Downhole Fluids Laboratory - Schlumberger/media/Files/resources/... · 38 Oilfield Review Downhole...

38 Oilfield Review

Downhole Fluids Laboratory

Reservoir fluids rarely occur as simple liquids and gases filling monolithic structures.

Their generation, migration and accumulation are affected by various processes

that result in complex fluid compositions and distributions. In the past, failure to

account for the complexities of the reservoir and its fluids has often resulted in costly

production problems and disappointing results. Recent developments in formation

testing and sampling technologies provide asset teams with a downhole laboratory to

measure in situ fluid properties and gain insight into reservoir connectivity.

Jefferson CreekChevron Energy Technology CompanyHouston, Texas, USA

Myrt (Bo) CribbsChevron North AmericaHouston, Texas

Chengli DongOliver C. MullinsHouston, Texas

Hani Elshahawi Shell International Exploration & Production Houston, Texas

Peter HegemanSugar Land, Texas

Michael O’KeefeHobart, Tasmania, Australia

Kenneth PetersMill Valley, California, USA

Julian Youxiang ZuoEdmonton, Alberta, Canada

Organic material in source rocks is converted into the oil and gas that migrate into reservoirs. Variations in the composition of the original organic matter and the processes that occur dur-ing migration and accumulation of petroleum fluids often increase their compositional com-plexity. Once in place, reservoir fluids can equilibrate, yet still exhibit large compositional gradients. Frequently, however, fluids are in dis-equilibrium, disrupted by processes such as bio-degradation, multiple reservoir fluid chargings and seal breach. Downhole fluid analysis mea-surements, some of which have recently been introduced, can help resolve the complexity of these fluids at near-reservoir conditions. Armed with these data, asset managers can make informed decisions long before incurring huge expenses associated with field development and installation of production facilities.

Although field development plans depend on a thorough understanding of in situ properties, knowledge of the fluid characteristics alone is insufficient to maximize recovery. In particular, undetected barriers to fluid flow can create enormous problems for operators. For example, because pressure equilibration across sealing bar-riers can occur over geologic time, this equilibra-tion does not prove flow communication in production timescales. Failure to account for res-ervoir architectural complexity has often resulted in costly mistakes. New downhole fluid analysis

(DFA) technologies are available that enable iden-tification of reservoir compartmentalization and connectivity, along with fluid heterogeneities.

To determine the fluid properties required for effective reservoir development, engineers use DFA techniques extensively.1 Although fluid properties are derived from a number of sensors, optical spectroscopy, based on visible and near-infrared (Vis-NIR) light, is the foundation of DFA measurements for hydrocarbons.2 The technique utilizes the light-absorption properties of fluids as well as light scattering from different materi-als to identify fluid composition (C1, C2, C3-5, C6+ and CO2), gas/oil ratio (GOR), relative asphal-tene content and water fraction. Other DFA mea-surements and capabilities include determination of pH and resistivity (if the fluid is water), index of refraction, fluorescence and live-fluid density.

Prior to the availability of DFA measurements, operators collected a limited number of samples, sent them to a laboratory and, after an often lengthy period of time, received a report describ-ing the reservoir fluids. Without real-time analysis to establish the extent of fluid complexity, ana-lysts often presumed fluid simplicity. Although the typical outcome was a simplified evaluation program, which initially appeared to be cost-effective, it came at the expense of adequate understanding of reservoir complexities. Too often the result was increased total project costs. With real-time DFA, the complexity and cost of

Oilfield Review Winter 2009/2010: 21, no. 4. Copyright © 2010 Schlumberger.For help in preparation of this article, thanks to Richard Byrd, Martin Isaacs and Michelle Parker, Sugar Land; and Dietrich Welte, Aachen, Germany.Fluid Profiling, InSitu Density, InSitu Family, InSitu Fluid Analyzer, InSitu Fluorescence, InSitu pH, InSitu Pro, MDT and Quicksilver Probe are marks of Schlumberger.

1. For information on fluid sampling and DFA: Betancourt S, Davies T, Kennedy R, Dong C, Elshahawi H, Mullins OC, Nighswander J and O’Keefe M: “Advancing Fluid-Property Measurements,” Oilfield Review 19, no. 3 (Autumn 2007): 56–70.

Betancourt S, Fujisawa G, Mullins OC, Carnegie A, Dong C, Kurkjian A, Eriksen KO, Haggag M, Jaramillo AR and Terabayashi H: “Analyzing Hydrocarbons in the Borehole,” Oilfield Review 15, no. 3 (Autumn 2003): 54–61.

2. Hydrocarbons are defined as organic compounds comprising hydrogen and carbon. The simplest form is methane [CH4]. The most common hydrocarbons are natural gas, oil and coal. Petroleum, a form of hydrocarbon, is a term generally applied to liquid crude oil.

3. Muggeridge AH and Smelley PC: “A Diagnostic Toolkit to Detect Compartmentalization Using Time-Scales for Reservoir Mixing,” paper SPE 118323, presented at the SPE International Petroleum Exhibition and Conference, Abu Dhabi, UAE, November 3–6, 2003.

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the fluid analysis program are matched to the complexity of the fluid column. This improvement in sampling and testing efficiency enables opera-tors to detect fluid complexity and resolve ques-tions arising from the downhole information.

Fluid complexities occur for many reasons. Kerogen, the major global precursor of petro-leum, consists of selectively preserved, resistant, cellular organic materials (algae, pollen, spores and leaf cuticles) and degraded residues of biological organic matter (amorphous material). The conversion from kerogen and the migration of fluids from source rock to reservoir rock impact fluid properties and composition. In addition, reservoir-scale fluid complexity can be caused by differences in temperature, pressure, gravity, biodegradation, phase transitions and reservoir charging history.

During early deepwater development, much of the interest in fluid composition measure-ments focused on flow assurance into the well-bore, through pipelines and within production facilities. However, it became evident that even more-significant problems occur in the reservoir. Consequently, the emphasis of fluid analysis has shifted to the reservoir, where knowledge of in situ fluid properties has considerable bearing on well placement, reservoir development, comple-tion strategies and surface-facilities design.

Using the downhole laboratory provided by DFA sensors, reservoir engineers quantify fluid properties with an accuracy that approaches that of surface-laboratory measurements. The advan-tage of DFA is that fluid properties are measured under reservoir conditions. Unlike equivalent

measurements in a surface laboratory, engineers can repeat, validate or use measurements to explain reservoir heterogeneities. A surface labo-ratory can repeat measurements, but only on the same sample. Moreover, DFA employs the same tool, time, temperature, calibration and technical operator—but with different fluids—from one DFA station to the next.

DFA measurements can also enable identifica-tion of reservoir compartmentalization, which is defined as lack of free-fluid flow between different regions of a field over production timescales.3 Flow units within a reservoir can range from massive to minute, and effective drainage during production requires that the well contact as many compart-ments as is economically feasible. Because com-partments are a major cause of reservoir underperformance, some experts suggest that this

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40 Oilfield Review

is the biggest problem facing deepwater operators in terms of strategic reservoir development.4

This article reviews the creation and migra-tion of reservoir fluids, including reservoir charg-ing, and the resulting effects on fluid properties. Compositional grading—the smooth and contin-uous variation of fluid properties with depth—is discussed, along with methods to detect reservoir compartmentalization. Also described are recent developments using asphaltene equilibrium dis-tribution as an indicator of reservoir connectiv-ity.5 Case studies from the deepwater Gulf of Mexico, the North Sea and offshore Africa dem-onstrate the application of new sampling meth-ods and technologies.

Fluid ComplexityOutside the oil and gas industry there are signifi-cant misconceptions about the habitat of hydro-carbons in nature. Perhaps such works as Jules Verne’s Journey to the Center of the Earth, or

similar portrayals, have given the general public the impression that oil lies in vast lakes below the Earth’s surface, awaiting the adventurous oil company’s drill bit to pop in and drain the oil, like sucking soda through a straw. The petroleum technologist harbors no such illusion, under-standing that hydrocarbons trapped within the pore spaces of reservoir rocks must be coaxed from their hiding places through exacting effort and time-tested methodologies.

Even among professionals, however, there is often a simplistic view of the oil or gas in a reser-voir. Although it is recognized that oil is not found in a subsurface lake, many in the industry con-sider a reservoir as something akin to a large porous container filled with homogeneous fluids. Reservoir architectural heterogeneity and fluid compositional complexity not only exist in nature but are the rule rather than the exception. This is especially true in deep reservoir structures where time and natural forces create ideal conditions for such heterogeneity.

