WO2015200789A1

WO2015200789A1 - Systems and methods for determining a property of a subsurface formation using superparamagnetic particles

Info

Publication number: WO2015200789A1
Application number: PCT/US2015/037969
Authority: WO
Inventors: Chun Huh; Thomas E. Milner; Steven L. Bryant; Bharadwaj MURALIDHARAN
Original assignee: Board Of Regents, The University Of Texas System
Priority date: 2014-06-26
Filing date: 2015-06-26
Publication date: 2015-12-30

Abstract

Disclosed herein are systems and methods of determining properties of subsurface formations. In some examples, the methods can comprise using superparamagnetic particles to determine a property of a subsurface formation.

Description

SYSTEMS AND METHODS FOR DETERMINING A PROPERTY OF A SUBSURFACE FORMATION USING SUPERPARAMAGNETIC

PARTICLES

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims benefit of U.S. Provisional Application No. 62/017,663, filed

June 26, 2014, which is hereby incorporated herein by reference in its entirety.

BACKGROUND

When drilling boreholes into formations in the earth's subsurface it is desirable to obtain information related to the nature and structure of the formations penetrated by the borehole. To this end, many different tools have been developed to measure (log) certain physical properties of the borehole and surrounding formations. For example, the depth location, borehole size, hydrocarbon pore volume, porosity, lithology, and permeability of a subsurface formation are often deduced from measurable quantities during drilling such as electrical resistivity, density, photoelectric factor (Pe), hydrogen index, natural (spontaneous) radioactivity, acoustic velocity, nuclear magnetic resonance, and thermal neutron capture cross section (Sigma), among others.

Logging tools typically carry a source that emits energetic radiation into the formation and one or more detectors that can sense the resulting interactions of the radiation. Detected signal data are typically transmitted uphole, temporarily stored downhole for later processing, or combined in both techniques, to evaluate the formation from which the data were gathered.

Current well logging operations suffer from many limitations, and a need for improved accuracy and resolution when determining properties of subsurface formations still exists. The systems and methods discussed herein address these and other needs.

SUMMARY

Disclosed herein are systems and methods for determining properties of subsurface formations, such as for oilfield applications.

In some examples, the methods can comprise injecting a superparamagnetic tracer fluid into a subsurface formation. The superparamagnetic tracer fluid can, for example, comprise a plurality of superparamagnetic particles. The amount of superparamagnetic particles used can depend on the application. In some examples, the concentration of the plurality of

superparamagnetic particles in the superparamagnetic tracer fluid can be from 0.00001% by weight to 20% by weight. The plurality of superparamagnetic particles can comprise any suitable material, for example an iron oxide (e.g., Fe30₄). The plurality of superparamagnetic particles can, for example, be a plurality of superparamagnetic nanoparticles (SPMNPs). In some examples, the plurality of superparamagnetic particles have an average maximum dimension (e.g., an average diameter for spheroidal particles) of from 1 nm to 200 nm. In some examples, mixtures of superparamagnetic particles of different sizes can be used. For example, the plurality of superparamagnetic particles can be mono-disperse, bi-disperse, tri-disperse, tetra-disperse, or multi-disperse. In some examples, the plurality of superparamagnetic particles can be adapted to withstand downhole conditions (e.g., temperature, pressure, salinity, etc.). In some examples each of the plurality of superparamagnetic particles can further comprise a coating. The coating can, for example, comprise a polymer. The coating, for example, can selectively adsorb to a target (e.g., a target rock, a target fluid, etc.) in the subsurface formation.

In some examples, the superparamagnetic tracer fluid can further comprise a liquid carrier fluid. Examples of suitable liquid carrier fluids include, but are not limited to, spacer fluids, drilling fluids, cementing fluids, fracturing fluids, mud fluids, synthetic fluids, aqueous solutions (e.g., solutions of surfactants and/or polymers injected into the subsurface formation for enhanced oil recovery), water (e.g., water injected into the subsurface formation for enhanced oil recovery), and combinations thereof.

In some examples, the methods can further comprise applying a variable magnetic field to the subsurface formation. The variable magnetic field applied to the subsurface formation, for example, can be supplied by a logging tool that is inserted into the subsurface formation (e.g., inserted into a wellbore in the subsurface formation). The variable magnetic field can, for example, be generated using an electric coil, an electromagnet, or combinations thereof. In some examples, the variable magnetic field can be sinusoidal. The frequency of the variable magnetic field can depend on the application. In some examples, the variable magnetic field can have a frequency of from 0.001 Hz to 10 MHz. In some examples, the variable magnetic field can have a strength of from 1 μΤ to 100 T.

In some examples, the methods can further comprise detecting a magnetic response signal from the subsurface formation, the plurality of superparamagnetic particles, or combinations thereof. Detecting the magnetic response signal can comprise, for example, detecting a voltage induced in a receiver coil. The magnetic response signal can comprise, for example, magnetization. In some examples, the magnetic response signal can comprise a nonlinear magnetization response of the plurality of superparamagnetic particles. In some examples, the methods can further comprise processing the magnetic response signal to obtain a property of the subsurface formation.

In some examples, the applying and detecting steps can occur before the injecting step, and the magnetic response signal of the subsurface formation can be processed to obtain a reference property of the subsurface formation. In some examples, the applying and detecting steps can occur after the injecting step, and the magnetic response signal of the plurality of superparamagnetic particles can be processed to obtain a sample property of the subsurface formation. In some example, processing the magnetic response signal can comprise comparing the reference property of the subsurface formation to the sample property of the subsurface formation. In some examples, the applying, detecting, and processing steps are performed at several time points to determine a change in the property of the subsurface formation over time.

In some examples, processing the magnetic response signal comprises directly imaging the plurality of superparamagnetic particles. In some examples, processing the magnetic response signal comprises accounting for the Langevin magnetization behavior of the plurality of superparamagnetic particles. In some examples, processing the magnetic response signal can comprise removing at least a portion of the magnetic noise from the subsurface formation from the magnetic response signal of the plurality of superparamagnetic particles.

In some examples, processing the magnetic response signal can comprise determining the concentration, spatial resolution, penetration depth, or combinations thereof, of the plurality of superparamagnetic particles in the subsurface formation. In some examples, processing the magnetic response signal can comprise mapping the presence, location, distribution, evolution, or combinations thereof, of a target reservoir rock, a target fluid (e.g., water, hydrocarbon fluid, an oil/water interface, etc.), a fracture, or combinations thereof. In some examples, processing the magnetic response signal can comprise the mapping internal geometry of a target reservoir rock in the subsurface formation. In some examples, processing the magnetic response signal can comprise mapping the oil/water interfaces, oil saturation distribution, evolution of oil distribution/displacement, or combinations thereof.

The property of the subsurface formation can comprise any property of interest. In some examples, the property of the subsurface formation can comprise porosity, solid content (e.g., solid content of certain mineral components), water content, fluid content, fluid composition, hydrocarbon location, hydrocarbon content, contaminant location, contaminant content, permeability, or combinations thereof. In some examples, the property of the subsurface formation can comprise the presence, location, distribution, evolution, or combinations thereof of a target reservoir rock, a target fluid (e.g., water, hydrocarbon fluid, an oil/water interface, etc.), a fracture, or combinations thereof.

Also disclosed herein are methods of determining a property of a subsurface formation comprising obtaining a sample comprising a portion of a subsurface formation and a reference sample. In some examples, the reference sample can comprise at least a portion of the subsurface formation. In some examples, the sample, the reference sample, or combinations thereof can comprise a drill cutting.

In some examples, the methods can further comprise contacting a superparamagnetic tracer fluid comprising a plurality of superparamagnetic particles with the sample, thereby forming a superparamagnetic sample.

In some examples, the methods can further comprise applying a variable magnetic field to the superparamagnetic sample and the reference sample. The variable magnetic field can for example, be supplied by a mud logging tool or a magnetic coil inside of which the

superparamagnetic sample and reference sample are placed.

In some examples, the methods can further comprise detecting a magnetic response signal from the superparamagnetic sample and the reference sample. In some examples, the magnetic response signal of the superparamagnetic sample can comprise a magnetic response signal from the plurality of superparamagnetic particles.

The methods, in some examples, can further comprise processing the magnetic response signal to obtain a property of the subsurface formation. In some examples, the magnetic response signal of the reference sample is processed to obtain a reference property. In some examples, the magnetic response signal from the superparamagnetic sample is processed to obtain a sample property of the subsurface formation. In some examples, processing the magnetic response signal comprises comparing the reference property to the sample property of the subsurface formation. In some examples, processing the magnetic response signal can comprise removing at least a portion of the magnetic noise from the magnetic response signal. In some examples, the methods can comprise determining a property of a subsurface formation via differential sensing of the sample and the reference sample.

Processing the magnetic response signal can, in some examples, comprise determining the concentration, spatial resolution, penetration depth, or combinations thereof, of the plurality of superparamagnetic particles in the superparamagnetic sample.

The property of the subsurface formation can, in some examples, comprise a solid content, hydrocarbon content, contaminant content, or combinations thereof. In some examples, the property of the subsurface formation can comprise the presence of a target reservoir rock, a target fluid, or combinations thereof.

Also disclosed herein are methods of determining a property of a subsurface formation comprising drilling a wellbore in the subsurface formation thereby forming a drill cutting. The drill cutting can, for example, comprise at least a portion of the subsurface formation. In some examples, a drilling fluid is used concurrent with at least a portion of said drilling. In some examples, the methods can further comprise adding a superparamagnetic tracer fluid comprising a plurality of superparamagnetic particles to the drilling fluid. In some examples, the concentration of the plurality of superparamagnetic particles in the superparamagnetic tracer fluid can be 0.00001% by weight or more (e.g., from 0.00001% by weight to 20% by weight). In some examples, the methods can further comprise removing the drill cutting from the wellbore; applying a variable magnetic field to the drill cutting; detecting a magnetic response signal from the drill cutting, the plurality of superparamagnetic nanoparticles, or combinations thereof; and processing the magnetic response signal to obtain a property of the subsurface formation. In some examples, the variable magnetic field can be supplied by a mud logging tool or a magnetic coil through which the drill cutting passes. In some examples, processing the magnetic response signal comprises determining the concentration, spatial resolution, penetration depth, or combinations thereof, of the plurality of superparamagnetic particles in the drill cutting. In some examples, the property of the subsurface formation comprises the presence of a target reservoir rock, a target fluid, or combinations thereof.

The details of one or more embodiments are set forth in the description below. Other features, objects, and advantages will be apparent from the description and from the claims and the drawings.

DESCRIPTION OF FIGURES

Figures la to le illustrate the physical effects exploited in MPI. A sinusoidal magnetic field H(t) (Figure la) is applied to particles with a non-linear magnetization curve (Figure lb). The anharmonic magnetization (Figure lc) induces a signal u(t) oc dM(t)/dt in a response measuring coil (Figure Id). Due to the non-linear magnetization curve, the spectrum (Figure le) of the acquired signal contains the excitation frequency fo as well as higher harmonics.

Figure 2 shows a sinusoidal current applied to a magnetic-field generating coil.

Figure 3 shows a sinusoidal applied magnetic field generated by a generator coil.

Figure 4 shows the magnetization curves of SPMNPs at various diameters. Figure 5 shows the excited magnetization of SPMNPs of various diameters.

Figure 6 shows the induced voltage signal generated by SPMNPs of various diameters at unit concentration.

Figure 7 shows a schematic representation of the signature spectrum detected from a pick-up coil in (A) the absence and (B) the presence of magnetic nanoparticles.

