Well logging was invented in 1927 by two French brothers—Conrad and Marcel Schlumberger—and has been rapidly developed and plays an important role in the oil and gas industry. Well logging is involved in the whole life cycle of a well from its starting point of drilling to its abandonment and therefore is involved in the whole life cycle of an oil field, from the first well to the last. There are many kinds of logging methods, with different capabilities to evaluate different kinds of reservoirs. This chapter summarizes a brief history of well logging, the elements of a logging unit, the development of wireline logging to logging while drilling, and the mainstream technology in well logging. The technological difficulties of conventional logging are outlined and nuclear magnetic resonance is introduced to solve these problems, from which two questions are answered: why nuclear magnetic resonance is needed and how nuclear magnetic resonance works.
1.1 Introduction to Well Logging
Well logging1 has been developed and widely applied as a thorough technology chain for oil and gas exploration in downhole conditions and environments. It provides various measurements, such as the electric, acoustic, radioactive, and nuclear magnetic resonance properties of a reservoir formation and fluids. From these measurements, the estimation of petrophysical parameters, formation evaluation, and fluid typing can be achieved. Well logging is often referred to as the “eye” of petroleum geologists and reservoir engineers, as it allows them to visualize the formation downhole.
Figure 1.1 illustrates the fundamental components of downhole well logging measurements, which include the downhole tools to collect physical properties of reservoir formation, a cable for power supply and telecommunication between downhole tools and surface equipment, surface equipment in a truck for downhole tool control and data acquisition, as well as data processing and analysis based on formation petrophysical models. The resulting interpretations and applications enable oil and gas characterization, quantitative formation evaluation, and help answer the following crucial questions. Where are the oil and gas located? How much of them is present? How much of them will be producible? How can they be produced? The oil and gas industry has confirmed and believes that well logging is a highly effective, economical, repeatable, and reliable technology, with a high spatial resolution for downhole oil and gas exploration.
One of the important milestones in well logging history was the discovery of the Archie equation in 1942, which has been the foundation of quantitative log analysis and formation evaluation. Archie successfully built an empirical correlation between formation resistivity, porosity, and water saturation, as shown in eqn (1.1), which created a discipline called petrophysics and has been guiding the research and development of well logging technology for more than seven decades.
Improving the reliability of downhole logging tools for porosity and resistivity measurements and developing more accurate petrophysical models for resistivity, porosity and water saturation estimations have always been the key objectives of well logging technology evolution.2 On the other hand, the Archie technological paradigm has gained popularity by using minimal parameters to build the simplest empirical correlation in conventional well logging applications, as shown in Figure 1.2.
Another important feature of oil and gas reservoirs, which affects the wireline logging responses, is so-called invasion, as shown in Figure 1.3.
The oil and gas reservoirs are normally permeable, which means that oil and gas will flow under a pressure difference. When the pressure of the formation is greater than the wellbore, fluids such as oil, gas and water will flow from the formation into the wellbore. Otherwise, when the pressure of the wellbore is greater than that of the formation, drilling fluids will invade the formation through pore channels and form an invaded zone, which will be time-dependent and permeability related. The obvious change at the invaded zone is the fluid saturation due to drilling fluid displacement, from which a profile of water saturation may be observed. Consequently, as implied in the Archie equation, the resistivity at the invaded zone will be a profile. In practice, there are normally two types of drilling fluids, water-based and oil-based. For water-based drilling fluid, the filtrate to the invaded zone will be water, and for oil-based drilling fluid, the filtrate will be oil.
The drilling fluid filtrate and the invasion process will affect logging tool response significantly. Therefore, care must be taken when reading the logs, and usually an invasion correction should be performed. In contrast, taking advantage of the difference in invasion between oil and water zones, resistivity logging tools with different depths of investigation have been developed for the qualitative identification and quantitative evaluation of pay zones.
In the last nine decades, well logging technology developed very fast and formed several generations of logging systems and downhole tools, to meet the strong demands of borehole measurements. Among the developed logging methods, the most popular and mature techniques can be divided into two categories. One is conventional logging, or the so-called triple-combo, which includes gamma ray (GR), spontaneous potential (SP), caliper, and resistivity from measurements with shallow, medium and deep depths of investigations respectively, and porosity measurements from acoustic, density and neutron logging, respectively, as shown in Figure 1.4.
Another category is so-called imaging logging, which conducts measurements with array detectors, such as micro-resistivity scanning logging, array induction logging, array acoustic logging, pulsed neutron logging, etc.
The theoretical response behind conventional logging is based on the homogeneity of the formation (media). The imaging logging, however, is based on the heterogeneity of the formation (media). The response function for both conventional and imaging logging has been well developed. By the given formation models, one can easily build the response charts with forward modeling, as shown in Figure 1.5.
As the usage of horizontal wells has dramatically increased, downhole logging has been both technically and commercially divided into two important categories: wireline logging and logging while drilling (LWD), as shown in Figure 1.6. So far, most of the downhole measurements acquired by wireline logging can be implemented with LWD.
1.2 Why We Need NMR Logging
Formation evaluation, fluid typing, and reservoir descriptions in oil and gas exploration are very complicated, especially for complicated/special lithology, low porosity/low permeability, low-resistivity/low-contrast resistivity/low hydrocarbon saturation, complicated fluids, and complicated pore structure reservoirs. There are still a lot of serious challenges when using the borehole measurements mentioned above.
Assume that the matrix of rock has three mineral components according to the volumetric model (as illustrated in Figure 1.7), the response functions for acoustic, density and neutron logging can be written as:
where Δt, ρb, and ϕN are log readings from acoustic, density, and neutron logging respectively.
Vmai, i = 1, …, n, is the ith matrix volume (unit in %), which is unknown. Δtmai, ρmai, ϕmai, i = 1, …, n, are solid matrix responses to acoustic, density, and neutron logging respectively, which are usually a set of previously known parameters. And the material balance equation is defined as ϕ = (1 − Vma1 − Vma2 − Vma3).
