person, E. Merino, M. Szpakiewcz, Evaluation of soluble benzene

953

migration in the Uinta Basin, Geofluids, 2005b, 5, 2, 106-123

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Zuo, J.Y., Mullins, O.C., Freed, D.E., Dong, C., Elshahawi, H., Seifert, D.J.,

956

Advances of the Flory-Huggins-Zuo equation of state for asphaltene gradients and

957

formation evaluation, Energy & Fuels, 2013, 27, 1722–1735

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Zuo, J.Y.; Jackson, R.; Agarwal, A.; Herold, B.; Kumar, S.; De Santo, I.; Dumont,

960

H.; Beardsell, M.; Mullins, O.C.; A diffusion model coupled with the Flory-

961

Huggins-Zuo Equation of State and Yen-Mullins model accounts for large viscosity

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and asphaltene variations in a reservoir undergoing active biodegradation, Energy

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