As a sedimentary basin matures, the processes that affect hydrocarbon generation, migration and accumulation result in complex fluid composi-tions. Understanding the complexity of hydro-carbon distributions in a reservoir begins at the source rock. Of the estimated 6 × 1015 tons of organic matter found in the Earth’s crust, 95% is in the form of kerogen.6 It is from this building block that most hydrocarbons are generated.

Kerogen consists of plant remains, such as algae, spores, higher plant debris, pollen, resins and waxes.7 Thermal maturation of kerogen expels fluids, such as oil and gas, and leaves behind a solid, mature form of kerogen (left). Type I kerogens are rather uncommon. They are oil prone and are made up of mainly algal and bacterial remains. The kerogen in the lacustrine Green River Shale, found in the central USA, is an example of this group. Comprising a mixture of terrigenous and marine sources, Type II kerogens may be prone to oil or gas depending on the tem-perature and proportions of constituents. Gas-prone Type III kerogens are composed of woody terrigenous source material. Many North American and European coals contain Type III kerogen. The hydrocarbon gas from this kerogen type is dominated by methane but may also con-tain ethane, propane, butane and pentane. Type IV kerogen, dead carbon, has almost no potential for hydrocarbon generation and commonly consists of recycled organic matter that has undergone previ-ous burial and maturation.8

As kerogen-rich source rock is buried and compacted, increased temperature and pressure convert the organic material into petroleum through catagenesis. Migration of the fluids into permeable rocks is controlled by three primary parameters: capillary pressure, buoyancy and hydrodynamics.9 As fluids charge into the reser-voir, they may be significantly out of equilibrium (next page, top right).10 For example, if the fluids enter a reservoir via a high-mobility path such as a fault, then poor fluid mixing takes place. Over geologic time, through molecular diffusion and gravity segregation, fluid equilibrium of the hydrocarbons can be established. Light gases will rise to the highest level in the reservoir, water generally fills the lowest level, and hydrocarbons of various densities are distributed in between.

With rare exceptions, kerogen Types I and II are required for generation of liquid hydrocar-bons. In the initial stages of conversion at low heat, heavy oils are created and can be preserved as asphalt or tar deposits. Increased temperature leads to generation of lighter oils, often cracked from early-stage heavy oils. There is, however, a

> Kerogen conversion to hydrocarbons. The Van Krevelen diagram classifies kerogen types by crossplotting ratios of oxygen and hydrogen to carbon. During the maturation process, kerogen is thermogenically converted to hydrocarbons. The evolutionary paths of increasing maturity (green arrows) indicate the type of hydrocarbons generated from each kerogen source type. Additional early-stage by-products of the conversion process are water and CO2.

Dry gas

Increasingmaturation

CO2, H2O

Oil

Wet gas

No hydrocarbonpotential

Kerogen maturationproducts

1.5

Hydr

ogen

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ratio

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0.5

0 0.1 0.2 0.3

Type I

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Oxygen/carbon ratio

Oilfield ReviewAutumn 09FluidsLab Fig. 1ORWIN09/10-FluidsLab Fig. 1

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temperature limit to oil generation. When the temperature exceeds the upper limit of the oil window—in excess of approximately 150°C [300°F]—condensate and wet gas result. At higher temperatures, through a more extreme thermal process termed metagenesis, less com-plex gases are generated, and methane gas even-tually becomes the primary hydrocarbon produced (below right).

In contrast to the limited window for oil generation—restricted to certain kerogen types and a specific temperature range—natural gas originates under a variety of conditions. It is gener-ated from all source rocks and across a broad tem-perature range. During diagenesis (early burial), anaerobic microorganisms can convert source-rock organic matter into methane. During cata-genesis and metagenesis, significant amounts of natural gas are produced.11

The maturation process lends itself to poten-tially complex fluid columns and compositional gradients. The natural forces of gravitational buoy-ancy and solubility can create asphaltene gradi-ents in the fluid column. Gravity drives the less dense hydrocarbons, especially gas, to the top of the reservoir. Because asphaltenes are not soluble in gas, the presence of a large GOR gradient results in an asphaltene gradient with higher concentra-tions at a lower point in the column.

Transport processes of both convection and diffusion may also be active. Unlike diffusion,

4. Mullins OC: The Physics of Reservoir Fluids: Discovery Through Downhole Fluid Analysis. Sugar Land, Texas: Schlumberger (2008): 43.

5. Asphaltenes are organic materials consisting of aromatic and naphthenic ring compounds along with peripheral alkanes and contain small quantities of nitrogen, sulfur and oxygen molecules. They exist as a colloidal suspension in oil. Asphaltenes can be problematic in production whenever they precipitate as a result of pressure drop, shear (turbulent flow), acids, solution CO2, condensate charging, mixing of incompatible crude oils or other conditions that break the stability of the asphaltic suspension.

6. Welte DH: “Organischer Kohlenstoff und die Entwicklung der Photosynthese auf der Erde,” Naturwissenschaften no. 57 (1970): 17–23.

7. Tissot BP and Welte DH: Petroleum Formation and Occurrence. Berlin: Springer-Verlag, 1984.

8. Peters KE and Cass MR: “Applied Source Rock Geochemistry,” in Magoon LB and Dow WG (eds): The Petroleum System—From Source to Trap. Tulsa: AAPG, AAPG Memoir 60 (1994): 93–119.

9. Welte DH and Yukler MA: “Petroleum Origin and Accumulation in Basin Evolution—A Quantitative Model,” AAPG Bulletin 65, no. 8 (August 1981): 1387–1396.

10. Equilibrium, in this article, is defined as a condition in which fluids are stable and modest changes in conditions result in modest changes in fluid properties. In contrast, metastable conditions are those in which modest changes may produce dramatic changes in fluid properties.

11. Grunau HR: “Abundance of Source Rocks for Oil and Gas Worldwide,” Journal of Petroleum Geology 6, no. 1 (1983): 39–53.

> Stainforth charge history model. According to the Stainforth model, charge history determines hydrocarbon distribution. In the early stage, low-maturity source rock (left) generates heavier oil, medium-maturity source rock (center) produces lighter oils along with gas and, finally, high-maturity source rock (right) generates light oil and gas. Lighter fluids rise to the top of the reservoir and push down fluids that migrated earlier. The extent of dissolved gas (as reflected in the GOR) in the hydrocarbon column is controlled by pressure and temperature. In this model the fluids are not in equilibrium. Whether the reservoir fluids attain equilibrium is a function of parameters such as vertical permeability and thermal gradients. (Adapted from Mullins, reference 4.)


Low-maturitysource rock

Medium-maturitysource rock

High-maturitysource rock

Oil window, low-temperature limit

Cap rock

Gas

Lighter oil

Medium oil

Heavier oil

Water

> Hydrocarbon maturation. Early-stage hydrocarbon creation occurs in immature source rock in a process of diagenesis, whereby organic materials are buried, compressed and undergo chemical alteration. Bacterial diagenesis can also occur through anoxic microbial conversion of organic material to methane. As temperatures rise above 50°C with deeper burial, microbes die off and catagenesis predominates. This process is similar to the high-temperature cracking and distillation in oil refineries, where heavy oils are converted to lighter petroleum products, but can occur at much lower temperatures over geologic time. Metagenesis is a later phase of hydrocarbon generation, occurring above 150°C, in which organic materials and previously generated petroleum are converted into natural gas, predominantly methane, at higher temperatures.


Hydr

ocar

bons

gen

erat

ed

CO2, H2O

Oil,Types l and ll

kerogen

Wet gas andcondensate Dry gas

Biogenic methane

MetagenesisCatagenesisDiagenesis

Immature zone Oil window Gas window

All kerogen types

50°C 150°C

Increasing depth and temperature

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convection requires a sufficient thermal gradi-ent, or inverted density gradient, to effect change in the fluid distribution.

These normal processes commonly result in gravitationally ordered fluid gradients progress-ing upward from heavy oils, medium oils, light oils, condensate, wet gas, lighter gas and finally to methane. However, nonequilibrium conditions often exist—even given geologic timescales for fluids to equilibrate. Fluid mixing processes in the reservoir may be extremely slow. The added effects of tectonism, faulting and reservoir het-erogeneity contribute to complicated fluid distri-butions. The processes acting on reservoir fluids can preserve a nonequilibrium condition.