To solve the equations and get an estimation of porosity, one should know the response parameters of the solid matrix. In many cases, however, like complex/special lithology reservoirs, the matrix response parameters are difficult to obtain. In addition, the response functions indicate that acoustic travel time and bulk density are more sensitive to matrix materials than to fluids in pores.6,7 The measurements may lose sensitivity when reservoir porosity is less than 10%.
On the other hand, resistivity logging tools are sensitive to water-filled pore spaces and strongly influenced by the presence of conductive minerals. The water saturation from the Archie equation with resistivity logging is an estimation of the total water saturation without consideration of irreducible water. From a reservoir engineering point of view, there are two types of water in the pore system. One is movable, called movable water, and the other is unmovable, called irreducible water. Resistivity logging is unable to distinguish movable and irreducible water. For proper interpretation of the responses of these tools, detailed prior knowledge regarding the properties of both the logged formation and the water in the pore space is required.
Moreover, there are no indicators of either permeability or pore structure from the resistivity, acoustic, and radiation (density and neutron) logs. Permeability and pore structure are so important for oil and gas recovery that any treatment for production and recovery enhancement will rely on a priori knowledge of these properties.
Nuclear magnetic resonance (NMR) provides information on fluids and pore structure, which resolves the problems mentioned above. In addition, there are many other advantages and benefits for borehole applications by using NMR measurements.
Magnetic resonance imaging (MRI) has been one of the most valuable clinical diagnostic tools in health care for many years. An MRI of a human head, as shown in Figure 1.8, demonstrates four important characteristics of NMR technology:
First, it is a sliced measurement. The signal for imaging comes from a well-defined region, usually a thin slice or cross section of the target. The physical principles of NMR in a gradient magnetic field define that each slice of the image contains only information from the imaged cross section, with materials in front or behind being essentially invisible.
Second, only fluids, such as blood in vessels, body cavities, and soft tissues, are visible, while solids, such as bone, produce a signal that typically decays too fast to be recorded.
Third, different fluids have different NMR properties, such as the longitudinal relaxation time (T1), transverse relaxation time (T2) and more generally the diffusion coefficient (D) of fluids, and each of them can be used as a contrast to the image. That means there are multi-contrast mechanisms available to differentiate fluids and study their surroundings. Based on the multi-contrast mechanism, different kinds of methodologies have been developed for clinical diagnoses of injury and disease.
Fourth, non-interrupted multi-slice images can be easily achieved so one can construct and visualize the measured target in convenient and creative ways.
By taking advantage of these four characteristics, MRI has become physicians’ most welcomed diagnostic tool for many incurable diseases, especially cancers.
These same NMR principles, used to diagnose anomalies in the human body, can be used to analyze the fluids in the pore spaces of reservoir rocks in the laboratory and formations downhole. And, just as physicians do not need to be NMR experts to use MRI technology for effective diagnosis, neither do geologists, geophysicists, petroleum engineers, nor reservoir engineers need to be NMR experts to use borehole NMR technology for reliable formation evaluation and fluid typing, although there is always a demand for a strong expert NMR team to investigate the principles, develop the instruments and methodologies, and support the applications.
NMR logging takes the medical MRI or laboratory NMR equipment and turns it inside out. So, rather than placing the subject at the center of the instrument, the instrument itself is placed, in a wellbore, at the center of the formation to be analyzed.
At the center of an NMR logging tool, a permanent magnet produces a magnetic field that magnetizes formation materials. An antenna surrounding this magnet transmits into the formation precisely timed bursts of radio-frequency energy in the form of an oscillating magnetic field. Between these pulses, the antenna is used to listen for the decaying “echo” signal from those hydrogen protons that are resonant with the field from the permanent magnet.
Because a linear relationship exists between the proton resonance frequency and the strength of the magnetic field produced by the permanent magnet, the frequency of the transmitted and received energy can be tuned to investigate well-defined regions such as cylindrical or sector fan-shaped regions at different diameters around the NMR tool. This tuning of an NMR probe to be sensitive to a specific frequency allows NMR instruments to image narrow slices of either a hospital patient or a rock formation. In addition, both medical MRI devices and NMR logging tools can be run with specific pulse sequence settings, or “activations”, that dramatically enhance their ability to detect and analyze the formation petrophysical and fluid properties due to the magic of NMR using radio-frequency pulses to editor and/or operate the molecular spins.
Figure 1.9 illustrates the “cylinders of investigation” for the MRIL-Prime (Magnetic Resonance Imaging Logging) tool of Halliburton.3,4 The diameter and thickness of each thin cylindrical region are selected by simply specifying the central frequency and bandwidth to which the MRIL transmitter and receiver are tuned. The diameter of the cylinder is temperature-dependent but is typically approximately 14 to 16 in.
Because only fluids in rocks are visible to NMR, the porosity measured by an NMR logging tool contains no contribution from the solid matrix materials and does not need to consider the influence of formation lithology.5 This response characteristic makes an NMR logging tool fundamentally different from conventional logging and any other imaging logging tools. Conventional acoustic travel time, bulk density, and neutron porosity logging tools are influenced by all components of a reservoir rock.
NMR logging tools can provide three types of information, each of which makes these tools unique among logging devices:
information about the quantities of the fluids in the formation rock;
information about the properties of these fluids, such as viscosity;
information about the sizes and connectivity of the pores that contain these fluids.
1.2.1 Fluid Quantity
An NMR tool can directly measure the density of hydrogen nuclei in reservoir fluids.8 Because the density of hydrogen nuclei present in water is known, data from an NMR tool can be directly converted to an apparent water-filled porosity. This conversion can be done without any knowledge of the minerals that make up the solid fraction of the rock, and without any concern about trace elements in the fluids (such as boron) that can perturb neutron porosity measurements.
1.2.2 Fluid Properties
Medical MRI relies on the ability to link specific medical conditions or organs in the body to different NMR behavior. A similar approach can be used with NMR logging tools to study fluids in a thin zone a few inches from the borehole wall. NMR tools can determine the presence and quantities of different fluids (water, oil and gas),9–13 as well as some of the specific properties of the fluids (for example, viscosity14). And the abundant pulse sequence settings of both medical MRI devices and NMR logging tools can further enhance their ability to detect particular fluid conditions.