Figure 8 shows the spectrum of Fourier amplitudes of total induced voltage signal.

Figure 9 is a schematic of an exemplary processing device.

Figure 10 is a schematic diagram of the "coils-in-sample" magnetization-response measurement set-up or apparatus, for which the magnetic-field generating coil and the response- measuring coil are inserted inside of a hole in the sample (e.g., a hole or well drilled at the center of a block of the SPMNP-containing rock sample, or of a subsurface formation).

Figure 1 1 is the plan-view schematic diagram of rectangular-shaped generator and receiver coils, which allow the projection of the applied magnetic field into the surrounding sample volume, for the "coils-in-sample" magnetization-response measurement set-up. The magnetic field lines generated from the generator coil are schematically shown, which excite the magnetization response of the SPMNPs that is recorded by the response measuring coil.

Figure 12 is the plan-view schematic diagram of an array of rectangular-shaped generator and receiver coils inside of a wellbore, which can measure the spatial distribution of the SPMNP concentration at the near-wellbore reservoir zone.

Figure 13 is a schematic diagram of the "sample-in-coils" magnetization-response measurement set-up or apparatus, for which the sample is inserted inside of the magnetic-field generating coil and the response-measuring coil.

Figure 14 is a photograph of a simple "sample-in-coils" set-up.

Figures 15 is a schematic representation of obtaining a property of a target (e.g., a property of a target rock or fluid) in the subsurface formation (e.g., along a well in a near- wellbore reservoir zone) by (a) injecting superparamagnetic nanoparticles and (b) detecting the particles attached to a target.

Figure 16 is a schematic representation of detecting the presence and/or distribution of fractures in the subsurface formation (e.g., along a well in a near-wellbore reservoir zone).

Figure 17 is a schematic representation of detecting the presence of a target (e.g., desired rock or fluid constituent) from drill cuttings. Figure 18 shows the equilibrium relation between the amount of SPMNP adsorbed on the Barnette shale No. 6 sample and the SPMNP concentration in water (e.g., the SPMNP adsorption isotherm).

Figure 19 shows the adsorption capacity of different types of shale rocks at two different initial EMG-605 concentrations. The adsorption capacity for sand was virtually zero, and is therefore not shown.

Figure 20 shows the adsorption capacity of different types of shale rocks at two different initial EMG-700 concentrations. The adsorption capacity for sand was virtually zero, and is therefore not shown.

Figure 21 show a time-domain magnetization response signal in which the response from a SPMNP is degraded with noise.

Figure 22 shows a frequency-domain spectrum of a magnetization response signal in which the response from a SPMNP is degraded with noise.

Figure 23 shows the results of filtering the magnetization response signal of Figure 22 to remove the noise.

Figure 24 shows a time-domain response signal reconstructed from the noise-filtered frequency domain spectrum of Figure 23.

Figure 25 shows (a) a pick-up coil for a differential sensing configuration and (b) the corresponding signal spectra.

Figure 26 shows a differential sensing MPI system setup.

Figure 27 shows the amplitude of the harmonics versus the harmonics for various concentrations of MNP 1.

Figure 28 shows the amplitude of the harmonics versus the harmonics for various concentrations of MNP2.

Figure 29 shows a comparison of the amplitude of the harmonics versus the harmonics for various concentrations of MNP 1 and MNP2.

Figure 30 shows the amplitude of the harmonics versus the harmonics for MNP3 at various excitation amplitudes.

DETAILED DESCRIPTION

For oil and gas exploration and production, it can be challenging to accurately map the internal geometry of porous rocks as well as to track the distribution and dynamics of fluids within the porous rocks. Because the inside of porous rocks cannot be easily optically imaged due to strong light scattering, a number of tools originally developed to map the human body have been employed, such as x-ray radiography, computed tomography (CT), and magnetic resonance imaging (MRI). An imaging technique proposed by Gleich and Weizenecker (Gleich B and Weizenecker J. Nature, 435, 1214-1217, 2005) and known as magnetic particle imaging (MPI) has been developed for medical imaging purposes (Buzug TM and Borgert J, ed.

Magnetic Particle Imaging. A Novel SPIO Nanoparticle Imaging Technique, Springer, 2012; Gleich B. Principles and Applications of Magnetic Particle Imaging, Springer Vieweg, 2014). The MPI technique employs superparamagnetic nanoparticles as contrast agents, which are specially surface-coated so that the nanoparticles can be delivered to target locations.

Superparamagnetic nanoparticles (SPMNPs) have a non-linear magnetization response to an external applied magnetic field (known as the Langevin function). Therefore, application of a sinusoidal magnetic field of prescribed frequency and amplitude, and measurement of the magnetization response of the SPMNPs, can provide the concentration of said SPMNPs with better spatial resolution and penetration depth, and a faster processing time, than other currently available techniques.

Disclosed herein are systems and methods for determining properties of subsurface formations. The methods discussed herein, for example, can use MPI to map the internal geometry of reservoir rocks and the distribution and dynamics of fluids in them. The systems and methods discussed herein using this MPI modality can provide benefits to the upstream oil industry. For example, the methods discussed herein can map the natural micro-fractures in shale and their growth in response to external stress loading. In some examples, the methods discussed herein can map oil saturation distribution and its evolution during oil displacement in pores in the subsurface formation via selective adsorption of magnetic nanoparticles at the oil/water interfaces of the oil in the subsurface formation (e.g., in the reservoir rock). The systems described herein (e.g., the MPI hardware) can be customized such that a detector (e.g., a scanner) can be inserted into the subsurface formation, for example via a wellbore, and can measure at specific target locations in the subsurface formation the spatial distribution (e.g., radial and along the well) of the superparamagnetic tracers (e.g., a concentration of a plurality of

superparamagnetic particles) that had been injected into the subsurface formation. The systems and methods described herein can benefit the upstream oil industry, for example, as enhanced results (in space and time) can be obtained with similar operational input used in current techniques. For example, an MPI scanner can be adapted into a custom MPI logging tool whose operation would use similar knowledge and skills as NMR logging tools. Thus, existing users can adapt their knowledge to the methods and systems discussed herein with enhanced resolution in space and time.

In some examples, the methods discussed herein can be used to determine the presence and amount of the particular kinds of rock or fluids from the continuous stream of drill cuttings generated from a well being drilled, for example by measuring the magnetization response from superparamagnetic particles added to the drilling fluid.

Overview of MPI

To image the structure and function of tissues and other biological materials, various tomographic platforms can be employed, such as x-ray CT, MRI, positron emission tomography (PET), and single photon emission computed tomography (SPECT). In the upstream oil industry, x-ray CT and MRI have been successfully employed to image the inside of rock core samples with different fluids therein (DiCarlo DA et al. Geophys. Res. Lett., 38, L24404, 201 1 ;

Aminzadeh B et al. SPE Paper 160052, SPE Annual Tech. Conf, San Antonio, TX, Oct. 8-10, 2012). NMR logging has been employed for oil exploration and production. NMR logging has been used, for example, to measure the properties of reservoir formations and fluids near wellbores (Coates GR et al. NMR Logging. Principles and Applications, Halliburton Energy Services, Houston, 1999).

MPI is a tomographic imaging technique that can measure the spatial distribution of superparamagnetic particles with high sensitivity and sub-millimeter spatial resolution.

Furthermore, the acquisition time can be short, which can allow for real time applications. For example, three-dimensional real time in vivo MPI experiments have been carried out to image a beating mouse heart (Weizenecker J et al. Phys. Med. Biol, 54(5), L1-L10, 2009). Table 1, below, which is an abbreviated version of a table published by Pablico-Lansigan et al. (Pablico- Lansigan MH et al. Nanoscale, 5, 4040-4055, 2013), compares certain characteristics of a selection of medical imaging methods, including MPI.

Table 1. Summary of Imaging Modalities.

The MPI technique employs superparamagnetic nanoparticles as contrast agents, which are specially surface-coated so that the nanoparticles can be delivered to target locations.

Magnetic nanoparticles with diameters of a few nanometers up to a few hundreds of nanometers (e.g., from 1 nm to 200 nm) can exhibit a material property of superparamagnetism. When an external magnetic field is applied to these particles, a magnetization response is generated based on the magnetic moment of these particles. This relationship between the magnetization and the external magnetic field is called the magnetization curve. In the case of superparamagnetic particles, the magnetization curve can be effectively mathematically mapped using the Langevin function, and hence this magnetization is also referred to as the Langevin curve.

In other words, superparamagnetic nanoparticles (SPMNPs) have a non-linear magnetization response to an external applied magnetic field (e.g., the Langevin function). Application of a sinusoidal magnetic field of prescribed frequency and amplitude, and measurement of the magnetization response of the SPMNPs, can provide the concentration of said SPMNPs with better spatial resolution and penetration depth, and a faster processing time, than other currently available techniques.

The fundamental principles of MPI are illustrated in Figures 1 a to 1 e (adapted from Gleich B and Weizenecker J. Nature, 435, 1214-1217, 2005). Utilizing a transformer setup, for example, a primary or an excitation coil can be excited by a time varying signal. This signal generates a time varying magnetic field, H, around the coil. For example, when a sinusoidal current (with fixed frequency and fixed amplitude) is applied to a magnetic-field generating coil (Figure 2), the magnetic field generated by the generator coil (e.g., solenoid) is also sinusoidal (Figure 3). Using the law of Biot-Savart, the axial magnetic field near the center of a solenoid with length /, radius r and N windin

where i(t) denotes the current applied to the coil.

Applying a time varying magnetic field (Figure 1 a) to SPMNPs can generate a magnetization response, M, as represented by the Langevin function in Equation 2 and shown in Figure lb.

— = coth(a) - -≡ L(a) [2] where M_s is the saturation magnetization of the dispersion which is equal to φΜά, where φ is the volume fraction of nanoparticles and Md is the bulk magnetization of the nanoparticle solid (e.g., bulk magnetization of the material the nanoparticle is made from); and a is as defined in Equation 3 : a≡ ^πμ°^Μάά3" [3]

6kt ^{1 J} where μ_α is the vacuum permeability; d is the diameter of the particles; and T is the absolute temperature.

As can be seen from the Langevin relation given above, the magnetization response from the nanoparticle dispersion can depend on the nanoparticle size and the bulk magnetization of the material forming the nanoparticle (e.g., the metal oxide core). Therefore, different nanoparticles (e.g., nanoparticles with different sizes and/or nanoparticles made from different materials) can be distinguished magnetically, for example as described in U.S. Patent

Application No. 2014/01 1654 which is hereby incorporated herein by reference. Figure 4 shows the magnetization curves of SPMNPs of various diameters. Figure 5 shows the excited magnetization of SPMNPs of various diameters. For the basic MPI model described in this section, mono-disperse particles may be considered, e.g., particles having the same diameter.

As shown in Figure lb, for superparamagnetic nanoparticles, the Langevin curve goes through the coordinate origin (H = 0, M = 0), without any hysteresis effects. The magnetization of the particle initially increases with the increase in the applied magnetic field intensity.

However, as the magnetic field intensity increases further, the magnetization of the particle starts to saturate. Hence the Langevin curve has two zones - a linear zone at low magnetic field strengths, where frequencies of the time varying magnetization of the particle match those of the time varying magnetic field intensity, and a saturation zone, e.g., a non-linear zone, where the time varying magnetization response contains the excitation frequency fo of the time varying magnetic field as well as harmonics of the excitation frequency (e.g., integer multiples of the excitation frequency).