Another contributor to nonequilibrium condi-tions is biodegradation, which occurs at the oil/water contact (OWC). Biodegradation results from the metabolic conversion of saturated hydrocarbons, primarily by methanogenic and sulfate-reducing bacteria in anoxic conditions. Preferential removal of alkanes at the OWC by biodegradation yields an increased asphaltene concentration causing large, nonequilibrium viscosity gradients. The OWC may change with subsequent charging of the reservoir or with seal leaks, but biodegradation remains active only below about 80°C [175°F]; above this tempera-ture the microbes are no longer viable. Among other effects, biodegradation raises oil viscosity, lowers API gravity, increases asphaltene and sul-fur content, and increases concentrations of met-als.12 Biodegradation can exert major control over the quality of the oil as well as its producibility.13

Biodegraded oil may be found as a mix of oils. For example, primary oil arrives first, is biodegraded, and is followed by oil from subsequent reservoir charges. The secondary oils may be unaffected, appearing after biodegrada-tion has ceased, creating spatial variations in fluid properties.

In addition, biogenic or thermogenic gas may override existing oil in the reservoir, move updip and disrupt the existing reservoir fluid gradients.14 The GOR of the primary oil changes with this influx, creating compositional varia-tions.15 Detection of these gradient disruptions from charging and recharging may indicate the presence of compartments, a topic to be dis-cussed later.

Ultimately, rather than an open container filled with layers of water, oil and gas, the reser-voir is a complex architectural structure contain-ing mixtures of fluids. There is no single tool to identify these complexities, and engineers create completion strategies and reservoir development plans using data from many sources. DFA mea-surements, however, have proved highly effective as a tool for understanding both reservoir fluids and architectural complexity.

Application-Driven InnovationWireline formation testing tools (WFTs) first appeared in the 1950s as a means to retrieve fluid samples for surface analysis. Laboratory testing of these samples was hampered by contamina-tion, particularly with filtrate from the drilling fluid, and by alteration of the fluids during the sampling and transfer process. Successive tool generations led to the development of more-advanced tools, such as the MDT modular formation dynamics tester, which incorporated

innovations such as multiple chambers, the abil-ity to pump fluid into the wellbore before captur-ing a sample, improved accuracy and resolution, a variety of probe styles, dual-packer assemblies and focused sampling to significantly reduce mud-filtrate contamination (below left). The MDT tool is also the primary platform for fluid property measurements.

Reservoir engineers need accurate assess-ment of fluid properties for reservoir evaluation, flow assurance, reservoir simulation and model-ing, facilities design, production strategies, reserves calculations and recovery estimates. Early sampling methods sometimes yielded sub-optimal results. Relatively few samples were used with simplistic fluid models to explain fluid dis-tributions in the reservoir. In addition, engineers resorted to analytical methods to correct labora-tory measurements for phase changes and mud- filtrate contamination, which often led to erroneous fluid characterization. This limitation has been partially overcome by the ability to pump contaminated fluids from the formation prior to sample initiation.

The MDT tool’s pumpout module is used to flow reservoir fluids into and through the tool. This enables reduction of filtrate contamination to obtain nearly virgin native fluids, as deter-mined through the DFA measurements, as well as the acquisition of reservoir fluids in sample bot-tles carried in the tool. One such operation in Kuwait pumped 2,100 liters [555 galUS] over a 66.5-hour interval to acquire uncontaminated samples. Although the volume of moved fluid is considerable, this is not an efficient method if multiple samples are needed or if DFA fluid profiling with multiple test points is the goal.

A focused-sampling probe, added to the MDT tool in 2006, greatly improved wellsite efficiency, allowing the timely acquisition of fluid samples free or nearly free of mud-filtrate contamina-tion.16 Using a concentric sampling arrangement and two synchronized pumps, the Quicksilver Probe tool acquires uncontaminated samples in a much shorter time frame (next page, top). An outer guard ring extracts fluids—primarily fil-trate and contaminated formation fluids—that enter the probe peripherally. Fluid flowing through the central probe quickly transitions from filtrate-contaminated fluids to formation fluids of acceptable quality for in situ fluid prop-erty measurements.

Low-contamination fluids are quickly avail-able for downhole analysis and more samples can be taken in a reasonable time frame. Tool sensors and fluid analysis capabilities have also advanced

> The MDT tool. The InSitu Family service is delivered downhole by the MDT tool. Along with the InSitu Fluid Analyzer module are the Quicksilver Probe tool for quick fluid-sample cleanup, dual pumpout modules for flowing sample- and guard-probe fluids and a sample-bottle module. Recovered samples are used for surface-laboratory analysis of reservoir fluids.

Oilfield ReviewAutumn 09FluidsLab Fig.4ORWIN09/10-FluidsLab Fig.4

Power cartridge

Sample-bottlemodule

Sample-pumpoutmodule

InSitu FluidAnalyzer module

Hydraulic module

QuicksilverProbe tool

Guard-fluidanalyzer module

Guard-pumpoutmodule

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to the point that fluid properties can be recorded and evaluated while the tool is still in the well. Because of this, sample recovery to the surface is not always necessary. In addition, engineers can create a Fluid Profiling log throughout the reser-voir interval from laboratory-quality measure-ments acquired at downhole conditions.

The Downhole LaboratoryMost major service companies have some form of downhole fluid analysis service. Each company has chosen specific methods to analyze the fluids, including optical absorption and magnetic reso-nance. The InSitu Family sensors in the MDT tool provide the following measurements:• hydrocarbon fluid composition (C1, C2, C3-5

andC6+)• gas/oilratio• CO2 concentration• color(andrelativeasphaltenecontent)• fluorescence• pH(forwatersamples)• live-fluiddensityandviscosity• oil-basemud(OBM)contamination• resistivity• pressureandtemperature(atsampledepth).

However, thebasicmethod forfluidanalysis is optical spectroscopy from the InSitu Fluid Analyzer module (bottom right).17 Optical spec-trometers measure light absorption at different wavelengths for fluids passing through the sensor

> Quicksilver Probe focused-sampling tool. Concentric intake flow areas of the Quicksilver Probe tool are connected to independent pumps in the MDT tool (right). The outer, or guard, probe extracts filtrate and continues to pump during sampling to keep contaminated fluids from migrating to the main probe. In addition to lower levels of sample contamination (graph, right), this assembly can produce acceptable samples more quickly than conventional probe assemblies (left).

Oilfield ReviewAutumn 09FluidsLab Fig.5ORWIN09/10-FluidsLab Fig.5

Conventional Probe Tool

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> Downhole fluids laboratory. As fluid moves through the MDT tool, the InSitu Fluid Analyzer service acts as a portable fluids laboratory. Two spectrometers measure light-absorption properties of the fluid as well as its color. Fluorescence sensors provide retrograde condensation detection and can differentiate oil type when the fluids are in an emulsion. The pH of water samples is measured by injecting a pH-sensitive dye into the flow stream (not shown) and detecting the color change. Pressure, temperature and resistivity sensors acquire data as fluid flows through the tool. A live-fluid density sensor is located in the flowline, and a second sensor can be placed in the probe assembly as well.

Fluid flow

Filter array spectrometerGrating

spectrometer

Fluorescence detector Pressure andtemperature

sensor

Live-fluiddensity sensor

Resistivitysensor

Space forfuture sensor

Light source


12. Connan J: “Biodegradation of Crude Oils in Reservoirs,” in Brooks J and Welte DH (eds): Advances in Petroleum Geochemistry, vol. 1. London: Academic Press (1984): 299–335.

13. Mullins, reference 4: 26.14. Biogenic methane can be differentiated from

thermogenic methane by stable carbon isotope ratios.15. Mullins, reference 4: 52.

16. For more on focused-probe sampling: Akkurt R, Bowcock M, Davies J, Del Campo C, Hill B, Joshi S, Kundu D, Kumar S, O’Keefe M, Samir M, Tarvin J, Weinheber P, Williams S and Zeybek M: “Focusing on Downhole Fluid Sampling and Analysis,” Oilfield Review 18, no. 4 (Winter 2006/2007): 4–19.