1.2.3 Pore Size and Porosity
The NMR behavior of a fluid in the pore space of a reservoir rock is different from the NMR behavior of the fluid in bulk form. For example, as the size of pores containing water decreases, the differences between the apparent NMR properties of the water in the pores and the water in bulk form increases.15 Simple methods can be used to extract enough pore size information from NMR logging data to greatly improve the estimation of such key petrophysical properties as permeability and the volume of capillary-bound water.16,17
The micro-porosity associated with clays and some other minerals that typically contain water means that, from an NMR perspective, they appear almost as solids. Water in such micro-pores has a very rapid “relaxation time”. Because of this rapid relaxation, this water is more difficult to see than, for example, producible water associated with larger pores. Earlier generations of NMR logging tools were unable to see water in these micro-pores, and because this water was associated most often with clays, the porosity measured by these earlier tools was often characterized as being an “effective porosity”. Modern NMR logging tools can see essentially all the fluids in the pore space, and the porosity measurements made by these tools are thus characterized as being “total porosity” measurements. Pore size information supplied by modern tools is used to calculate an effective porosity that mimics the porosity measured by the older NMR tools.18
In addition, one of the key features of the NMR logging tool design philosophy is that the NMR measurements of the formation made when the logging tool is in the wellbore can be duplicated in the laboratory by NMR measurements made on rock cores recovered from the formation. This ability to make repeatable measurements under very different conditions is what makes it possible for researchers to calibrate the NMR measurements to the petrophysical properties of interest, such as pore size, to the end user of NMR data.19–22
Figure 1.10 compares NMR logging responses with those of conventional logging tools. The volumetric model is widely used in logging formation evaluation. An idealized volumetric model consists of a matrix component and a fluid-filled pore space. The matrix component is composed of clay minerals and non-clay minerals, and the pore fluid component is composed of water and hydrocarbons. Conceptually, the pore fluids can be more finely divided into clay-bound water, capillary-bound water, movable water, gas, light oil, medium-viscosity oil, and heavy oil.
Although conventional porosity tools, such as acoustic, density, and neutron logging, exhibit a bulk response to all components of the volumetric model, as mentioned above, they are more sensitive to matrix materials than to pore fluids. Furthermore, the responses of these tools are highly affected by the borehole and mudcake, the sensitive volumes of these tools are normally not well defined, and nobody really knows where the measured signals are from.
Resistivity tools, such as induction and laterolog, respond to conductive fluids such as clay-bound water, capillary-bound water, and movable water. Based on the conductivity contrast between (1) clay-bound water and (2) capillary-bound and movable water, the dual-water and Waxman–Smits models were developed for better estimation of water saturation than Archie. Even with these models, recognition of pay zones is still difficult because no conductivity contrast exists between capillary-bound water and movable water. As with conventional porosity tools, resistivity tools are very sensitive to the borehole and mudcake, and their sensitive volumes are also poorly defined.
Conventional log interpretation uses environmentally corrected porosity and resistivity logs to determine formation porosity and water saturation. Assessing the accuracy of tool responses, selecting reliable values for model parameters, and matching the vertical resolutions and depths of investigation of the various measurements all add to the challenge of successfully estimating porosity and water saturation. Additionally, with conventional logs, distinguishing light oil, medium-viscosity oil, and heavy oil is usually impossible.
As mentioned before, NMR porosity is essentially mineralogy-independent—that is, NMR tools are sensitive only to pore fluids. The difference in various NMR properties—such as relaxation times (T1 and T2) and diffusivity (D)—among various fluids makes it possible to distinguish (in the sensitive zone) among bound water, movable water, gas, light oil, medium-viscosity oil, and heavy oil. The sensitive volumes of NMR tools are very well defined; thus, if the borehole and mudcake are not in the sensitive volumes, then they will not affect NMR measurements.
The volumetric model of Figure 1.10 does not include other parameters that can be estimated from NMR measurements: pore size; formation permeability; the presence of clay, vugs, and fractures; and hydrocarbon properties such as viscosity. These factors affect NMR measurements, and their effects can be extracted to provide very important information for reservoir description and evaluation (an example is illustrated in Figure 1.11). Conventional logging measurements are insensitive to these factors.
1.2.4 NMR Logging Raw Data
Before a formation is logged with an NMR tool, the protons in the formation fluids are randomly oriented. When the tool passes through the formation, the tool generates magnetic fields that activate those protons. First, the tool’s permanent magnetic field aligns, or polarizes, the spin axes of the protons in a particular direction. Then the tool’s oscillating magnetic field is applied to tip these protons away from their new equilibrium position. When the oscillating field is subsequently removed, the protons begin tipping back, or relaxing, toward the original direction in which the static magnetic field aligned them.23 Specified pulse sequences are used to generate a series of so-called spin echoes, which are measured by the NMR logging tool and are displayed on logs as spin-echo trains. These spin-echo trains constitute the raw NMR data.
To generate a spin-echo train such as the one in Figure 1.12 (upper section), an NMR tool measures the amplitude of the spin echoes as a function of time. Because the spin echoes are measured over a very short time, an NMR tool travels no more than a few centimeters in the well while recording the spin-echo train. The recorded spin-echo trains can be displayed on a log as a function of depth, as shown in the lower section of Figure 1.12.
The initial amplitude of the spin-echo train is proportional to the number of hydrogen nuclei associated with the fluids in the pores within the sensitive volume. Thus, this amplitude can be calibrated to give porosity. The observed echo train can be linked both to data acquisition parameters and to properties of the pore fluids located in the measurement volumes. Data acquisition parameters include inter-echo spacing (TE) and polarization time (TW). TE is the time between the individual echoes in an echo train. TW is the time between the cessation of measurement of one echo train and the beginning of measurement of the next echo train. Both TE and TW can be adjusted to change the information content of the acquired data.
The properties of the pore fluids that affect the echo trains are the hydrogen index (HI), longitudinal relaxation time (T1), transverse relaxation time (T2), and diffusion coefficient (D). HI is a measure of the density of the hydrogen atoms in the fluid. T1 is an indication of how fast the tipped protons in the fluids relax longitudinally (relative to the axis of the static magnetic field), while T2 is an indication of how fast the tipped protons in the fluids relax transversely (again relative to the axis of the static magnetic field). D is a measure of the extent to which molecules move at random in the fluid.