The time varying magnetization response (Figure lc) can induce a signal (e.g., an emf) in a detector coil (Figure Id). As used herein, the terms "response measuring coil", "detector coil", "receiver coil" or "signal response coil" are used interchangeably and refer to a coil that is used to detect a signal returned by the magnetic nanoparticles (e.g., superparamagnetic nanoparticles) after activation with a "magnetic-field generating coil", which may also be referred to as simply a "generating coil" or "source coil". As shown herein, the generating coil and the receiver coil may be configured in a "sample-in-coils" or a "coils-in-sample" configuration, or both.

The voltage u(t) induced in a receiver coil by the time varying magnetization M(t) for SPMNPs of different sizes at unit concentration is shown in Figure 6. The received signal can be calculated using the reciprocity principle for magnetic recording:

u{t) = μ₀5₀ν _t M{t) [4] where u(t) is the induced emf due to the presence of the SPMNPs, is the sample volume, and So is coil sensitivity. The induced voltage signal is periodic with base frequency,/, and is directly proportional to the time derivative of the particle magnetization. An emf w(¾) is generated in the secondary coil even in the absence of the SPMNPs due to the time varying signal in the primary coil as governed by Faraday's law of induction. Hence, the total emf induced v(t) in the pick-up coil is the sum oi u(t) and u(t) (Figure 7).

Due to the non-linearity of the magnetization curve, the induced voltage signal contains the excitation frequency fo as well as harmonics (i.e., integer multiples) of/o. By spectral analysis of the induced signal, the harmonics can be determined (Figure l e and Figure 8) and from the amplitude of this induced voltage signal, the concentration of the nanoparticles, for example in the solvent medium, can be determined, as described in more detail below.

Magnitude squared of the Fourier transform of the induced emf v(t) gives the power spectrum of the signal V(f), which gives details of the relative energy (or power) contained in the frequency components available in the signal. Ideally, the spectrum corresponding to u(t) only has the frequencies of the excitation signal. That is, in the absence of non-linearities, only the frequencies of the excitation signal are present in u(t). Whereas, the spectrum corresponding to u(t) has the fundamental frequency and harmonics of the fundamental frequency driving the primary coil. The harmonics in u(t) become larger in magnitude if the excitation is large enough to operate in the saturation region. Based on this difference, the detection of the SPMNPs is feasible by looking at the spectrum of the harmonics of the excitation frequency to determine the concentration of the SPMNPs, provided the SPMNPs are excited with a sufficiently high excitation field.

An advantage of the MPI method (e.g., for oilfield applications), is that the magnetization response from a particular kind of nanoparticles is unique in time. That is, the received signal has its own spectral fingerprint, so that the signal can be extracted from any noise present. Oil reservoirs and surface equipment can have magnetic noise sources, such as well casings and production facilities made of steel and electrical currents used to operate equipment. The MPI method allows measurement and processing of the unique power spectrum signal emanating directly from the nanoparticles. In contrast, methods of using superparamagnetic nanoparticles as reservoir-sensing contrast agents were recently proposed that relied on the magnetic wave inversion method to deduce the presence of the nanoparticles in the reservoir. By obtaining a series of Maxwell equation solutions, the most likely of the possible configurations of the nanoparticle ensemble, that matches the measured perturbation of the magnetic field from the nanoparticle-free case, is chosen. However, the magnetic noise sources that are prevalent in oil reservoirs and surface equipment make an accurate inversion difficult, thus making such a technique impractical for such real world oil field applications.

The systems and methods disclosed herein can be used to determine properties of subsurface formations via the MPI technique. For example, the systems and methods discussed herein can be used to map the internal structure of porous rock, and/or the distribution and realtime dynamics of fluids in a subsurface formation.

Methods

Disclosed herein are methods of determining properties of subsurface formations, such as for oilfield applications. While the MPI tools used for medical imaging purposes can, in theory, also be utilized for oilfield application purposes, there are a number of factors that should be considered. For example, there can be a vast difference in size, scope, temperature, pressure, distance, position, and equipment for oilfield applications compared to medical imaging applications. Other considerations can, for example, include selecting and/or designing the surface coatings on the nanoparticles such that the nanoparticles can maintain their dispersion stability for the duration of their delivery to the target and their imaging. In some examples, the surface coatings on the nanoparticles can be selected and/or designed such that the nanoparticles can attach selectively to a selected target, such as the oil/water interface of the resident oil, or specific organic matter in shale pores. In some examples, the source magnetic oscillation and the induced magnetic oscillation generated by the magnetic nanoparticles can pass through the subsurface formation, which can, for example, comprise porous rock filled with multi-phase fluids. In these examples, accurately determining the concentration and/or spatial distribution of the magnetic nanoparticles can include correcting the recorded signal from the response measuring coil(s) to account for the nanoparticle's Langevin curve.

Disclosed herein are methods of determining properties of subsurface formations. In some examples, the methods can comprise injecting a superparamagnetic tracer fluid into a subsurface formation. The subsurface formation can be any formation of interest. In some examples, the formation can be adjacent to a well (e.g., petroleum, natural gas, water, CO2), an aquifer, a mineral deposit, a contamination site, or combinations thereof. The formation can be on- or off-shore.

The superparamagnetic tracer fluid can, for example, comprise a plurality of

superparamagnetic particles (SPMPs). The plurality of superparamagnetic particles can comprise superparamagnetic particles of any shape (e.g., a sphere, a rod, a quadrilateral, an ellipse, a triangle, a polygon, etc.). The plurality of superparamagnetic particles can comprise any suitable material, for example iron, cobalt, zinc, nickel, manganese, silver, gold, carbon, cadmium, or combinations thereof. In some examples, the plurality of superparamagnetic particles can comprise an oxide of iron, cobalt, zinc, nickel, manganese, or combinations thereof. In some examples, the plurality of superparamagnetic particles can comprise an iron oxide, for example Fe30₄. Superparamagnetic nanoparticles of iron oxide can, in some examples, be referred to as superparamagnetic iron oxide (SPIO) particles.

The plurality of superparamagnetic particles can, for example, be a plurality of superparamagnetic nanoparticles (SPMNPs). In some examples, the plurality of

superparamagnetic particles have an average maximum dimension (e.g., an average diameter for spheroidal particles) of 1 nm or more (e.g., 2 nm or more, 3 nm or more, 4 nm or more, 5 nm or more, 6 nm or more, 7 nm or more, 8 nm or more, 9 nm or more, 10 nm or more, 15 nm or more, 20 nm or more, 25 nm or more, 30 nm or more, 35 nm or more, 40 nm or more, 45 nm or more, 50 nm or more, 60 nm or more, 70 nm or more, 80 nm or more, 90 nm or more, 100 nm or more, 110 nm or more, 120 nm or more, 130 nm or more, 140 nm or more, 150 nm or more, 160 nm or more, 170 nm or more, 180 nm or more, or 190 nm or more). In some examples, the plurality of superparamagnetic particles can have an average maximum dimension of 200 nm or less (e.g., 190 nm or less, 180 nm or less, 170 nm or less, 160 nm or less, 150 nm or less, 140 nm or less, 130 nm or less, 120 nm or less, 110 nm or less, 100 nm or less, 90 nm or less, 80 nm or less, 70 nm or less, 60 nm or less, 50 nm or less, 45 nm or less, 40 nm or less, 35 nm or less, 30 nm or less, 25 nm or less, 20 nm or less, 15 nm or less, 10 nm or less, 9 nm or less, 8 nm or less, 7 nm or less, 6 nm or less, 5 nm or less, 4 nm or less, 3 nm or less, or 2 nm or less).

The average maximum dimension of the plurality of superparamagnetic particles can range from any of the minimum values described above to any of the maximum values described above. For example, the plurality of superparamagnetic particles can have an average maximum dimension of from 1 nm to 200 nm (e.g., from 1 nm to 100 nm, from 100 nm to 200 nm, from 1 nm to 50 nm, from 50 nm to 100 nm, from 100 nm to 150 nm, from 150 nm to 200 nm, from 10 nm to 190 nm, or from 5 nm to 25 nm). In some examples, the plurality of superparamagnetism particles can have an average maximum of greater than 200 nm (e.g., up to 500 nm), provided the plurality of particles is superparamagnetic

In some examples, mixtures of superparamagnetic particles of different sizes can be used. For example, the plurality of superparamagnetic particles can be mono-disperse (e.g., the plurality of superparamagnetic particles have substantially the same average maximum dimension), bi-disperse (e.g., the plurality of superparamagnetic particles can comprise a first population and a second population, wherein the first population and second population have substantially different average maximum dimensions), tri-disperse, tetra-disperse, or multi- disperse.

In some examples, the plurality of superparamagnetic particles can be adapted to withstand downhole conditions (e.g., temperature, pressure, salinity, etc.). In some examples, each of the plurality of superparamagnetic particles can further comprise a coating. The coating can, for example, comprise a polymer. The coating, for example, can selectively adsorb to a target (e.g., a target rock, a target fluid, etc.) in the subsurface formation. For example, SPMNPs can undergo versatile surface modification through application of a suitable polymer coating. A variety of SPMNP surface coating materials and methods are available that are appropriate for oilfield uses.

In some examples, the plurality of superparamagnetic nanoparticles can be adapted to selectively adsorb to a target (e.g., a target rock, a target fluid, etc.) in the subsurface formation, such as through a coating (e.g., a polymer coating). In some examples, the plurality of superparamagnetic particles can comprise a first population with a first coating that can selectively adsorb to a first target. In some examples, the plurality of superparamagnetic particles can further comprise a second population with a second coating that can selectively adsorb to a second target. In some examples, the first population and the second population can have different average maximum dimensions (e.g., the plurality of superparamagnetic particles can be bi-disperse with each size population comprising a different coating) and/or be formed from different materials, such that the two populations can be distinguished by their respective magnetic response signals.

The amount of superparamagnetic particles used can depend on the application. In some examples, the concentration of the plurality of superparamagnetic particles in the

superparamagnetic tracer fluid can be 0.00001% by weight or more (e.g., 0.00005% or more, 0.0001% or more, 0.0005% or more, 0.001% or more, 0.005% or more, 0.01% or more, 0.05% or more, 0.1% or more, 0.5% or more, 1% or more, 5% or more, 10% or more, or 15% or more). In some examples, the concentration of the plurality of superparamagnetic particles in the superparamagnetic tracer fluid can be 20% by weight or less (e.g., 15% or less, 10% or less, 5% or less, 1% or less, 0.5% or less, 0.1% or less, 0.05% or less, 0.01% or less, 0.005% or less, 0.001% or less, 0.0005% or less, 0.0001% or less, or 0.00005% or less).

The concentration of the plurality of superparamagnetic particles in the

superparamagnetic tracer fluid can range from any of the minimum values described above to any of the maximum values described above. For example, the concentration of the plurality of superparamagnetic particles in the superparamagnetic tracer fluid can be from 0.00001% by weight to 20% by weight (e.g., from 0.00001% to 0.05%, from 0.05% to 20%, from 0.00001% to 0.001%, from 0.001% to 0.1%, from 0.1% to 20%, or from 0.01% to 1%).