17. For more on optical spectroscopy: Crombie A, Halford F, Hashem M, McNeil R, Thomas EC, Melbourne G and Mullins OC: “Innovations in Wireline Fluid Sampling,” Oilfield Review 10, no. 3 (Autumn 1998): 26–41.

Betancourt et al, reference 1.

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and can distinguish between water, gas, crude oil and OBM filtrate (above). Introduced originally to monitor contamination, downhole spectros-copy measurements have undergone a number of advances. The current tool includes two spec-trometers—filter array and grating array. Both spectrometers share the same optical cell, but they cover different wavelength ranges and pro-vide complementary functions. Wavelengths of the 20 channels in the filter array cover the visi-ble and near-infrared spectrum (Vis-NIR) range from 400 to 2,100 nm. These channels indicate the color and molecular vibration absorptions of the fluid and show the main absorption peaks of water and CO2. The sensor also detects color change for the pH measurement. The grating spectrometer has 16 channels that focus on the NIR spectrum of 1,600 to 1,800 nm where reser-voir fluid has characteristic absorptions that reflect molecular structure. For oilfield fluids of interest, much of the information is found in the NIR spectrum.18

Color, ranging from very dark in heavy crudes to clear or very light for gas condensates, is used to distinguish oil types. The term color should not be confused with hue, such as red, green or blue. These more exotic colors are produced when

crude oils are observed in background light that induces some fluorescence, and light absorption creates a variety of colors. In fact, a blue crude oil has been produced for many years in the Gulf of Mexico; its blue color is due to strong fluorescence under illumination (next page, top left). Measured properly, crude oils are typically brown, and color-ation refers to degree of brown absorption.

One use of coloration is to determine contami-nation of fluid samples from OBM filtrate, which contains little to no asphaltene and thus has little color. The degree of contamination is determined by monitoring the increase in color over time while the MDT tool pumps fluid from the tested interval through the DFA module. In addition to having little color, OBM filtrate generally has negligible dissolved gas—low GOR—whereas most native oils have appreciable amounts of dissolved gas. During pumpout, sampled fluids transition from low to high GOR, indicating that the level of con-tamination decreases while the percentage of native oil increases. Useful for contamination determination, the GOR measured downhole, before temperature and pressure effects occur, is also an important in situ fluid property.

Sample contamination is only one aspect of the optical spectroscopy measurement. Molecules interact with electromagnetic waves, such as those in the visible and NIR spectrum, as a func-tion of their complexity. Oils that are high in asphaltenes and resins are darker and more absorptive than simpler hydrocarbons.

In the NIR range, light absorption excites molecular vibration in a manner that is analo-gous to exciting other mechanical oscillators, such as a guitar string. Maximum absorption occurs at characteristic frequencies that are a function of the molecular structure of the hydro-carbon. Methane [CH4]—the simplest hydrocar-bon, with a unique hydrogen/carbon ratio—has a distinct spectral signature. Ethane is composed of two –CH3 groups (the methyl group) and has a different signature. Most hydrocarbon gases are dominated by their –CH3 chemical group. In con-trast, liquid hydrocarbons are dominated by the –CH2– chemical group (the methylene group). The spectral signal is used to differentiate meth-ane and ethane from other gases and liquids. Carbon dioxide [CO2] has its own characteristic frequency of excitation and can be identified from InSitu Fluid Analyzer data.

> Optical density of fluids from spectroscopy measurements. The InSitu Fluid Analyzer tool incorporates two optical spectrometers: a filter array spectrometer that covers a frequency range from 400 to 2,100 nm and a grating spectrometer that focuses on a narrow range of 1,600 to 1,800 nm where reservoir fluids have characteristic absorptions that reflect their molecular structures. The frequency of visible light is about 500 nm, and NIR light ranges from 750 to 2,500 nm. Oilfield fluids have specific spectral optical density (OD) characteristics that are functions of the frequency of light passing through them. Visible (Vis) light is best suited for distinguishing relative asphaltene content. The NIR spectrum is useful for water detection, distinguishing water from oil and identifying the type of oil. Optical spectroscopy was originally introduced to determine sample quality, especially the transition from OBM filtrate to reservoir fluids during sampling. OBM filtrates do not contain asphaltenes or significant dissolved gas. Thus, OBM filtrates are differentiated from crude oil using asphaltene concentration determined from OD of visible light measurements. Dissolved gas content from NIR measurements is an additional sample quality indicator.

Oilfield ReviewAutumn 09FluidsLab Fig.7ORWIN09/10-FluidsLab Fig. 7

Heavy oil

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18. Mullins, reference 4: 74.19. Retrograde condensation is the formation of liquid

hydrocarbons in a gas when the pressure drops below the dewpoint pressure. It is called retrograde because some of the gas condenses into a liquid under isothermal conditions instead of expanding or vaporizing

As the molecular complexity of hydrocarbons increases beyond ethane, the frequency signature is more complex. Thus, the group comprising propane, butane and pentane—the C3-5 group—is combined for analysis. Liquid hydrocarbons include the hexane and heavier hydrocarbons—the C6+ group.

Optical absorption of water covers a broad spectrum in the NIR range and overlaps many of the hydrocarbon peaks. The presence of water can mask other fluids, especially CO2, from the detector.

Hydrocarbon fluorescence results from the aromatic fraction of crude oils, and its color and intensity are characteristics of the oil type (above right). Ultraviolet (UV) light and fluores-cence have been used by the oil industry for many years. At one time a black light, or UV light, was common on wireline logging units, primarily for core analysis and detection of trace amounts of hydrocarbon in formation fluid samples when mostly filtrate was recovered. Mud loggers still use black lights to detect fluorescence in cuttings.

The InSitu Fluorescence sensor allows the measurement of fluorescence to be made down-hole. Although it retains some of the early appli-cations, this sensor offers new utilities, including fluid-phase detection and oil typing. One applica-tion of the fluorescence measurement is the detection of retrograde condensation, also known as retrograde dew, a condition that can occur upon pressure reduction with each stroke of the pumpout tool.19

A recent innovation using fluorescence is fluid typing in emulsions.20 Emulsions often form

in sample acquisition of heavy oils because the asphaltenes in the oil act as a surfactant for both formation water and water-base mud (WBM) filtrate. When these emulsions form, significant light scattering occurs, making optical density measurements difficult to interpret. In the labo-ratory, centrifuges and chemicals are used to demulsify the liquids and analyze the oil portion. This approach is not always successful nor is it an option downhole.

The fluorescence measurement, however, unlike the optical density measurement, is rela-

> Blue crude. The blue coloration of this unusual variety of Gulf of Mexico crude oil is caused by strong fluorescence under ambient light from a high concentration of perylene, a polychromatic hydrocarbon. Typically, oils are brown, and their color, as measured by optical spectroscopy, is their degree of “brownness.”


> Hydrocarbon fluorescence. Chromophores are molecules that absorb light; fluorophores, a subset of chromophores, absorb light and then fluoresce. For crude oil, virtually all chromophores and fluorophores have some aromatic carbon. Graphite is an aromatic carbon in large ring systems and is correspondingly black. In the visible light spectrum, light-absorbing heavy oils appear dark, and lighter oils have less color because they absorb less light (top). Under UV radiation (bottom), the heavy oils produce a dull, reddish brown fluorescence. Light oils appear blue and produce fluorescence with greater intensity. Being clear, the lightest oil absorbs little visible light and some UV radiation, and thus fluoresces, but at a low level.


when pressure is decreased, as would be the case for a single-phase fluid.

20. Andrews AB, Schneider MH, Cañas J, Freitas E, Song YQ and Mullins OC: “Methods for Downhole Fluid Analysis of Heavy Oil Emulsions,” Journal of Dispersion Science and Technology 29, no. 2 (February 2008): 171–183.

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tively independent of the state of the emulsion and gives a qualitative indicator of oil type (above). This is particularly useful in identifying compositionally graded fluids in heavy-oil reser-voirs, such as those affected by biodegradation, without the requirement of pumping to obtain an emulsion-free sample.21

Another important property of reservoir fluids is water pH. The pH of water is used for predicting scaling and corrosion potential and for petrophys-ical evaluation, and it can also contribute impor-tant information about reservoir connectivity.22 The measurement concept is similar to that of classroom experiments, in which the color change in litmus paper indicates the pH of a liquid. For the InSitu pH measurement, a colorimetric dye is injected directly into the flow stream where the optical spectrometer detects the color change. Making the measurement downhole is important because irreversible changes can occur when water samples are brought to the surface for labo-ratory testing. The measurement not only reflects the condition of the water at formation tempera-ture and pressure, but also includes the effects of

hydrogen sulfide [H2S] and CO2. Typically, these gases are flashed and missing when water is ana-lyzed at surface conditions. Errors in measure-ment caused by precipitation of pH-altering solids, which can occur at lower temperatures, are also overcome.