1.2.5 NMR Porosity
The initial amplitude of the raw decay curve is directly proportional to the number of polarized hydrogen nuclei in the pore fluid. The raw reported porosity is provided by the ratio of this amplitude to the tool response in a water tank (which is a medium with 100% porosity). This porosity is independent of the lithology of the rock matrix and can be validated by comparing laboratory NMR measurements on cores with conventional laboratory porosity measurements.
The accuracy of the raw reported porosity depends primarily on three factors:19
A sufficiently long TW to achieve complete polarization of the hydrogen nuclei in the fluids.
A sufficiently short TE to record the decays for fluids associated with clay pores and other pores of similar size.
The number of hydrogen nuclei in the fluid being equal to the number in an equivalent volume of water, that is, HI = 1.
Provided the preceding conditions are satisfied, the NMR porosity is the most accurate available in the logging industry.
The first and third factors are only an issue for gas or light hydrocarbons. In these cases, special activations can be run to provide information to correct the porosity. The second factor was a problem in earlier generations of tools. They could not, in general, see most of the fluids associated with clay minerals. Because in shaly sand analysis, the non-clay porosity is referred to as effective porosity, the historical MRIL porosity (MPHI) was also called effective porosity. Current NMR logging tools now capture a total porosity (MSIG) by using both a short TE (0.6 ms) with partial polarization and a long TE (1.2 ms) with full polarization. The difference between MSIG and MPHI is taken as clay-bound water (MCBW). This division of porosity is useful in analysis and often corresponds to other measures of effective porosity and clay-bound water. The division of porosity into clay-bound porosity and effective porosity depends to some extent on the method used; thus, other partitions can differ from that obtained from the NMR porosity, as shown in Figure 1.13.
NMR measurements on rock cores are routinely made in the laboratory. The porosity can be measured with a sufficiently short TE and sufficiently long TW to capture all the NMR-visible porosity. Thousands of laboratory measurements on cores verify that the agreement between the NMR porosity and a Helium Boyles Law porosity is better than 1 p.u. Figure 1.14 illustrates such an agreement.
1.2.6 NMR T2 Distribution
The amplitude of the spin-echo-train decay can be fit very well by a sum of decaying exponentials, each with a different decay constant. The set of all the decay constants forms the decay spectrum or transverse relaxation time (T2) distribution. In water-saturated rocks, it can be proven mathematically that the decay curve associated with a single pore will be single exponential with a decay constant proportional to pore size; that is, small pores have small T2 values and large pores have large T2 values.15,24 At any depth in the wellbore, the rock samples probed by the NMR logging tool will have a distribution of pore sizes. Hence, the multi-exponential decay represents the distribution of pore sizes at that depth, with each T2 value corresponding to a different pore size. Figure 1.15 shows the T2 distribution that was derived from the spin-echo train in Figure 1.12.
Properly defined, the area under the T2 distribution curve is equal to the initial amplitude of the spin-echo train. Hence, the T2 distribution can be directly calibrated in terms of porosity. In essence, a key function of the NMR tool and its associated data acquisition software is to provide an adequate description of the T2 distribution at every depth in the wellbore. In terms of the T2 distribution, MPHI is the area under the part of the curve for which T2 is 4 ms, MCBW is the area for which T2 < 4 ms, and MSIG is the total area.
The NMR T2 distribution can be displayed in three ways: waveform presentation, image format, and bin-distribution plot. Each represents the distribution of the porosity over T2 values and, hence, over the pore sizes. The three displays reflect different visualizations of the same set of data. The lower section of Figure 1.15 shows an example of these displays.
1.2.7 NMR Free-fluid Index and Bulk Volume Irreducible
The porosity and pore size information from NMR measurements can be used to estimate both the permeability and the potentially producible porosity (that is, the movable fluids).
The NMR estimate of producible porosity is called the free-fluid index (MFFI and also FFI). The estimate of MFFI is based on the assumption that the producible fluids reside in large pores, whereas the bound fluids reside in small pores. Because T2 values can be related to pore sizes, a T2 value can be selected below which the corresponding fluids are expected to reside in small pores and above which the corresponding fluids are expected to reside in larger pores. This T2 value is called the T2 cutoff (T2cutoff).25,26
Through the partitioning of the T2 distribution, T2cutoff divides MPHI into the free-fluid index (MFFI) and bound-fluid porosity, or bulk volume irreducible (BVI), as shown in Figure 1.16.
The T2cutoff can be determined with NMR measurements on water-saturated core samples. Specifically, a comparison is made between the T2 distribution of a sample in a fully water-saturated state, and the same sample in a partially saturated state, the latter typically being attained by centrifuging the core at a specified air-brine capillary pressure.25 Although capillary pressure, lithology, and pore characteristics all affect T2cutoff values, common practice establishes local field values for T2cutoff. For example, in the Gulf of Mexico, T2cutoff values of 33 and 92 ms are generally appropriate for sandstones and carbonates, respectively.25 Generally though, more accurate values are obtained by performing measurements on core samples from the actual interval logged by an NMR tool.
1.2.8 NMR Permeability
NMR relaxation properties of rock samples are dependent on porosity, pore size, pore fluid properties and mineralogy. The NMR estimate of permeability is based on theoretical models that show that permeability increases with both increasing porosity and increasing pore size.26–31 Two related kinds of permeability models have been developed. The free-fluid or Coates model can be applied in formations containing water and/or hydrocarbons. The average-T2 or SDR (Schlumberger–Doll Research) model can be applied to pore systems containing only water.32
Measurements on core samples are necessary to refine these models and produce a model customized for local use. Figure 1.17 shows that the decay of an echo train contains information related to formation permeability. Figure 1.18 shows how the Coates model can be calibrated with laboratory core data. Figure 1.19 demonstrates NMR permeability derived from a customized Coates model.32
1.2.9 NMR Capillary Pressure Curve
The mercury injection experiment is an important method for evaluating reservoir pore structure. Mercury injection capillary pressure curves are obtained by mercury injection experimental analysis of cores to evaluate reservoir pore structure. However, the use of core analysis to obtain mercury injection capillary pressure data is very expensive and toxic mercury, which is used as the experimental medium, results in permanent damage to the cores. Therefore, it is impossible to apply continuous core analysis to the entire well, and only limited cores from the main formations of interest are selected for the mercury injection experiment, which cannot result in a continuous evaluation of the pore structure of the entire reservoir intervals.