In some examples, the superparamagnetic tracer fluid can further comprise a liquid carrier fluid. Examples of suitable liquid carrier fluids include, but are not limited to, spacer fluids, drilling fluids, cementing fluids, fracturing fluids, mud fluids, synthetic fluids, aqueous solutions (e.g., solutions of surfactants and/or polymers injected into the subsurface formation for enhanced oil recovery), water (e.g., water injected into the subsurface formation for enhanced oil recovery), or combinations thereof. Drilling fluids include, for example, foams and aerated liquids; water-based fluids that can use clay, a biopolymer, or combinations thereof to viscosify the water-based fluid; and non-aqueous fluids which can include, but are not limited to, all-oil systems and water-in-oil emulsions. The continuous phase of a non-aqueous oil-based fluid can be, for example, an oil such as diesel oil, a refined mineral oil, or a chemically modified mineral oil. Non-aqueous synthetic -based fluids (e.g., muds) can use synthetic oils such as synthetic paraffins, olefins, esters, acetals, or combinations of these as a base fluid. Reservoir drilling fluids and completion fluids can be brine based fluids that can use salts such as sodium chloride, potassium chloride, calcium chloride, calcium bromide, zinc bromide, sodium formate, potassium formate, cesium formate, or combinations thereof to increase the density and minimize solids in the fluid. Spacer fluids include, for example, pre-flushes that can be used ahead of the cementing fluid and can be used to remove left-over mud from the borehole and water wet surfaces of the casing and wellbore. The spacer fluid can be, for example, a diluted low-density cement slurry; a water-based fluid that can contain a polyacrylamide, a cellulose derivative, and/or a biopolymer (e.g., guar and its derivatives, xanthan gum, scleroglucan, welan, diutan gum, and the like). Cementing fluids can include, for example, hydraulic cements. The hydraulic cement can include any hydraulic cement that is known in the art for use in wells. Suitable hydraulic cements include calcium aluminate cements (e.g., sold as Lumnite or Ciment Fondu), Portland cements, epoxy cements, silicone cements (geothermal cements), and combinations thereof.

In some examples, the methods can further comprise applying a variable magnetic field to the subsurface formation. The variable magnetic field applied to the subsurface formation, for example, can be supplied by a logging tool that is inserted into the subsurface formation (e.g., inserted into a wellbore in the subsurface formation).

In some examples, the superparamagnetic tracer fluid is injected into the subsurface formation from a wellbore (e.g., at one or more locations along a casing within the wellbore) and the variable magnetic field is applied to the subsurface formation from the same wellbore (e.g., at one or more locations along a casing within the wellbore). In some examples, the

superparamagnetic tracer fluid is injected into the subsurface formation from a wellbore and the variable magnetic field is applied from a different wellbore.

The variable magnetic field can, for example, be generated using an electric coil, an electromagnet, or combinations thereof. In some examples, the variable magnetic field can be sinusoidal.

The frequency of the variable magnetic field can depend on the application. In some examples, the variable magnetic field can have a frequency of 0.001 Hertz (Hz) or more (e.g., 0.005 Hz or more, 0.01 Hz or more, 0.05 Hz or more, 0.1 Hz or more, 0.5 Hz or more, 1 Hz or more, 5 Hz or more, 10 Hz or more, 50 Hz or more, 100 Hz or more, 500 Hz or more, 1 kHz or more, 5 kHz or more, 10 kHz or more, 50 kHz or more, 100 kHz or more, 500 kHz or more, 1 MHz or more, or 5 MHz or more). In some examples, the variable magnetic field can have a frequency of 10 MHz or less (e.g., 5 MHz or less, 1 MHz or less, 500 kHz or less, 100 kHz or less, 50 kHz or less, 10 kHz or less, 5 kHz or less, 1 kHz or less, 500 Hz or less, 100 Hz or less, 50 Hz or less, 10 Hz or less, 5 Hz or less, 1 Hz or less, 0.5 Hz or less, 0.1 Hz or less, 0.05 Hz or less, 0.01 Hz or less, or 0.005 Hz or less).

The frequency of the variable magnetic field can range from any of the minimum values described above to any of the maximum values described above. For example, the variable magnetic field can have a frequency of from 0.001 Hz to 10 MHz (e.g., from 0.001 Hz to 100 Hz, from 100 Hz to 10 MHz, from 0.001 Hz to 0.1 Hz, from 0.1 Hz to 10 Hz, from 10 Hz to 1 kHz, from 1 kHz to 100 kHz, from 100 kHz to 10 MHz, or from 1 Hz to 1 MHz).

The frequency of the variable magnetic field used can depend in part on the average maximum dimension of the plurality of superparamagnetic particles in the superparamagnetic tracer fluid. In some examples, the frequency of the applied variable magnetic field can be selected to minimize attenuation of the applied variable magnetic signal as it penetrates the subsurface formation. In some examples, the frequency of the applied variable magnetic field can be selected to improve the resolution of the detected magnetic response signal.

The systems and methods disclosed herein (e.g., MPI-based imaging) can probe deeper into the subsurface formation with frequency modulation. That is, by applying a lower frequency magnetic signal which can penetrate deeper into the subsurface formation, and by collecting similar frequency responses, which would also attenuate less, the concentration of the superparamagnetic tracer fluid placed deep in the subsurface formation (e.g., reservoir) can be measured. Such a modification can provide an improvement over existing subsurface logging tools. For example, NMR logging utilizes sharp magnetic pulses to record the spin responses from the "paramagnetic tracer" molecules, which can attenuate quickly, meaning NMR logging generally has a shallow penetration depth.

In some examples, the variable magnetic field can have a strength of 1 microTesla (μΤ) or more (e.g., 5 μΤ or more, 10 μΤ or more, 50 μΤ or more, 100 μΤ or more, 500 μΤ or more, 1 mT or more, 5 mT or more, 10 mT or more, 50 mT or more, 100 mT or more, 500 mT or more, 1 T or more, 5 T or more, 10 T or more, or 50 T or more). In some examples, the variable magnetic field can have a strength of 100 T or less (e.g., 50 T or less, 10 T or less, 5 T or less, 1 T or less, 500 mT or less, 100 mT or less, 50 mT or less, 10 mT or less, 5 mT or less, 1 mT or less, 500 μΤ or less, 100 μΤ or less, 50 μΤ or less, 10 μΤ or less, or 5 μΤ or less).

The strength of the variable magnetic field can range from any of the minimum values described above to any of the maximum values described above. For example, the variable magnetic field can have a strength of from 1 μΤ to 100 T (e.g., from 1 μΤ to 10 mT, from 10 mT to 100 T, from 1 μΤ to 50 μΤ, from 50 μΤ to 1 mT, from 1 mT to 50 mT, from 50 mT to 1 T to 100 T, or from 10 μΤ to 50 T).

The strength of the variable magnetic field used can depend in part on the concentration of the plurality of superparamagnetic particles in the superparamagnetic tracer fluid (e.g., lower concentrations of the plurality of superparamagnetic particles can call for higher variable magnetic field strengths, and vice-versa).

In some examples, the methods can further comprise detecting a magnetic response signal from the subsurface formation, the plurality of superparamagnetic particles, or combinations thereof. The magnetic response signal can be detected by any magnetic field sensor known in the art. Examples of magnetic field sensors include, but are not limited to, Hall effect sensors, magneto-diodes, magneto-transistors, AMR magnetometers, GMR

magnetometers, magnetic tunnel junction magnetometers, magneto-optical sensors, Lorentz force based MEMS sensors, Electron Tunneling based MEMS sensors, MEMS compasses, Nuclear precession magnetic field sensors, optically pumped magnetic field sensors, fluxgate magnetometers, coil magnetic field sensors, SQUID magnetometers, and combinations thereof. Detecting the magnetic response signal can comprise, for example, detecting a voltage induced in a receiver coil. The magnetic response signal can comprise, for example, a magnetization response. In some examples, the magnetic response signal can comprise a non-linear magnetization response of the plurality of superparamagnetic particles.

In some examples, the methods can further comprise processing the magnetic response signal to obtain a property of the subsurface formation.

In some examples, the applying and detecting steps can occur before the injecting step, and the magnetic response signal of the subsurface formation can be processed to obtain a reference property of the subsurface formation. In some examples, the applying and detecting steps can occur after the injecting step, and the magnetic response signal of the plurality of superparamagnetic particles can be processed to obtain a sample property of the subsurface formation. In some examples, the applying, detecting, and processing steps are performed at several time points to determine a change in the property of the subsurface formation over time.

In some examples, the applying and detecting steps can occur before the injecting step, and the magnetic response signal of the subsurface formation can be processed to obtain a reference property of the subsurface formation; the applying and detecting steps can be repeated after the injecting step, and the magnetic response signal of the plurality of superparamagnetic particles can be processed to obtain a sample property of the subsurface formation; and processing the magnetic response signal can comprise comparing the reference property of the subsurface formation to the sample property of the subsurface formation.

In some examples, the reference property and the sample property can be obtained concurrently, for example using the differential sensing systems described herein below.

In some examples, processing the magnetic response signal comprises directly imaging the plurality of superparamagnetic particles. In some examples, processing the magnetic response signal comprises accounting for the Langevin magnetization behavior of the plurality of superparamagnetic particles. In some examples, processing the magnetic response signal can comprise removing at least a portion of the magnetic noise from the magnetic response signal. In some examples, processing the magnetic response signal can comprise removing at least a portion of the magnetic noise from the subsurface formation from the magnetic response signal of the plurality of superparamagnetic particles.

In some examples, processing the magnetic response signal can comprise determining the concentration, spatial resolution, penetration depth, or combinations thereof, of the plurality of superparamagnetic particles in the subsurface formation. In some examples, processing the magnetic response signal can comprise mapping the presence, location, distribution, evolution, or combinations thereof, of a target reservoir rock, a target fluid (e.g., water, hydrocarbon fluid, an oil/water interface, etc.), a fracture, or combinations thereof. Examples of target fluids include, but are not limited to, hydrocarbon fluids (e.g., oil, natural gas, etc.), water, and combinations thereof (e.g., oil/water interfaces). In some examples, processing the magnetic response signal can comprise mapping the internal geometry of a target reservoir rock in the subsurface formation. In some examples, processing the magnetic response signal can comprise mapping the oil/water interfaces, oil saturation distribution, evolution of oil

distribution/displacement, or combinations thereof.

In some examples, processing the magnetic response signal can comprise accounting for the induced magnetic oscillation generated by the superparamagnetic nanoparticles that pass through a porous rock filled with multi-phase fluids to thereby accurately determine the concentration of the plurality of superparamagnetic particles in the subsurface formation.

In some examples, using the plurality of superparamagnetic particles in the methods described herein can increase the probe depth (e.g., increase the penetration depth); improve spatial resolution; provide real-time imaging; deliver one or more additional superparamagnetic tracer fluids to at least one target site to increase the clarity of a target's image in the subsurface formation; or combinations thereof. The property of the subsurface formation can comprise any property of interest. In some examples, the property of the subsurface formation can comprise porosity, solid content (e.g., solid content of certain mineral components), water content, fluid content, fluid composition, hydrocarbon location, hydrocarbon content, contaminant location, contaminant content, permeability, or combinations thereof. In some examples, the property of the subsurface formation can comprise the presence, location, distribution, evolution, or combinations thereof of a target reservoir rock, a target fluid (e.g., water, hydrocarbon fluid, an oil/water interface, etc.), a fracture, or combinations thereof.

As discussed in detail above, in some examples the plurality of superparamagnetic particles can be adapted to selectively adsorb to a target (e.g., a target rock, a target fluid, etc.) in the subsurface formation, for example through a coating (e.g., a polymer coating). In these example, detecting a magnetic response signal from said selectively adsorbed plurality of superparamagnetic particles can allow for the presence, location, distribution, evolution, or combinations thereof of said target to be obtained. For example, the presence of the target can be determined by detecting the magnetic response signal from the plurality of superparamagnetic particles in the subsurface formation, and vice-versa (e.g., if the magnetic response signal of the plurality of superparamagnetic particles is not detected in the subsurface formation, the target is not present in the subsurface formation). In some examples, the methods can comprise determining the particular kinds of reservoir rocks, the distribution of the reservoir rocks, or particular kinds of reservoir fluids in the subsurface formation based on the magnetic response signal detected from the subsurface formation, the plurality of superparamagnetic particles, or combinations thereof.