The InSitu pH measurement has proved use-ful in differentiating WBM filtrate from connate water. Filtrate from WBM systems is generally basic, with a pH range from 8 to 10, and formation waters are usually more acidic. In the past, resis-tivity of the fluid was used to identify formation water, but this method is not effective when the resistivity of the WBM filtrate is similar to that of the connate waters. Engineers use the pH sensor to detect fluid transitions and contacts.

The conventional method for determining fluid transitions and contacts is plotting MDT pressure data versus depth. Although this method is widely used, its precision depends on the abil-ity to measure true formation pressure. Pressure- gradient plots may be affected by the number and spacing of pressure points, measurement accu-racy, depth accuracy and freedom from external

perturbations that include supercharging, tool movement and tool seal failures. In addition, it is often difficult to establish pressure gradients in layered reservoirs with varying permeability, formations containing viscous oils and rocks of low permeability.23

The InSitu Density measurement overcomes many of the limitations inherent in pressure plots. Live-fluid density data are acquired from two independent sensors, one placed in the sam-ple probe and the other located in the flowline. Profiling the fluid density quantifies the varia-tions in fluids versus depth.

Compartmentalization, sealing elements and barriers to flow can be identified from abrupt changes in fluid properties. The accuracy and resolution of the data make it possible to com-pare fluids from different wells within a field, establishing connectivity or lack thereof. The InSitu Density sensor can be placed in the fluid analyzer section as well as in the Quicksilver Probe tool, providing independent confirmation of the measurement.24

> Fluorescence measurement and emulsions. Surface laboratories use centrifuges and chemical agents to break down emulsions and measure properties of the native hydrocarbons. NIR measurements from six heavy-oil emulsion samples are shown before (top left) and after (bottom left) attempts at demulsification. Emulsion Samples D, E and F exhibit strong light scattering, which produces a shift in their optical densities. There is also a noticeable water peak after 2,200 nm. Samples B (yellow) and D (green) have different spectral signatures as emulsions, yet the oil portions are similar after demulsification based on their optical characteristics. Downhole optical spectroscopy measurements have no provision for demulsification. However, the fluorescence measurement spectrum is unaffected by the emulsion (right), and the responses are identical to those of demulsified oils (not shown). Fluorescence spectra of Samples B and D clearly indicate the oils in the emulsion are similar in type, which is not apparent in the optical spectroscopy data from emulsified samples. (Adapted from Andrews et al, reference 20.)

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Schlumberger engineers have also developed the InSitu Pro software to integrate data from the InSitu Family sensors, providing both real-time analysis and postacquisition processing (above). These real-time capabilities help identify anoma-lous readings, fluid contacts and potential reser-voir heterogeneity. With this intuitive application, the engineer can develop a deeper understanding of the reservoir fluids as well as identify connec-tivity problems related to reservoir architecture. Data integration, based on a recognized equa-tion-of-state (EOS) model with fluid property cor-rections, allows real-time modification of the testing and sampling program while the MDT tool is still in the well.25 Indications of compartmen-talization can be validated before completing the well and performing extensive well tests.

Although the measurement capabilities of the InSitu Family system continue to expand, there is still no single sensor or tool that can supply reser-voir engineers with all the information needed to efficiently develop and produce hydrocarbons from a reservoir. These measurements must be

integrated with drilling data, reservoir models, production tests and time-dependent analyses to arrive at the best course of action.

CompartmentalizationOn Alaska’s North Slope, just 35 mi [55 km] east of the prolific Prudhoe Bay field, lies the Badami oil field. Discovered in 1990 and brought online in 1997, the field is estimated to contain more than 120 million bbl [19.1 million m3] of recoverable reserves. The excitement of this major discovery was quickly extinguished after production, which briefly peaked at 18,000 bbl/d [2,860 m3/d] in

1998, plummeted to 1,350 bbl/d [214 m3/d]. The field was eventually mothballed in August 2003, and subsequent attempts to restart operations two years later were unsuccessful.26 After spend-ing more than US $300 million in development costs, the operating company representatives cited one major problem: The reservoir is more highly compartmentalized than initially thought, thus preventing the oil from flowing between the zones targeted for production.27 This is just one example of the high cost of recognizing compartmentalization after field development has commenced.

21. Mullins, reference 4: 139.22. Raghuraman B, O’Keefe M, Eriksen KO, Tau LA,

Vikane O, Gustavson G and Indo K: “Real-Time Downhole pH Measurement Using Optical Spectroscopy,” paper SPE 93057, presented at the SPE International Symposium on Oilfield Chemistry, The Woodlands, Texas, February 2–4, 2005.

23. O’Keefe M, Godefroy S, Vasques R, Agenes A, Weinheber P, Jackson R, Ardila M, Wichers W, Daungkaew S and De Santo I: “In-Situ Density and Viscosity Measured by Wireline Formation Testers,” paper SPE 110364, presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, October 30–November 1, 2007.

> InSitu Pro software with real-time analysis. Field engineers can perform quality control checks of field data in a format that offers a clear representation of downhole fluid and reservoir properties using InSitu Pro software. Pressure plots provide fluid gradients and transitions, and an excess-pressure plot is also available (Track 1). Fluid compositional gradients from pressure data can be observed along with fluid analysis (Tracks 3 and 4) at true depth. Additional InSitu Fluid Analyzer measurements are shown at depth for easy reference. This software can be used to process postacquisition data and generate comprehensive interpretation reports.

Oilfield ReviewAutumn 09FluidsLab Fig.11ORWIN09/10-FluidsLab Fig. 11

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24. O’Keefe et al, reference 23.25. An equation of state is useful in describing the

properties of fluids and mixtures of fluids. These mathematical relationships describe the state of matter under a given set of physical conditions, in this case hydrocarbons at pressure and temperature.

26. Nelson K: “Back to Badami,” Petroleum News 10, no. 23 (2005), http://www.petroleumnews.com/pntruncate/ 369854151.shtml (accessed November 11, 2009).

27. “BP Will Postpone Restarting Badami Oil Field,” Anchorage Daily News, September 1, 2009, http://www.adn.com/money/industries/oil/story/919225.html (accessed November 11, 2009).

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The term compartmentalization covers a variety of conditions that include continuous sealing barriers from sedimentary features, seal-ing faults, discontinuous sand lenses, pressure communication in the absence of flow communi-cation and regions of low permeability that inhibit fluid flow.28

A discontinuous fluid distribution is indica-tive of a disruption of the normal fluid gradients that result from primary and secondary migration

of fluids during the hydrocarbon maturation pro-cess. This situation is further complicated by nonuniform temperature gradients; by reservoir restructuring during burial, uplift and erosion; and by other hydrodynamic events. If these processes cease, the fluids will return to their steady-state condition over geologic time. The absence of a continuous fluid gradient implies nonequilibrium fluid distribution and possible compartmentalization.29

In a normal burial sequence, later-stage hydro-carbon generation produces lighter hydrocarbons that rise until they encounter a sealing element. The anomalous presence of lighter or lower den-sity fluids at a point lower than expected in the oil column suggests stacked reservoirs or vertical compartmentalization. Discontinuous distribution of asphaltenes is also an indicator of compart-ments. In particular, increased concentrations of asphaltenes higher in the oil column indicate the presence of a sealing barrier (left). These dense asphaltene particles tend to sink, not float, in a single hydrocarbon column.

The consequences of undetected compart-mentalization are reduced drainage efficiency and flow. With early identification of the degree and complexity of compartmentalization, engi-neers can design appropriate development schemes to mitigate its impact. They can also make better-informed decisions related to pro-duction facilities and reservoir economics.30 In some cases, developing heavily compartmental-ized reservoirs may be uneconomical, at least with current technology and pricing.31

In the past, compartments were usually iden-tified by well testing—drillstem tests (DSTs) and extended well tests. In deep water DSTs can become impractical, with costs approaching those of drilling a new well. Environmental issues from potential spills are also a concern. The most conclusive detection method is long-term production surveillance, but this may come too late for mitigation.32 These hurdles to identi-fying compartmentalization are being addressed today through DFA Fluid Profiling techniques.33

Before the availability of DFA, reservoir engi-neers looked at pressure communication to assess compartmentalization and connectivity. This approach is better suited to detecting isolated or unconnected pockets in producing fields. In virgin reservoirs there may be no pressure differential between unconnected elements. Relying on pres-sure differentials can also be misleading because compartments may have pressure communication in geologic time without flow communication in production time. A recent development in fluid analysis uses asphaltene concentration to indicate connectivity and flow communication.