Reservoir pore structure can be directly evaluated by analyzing the morphological characteristics of T2 distribution. That means NMR logging data can be effectively converted to construct continuous reservoir capillary pressure curves, which are named NMR capillary pressure curves. Figure 1.20 shows the NMR capillary pressure curves and their application in both core analysis and well logs.
1.2.10 NMR Properties of Reservoir Fluids
Clay-bound water, capillary-bound water, and movable water occupy different pore sizes and locations. Hydrocarbon fluids differ from brine in their locations in the pore spaces, usually occupying the large pores and relying on wettability. They also differ from each other and brine in viscosity and diffusivity. NMR logging uses these differences to characterize the fluids in the pore space. Figure 1.21 qualitatively indicates the NMR properties of different fluids found in rock pores.33–35 In general, bound fluids have very short T1 and T2 times, along with slow diffusion (small D) that is due to the restriction of molecular movement in small pores. Free water commonly exhibits medium T1, T2 and D values. Hydrocarbons, such as natural gas (methane), light oil, medium-viscous oil, and heavy oil, have very different NMR characteristics. Natural gas exhibits very long T1 times but short T2 times and single-exponential relaxation decay. NMR characteristics of oils are quite variable and are largely dependent on oil viscosities. Lighter oils are highly diffusive, have long T1 and T2 times, and often exhibit single-exponential decay. As viscosity increases and the hydrocarbon mix becomes more complex, diffusion decreases, as do the T1 and T2 times, and events are accompanied by increasingly complex multi-exponential decays. Based on the unique NMR characteristics of the signals from pore fluids, applications have been developed to identify and, in some cases, quantify the type of hydrocarbon present by use of multi-contrast and multidimensional NMR logging.
1.2.11 NMR Hydrocarbon Typing
Despite the variability in the NMR properties of fluids, the locations of signals from different types of fluids in the T2 distribution can often be predicted or, if measured data are available, identified. This capability provides important information for NMR data interpretation and makes many applications practical.
Figure 1.22 shows two methods for differentiating fluids. In one method, different TW values are used with a T1-weighted mechanism to differentiate light hydrocarbons (light oil or natural gas, or both) from water. In the second method, different TE values are used with a diffusivity-weighted mechanism in a well-defined gradient magnetic field to differentiate viscous oil from water or to differentiate gas from liquid.
The Differential Spectrum Method (DSM) is an example of a T1-weighted mechanism in which two echo trains are gathered over the same interval using two different polarization times. The echo train recorded after the short TW contains almost all of the water signals but only some of the light hydrocarbon signals. However, the echo train recorded after the long TW contains all of the signals from both the water and the light hydrocarbons that are present. A differential spectrum that contains only light hydrocarbon components can be created by taking the difference between the T2 distributions computed separately from the echo trains acquired at the two different polarization times.7–9
The two echo trains used to compute a differential spectrum can also be subtracted from one another and the resulting echo train is examined through a process referred to as Time Domain Analysis (TDA).36 TDA starts by resolving the exponential decay associated with light hydrocarbons (oil and/or gas), thereby confirming the presence of these fluids, and then provides estimates of the fluid volumes. TDA is a more robust process than DSM.
The log in Figure 1.23 provides an example combining both DSM and TDA results.38–40 Because NMR analysis does not rely on formation water salinity to obtain water saturation, it has an advantage over conventional resistivity analysis in mixed or unknown salinity conditions. This feature can be extremely useful in waterflood projects to evaluate residual oil saturation (ROS) after the waterflood or to look for bypassed oil.
1.2.12 NMR Enhanced Water Saturation with Resistivity Data
Because resistivity tools have a large depth of investigation, a resistivity-based water saturation model is preferred for determining water saturation in the virgin (uninvaded) zone of a formation. However, resistivity measurements cannot distinguish between capillary-bound water and movable water. This lack of contrast makes it difficult to recognize hydrocarbon-productive low-resistivity and/or low-contrast pay zones from data provided by traditional logging suites, whereas NMR will help to solve these problems, as shown in Figures 1.24 and 1.25.
The unique information, such as BVI and MCBW, provided by NMR logging can significantly enhance the estimation of resistivity-based water saturation and can greatly assist in the recognition of pay zones that will produce water-free deposits.
Through an MRI analysis process referred to as “MRIAN” (an interpretation method that incorporates deep resistivity data, MRIL standard T2 logging measurements, and the dual water model),37 the NMR data and the deep resistivity data are integrated to determine whether producible water is in the virgin zone, or whether an interval with high water saturation may actually produce water-free hydrocarbons. The log shown in Figure 1.26 includes MRIAN results.
1.2.13 Multi-dimensional NMR
A rock consists of the rock matrix, dry clay, bound water, movable water and oil or gas and different types of fluids with different relaxation times and diffusion coefficients, which can be easily reflected by multi-dimensional NMR measurements. Usually, T2 distribution is the direct means for the calculation of porosity, permeability and saturation, etc., and it can also be used to differentiate the fluids by setting different TW or TE, as shown in Figure 1.27. However, if the relaxation time or diffusion coefficients of fluids (bound water, oil or gas) are similar, a single relaxation or diffusion contrast or so-called one-dimensional measurement is not enough, so a T1–T2 or T2–D correlation map (a two-dimensional measurement) should be introduced to differentiate the properties of fluids and calculate the saturation, viscosity, and other evaluation parameters. Three-dimensional methods, such as T1–D–T2, will be more visible for distinguishing complex fluids, because three key parameters are obtained simultaneously. An example is demonstrated in Figure 1.28, in which the water-based filtrate, oil-based filtrate, oil and gas can be clearly identified on the two-dimensional projection obtained from three-dimensional measurements.