As discussed above, in some examples the plurality of superparamagnetic particles can comprise a first population and a second population, wherein the first population selectively adsorbs to a first target and the second population selectively adsorbs to a second target. In these examples, the first population and the second population can have different average maximum dimensions (e.g., the plurality of superparamagnetic particles can be bi-disperse) and/or be formed from different materials, such that the two populations can be distinguished by their respective magnetic response signals (e.g., by their respective Langevin curve characteristics). In these examples, the presence, location, distribution, evolution, or combinations thereof of multiple targets can be obtained (e.g., the presence, location, distribution, etc. of multiple types of reservoir rocks, multiple fluids, or combinations thereof). In other words, in some examples of the methods and systems disclosed herein, the plurality of superparamagnetic particles employed can comprise differently sized

superparamagnetic nanoparticles, which have different Langevin curve characteristics, with different surface coatings so that each type of superparamagnetic nanoparticle can be targeted for attachment to different kinds of rock or fluid. The resolution of the individual concentrations of the different SPMNPs (e.g., the different populations within the plurality of superparamagnetic particles) can be carried out. In some examples, the systems and methods disclosed herein can be used for the identification of reservoir zones that have the combined characteristics of two or more target properties (e.g., sandstone with oil, shale with oil). For example, consider two populations of SPMNPs, each of which have a different average maximum dimension (e.g., a different size) and thus which have different Langevin curve characteristics, and with different surface coatings, so that each population is preferentially attached to a different target rock or target fluid (e.g., one population will selectively adsorb to sandstone rock and the other population will selective adsorb to the oil/water interface). When the method is carried out, if the magnetization response shows significant signals from both kinds of SPMNPs, it means that both targets are present (e.g., the reservoir zone has oil-containing sandstones).

superparamagnetic sample and reference sample are placed.

In some examples, the methods can further comprise detecting a magnetic response signal from the superparamagnetic sample and the reference sample. In some examples, the magnetic response signal of the superparamagnetic sample can comprise a magnetic response signal from the plurality of superparamagnetic particles. The methods, in some examples, can further comprise processing the magnetic response signal to obtain a property of the subsurface formation. In some examples, the magnetic response signal of the reference sample is processed to obtain a reference property. In some examples, the magnetic response signal from the superparamagnetic sample is processed to obtain a sample property of the subsurface formation. In some examples, processing the magnetic response signal comprises comparing the reference property to the sample property of the subsurface formation. In some examples, processing the magnetic response signal can comprise removing at least a portion of the magnetic noise from the magnetic response signal. In some examples, the methods can comprise determining a property of a subsurface formation via differential sensing of the sample and the reference sample.

The property of the subsurface formation can, in some examples, comprise a solid content, hydrocarbon content, contaminant content, or combinations thereof. In some examples, the property of the subsurface formation can comprise the presence of a target reservoir rock, a target fluid (e.g., a hydrocarbon fluid), or combinations thereof.

Also disclosed herein are methods of determining a property of a subsurface formation comprising drilling a wellbore in the subsurface formation, thereby forming a drill cutting. The drill cutting can, for example, comprise at least a portion of the subsurface formation. In some examples, a drilling fluid is used concurrent with at least a portion of said drilling. In some examples, the methods can further comprise adding a superparamagnetic tracer fluid comprising a plurality of superparamagnetic particles to the drilling fluid. In some examples, the concentration of the plurality of superparamagnetic particles in the superparamagnetic tracer fluid can be 0.00001% by weight or more (e.g., from 0.00001% by weight to 20% by weight).

In some examples, the methods can further comprise removing the drill cutting from the wellbore; applying a variable magnetic field to the drill cutting; detecting a magnetic response signal from the drill cutting, the plurality of superparamagnetic nanoparticles, or combinations thereof; and processing the magnetic response signal to obtain a property of the subsurface formation. In some examples, the variable magnetic field can be supplied by a mud logging tool or a magnetic coil through which the drill cutting passes.

In some examples, the applying and detecting steps can occur before the adding step, and the magnetic response signal of the drill cutting can be processed to obtain a reference property of the subsurface formation. In some examples, the applying and detecting steps can occur after the adding step, and the magnetic response signal of the plurality of superparamagnetic particles can be processed to obtain a sample property of the subsurface formation. In some examples, the applying, detecting, and processing steps can be performed at several time points to determine a change in the property of the subsurface formation over time.

In some examples, the reference property and the sample property can be obtained concurrently, for example using the differential sensing system described herein below.

In some examples, the applying and detecting steps can occur before the adding step, and the magnetic response signal of the drill cutting can be processed to obtain a reference property of the subsurface formation; the applying and detecting steps can be repeated after the adding step, and the magnetic response signal of the plurality of superparamagnetic particles can be processed to obtain a sample property of the subsurface formation; and processing the magnetic response signal can comprise comparing the reference property of the subsurface formation to the sample property of the subsurface formation.

In some examples, processing the magnetic response signal can comprise removing at least a portion of the magnetic noise from the magnetic response signal.

In some examples, processing the magnetic response signal comprises determining the concentration, spatial resolution, penetration depth, or combinations thereof, of the plurality of superparamagnetic particles in the drill cutting.

In some examples, the property of the subsurface formation comprises the presence of a target reservoir rock, a target fluid, or combinations thereof. Examples of target fluids include, but are not limited to, hydrocarbon fluids (e.g., oil, natural gas, etc.), water, and combinations thereof (e.g., oil/water interfaces).

As discussed in detail above, in some examples the plurality of superparamagnetic particles can be adapted to selectively adsorb to a target (e.g., a target rock, a target fluid, etc.) in the subsurface formation, for example through a coating (e.g., a polymer coating). As the drill cuttings can comprise at least a portion of the subsurface formation, in some examples at least a portion of the drill cuttings can comprise at least a portion of the target, which can have at least a portion of the plurality of superparamagnetic particles adsorbed thereon. As such, the presence of the target can be determined by detecting the magnetic response signal from the plurality of superparamagnetic particles in the drill cuttings (e.g., if the magnetic response signal of the plurality of superparamagnetic particles is not detected in the drill cuttings, the target is not present in the subsurface formation). In some examples, the methods can comprise determining the particular kinds of reservoir rocks, the distribution of the reservoir rocks, or particular kinds of reservoir fluids in the subsurface formation based on the magnetic response signal detected from the drill cuttings, the plurality of superparamagnetic particles, or combinations thereof.

Advantages of the methods described herein can include, for example: (1) The local concentrations of the superparamagnetic tracer fluids can be directly measured, because the magnetization response of the plurality of superparamagnetic particles comes directly from the plurality of superparamagnetic particles and is proportional to the mass/volume of the plurality of superparamagnetic particles in the measurement volume. (2) The concentrations of the different populations of superparamagnetic particles with different Langevin curves can be distinguished within a plurality of superparamagnetic particles comprising a mixture of different populations. (3) By modulation of the amplitude of the applied variable magnetic field, the resolution of the detected magnetic response signal can be refined.

The methods disclosed herein can be carried out in whole or in part on one or more processing devices. For example, one or more of the applying, detecting, and processing steps can be carried out in whole or in part on one or more processing devices. Figure 9 illustrates a suitable processing device upon which the methods disclosed herein may be implemented. The processing device 600 can include a bus or other communication mechanism for communicating information among various components of the processing device 600. In its most basic configuration, a processing device 600 typically includes at least one processing unit 612 (a processor) and system memory 614. Depending on the exact configuration and type of processing device, the system memory 614 may be volatile (such as random access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in Figure 9 by a dashed line 610. The processing unit 612 may be a standard programmable processor that performs arithmetic and logic operations necessary for operation of the processing device 600.

The processing device 600 can have additional features/functionality. For example, the processing device 600 may include additional storage such as removable storage 616 and nonremovable storage 618 including, but not limited to, magnetic or optical disks or tapes. The processing device 600 can also contain network connection(s) 624 that allow the device to communicate with other devices. The processing device 600 can also have input device(s) 622 such as a keyboard, mouse, touch screen, antenna or other systems configured to communicate with the camera in the system described above, etc. Output device(s) 620 such as a display, speakers, printer, etc. may also be included. The additional devices can be connected to the bus in order to facilitate communication of data among the components of the processing device 600.

The processing unit 612 can be configured to execute program code encoded in tangible, computer-readable media. Computer-readable media refers to any media that is capable of providing data that causes the processing device 600 (i.e., a machine) to operate in a particular fashion. Various computer-readable media can be utilized to provide instructions to the processing unit 612 for execution. Common forms of computer-readable media include, for example, magnetic media, optical media, physical media, memory chips or cartridges, a carrier wave, or any other medium from which a computer can read. Example computer-readable media can include, but is not limited to, volatile media, non-volatile media and transmission media.

Volatile and non- volatile media can be implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data and common forms are discussed in detail below. Transmission media can include coaxial cables, copper wires and/or fiber optic cables, as well as acoustic or light waves, such as those generated during radio-wave and infra-red data communication. Example tangible, computer- readable recording media include, but are not limited to, an integrated circuit (e.g., field- programmable gate array or application-specific IC), a hard disk, an optical disk, a magneto- optical disk, a floppy disk, a magnetic tape, a holographic storage medium, a solid-state device, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices.

For example, the processing unit 612 can execute program code stored in the system memory 614. For example, the bus can carry data to the system memory 614, from which the processing unit 612 receives and executes instructions. The data received by the system memory 614 can optionally be stored on the removable storage 616 or the non-removable storage 618 before or after execution by the processing unit 612.

The processing device 600 typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by device 600 and includes both volatile and non- volatile media, removable and non-removable media. Computer storage media include volatile and non-volatile, and removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. System memory 614, removable storage 616, and non-removable storage 618 are all examples of computer storage media. Computer storage media include, but are not limited to, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by processing device 600. Any such computer storage media can be part of processing device 600.

It should be understood that the various techniques described herein can be implemented in connection with hardware or software or, where appropriate, with combinations thereof. Thus, the methods, systems, and associated signal processing of the presently disclosed subject matter, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a processing device, the machine becomes an apparatus for practicing the presently disclosed subject matter. In the case of program code execution on programmable computers, the processing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. One or more programs can implement or utilize the processes described in connection with the presently disclosed subject matter, e.g., through the use of an application programming interface, reusable controls, or the like. Such programs can be implemented in a high level procedural or object-oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language can be a compiled or interpreted language and it may be combined with hardware implementations.

Systems

Also disclosed herein are systems, for example systems for carrying out the methods disclosed herein. For example, disclosed herein are coil systems for generating the applied variable magnetic field and/or detecting the magnetic response signal.

In some examples, the coil system that can generate the variable (e.g., oscillatory) magnetic field and detect (e.g., measure) the magnetic response signal (e.g., the magnetization response) from the plurality of superparamagnetic particles in the subsurface formation (e.g., reservoir rock) can be designed in two different configurations, e.g., the "sample-in-coils" mode and the "coils-in-sample" mode, depending on the applications as described above. For the "coils-in-sample" mode, the coil system is placed within the subsurface formation (e.g., via a wellbore within the subsurface formation) and the variable magnetic field generated (e.g., applied) can be emitted out of the coil and penetrate into the surrounding subsurface formation. For the "sample-in-coils" mode, two concentric circular coil configuration can be used where the sample is removed from the subsurface formation and inserted inside of the magnetic-field generating coil and the response-measuring coil to obtain a property of the sample.