Unlocking Reservoir Connectivity— Colloidal Nanoaggregates Asphaltene in oil is an example of a colloid—a mixture of one substance dispersed within another. Commonly consisting of an aromatic carbon core with peripheral alkane substituents, asphaltenes make heavy oils “heavy” and give oil color.34

28. Muggeridge and Smelley, reference 3.29. Muggeridge and Smelley, reference 3.30. Elshahawi H, Hashem M, Mullins OC and Fujisawa G:

“The Missing Link—Identification of Reservoir Compartmentalization Through Downhole Fluid Analysis,” paper SPE 94709, presented at the SPE Annual Technical Conference and Exhibition, Dallas, October 9–12, 2005.

31. Mullins OC, Rodgers RP, Weinheber P, Klein GC, Venkataramanan L, Andrews AB and Marshall AG: “Oil Reservoir Characterization via Crude Oil Analysis by Downhole Fluid Analysis in Oil Wells with Visible–Near-Infrared Spectroscopy and by Laboratory Analysis with Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry,” Energy & Fuels 20 (2006): 2448–2456.

32. Muggeridge and Smelley, reference 3.

> Identifying compartments. Pressure data show several disconnected sand intervals (Track 2). Large pressure differentials between Points C and D indicate lack of connectivity. DFA stations and fluid samples were taken at six depths: Points A through F. DFA color analysis (Track 3) shows distinct differences between zones, as do the fluorescence data (Track 4). Components with more color have a higher optical density and should be at the bottom of the interval. Their presence higher in the column suggests compartmentalization. Varying intensity levels of fluorescence indicate different oil types. The lack of continuity and gradient disruption strongly imply many small disconnected compartments, which ultimately led to abandonment of the well by the operator.

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33. Elshahawi H, Mullins OC, Hows M, Colacelli S, Flannery M, Zou J and Dong C: “Reservoir Fluid Analysis as a Proxy for Connectivity in Deepwater Reservoirs,” presented at the SPWLA 50th Annual Logging Symposium, The Woodlands, Texas, June 21–24, 2009.

34. For more on asphaltenes: Akbarzadeh K, Hammami A, Kharrat A, Zhang D, Allenson S, Creek J, Kabir S, Jamaluddin A, Marshall AG, Rodgers RP, Mullins OC and Solbakken T: “Asphaltenes—Problematic but Rich in Potential,” Oilfield Review 19, no. 2 (Summer 2007): 22–43.

35. Mullins OC: “The Modified Yen Model,” Energy & Fuels (January 19, 2010), http://pubs.acs.org/doi/full/10.1021/ef900975e (accessed January 29, 2010).

36. Mullins, reference 35.37. “Tahiti, Gulf of Mexico, USA,” http://www.offshore-technology.

com/projects/tahiti/ (accessed November 30, 2009).

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Asphaltene molecules readily combine—or aggregate—into small particles called nanoag-gregates, which are often their dominant form in crude oils. At high concentrations, nanoaggre-gates can further combine to form clusters (right). Both the nanoaggregates and clusters are found as colloidal dispersions in crude oil.35

Fluids specialists use color from DFA measure-ments to estimate the concentration of asphaltenes in reservoir fluids. Similarities in color can then be used to identify compositionally similar fluids from different locations within a reservoir. This infor-mation is being used to infer flow connectivity and understand reservoir architecture.

Asphaltene gradients are used to understand fluid distribution in a reservoir, and they can occur as a result of GOR gradients. A characteris-tic of low-GOR fluids is that they can dissolve (or disperse) large amounts of asphaltenes. High- GOR fluids can dissolve very little asphaltene; methane, the simplest alkane, dissolves no asphaltenes. In addition, gravity segregation tends to concentrate asphaltenes at the base of a fluid column; the magnitude of this effect is strongly influenced by the size of the asphaltene particles. Both GOR and gravity work to concen-trate asphaltenes at the lowest point in the reser-voir, while thermally driven entropy tends to disperse the asphaltenes.

Sealing barriers or flow restrictions disrupt the movement and migration of fluids and, as a consequence, segregate fluids with different asphaltene concentrations. The presence of a discontinuous asphaltene concentration laterally or vertically within the reservoir explicitly indicates a boundary to fluid flow.

If the asphaltene gradient is the same across a reservoir, and especially if it is in equilibrium, connectivity is implied because it takes geologic time and fluid movement to establish an equilibrated asphaltene gradient. Sealing barri-ers all but preclude equilibrium distributions of asphaltenes.

It is now possible to model the distribution of asphaltenes within a reservoir once the asphal-tene colloidal particle size has been determined.36 This requires not only accurate measurement of the relative asphaltene concentration, but also an accurate measurement of GOR vertically and laterally in the reservoir.

The InSitu Fluid Analyzer service provides measurements with sufficient resolution and accuracy to compare fluids across a reservoir. These data may then be incorporated into an equation of state (EOS) to model the asphaltene distribution. If the measured gradient fits the

EOS model, connectivity is indicated. The ability of DFA to link asphaltene concentrations to connectivity was demonstrated by a multiwell, multiyear study in the deepwater Gulf of Mexico Tahiti field.

Asphaltenes, Colloids and EquilibriumLocated approximately 190 mi [300 km] south of New Orleans, and in a water depth of 4,200 ft [1,280 m], the Tahiti field discovery well was drilled in 2002. With a total depth of 28,411 ft [8,660 m], the well epitomizes the potential risks and rewards of deepwater exploration, encoun-tering more than 400 ft [122 m] of net pay.

Subsequent appraisal wells found net-pay inter-vals in excess of 1,000 ft [300 m]. Data from what was at that time the world’s deepest successful well test indicated a single-well production rate greater than 30,000 bbl/d [4,800 m3/d].37

The reservoir consists of several stacked Miocene turbidite sand intervals buried beneath an 11,000-ft [3,353-m] thick salt canopy. After the initial discovery two appraisal wells with sidetracks were drilled, and extensive pressure data, DFA data and fluid samples were acquired for the producing intervals (below). The two pri-mary sand layers—the M21A and M21B—are in different pressure regimes, and pressure testing

> Asphaltene molecular structures. Asphaltenes (left) can take many forms but are characterized as aromatic rings (green) with alkane chains. The rings may be fused, meaning they share at least one side. The rings may also contain heteroatoms such as sulfur, nitrogen, oxygen, vanadium and nickel. The molecule on the left contains a nitrogen [N] heteroatom. Asphaltene molecules form nanoaggregates (center) in oils. High concentrations of nanoaggregates form clusters (right) in heavy oils.


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> A geologic model showing the upper and lower horizons of the Tahiti field. The steeply dipping beds of the deepwater Tahiti field, whose sands are shown here in this 3D facies model, lie beneath an 11,000-ft-thick salt canopy. Allochthonous salt buoyancy caused the field to tilt. Since the reservoir is not a rigid body, tilting the field results in faulting. The biggest risk factor in field development is whether these faults are transmissive and thus contribute to reservoir connectivity. Seismic models cannot provide this information, but DFA data have proved beneficial in identifying connectivity within the field.


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indicated these two main sand layers are com-partmentalized (below).

Lack of connectivity resulting from compart-mentalization is a significant risk in deepwater development because its existence requires additional wells to contact untapped reserves. Extremely high well costs can make a project uneconomical. Because of the thick salt canopy overlying the Tahiti field, delineating reservoir architecture and potential compartments from seismic data is challenging. Moreover, many sealing barriers are too thin to be visible in seis-mic data. To understand the reservoir connectiv-

ity, reservoir engineers have focused on the properties of sampled fluids.