1.2.14 Radial Profile NMR
If a mud filtrate invasion happens, the rock volume model can be described as demonstrated in Figure 1.29. The formation will consist of a flushed zone, an invaded zone and an undisturbed zone. Invasion frequently causes fluid saturations to vary over the first few inches of formation away from the wellbore in wells drilled with oil-based or water-based mud. For resistivity logs, shallow, medium and deep resistivity logging can be implemented to solve this issue. State-of-the-art NMR logging tools acquire data in thin shells at distinct radial depths of investigation (DOI) so that a radial profile will be obtained to efficiently describe the saturation variation due to the filtrate invasion, as shown in Figure 1.30.
1.2.15 NMR Logging Tools
NMR technique has been well defined and maturely developed in both high-field spectroscopy in chemistry and imaging in medicine. The basic components of the NMR technique include the “instruments”, “pulse sequences-data acquisition technique”, and the “data processing and interpretation technique”. The NMR logging, just like the NMR in chemistry and MRI in medicine, has the same components, as shown in Figure 1.31.
NMR logging actually has a relatively long history. Right after the discovery of NMR phenomena in 1946, Russell Varian filed a patent in 1948 to employ Earth’s magnetic field NMR in a wellbore for oil detection.43 Then, in the early 1960s, Robert Brown et al. at Chevron developed a logging device with Earth’s field NMR, called NML (Nuclear Magnetic Logging), and deployed it in oilfields.44,45 Unfortunately, the NML readings are not so obvious, and most of the time, it was not able to tell anything about the formation logged due to borehole fluids causing the majority of the measured signals. Considerable effort has been made since then to develop a better NML tool by service companies, such as Schlumberger and organizations in the former Soviet Union. Later, in the 1970s and early 1980s, Jasper Jackson at Los Alamos pioneered a new design called “Inside-out”, which takes the medical MRI or laboratory NMR equipment and turns it inside out, rather than placing the subject at the center of the instrument, the instrument itself is placed, in a wellbore, at the center of the formation to be analyzed.46 Jackson’s design established the fundamental of borehole NMR logging for real massive and commercial applications. Figure 1.32 shows the evolution of the downhole wireline NMR probes.
As mentioned before, the early version of the probe was based on the Earth’s magnetic field. A looped coil with DC will set up a high magnetic field to polarize the proton surrounding the coil. When the DC is turned off the polarized proton will precess in the Earth’s magnetic field and Free Induction Decay (called the FID signal) can be recorded. The recorded FID can be applied to extract information on porosity from amplitude and relaxation time from the decay rate. A critical problem for Earth’s field NMR logging is that the signal is from a very wide region and not well defined; furthermore, the main part of the signal is from the wellbore rather than the formation. This makes it difficult to use for applications. And Earth’s field NMR logging was never commercialized, although many companies were involved in the development of this technology, including Schlumberger, Chevron and companies in the former Soviet Union.
The most important step in downhole NMR development was made by Jasper Jackson who invented the “inside-out” concept, which put a permanent magnet in the wellbore and produced a well-defined “homogeneity” magnetic field in the measured formation.46 Then, radio-frequency (RF) pulses can be transmitted to the formation through coils. By choosing the frequency of the RF pulse to the Larmor frequency of the defined magnetic field, the NMR signal can be measured from the formation. The “inside-out” concept opened a new era to downhole NMR in two ways: the first is that the measured region can be designed and well defined; the second is that pulsed NMR with rich sequences can be adapted to downhole environments for much more convenient operation and better application.
The “inside-out” concept has been successfully employed and become the fundamental commercialized downhole NMR technology. Jasper Jackson’s prototype based on a “homogeneity” magnetic field design, however, had problems regarding signal intensity and signal-to-noise ratio. In addition, in wireline logging circumstances, the downhole tool will normally always move with a certain speed during the measurement; it is hard to keep the polarized protons within the measurement region with a homogeneous magnetic field during the time for recording a whole echo train.
Zvi Taicher alternatively utilized a gradient magnetic field instead of a homogeneous magnetic field to architecture the NUMAR’s MRIL, which has dramatically improved signal-to-noise ratio and made the application of many advanced NMR technologies, such as diffusion and multi-dimensional measurements, in downhole conditions possible and feasible.47,53,54 The MRIL tool series have been very successful in the past two decades.
Meanwhile, Schlumberger developed their Combinable Magnetic Resonance (CMR) tool based on a pad-type design with a homogeneous magnetic field. CMR also has successfully been commercialized in the last two decades and provided reasonable NMR measurements for petrophysical estimation and formation evaluation with high vertical resolution.48
In the early part of this century, new NMR logging probes shifted to a gradient magnetic field. Schlumberger kept their pad-type design and developed a so-called MR Scanner tool49 and Baker Hughes developed a so-called MREx tool.50
Usually, NMR application is based on the hypothesis that the formation is homogeneous. However, in many reservoirs, especially unconventional reservoirs, pores or fractures, and even fluids of the rock formation pose different properties not only along the depth and radial direction but around the borehole. These cases will result in unexpected errors in porosity and permeability values, as well as mistaken evaluations of oil/gas location using NMR technology. An early trial of borehole NMR azimuthal measurement was suggested by Prammer.59 It tried to rotate the single saddle-shaped coil or tool by 90 degrees to acquire additional directional information so that the system is capable of differentiating the petrophysical parameters from four azimuthal regions with special post-processing. However, the precision of the corresponding processing is easily affected by overlapping sensitive regions. For the strong demands of heterogeneous formation exploration with NMR technology, a new three-dimensional NMR logging tool has been designed, with corresponding wellbore MRI logging instruments and equipment with scanning functions proposed by Xiao,52 as shown in Figure 1.33.
The above-addressed NMR wireline logging tools are also used in other parts of the prospecting and reservoir characterization process. These tools include an LWD NMR tool for early reservoir identification and properties estimation, an NMR device built into a formation tester tool (at Halliburton, it is called a reservoir description tool, RDT™), advanced formation testing, and a device for fluid-sampling and analysis. The LWD NMR tool uses the same principles as the NMR wireline tool but provides information earlier and on uninvaded rocks. The NMR fluid analyzer provides fluid-property information in downhole reservoir conditions. All of the information from the LWD NMR tool, the wireline NMR tool, and the NMR fluid analyzer device can then be integrated at a reservoir decision center to give a more complete analysis. Figure 1.34 provides a schematic of this process.