In some examples, the MPI methods can be adapted as a logging tool, for example a system/tool that can be inserted into a wellbore (e.g., as schematically shown in Figure 10), and obtain a property of the subsurface formation (e.g., image the near- wellbore rock formation) with use of the superparamagnetic tracer fluids (e.g., comprising SPMNPs). Figure 10 is a schematic diagram of an example "coils-in-sample" magnetization-response measurement set-up or MPI detection apparatus 100. In the "coils-in-sample" set-up 100, the magnetic-field generating coil 102 and the response-measuring coil 104 are inserted inside of a hole (e.g., a well or wellbore) present in the formation (e.g., reservoir rock). The MPI detection apparatus 100 can be a useful tool for oil exploration and production. Compared to NMR logging (whose depth of penetration can be shallow) and other available logging tools, the advantages of the MPI detection apparatus 100 can include, for example,: (a) increased depth of penetration (e.g., increased probe depth); (b) better spatial resolution; (c) real-time processing potential; and (d) delivery of the superparamagnetic tracer fluids to the target sites for improved resolution of the target (e.g., an image or map of the target with improved resolution).

Rectangular-shaped generator and receiver coils can be effective for the "coils-in- sample" configurations (Li H et al. Paper IEEE-978-1-4673-1591-3/12, 2012). Referring now to Figure 1 1, the "coils-in-sample" magnetization-response measurement set-up or apparatus in the wellbore 508 can comprise a generator coil 500 (which can be a rectangular-shaped generator) and receiver or response measuring coils 502. The generator coil 500 can project the applied magnetic field, denoted by the applied magnetic field lines 504, into the surrounding sample volume 506 (e.g., the subsurface formation). The magnetic field lines 504 generated from the generator coil 500 can induce (e.g., excite) a magnetic response from the surrounding sample volume 506 (e.g., from the subsurface formation, the plurality of superparamagnetic particles, or combinations thereof), which can be detected (e.g., recorded) by the response-measuring coil 502.

The optimum coil configuration for a particular application (e.g., that allows the maximum penetration of the magnetic field into the surrounding reservoir rock) can be determined by carrying out magnetic field simulations using a commercial software such as COMSOL, for Maxwell equations solution.

If the wellbore radius is large, multiple sets or an array of such coil systems can be deployed inside of the well, so that the angular distributions of the SPMNP concentrations at the near-wellbore reservoir zone can be measured, for example as shown in Figure 12. Figure 12 is a schematic diagram of an array of rectangular-shaped generator and receiver coils 510 inside of the wellbore 508 in a reservoir rock 506 (e.g., subsurface formation), which can measure the spatial distribution of the SPMNP concentration at the near-wellbore reservoir zone. For such localized deployment, the coil configuration proposed by Sattel et al. (Sattel TF et al. J. Phys. D: Appl. Phys., 42, 022001-022005, 2009) for single-sided MPI imaging can be employed.

The MPI method can be employed for similar purposes to measure the magnetization response from the SPMNPs in rock and/or fluid samples that are passed through the MPI set-up or apparatus, as shown in Figure 13. Figure 13 is a schematic diagram of an example "sample-in- coils" magnetization-response measurement set-up or MPI detection apparatus 200, in which a sample 202 (e.g., drill cuttings, SPMNP-containing rock or fluid samples, etc.) can be inserted inside of the magnetic-field generating coil by a conveyor belt 204 and the response-measuring coil. In some examples, the MPI detection apparatus 200 can further comprise an AC current source with a single frequency 206. In some examples, the MPI detection apparatus 200 can further comprise a band-pass filter 208 and/or a spectrum analyzer 210. For example, the magnetic response signal can be detected by the response-measuring coil, and then sent through the band-pass filter 208 to the spectrum analyzer 210. The spectrum analyzer 210 is shown with displayed graphs that show signature spectrums 212a and 212b.

MPI detection apparatus 200 can be used, for example, for a non-invasive, continuous and automatic method that can determine the presence of oil or other specified kinds of rock from a continuous stream of drill cuttings that can be removed from the subsurface formation during drilling operations. Such drill cuttings are often removed from the subsurface formation during drilling and simply disposed. Other exemplary "samples-in-coils" systems are shown, for example in Figure 7 and Figure 14. Referring now to Figure 14, a coil system 700 used to measure the concentration of SPMNPs either dispersed in a liquid or inside of rock pores within the sample 702. The coil system 700 is shown on a non- magnetic base 704.

Also described herein are systems comprising a differential transformer and methods of using said system for differential sensing (e.g., as described above). Exemplary differential transformers are shown, for example, in Figure 25 and Figure 26. The differential transformer configuration has two pick-up coils. The excitation and differential pick-up coils are connected in such a way that the output voltage Vout(t) induced by the primary coil and induced in the pickup coils subtracts one pick-up coil from the other. When a sample is placed inside one of the pick-up coils and a control sample is placed in the other, the resultant output can substantially reduce the common-mode noise that is induced by the primary frequency in both coils, and the signature of the sample can be obtained. Some advantages of the differential sensing system can include: (1) the noise floor of the pick-up signal can be reduced; (2) increased sensitivity and a higher signal-to-noise ratio (SNR); (3) lower concentration samples can be measured; (4) the concentration and SPMNP determination is not fixed to one or one set of reading(s); and (5) the SPMNP concentration can be determined by varying the amplitude of the excitation current in the primary coil, and examining the slope of different harmonics in the pick-up signal.

The examples below are intended to further illustrate certain aspects of the systems and methods described herein, and are not intended to limit the scope of the claims.

EXAMPLES

The following examples are set forth below to illustrate the methods and results according to the disclosed subject matter. These examples are not intended to be inclusive of all aspects of the subject matter disclosed herein, but rather to illustrate representative methods and results. These examples are not intended to exclude equivalents and variations of the present invention which are apparent to one skilled in the art.

Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.) but some errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, temperature is in °C or is at ambient temperature, and pressure is at or near atmospheric. There are numerous variations and combinations of measurement conditions, e.g., component concentrations, temperatures, pressures and other measurement ranges and conditions that can be used to optimize the described process.

Example 1

Described herein are methods for measuring a desired rock or fluid property along a well in a reservoir zone. Such detection can be achieved, for example, using the methods described herein above. In some examples, the methods can comprise the following steps: (Step la). Obtaining or preparing SPMNPs whose surface coating is such that, among various constituents of the near-wellbore formation, the SPMNPs preferentially and selectively adsorb to the surface of a particular kind of the rock or fluid constituent, e.g., at the oil/water interface of the oil in the rock pores. (Step lb). Injecting a pre-designed volume of an aqueous dispersion of the SPMNPs into the reservoir formation at the well, so that they flow into all layers of the reservoir and adsorb on the surface of the desired kind of the rock or fluid constituent. (Step lc). Flushing the well back, so that any SPMNPs not attached to the target locations are removed from the reservoir layers and the wellbore. (Step Id). Lowering an MPI detection apparatus, for example MPI apparatus 100 as schematically shown in Figure 10, into the well, and obtaining and recording the magnetization response data from the near-wellbore formations along the well using an MPI detection apparatus. (Step le). Obtaining the spatial distributions of the adsorbed SPMNPs (e.g., vertical distribution for a vertical well, and horizontal distribution for a horizontal well), and thereby the concentration or density of the desired reservoir formation constituent based on the MPI method described above. A graphic description of the above steps (Steps la- le) is shown in Figure 15. (Step If). If desired, the effluent concentration of the removed SPMNPs can be measured as a function of the flush-back volume, in the manner of the single- well tracer test, again employing the MPI method, non-invasively, continuously and

automatically. For this purpose, the flush-back effluent is flowed through the magnetization- measurement coils, in a manner similar to Figure 13. From the analysis of the effluent curve, the amount and retention characteristics of the SPMNPs still remaining in the near-wellbore formation can be further confirmed.

Figures 15 is a graphic representation of one embodiment of measuring the desired rock or fluid property 300 along the well 302 in the near-wellbore reservoir zone 304. Figure 15a shows that a small bank or slug of the surface-coated SPMNPs 306 is injected 308 into a well so that the particles go into all layers of the near-wellbore zone 304. The SPMNPs 306 selectively adsorb at the target locations such as the water/oil interfaces of the oil in the reservoir. Figure 15b shows the SPMNPs 306 that are not attached and still freely floating are flushed out; the "coils-in-sample" magnetization-response measurement tool or MPI detection apparatus 310 is lowered into the well; and the SPMNP 306 distribution (and accordingly that of the target constituent) along the well is determined.

Example 2

Described herein are methods for measuring the injection allocation of enhanced oil recovery (EOR) chemicals in different layers of a reservoir. When the chemicals for EOR processes, such as the polymer solutions, polymer gels or microemulsions formed from surfactants, are injected into the reservoir, it can be important to understand what portion of the injected chemical is going into which layer of the reservoir. Even when the "layer-by-layer" distributions of the porosity and permeability are known from other conventional logging measurements, the determination of the chemical's injection allocation can be difficult to obtain, for example due to the non-Newtonian rheology of those chemicals.

Such detection can be achieved, for example, using the methods described herein above.

In some examples, the methods can comprise the following steps: (Step 2a). Adding a low concentration of the SPMNPs that is detectable by the MPI apparatus into a small bank of the injectant formulation at the beginning of the chemical injection, so that the SPMNPs can flow into all reservoir layers in proportions according to their porosity and permeability variations. (Step 2b). Lowering the magnetization-response measurement coil(s), for example as schematically shown in Figure 10, into the well, and obtaining and recording the magnetization response data from the near-wellbore formations along the well. (Step 2c). Obtaining the spatial distributions of the injected SPMNPs (e.g., vertical distribution for a vertical well, and horizontal distribution for a horizontal well), and thereby the injection allocation of the EOR chemical into each reservoir layer, based on the MPI method described above.

Example 3

Described herein are methods for detecting the presence and distribution of natural and induced fractures in a subsurface formation, for example at the near-wellbore reservoir zone. Such detection can be achieved, for example, using the methods described herein above. In some examples, the methods for determining the distribution of natural and hydraulically induced fractures at the near-wellbore reservoir zones can comprise the following steps: (Step 3a).

Adding a low concentration of the SPMNPs that is detectable by the MPI detection apparatus, into a small bank of the injection fluid, such as water for waterflooding, so that the SPMNPs flow into the reservoir in proportions according to their porosity and permeability variations. When a fracture intersects the well, a substantial portion of the SPMNPs will go into the fracture. (Step 3b). Lowering the magnetization-response measurement coils, for example as schematically shown in Figure 10, into the well, and obtaining and recording the magnetization response data from the near-wellbore formations along the well. (Step 3c). Obtaining the spatial distributions of the injected SPMNPs (e.g., vertical distribution for a vertical well, and horizontal distribution for a horizontal well), and thereby the presence and distribution of the fractures at the near-wellbore zone, based on the MPI method described above. Figure 16 is a graphic description of this embodiment of detecting the presence and distribution of the fractures along the well in the near-wellbore reservoir zone 304. A small bank of the SPMNPs is injected into a well so that the particles go mainly into the fractures 312 of the near-wellbore zone 304; and the "coils-in-sample" magnetization- response measurement tool or MPI detection apparatus 310 is lowered into the well 302, and the SPMNP distribution (and accordingly that of the fractures) along the well 302 and/or in the fractures 312 is determined.