Data from 14 DFA sample stations in the M21 sands were analyzed in the study.38 Downhole and laboratory measurements show undersatu-rated black oil with GORs ranging from 550 to 650 ft3/bbl [99 to 117 m3/m3]. Geochemical fin-gerprinting from gas chromatography confirmed pressure-data results: The M21A samples are similar to but distinct from those recovered from the M21B sand. The DFA data indicated an asphaltene compositional gradient, as revealed by an increase in fluid color with depth, in both

sand bodies. This gradient was corroborated by laboratory fluids measurements.

For development-well planning, engineers integrated information from this study to predict the DFA measurements at proposed well loca-tions. Synthetic Fluid Profiling logs, based on asphaltene analysis, were generated for a subse-quent well and matched the DFA data. This vali-dated the model and verified connectivity within the sand layers found in the new well. Had there been no match, DFA stations could have been reacquired for validation or the geologic model adjusted to account for differences.

In the Tahiti field, crude oil has a low GOR and is fairly incompressible. Consequently, gravity determines the asphaltene distribution. In an EOS the gravity component consists of Archimedes buoyancy for the asphaltene nano aggregate in a Boltzmann distribution. Fluids experts developed an EOS model based on a fixed asphaltene particle size, correlating optical density to depth. As an indication of connectivity, a simple equation was developed from field data that accounted for the asphaltene distribution in almost the entire field.

> Tahiti field, two separate sands. The petrophysical cross section (bottom) of the Tahiti field, developed from several wells and sidetracks (STs), exhibits considerable heterogeneity. The M21A and M21B sands are the primary targets and, although similarly pressured, are in two different pressure regimes (top left). The two primary sands are thus disconnected. The gas chromatography (GC) starplot diagram (top right) indicates geochemical fingerprints that distinguish M21A crude oils (blue) from those from the M21B sand (red). Oil from the M21A sand in a subsequent well, drilled in the north area of the field, had its own GC fingerprint (green), indicating possible separation from the rest of the reservoir.


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> Optical density trends and asphaltene modeling. The Boltzmann distribution model predicted color (OD) using a fixed particle size but with different asphaltene concentrations. Data from samples and the predictive model again demonstrate that the M21A (blue) and M21B (red) are two separate sands. Data from a subsequent well drilled in the northern area of the field (green) yields a different trend because oil from the M21A sand in the northern section has a lower asphaltene concentration than that in the south and central regions.


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The first production well encountered black oil that correlated with the asphaltene concen-tration predicted from the discovery- and appraisal-well data (right). This analysis con-firms that the asphaltenes are in an equilibrium distribution in both the M21A and M21B sands. Consequently, each sand is predicted to have large-scale connectivity. This prediction was later confirmed during production.

Distinct asphaltene trends are visible in the data from the M21A and M21B sands (previous page, top right). A subsequent well drilled in the north section of the field revealed a lower con-centration of asphaltenes in the M21A sand than is found in wells drilled elsewhere. There was no pressure differential within the sand because the reservoir was at virgin pressure. With almost all other hydrocarbon properties being equal, the asphaltene distribution was the primary means of determining a lack of connectivity between the northern well and the rest of the reservoir. Interpretation following reprocessing of the seismic data confirmed the possibility of fault separation between the regions (below right).

Integration Is the KeyThe downhole laboratory provides a wealth of real-time information. But if DFA data are to be maximally utilized, it is important to treat them as pieces of a larger puzzle. Reservoir engineers integrate measured fluid properties with existing geologic models. Fluid predictions based on EOS models are either corroborated by the downhole measurements or the models can be adapted to fit the data.

For example, in 2002 a North Sea operator identified a large compositional gradient in a dis-covery well containing oil and gas.39 DFA technol-ogy was fairly new, and the original sampling program was modified in real time to profile the complex and depth-variant fluid properties. From analysis of the data, reservoir engineers picked the depth of the gas/oil contact (GOC) higher in the reservoir and moved the oil/water contact

38. Betancourt SS, Dubost F, Mullins OC, Cribbs ME, Creek JL and Matthews SG: “Predicting Downhole Fluid Analysis Logs to Investigate Reservoir Connectivity,” paper IPTC 11488, presented at the International Petroleum Technology Conference, Dubai, December 4–6, 2007.

39. Gisolf A, Dubost F, Zuo J, Williams S, Kristoffersen J, Achourov V, Bisarah A and Mullins OC: “Real Time Integration of Reservoir Modeling and Formation Testing,” paper SPE 121275, presented at the SPE EUROPEC/EAGE Annual Conference and Exhibition, Amsterdam, June 8–11, 2009.

> Predicting DFA response. The DFA spectrometer measures the optical density from discrete channels focused on specific frequencies. The OD is computed from these data and used to quantify oil color. Asphaltenes are the primary source of this color. Using a modified Boltzmann distribution equation from nanoaggregate particle-size estimations of the asphaltenes, engineers developed a predictive color model. This model used DFA data from the original Tahiti discovery well to predict the response of spectrometer channels (shown as color bands in Track 3) for oil in a subsequent development well. The DFA data from the M21A and M21B sands (Track 2) matched the model, suggesting reservoir connectivity. Recent production data confirmed this connectivity, validating the original model.

Oilfield ReviewAutumn 09FluidsLab Fig. NEW 16ORWIN09/10-FluidsLab Fig. NEW 16

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> Field-wide asphaltene concentrations. This 3D model of the M21A reservoir shows asphaltene concentration versus depth that is consistent with an equilibrium distribution of asphaltenes and indicates reservoir connectivity in the central and southern clusters of wells. The two well penetrations in the north show a similar but different distribution, which could indicate that this area is separated by a fault. A recent seismic reinterpretation also indicates a possible fault in this orientation.


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(OWC) lower than originally modeled (left). The result was an increased reserves estimate. An EOS fluid model was later developed from the DFA data.

In 2008 the operator drilled an injector well in the field. Reservoir engineers used the EOS model from the discovery well to predict pres-sures, fluid gradients, fluid contacts and DFA log response for the new well. Engineers developed a predictive modeling workflow that integrated reservoir, EOS and fluid models (next page, top right). Both fluid equilibrium and flow connectiv-ity were assumed. When the measured data from the new well were compared with those from the model, an outlier near the GOC did not match. An extra station was selected, validating the original fluid model and allowing the erroneous data point to be discarded. However, even with this correction, the second well encountered the GOC at a depth that was 18 m [59 ft] higher than predicted, which required further refinement of the reservoir model.

There were also significant differences between the predicted composition and the DFA measurements (left). Analysis of DFA data from a point just above the GOC indicated that slugging during pumpout was affecting the measurement. A spike in the fluorescence measurement caused by this two-phase flow was not being accounted for in the model. Correcting the model for this condition improved the correlation with mea-sured data but a discrepancy remained.

Geologists believed that the two wells had their own separate gas caps but assumed they shared a common oil reservoir with flow and pressure communication. The unexpected 18-m difference can be explained by two scenarios: lateral disequilibrium or compartmentalization.

To distinguish between these two possibilities, a color analysis of the heavy ends, or heavy compo-nents, of the fluids was performed. The heavy ends would be mostly unaffected by two different GOCs; there is no heavy-end component in the gas. If the sand is in a single compartment, then the heavy ends should grade continuously across the reservoir; if the sand is compartmentalized, the heavy ends should show a discontinuous change. Data show that the color is generally con-tinuous (next page, bottom right). In addition, the EOS data suggest equilibrated heavy ends, indi-cating connectivity. This has since been con-firmed by production data.

> Vertical compositional gradient in a discovery well. Pressure data and fluid analysis (left) show a transition from water (blue) to oil (green) to gas (red), indicated by changes in the slope of the line. Fluid analysis (center) from DFA data shows a gradient with increasing GOR (higher concentration of C1 and C2-5 gas versus C6+ liquids) from the bottom to the top of the reservoir section. This was confirmed by laboratory GOR measurements (right). DFA measurements indicate a compositional gradient in the oil that was not apparent in the pressure data. An equation of state (EOS) was developed from these data to predict the response in subsequent development wells.


C1 C2-5 C6+ Water

Composition, weight %Pressure gradients

Dept

h, m

LaboratoryGOR

ToolGOR

X,X68.2

X,Y75.1

X,Z85.6

Y,X00.0

Y,Y06.3

0.374 g/cm3

0.599 g/cm3

0.982 g/cm3

Increasing pressure

1,085

336

312

284

265

1,410

450

360

320

270

m3/m3 m3/m3

> Equation of state model. Engineers developed an EOS model from the discovery well data (top). The calculated values (blue, red and black curves) were compared with the DFA tool’s C1, C2-5 and C6+ response (blue, red and black symbols). The model was then used to predict fluid composition for the injector well (bottom). Although the C1 and C2-5 data agree with the model, the DFA C6+ data (green circles) are considerably different from model predictions above the GOC. Slugging was determined to be the cause of the discrepancy, and the data were later reprocessed and corrected for this effect.