1.2.16 LWD NMR
With the rapid growth in the number of horizontal wells for enhanced oil recovery, there were more and more applications needed in the oil industry to provide real-time measurements and evaluations while drilling.
NMR LWD has the ability to carry out real-time measurements when instruments are drilling on the way to the target zone and is compatible with operation environments in horizontal or high-angle wells. Compared to wireline NMR, there are advantages for LWD that the original measurements before the deterioration of the drilling fluids invasion can be acquired by LWD instruments. Nowadays, the representative LWD NMR tools are the Halliburton MRIL-WD (Halliburton),55 Schlumberger pro-VISION,56 and Baker Hughes Mag-Trak,57,58 as shown in Figure 1.35. MRIL-WD has a similar magnet configuration compared with the MRIL-Prime, to produce a dipole magnetic field with a gradient of about 14 Gauss cm−1. Due to the thinner sensitive region, only T2 can be measured when the tool is in a sliding state (without strong radial vibrations) except for the T1 measurement, the results of which are less sensitive to the radial vibrations. The Jackson design is introduced into the pro-VISION and Mag-Trak, which can produce relatively homogeneous magnetic fields with gradient fields lower than 2 Gauss cm−1. The advantage is obvious with high formation resolution, wide resonant volume, and less sensitivity to the radial variations when T2 measurements are conducted.
1.2.17 Downhole NMR Fluids Analyzer
The fluid environment possesses different properties before they are extracted into the laboratory to be analyzed. Those properties, such as oil and gas ratio, viscosity, molecular structure, etc., are very difficult to be recovered in the original state with high temperature and pressure on the ground. Fluid in situ analysis is significant to the evaluation of reservoir contamination degree, fluid characterization, oil and gas quantification, and oil recovery estimation. That is why a multi-functional fluid analyzer that can be used to analyze the reservoir fluids in situ in real-time is a long-term goal of petroleum scientists. Early fluid analysis devices were developed by service companies, such as Halliburton’s Reservoir Description Tool (RDT),60–62 Schlumberger Modular Formation Tester (MDT),64,65 and the Baker Hughes Reservoir Characterization Instrument (RCI).63 The methods used for fluid analysis were optical and NMR. The former did not perform well in the cases where the drilling fluid filtrate was oil-based, or the fluids in the pores were mixed phase. In contrast, NMR is sensitive to fluids, which makes it suitable for testing with different drilling fluids. Moreover, downhole in situ fluid analysis is only one of the applications for an NMR fluid analyzer laboratory.66,67 Expanded applications on the ground include drilling fluid monitoring for oil content detection68 and multi-phase flow separation detection system for flow velocity monitoring, fluids quantification and characterizations for pipelines.69 Figure 1.36 demonstrates the in situ NMR fluid analyzer developed by Halliburton.
1.3 NMR Logging Application Examples
1.3.1 Porosity and Permeability
Figure 1.37 presents data from a shaly sand formation in Egypt. Track 1 contains MRIL permeability (green curve) and core permeability (red asterisks). Track 2 contains MRIL porosity (blue curve) and core porosity (black asterisks). In this reservoir, the highly variable grain sizes lead to a considerable variation in rock permeability. Capillary pressure measurements on rock samples yielded a good correlation between the pore bodies and the pore throat structures. This correlation indicates that the NMR T2 distribution is a good representation of the pore throat size distribution when the pores are 100% water saturated.
Figure 1.38 shows an MRIL log through a massive low porosity (approximately 10 p.u.), low permeability (approximately 1 to 100 md) sandstone reservoir in Australia’s Cooper Basin. Track 1 contains GR and caliper logs. Track 2 contains deep- and shallow-reading resistivity logs. Track 3 presents the MRIL calculated permeability and core permeability. Track 4 shows the MRIL porosity response, neutron and density porosity readings (based on a sandstone matrix), and core porosity. This well was drilled with a potassium chloride (KCl) polymer mud [48 kppm sodium chloride (NaCl) equivalent] and an 8.5 in. bit. MRIL data were acquired with TW = 12 s and TE = 1.2 ms.
Over the interval depicted, the log shows a clean sandstone formation at the top, a shaly sandstone at the bottom, and an intervening shale between the two sandstones. The agreement between MPHI and the core porosity is good. The slight underestimation of MPHI relative to core porosity is attributed to residual gas in the flushed zone. The MRIL permeability curve was computed using a model customized to this area. The agreement between MRIL permeability and core permeability is very good.
Figure 1.39 compares core data with MRIL porosity and permeability recorded in a gas reservoir.23 Track 1 contains GR and caliper logs. Track 2 contains deep- and shallow-reading resistivity logs. Track 3 presents the MRIL-derived permeability and core permeability. Track 4 presents the core porosity, MRIL porosity MPHI, neutron and density porosity (based on a sandstone matrix), BVI from a model customized to this reservoir, and bulk volume water (CBVWE) from resistivity logs. The MRIL log in this example was acquired with TW = 10 s, TE = 1.2 ms, and NE = 500, where NE is the number of echoes per echo train.
A gas/water contact at X220 is easily identified on the resistivity logs. Immediately above the contact, a large gas crossover (yellow) is observed between the neutron and density logs. A decrease in MRIL porosity occurs here because of the hydrogen index (HI) effect of the unflushed gas. Accurate data for BVI and MFFI are important for permeability calculations with the Coates model. The MPERM curve (permeability estimate obtained from MRIL measurements) in track 3 was calculated from the Coates model: MPHI was used for porosity, and the difference between MPHI and BVI was used for MFFI. Used in this way, the Coates model will give good estimates of permeability when the MRIL porosity is unaffected by gas. In zones where the MRIL porosity is affected by gas, MPERM is pessimistic because the difference between MPHI and BVI underestimates MFFI. In this situation, the difference between BVI and the porosity obtained from the nuclear logs gives a better estimate of MFFI for calculating permeability. The PMRI curve was computed in this manner. It is a more reasonable representation of permeability in the gas zones and, in this example, matched very well with the core permeability. Below the gas/water contact, MRIL porosity and permeability match core data quite well.