Example 4

Described herein are methods for detecting the presence of a specific rock or fluid constituent from the continuous stream of washed drill cuttings. When an oil well is drilled, the first tangible and definitive data on the nature of the rock and fluids in the subsurface formation can be obtained from the rock cuttings that come out of the well being drilled, carried by the drilling fluid. By retrieving and sampling the cuttings samples from the different depth of formation that had just been drilled through, and by analyzing the rock mineralogy and oil and gas content and composition, the properties of the particular subsurface layer can be concretely determined, either confirming or forcing to revise the earlier geophysical and geological information that was used for the drilling decision. In particular, the cuttings analysis can be employed to detect the first evidence of the presence of oil in the reservoir. Typically, the cuttings samples are transported to the on-site or main laboratory for microscope examination of the rock pieces, and chromatographic and other chemical analyses of the gas and oil contents and compositions. Sample taking and mineralogical and chemical analyses can be labor intensive and time consuming (Karimi M. SPE 165919, SPE Asia Pacific Oil & Gas Conf, Jakarta, Indonesia, Oct. 22-24, 2013).

The methods described herein can allow a non-invasive, continuous and automatic way of determining the presence of oil or other certain kinds of rock from the continuous stream of the cuttings that are removed from the drilling fluid for disposal. Such detection can be achieved, for example, using the methods described herein above. In some examples, the methods can comprise the following steps: (Step 4a). Obtaining or preparing SPMNPs whose surface coating is such that, among various constituents of drilled rock cuttings, the SPMNPs preferentially and selectively adsorb to the surface of a particular kind of rock cuttings, such as the shale pieces that have a high organic content. (Step 4b). Adding the SPMNPs into the drilling fluid so that, when the cuttings are generated and carried along by the drilling fluid, the SPMNPs adsorb on the surface of the desired kind of rock pieces. (Step 4c). Removing the cuttings from the drilling fluid and washing the cutting for the subsequent disposal, thereby removing any SPMNPs that had not been attached to the particular kind of rock pieces in the drill cuttings. (Step 4d).

Continuously moving/transporting the washed cuttings, e.g., on a conveyor belt, through the magnetization-measurement coils, for example as schematically shown in Figure 13, thereby continuously obtaining and recording the magnetization response data. (Step 4e). Obtaining the concentration of the SPMNPs that are adsorbed on the particular kind of rock cuttings when they go through the coils, based on the MPI method described above. A graphic description of this embodiment is shown in Figure 17.

Figure 17 is the graphic description of an embodiment for detecting the presence of the desired rock or fluid constituent from a continuous stream of washed drill cuttings 400. The surface-coated SPMNPs 402 are added into the drilling fluid so that, when the cuttings 404 are generated by the drill bit 406 and carried along by the drilling fluid 412 cut from the reservoir rock 410, the SPMNPs adsorb on the surface of selected kind of rock fragments 414; when the cuttings are removed from the drilling fluid 416 and washed for the subsequent disposal, any SPMNPs that had not been attached to the particular kind of rock fragments 414 are removed; and the washed cuttings that are continuously moved, e.g., on a conveyor belt 418, are then made to go through the inside of the magnetization-measurement coils or MPI detection apparatus 420, which continuously obtains and records the magnetization response data, and measures the SPMNP distribution.

In some examples, these methods can be used to determine the permeability distribution during or right after the drilling operation. When the SPMNPs are added to the drilling fluid for their selective adsorption to particular kinds of rock cuttings, some of the SPMNPs can penetrate into the near-wellbore formations as they (e.g., the SPMNPs) flow up to the well head, together with the drilling fluid, through the annular gap between the drill stem and the formation face. Since the extent of the penetration of the SPMNPs will vary with the permeability of the formation layers, the measurement of the SPMNP concentrations along the well (e.g., as described in Examples 1-3), can allow for the determination of the permeability distribution during or right after the drilling operation. Such a procedure can be accomplished without any additional use of tracers, and such measurements will also complement the drilling cuttings measurement described above. Example 5

One example application of the continuous drill cuttings analysis, as described above as Example 4, is the selective adsorption of the SPMNPs on the surface of shale cuttings but not on the surface of sand grains. Such a distinction can help automatically identify the presence of shale layers and sand or sandstone layers while the drilling progresses. To investigate this embodiment, the adsorption of SPMNPs on the surfaces of shale samples from the shale fields of Texas was measured and compared with the adsorption on the surface of sample sand grains.

Commercially available SPMNPs, namely EMG-605 (146 g/L of total Fe as received) and EMG-700 (243 g/L of total Fe as received), were purchased from Ferrotec, Germany. The surfaces of the SPMNPs in the EMG-605 and EMG-700 samples were coated with cationic and anionic surfactants, respectively, and suspended in water. Shales used as adsorbent solids were from the Barnette Shale (BS) and Eagle Ford Shale (EFS) fields, both of Texas. Crushed samples from two different cores from each shale were used for the adsorption tests: identified as Core no. 5 (BS-5) and 6 (BS-6) for BS; and as 120,000 ft (EFS-120) and 8600 ft (EFS-86) cores for EFS. The shale size used for the adsorption test ranged from 1000 to 1800 μιη. Sand (S) was also tested as an adsorbent, the size of which was > 65 μιη, to compare its SPMNP adsorption capacity to those on the BS and EFS samples. The inductively coupled plasma optical emission spectrometer (ICP) employed to determine the SPMNP concentrations only measures the amount of Fe. Therefore, both the amount of adsorbed SPMNP and the concentration of SPMNP in the dispersion are expressed in terms of Fe only, for consistency.

The Fe concentrations of the stock solutions of EMG-605 and EMG-700 used for the adsorption test were 1462 mg/L and 2431 mg/L, respectively. A designated volume of stock solution was added to a 14 mL glass vial containing about 0.025 g of adsorbents (BS, EFS, and S) for the EMG-605 tests and 0.05 g adsorbents for the EMG-700 tests. The glass vial was then filled with de-ionized water to a final volume of 10 mL. The mixtures were placed on an orbital shaker and mixed for 5 days at 150 rpm. After 5-day adsorption period, a certain volume of solution was added to another 14 mL glass vial, then, 4% FTN03 was added to make up 10 mL of total volume. The 4% FTN03 (trace metal grade, Fisher chemical) was added to dissolve the SPMNPs for iron analysis. Ten mL of 4% FTN03 was also added to the reactors used for adsorption tests to dissolve any iron adsorbed on the wall of the reactor vial. The acid-iron mixtures were placed on the shaker and mixed for 24 hours at 150 rpm. The dissolved iron was analyzed using ICP (Varian 710-ES). The Fe detection limit was 0.2 μg/L at a wavelength of 283.204 nm. The Fe standard calibration curve ranged from 0.1 to 10 mg/L. Adsorption capacity can be calculated using Equation 5:

^adsorbed ^τ(^Ό~ ^5θίη) j-^j

^adsorbent ^adsorbent

where q = adsorption capacity (mg/g); Madsorbed = Fe mass adsorbed on adsorbent (mg); Madsorbent = adsorbent (shale or sand) mass added (g); VT = total volume in the adsorption reactor (10 mL); Co = initial Fe concentration added (mg/L); and Csoin = Fe concentration remained in solution after 5-day adsorption period (mg/L).

Figure 18 shows the equilibrium relation between the amount of SPMNP adsorbed on the Barnette shale No. 6 sample and the SPMNP concentration in water (e.g., the SPMNP adsorption isotherm). Figure 19 shows the adsorption capacity of different types of shale rocks at two different initial SPMNP concentrations for EMG-605. Figure 20 shows the adsorption capacity of different types of shale rocks at two different initial SPMNP concentrations for EMG-700. The adsorption capacity for sand was virtually zero, and is therefore not shown in Figure 19 or Figure 20.

The SPMNP adsorption data shown by Figures 18-20 demonstrate the targeted attachment of SPMNP, with a proper surface coating, to the shale surfaces.

Example 6

As electrical power is frequently used to operate drilling and production hardware and logging and other monitoring tools, the downhole environment for the magnetization response measurement could be quite noisy magnetically. To demonstrate the advantage of the MPI method to be able to extract the unique response spectrum from a noisy measurement environment, a simulated time-domain response signal was generated by combining a response signal from a SPMNP with a high-level noise, for a blind test. Figure 21 displays the noisy time- domain signal and Figure 22 displays the corresponding noisy frequency-domain spectrum. Without the noise, the response spectrum includes only a series of discrete frequencies (e.g.,/o, 3fo, 5fo. . ., where fo is the excitation frequency). Because the unique frequency spectrum from the SPMNP used is known a priori, other than its overall magnitude, the noise can be removed by filtering. Figure 23 is the filtered frequency-domain spectrum with model-based digital filter, and Figure 24 is the reconstructed time-domain response signal.

The magnetization noise can also arise from the iron-containing minerals in the reservoir rock. Because the magnetization responses from those minerals will be ferromagnetic with a coercivity in general, and not superparamagnetic, noise from such sources can also be filtered. Furthermore, their magnetization response will be in general small compared to the signal from the superparamagnetic nanoparticles.

Example 7

Also described herein are systems comprising a differential transformer and methods for differential sensing.

Based on the setup described by Biederer S et al. (Biederer S et al. J. Phys. D: Appl Phys, 42, 205007, 2009), an excitation solenoid coil was chosen that can generate about a 30 mT magnetic field. The pick-up coil was designed with a varying diameter on a 3D spool that fits into the excitation coil. This coil setup was excited using a waveform generator and an amplifier system. The output of the pick-up coils was measured using a lock- in amplifier which can phase lock to a harmonic and the corresponding data can be read. The noise floor of this system is in mV and therefore it was possible to detect response signals for samples having a concentration of SPMNPs of 1000 ppm and above effectively. The response signals for samples with lower SPMNPs were masked by noise. To reduce the noise for such low concentration samples, differential sensing was utilized (e.g., utilizing a differential transformer).

The differential transformer configuration has two pick-up coils. The excitation and differential pick-up coils are connected in such a way that the output voltage Vout(t) induced by the primary coil and induced in the pick-up coils subtracts one pick-up coil from the other. If vi(t) and V2(t) are the voltage outputs of the two coils, the output voltage of the differential coil is described by Equation 6.

Vout(t = v^t - v₂ (t) [6]

When a sample is placed inside one of the pick-up coils and a control sample is placed in the other, the resultant output can substantially reduce the common-mode noise that is induced by the primary frequency in both coils, and the signature of the sample can be obtained (Figure 25).

The pick-up coils for the differential sensing were wound on a 3D printed spool (Figure 25) such that it fitted into the excitation coil. The only difference in the setup was that output from both the pick-up coils were connected to the lock-in amplifier such that differential sensing was performed (Figure 26).

Various commercially available and laboratory made SPMNP samples were used for measurements at varying concentrations. Commercially available SPMNPs, namely EMG-605 and EMG-705 purchased from Ferrotec, Germany, and are denoted as MNP1 and MNP3, respectively. A laboratory made sample, denoted as MNP2, was made from hydrated iron chloride dissolved in water. The sample was treated with citric acid monohydrate and ammonium hydroxide to make iron oxide magnetic nanoparticles. These iron oxide magnetic nanoparticles were amine functionalized and treated with polyacrylic acid (molecular weight, 8000 Daltons) to create magnetic nanoparticles with suitable coatings (Wang Q et al. IPTC- 17901-MS, Kaula Lumpur, Malaysia, 2014).