665

670

675

680

690

695

700

7050 10 20 30 40 50 60 70 10 20 30 40 50 60 70 80

Dept

h, m

685

C1 calculated

C2-5 calculated

C6+ calculated

C1

C2-5

C6+

Dept

h re

lativ

e to

GOC

, m

–75

–50

–25

0

25

50

GOC

Discovery Well Development Well

C1 predictedC2-5 predictedC6+ predicted

C2-5 (DFA)C1 (DFA)

C6+ (DFA)

Composition, weight % Composition, weight %

(DFA)

(DFA)

(DFA)


665

670

675

680

690

695

700

7050 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 80

Dept

h, m

685

C1 calculated

C2-5 calculated

C6+ calculated

C1

C2-5

C6+

Dept

h re

lativ

e to

GOC

, m

–75

–50

–25

0

25

50

GOC

Discovery Well Development Well

C1 predictedC2-5 predictedC6+ predicted

C2-5 (DFA)C1 (DFA)

C6+ (DFA)

Composition, weight % Composition, weight %

(DFA)

(DFA)

(DFA)

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Winter 2009/2010 53

Integration of data allows predictive testing of the reservoir to establish connectivity and fluid equilibrium. Fluids experts developed a sampling program beforehand from EOS fluid models and were able to validate results when data initially deviated from the model. The ability to adjust the program in real time provides the reservoir engi-neer with a diagnostic tool for data quality con-trol. In this case, revisiting an anomalous data point confirmed the original model. Similarly, analysis of color and asphaltene gradients con-firmed reservoir connectivity when initial test results were inconclusive.

Resolving Deepwater UncertaintiesDeepwater plays are becoming more common and fields are being discovered in areas whose water depths made them unreachable not long ago. The risk-reward scenario in deepwater E&P goes beyond the potential for finding large accu-mulations of untapped hydrocarbons; it encom-passes development decisions that must be made with limited datasets. Reservoir connectivity is often the largest uncertainty, and no single mea-surement can provide a complete solution.40

Pressure gradients have traditionally been used to confirm connectivity, as well as to com-pute fluid density and detect fluid contacts. The success of this technique depends on the number of data points as well as their locations within the reservoir column. Discontinuous reservoir sec-tions, thinly laminated sands and supercharging can distort or confound the interpretation. Abrupt changes in fluid density within a fluid column are expected at the OWC and GOC, but when detected within the oil column, they indi-cate the potential for compartmentalization.

A new sensor that measures live-fluid density was employed in an offshore West Africa stacked-sand reservoir. The deepwater vertical appraisal well was drilled in a water depth of 1,000 m [3,280 ft]. The objectives of the well were to assess hydrocarbon potential, evaluate fluid properties, determine fluid contacts and identify the presence of compositional grading.41 Data were acquired from an MDT tool equipped with two InSitu Family sensors. One sensor was located in the focused-probe assembly and a sec-ond was in the InSitu Fluid Analyzer module.

40. Elshahawi et al, reference 33.41. O’Keefe et al, reference 23.

> DFA predictive modeling. Data acquired in the discovery well (bottom right) are combined with reservoir and EOS models to predict DFA measurements in an injector well drilled at a later date (top). Because Station 2 did not match the prediction, a fifth station was taken, which matched the predicted response and confirmed the original model. The off-trend station was judged to be erroneous and discarded. This is an example of real-time observations suggesting retesting. Without the predictive model, the erroneous data could have resulted in an incorrect conclusion, such as compartmentalization.

Pres

sure

, psi

Temperature, K

DiscoveryWell A

InjectorWell B

Modeloutput

DFA and SampleAnalysis Results

On

Extra

Off

On

On

EOS Modeling

1

5

2

3

4

Mea

sure

d

Stat

ion

2 of

f-tre

nd

DFA EquivalentMeasured Compositions

DFA EquivalentModeled Compositions Fluid Model


> Color analysis between wells. Well A color data from DFA measurements (blue dots) follow a consistent trend, although the deeper points have more color than modeled data predictions (red curve). The model assumes a fixed asphaltene particle size and outputs color based on asphaltene concentration. Data from DFA measurements taken from Well B (green) plot on the model trend line at the top of the reservoir but the deeper data points are above the line. The observation from Well A data, that fluids in the lower part of the reservoir have more color than expected, is reflected in Well B data. Although this could be an indication of compartmentalization, it could also be explained by disequilibrium of the fluids in the reservoir. From production data engineers concluded that the two wells were not in separate compartments.


True

ver

tical

dep

th, m

3,660

3,670

3,680

3,690

3,700

3,7100.50 1.0 1.5 2.0 2.5

Optical density

Optical density, model

Well AWell B

28237schD6R1.indd 53 2/4/10 2:04 PM

54 Oilfield Review

The pressure-sampling program included 56 pressure pretests along with fluid profiling and sampling at seven depths across the reservoir interval. A technique using an excess-pressure plot indicated pressure communication within the reservoir and a single producing unit with compositional grading. Three gradients were identified, corresponding to water, oil and gas—all in pressure communication (above). A mea-surement station that included the InSitu Density sensor was performed at 1,754.5 m [5,756 ft] MD, which is near the top of the oil zone.

Laboratory PVT analysis of the recovered fluid from that station yielded an oil density of 0.70 g/cm3. The InSitu Density sensor measured a density of 0.71 g/cm3. These values compare favorably with each other—within 0.01 g/cm3, the accuracy typi-cal of fluid density measurements made in the con-trolled environment of a laboratory.

With DFA data that included fluid density, the operator was able to quickly analyze the fluid composition, determine fluid contacts and assess

> Fluid contacts from pressure and InSitu Density data. Fifty-six pressure points were sampled to construct a pressure profile curve (Track 1). Data indicate fluid changes at 1,798 m and 1,748 m. The fluid composition data from the InSitu Fluid Analyzer module show oil and gas (Track 2). Stations A, B and C confirm that the oil density (red triangles) is consistent throughout the oil interval. From this analysis the operator confirmed the fluid density, quickly identified fluid contacts and developed a subsequent DST program that validated the DFA analysis.


PetrophysicalAnalysis

100,0000.1 cP

Drawdown Mobility

GammaRay

0 100gAPI

1,720

1,740

1,760

1,780

1,800

1,820

Composition

0 100

Methane Ethane Hexane

Formation Pressure

psi

0.5 1g/cm3

Fluid Density

2,900 3,200

Station C

Station B

Station A

Dept

h, m

wt %

Moved Gas

Gas

Water

Oil

Sand

Bound Water

Clay 1

GOC

OWC

reservoir connectivity. Because the Fluid Profiling technique revealed no sealing features or poten-tial compartmentalization, the operator was able to proceed with the original development plan.

Downhole Laboratory of the FutureWhat began as a means of quantifying sample qual-ity has evolved into laboratory-grade measure-ments that quantify in situ fluid properties. As the nature of DFA measurements such as the InSitu Family service expands, so too have applications.

The future of DFA may take two directions: LWD-based services and new measurements. Today, service companies have tools that can pro-vide pressure profiles while drilling. Eventually, elements of the downhole fluids laboratory will be incorporated into these services, enabling the measurement of real-time fluid properties before deep invasion of drilling fluids occurs.

New techniques are also in development, such as an accurate measurement of in situ fluid viscos-ity and concentrations of other components.

Viscosity, for example, has significant impact on fluid recovery and therefore field economics. However, surface measurements of viscosities often include a host of effects that may render them inaccurate or invalid. To better understand the reservoir and maximize production, reservoir engineers will be able to use viscosity measure-ments to analyze fluids flowing from the reservoir before they undergo phase changes due to pres-sure and temperature variations.

Reservoir development will never be as simple as inserting a long straw into a lake of crude oil and sucking it out. For now, however, the reservoir engineer has an exten-sive portable laboratory to send downhole and help unravel the complexity of in situ fluids, while also helping clarify understanding of reservoir architecture. —TS

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