1.3.2 Low-resistivity Reservoir Evaluation
An interval from a Gulf of Mexico well has been used several times throughout this chapter to illustrate various MRIL measurements. The same well is now discussed in the context of a specific case study.
The reservoir penetrated by the well consists of a massive medium- to fine-grained sandstone formation, which developed from marine shelf sediments. Intense bioturbation is observed within the formation. Air permeability typically ranges between 1 and 200 md, with core porosity varying between 20 and 30 p.u. The upper portion of the reservoir (Zone A) has higher resistivity (approximately 1 Ω m) than that of the lower reservoir (Zone B, approximately 0.5 Ω m). The produced hydrocarbons are light oil with viscosities from 1 to 2 cp. The well was drilled with water-based mud. Conventional logs are shown in Figure 1.40. MRIL results from both TDA and MRIAN are illustrated in Figure 1.41.
The operator was concerned about the decrease in resistivity in the lower portion of the reservoir. The question was whether the decrease was due to textural changes (smaller grain sizes, in which case the well might produce free of water) or to an increase in the volume of movable water. The ability to reliably answer this question could have significant implications on reserve calculations, well-completion options, and future field-development decisions. An additional piece of key information for this type of reservoir is that the actual cumulative production often far exceeds the initial calculated recoverable reserves based on a water saturation cutoff of 60%. If the entire zone in question were actually at irreducible water saturation, then the total net productive interval could be increased from 25 to 70 ft. The resulting increase in net hydrocarbon pore volume would be more than 200% and expected recoverable reserves would increase significantly.
The MRIL logs were incorporated into the logging suite for two principal reasons:
To distinguish zones of likely hydrocarbon production from zones of likely water production by establishing the bulk volume of irreducible water (BVI) and the volume of free fluids (MFFI).
To improve the estimation of recoverable reserves by defining the producible interval.
The MRIL data acquired in this well were to include total porosity to determine clay-bound water, capillary-bound water, and free fluids. Dual TW logging was to be used to distinguish and quantify hydrocarbons.
The MRIL data in Figure 1.41 helped determine that the resistivity reduction was due to a change in grain size and not to the presence of movable water. The two potential types of irreducible water that can cause a reduction in measured resistivity are clay-bound water (whose volume is designated by MCBW) and capillary-bound water (whose volume is indicated by BVI). The MRIL clay-bound-water measurement (track 3) indicates that the entire reservoir has very low MCBW. The MRIL BVI curve (track 7) indicates a coarsening upward sequence (BVI increases with depth). The increase in BVI and the corresponding reduction in resistivity are thus attributed to the textural change. Results of the TDA (track 6) and TDA/MRIAN (track 7) combination analysis imply that the entire reservoir contains no significant amount of movable water and is at irreducible conditions.
Based on these results, the operator perforated the interval from XX163 to XX234. The initial production of 2000 BOPD was water-free and thus confirmed the MRIL analysis.
A difference can be found between the TDA and TDA/MRIAN results in Figure 1.41. The TDA shows that the free fluids include both light oil and water, whereas the TDA/MRIAN results show that all of the free fluids are hydrocarbons. This apparent discrepancy is simply due to the different depths of investigation (DOI) of different logging measurements. TDA saturation reflects the flushed zone as seen by NMR logging measurement. The TDA/MRIAN combination saturation reflects the virgin zone as seen by deep resistivity measurements. Because water-based mud was used in this well, some of the movable hydrocarbons are displaced in the invaded zone by the filtrate from the water-based mud.
1.3.3 MRIL Response in Rugose Holes
As shown in Figure 1.42, an MRIL tool responds to the materials in a series of cylindrical shells, each approximately 1 mm thick. Borehole or formation materials outside these shells have no influence on the measurements, a situation similar to medical MRI. Hence, if the MRIL tool is centralized in the wellbore, and the diameter of any washout is less than the diameter of the inner sensitive shell, then the MRIL tool will respond solely to the NMR properties of the formation. In other words, borehole rugosity and moderate washouts will not affect MRIL measurements. Figure 1.43 provides an example of an MRIL log run in a rugose borehole.
The diameters of the response shells for an NMR tool are dependent on operating frequency and tool temperature. For an MRIL tool, the highest frequency of operation is 750 kHz, which corresponds to a diameter of investigation of approximately 16 in. at 100 °F. At the lowest operating frequency of 600 kHz, the diameter of investigation is about 18 in. at 100 °F. Charts that illustrate the dependence of the depth of investigation on operating frequency and tool temperature have been published.41,42
1.3.4 NMR Logging Applications Summary
Case studies and theory have shown that NMR logging tools furnish powerful data for:
distinguishing low-resistivity/low-contrast pay zones;
evaluating complex lithology oil and/or gas reservoirs;
identifying medium-viscosity and heavy oils;
studying low porosity/low permeability formations;
determining residual oil saturation;
enhancing stimulation design;
fracking optimization in low porosity reservoirs;
optimizing the selection of formation test and completion intervals;
synthetic capillary pressure curves;
oil viscosity for mobility determination;
oil viscosity/chemical components;
the wettability index;
water saturation profile with multi-depth investigations.
Particularly, NMR data provide the following valuable information:
porosity distribution, complete with a pore size distribution in water-saturated formations;
bulk volume irreducible (BVI) and free fluid when a reliable T2cutoff value is available;
permeability, determined from the free-fluid index and the BVI or average T2;
hydrocarbon typing through the use of (1) T1-weighted contrasts for water, gas, and/or light oil, (2) diffusivity-weighted contrasts for water and viscous oil;
NMR enhanced water saturation calculations for the virgin zone;
reservoir quality and productivity by Vsh, effective porosity, permeability, Swir, Sor.
To support the NMR logging interpretation and applications, model building and core calibration in the laboratory are always necessary and important.
In this book, we will introduce, summarize and explain all the details for each part of the concepts mentioned above, especially the advancement in understanding the NMR properties in porous rock and the development of downhole NMR tools and application case histories. The principles described in this book can be fully applied to all scenarios as shown in Figure 1.44.