The MNP1 and MNP2 samples, at varying concentrations, were investigated using the differential scanning setup, using DI water as the control. The voltage detected by the lock-in amplifier for odd harmonics (3-19) are shown in Figure 27 for MNP1 and Figure 28 for MNP2. The data from Figure 27 and Figure 28 are combined in Figure 29, where it can be seen that the slope of the amplitude-harmonics data can be used to distinguish the two samples, MNP1 and MNP2, from one another.

Furthermore, the strength of the excitation magnetic field can also affect the slope of the amplitude-harmonics data, for example as shown in Figure 30 for the MNP3 sample at a single concentration.

Based on the results discussed above, some advantages of the differential sensing system can include: (1) the noise floor of the pick-up signal is reduced; (2) increased sensitivity and a higher signal-to-noise ratio (SNR); (3) lower concentration samples can be measured; (4) the concentration and SPMNP determination is not fixed to one or one set of reading(s); and (5) the SPMNP concentration can be determined by varying the amplitude of the excitation current in the primary coil, and examining the slope of different harmonics in the pick-up signal.

Other advantages which are obvious and which are inherent to the invention will be evident to one skilled in the art. It will be understood that certain features and sub-combinations are of utility and may be employed without reference to other features and sub-combinations.

This is contemplated by and is within the scope of the claims. Since many possible embodiments may be made of the invention without departing from the scope thereof, it is to be understood that all matter herein set forth or shown in the accompanying drawings is to be interpreted as illustrative and not in a limiting sense.

The methods of the appended claims are not limited in scope by the specific methods described herein, which are intended as illustrations of a few aspects of the claims and any methods that are functionally equivalent are intended to fall within the scope of the claims. Various modifications of the methods in addition to those shown and described herein are intended to fall within the scope of the appended claims. Further, while only certain

representative method steps disclosed herein are specifically described, other combinations of the method steps also are intended to fall within the scope of the appended claims, even if not specifically recited. Thus, a combination of steps, elements, components, or constituents may be explicitly mentioned herein or less, however, other combinations of steps, elements, components, and constituents are included, even though not explicitly stated. The term

"comprising" and variations thereof as used herein is used synonymously with the term

"including" and variations thereof and are open, non-limiting terms. Although the terms "comprising" and "including" have been used herein to describe various embodiments, the terms "consisting essentially of and "consisting of can be used in place of "comprising" and

"including" to provide for more specific embodiments of the invention and are also disclosed. Other than in the examples, or where otherwise noted, all numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification and claims are to be understood at the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, to be construed in light of the number of significant digits and ordinary rounding approaches.

As used in the description and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a composition" includes mixtures of two or more such compositions, reference to "an agent" includes mixtures of two or more such agents, reference to "the component" includes mixtures of two or more such components, and the like.

"Optional" or "optionally" means that the subsequently described event or circumstance can or cannot occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.

Ranges can be expressed herein as from "about" one particular value, and/or to "about" another particular value. By "about" is meant within 5% of the value, e.g., within 4, 3, 2, or 1% of the value. When such a range is expressed, another aspect includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as

approximations, by use of the antecedent "about," it will be understood that the particular value forms another aspect. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.

It is understood that throughout this specification the identifiers "first" and "second" are used solely to aid in distinguishing the various components and steps of the disclosed subject matter. The identifiers "first" and "second" are not intended to imply any particular order, amount, preference, or importance to the components or steps modified by these terms.

The term "or combinations thereof as used herein refers to all permutations and combinations of the listed items preceding the term. For example, "A, B, C, or combinations thereof is intended to include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB.

Continuing with this example, expressly included are combinations that contain repeats of one or more item or term, such as BB, AAA, AB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context.

Claims

CLAIMS What is claimed is:

1. A method of determining a property of a subsurface formation comprising:

injecting a superparamagnetic tracer fluid comprising a plurality of

superparamagnetic particles into the subsurface formation;

applying a variable magnetic field to the subsurface formation;

detecting a magnetic response signal from the subsurface formation, the plurality of superparamagnetic particles, or combinations thereof; and

processing the magnetic response signal to obtain a property of the subsurface

formation.

2. The method of claim 1, wherein the applying and detecting steps occur before the

injecting step, and the magnetic response signal of the subsurface formation is processed to obtain a reference property of the subsurface formation.

3. The method of claim 1 or claim 2, wherein the applying and detecting steps occur after the injecting step, and the magnetic response signal of the plurality of superparamagnetic particles is processed to obtain a sample property of the subsurface formation.

4. The method of claim 3, wherein processing the magnetic response signal comprises comparing the reference property of the subsurface formation to the sample property of the subsurface formation.

5. The method of any one of claims 1-4, wherein the applying, detecting, and processing steps are performed at several time points to determine a change in the property of the subsurface formation over time.

6. The method of any one of claims 1-5, wherein the superparamagnetic tracer fluid is injected into the subsurface formation from a wellbore and the variable magnetic field is applied to the subsurface formation from the same wellbore.

7. The method of any one of claims 1-6, wherein the superparamagnetic tracer fluid is injected into the subsurface formation from a wellbore and the variable magnetic field is applied from a different wellbore.

8. The method of any one of claims 1-7, wherein the variable magnetic field applied to the subsurface formation is supplied by a logging tool that is inserted into the subsurface formation.

9. The method of any one of claims 1-8, wherein processing the magnetic response signal comprises determining the concentration, spatial resolution, penetration depth, or combinations thereof, of the plurality of superparamagnetic particles in the subsurface formation.

10. The method of any one of claims 1-9, wherein the property of the subsurface formation comprises porosity, solid content, water content, fluid content, fluid composition, hydrocarbon location, hydrocarbon content, contaminant location, contaminant content, permeability, or combinations thereof.

11. The method of any one of claims 1-10, wherein the property of the subsurface formation comprises presence, location, distribution, evolution, or combinations thereof of a target reservoir rock, a target fluid, an oil/water interface, a fracture, or combinations thereof.

12. The method of any one of claims 1-1 1, wherein processing the magnetic response signal comprises mapping the presence, location, distribution, evolution, or combinations thereof of a target reservoir rock, a target fluid, an oil/water interface, a fracture, or combinations thereof.

13. The method of claim 1 1 or 12, wherein the target fluid comprises a hydrocarbon fluid.

14. A method of determining a property of a subsurface formation comprising:

obtaining a sample comprising a portion of a subsurface formation and a reference sample;

contacting a superparamagnetic tracer fluid comprising a plurality of

superparamagnetic particles with the sample, thereby forming a

superparamagnetic sample;

applying a variable magnetic field to the superparamagnetic sample and the reference sample;

detecting a magnetic response signal from the superparamagnetic sample and the reference sample; and

processing the magnetic response signal to obtain a property of the subsurface

formation.

15. The method of claim 14, wherein the magnetic response signal of the reference sample is processed to obtain a reference property.

16. The method of claim 14 or claim 15, wherein the magnetic response signal of the superparamagnetic sample comprises a magnetic response signal from the plurality of superparamagnetic particles.

17. The method of any of claims 14-16, wherein the magnetic response signal from the

superparamagnetic sample is processed to obtain a sample property of the subsurface formation.

18. The method of claim 17, wherein processing the magnetic response signal comprises comparing the reference property to the sample property of the subsurface formation.

19. The method of any of claims 14-18, wherein the reference sample comprises at least a portion of the subsurface formation.

20. The method of any of claims 14-19, wherein the sample, the reference sample, or

combinations thereof comprise(s) a drill cutting.

21. The method of any one of claims 14-20, wherein the variable magnetic field is supplied by a mud logging tool or a magnetic coil inside of which the superparamagnetic sample and reference sample are placed.

22. The method of any one of claims 14-21, wherein processing the magnetic response signal comprises determining the concentration, spatial resolution, penetration depth, or combinations thereof, of the plurality of superparamagnetic particles in the

superparamagnetic sample.

23. The method of any one of claims 14-22, wherein the property of the subsurface formation comprises solid content, hydrocarbon content, contaminant content, or combinations thereof.

24. The method of any one of claims 14-23, wherein the property of the subsurface formation comprises the presence of a target reservoir rock, a target fluid, or combinations thereof.

25. The method of claim 24, wherein the target fluid comprises a hydrocarbon fluid.

26. The method of any one of claims 1-25, wherein the superparamagnetic tracer fluid

further comprises a liquid carrier fluid.

27. The method of claim 26, wherein the liquid carrier fluid comprises a spacer fluid, a

drilling fluid, a cementing fluid, a fracturing fluid, a mud fluid, a synthetic fluid, an aqueous solution, water, or combinations thereof.

28. The method of any one of claims 1-27, wherein the concentration of the plurality of superparamagnetic particles in the superparamagnetic tracer fluid is 0.00001% by weight or more.

29. The method of any one of claims 1-28, wherein the concentration of the plurality of superparamagnetic particles in the superparamagnetic tracer fluid is from 0.00001% by weight to 20% by weight.

30. A method of determining a property of a subsurface formation comprising:

drilling a wellbore in the subsurface formation thereby forming a drill cutting,

wherein a drilling fluid is used concurrent with at least a portion of said drilling; adding a superparamagnetic tracer fluid comprising a plurality of superparamagnetic particles to the drilling fluid;

removing the drill cutting from the wellbore;

applying a variable magnetic field to the drill cutting;

detecting a magnetic response signal from the drill cutting, the plurality of superparamagnetic particles, or combinations thereof; and

processing the magnetic response signal to obtain a property of the subsurface

formation.

31. The method of claim 30, wherein the applying and detecting steps occur before the

adding step, and the magnetic response signal of the drill cutting is processed to obtain a reference property of the subsurface formation.

32. The method of claim 30 or claim 31 , wherein the applying and detecting steps occur after the adding step, and the magnetic response signal of the plurality of superparamagnetic particles is processed to obtain a sample property of the subsurface formation.

33. The method of claim 32, wherein processing the magnetic response signal comprises comparing the reference property of the subsurface formation to the sample property of the subsurface formation.

34. The method of any one of claims 30-33, wherein the applying, detecting, and processing steps are performed at several time points to determine a change in the property of the subsurface formation over time.

35. The method of any one of claims 30-34, wherein the concentration of the plurality of superparamagnetic particles in the drilling fluid is 0.00001% by weight or more.

36. The method of any one of claims 30-35, wherein the concentration of the plurality of superparamagnetic particles in the drilling fluid is from 0.00001% by weight to 20% by weight.

37. The method of any one of claims 30-36, wherein the variable magnetic field is supplied by a mud logging tool or a magnetic coil through which the drill cutting passes.

38. The method of any one of claims 30-37, wherein processing the magnetic response signal comprises determining the concentration, spatial resolution, penetration depth, or combinations thereof, of the plurality of superparamagnetic particles in the drill cutting.

39. The method of any one of claims 30-38, wherein the property of the subsurface formation comprises solid content, hydrocarbon content, contaminant content, or combinations thereof.

40. The method of any one of claims 30-39, wherein the property of the subsurface formation comprises the presence of a target reservoir rock, a target fluid, or combinations thereof.

41. The method of claim 40, wherein the target fluid comprises a hydrocarbon fluid.

42. The method of any one of claims 1-41, wherein the magnetic response signal comprises a magnetization response.

43. The method of any one of claims 1 -42, wherein the magnetic response signal comprises a non-linear magnetization response of the plurality of superparamagnetic particles.

44. The method of any one of claims 1-43, wherein processing the magnetic response signal comprises accounting for the superparamagnetic particles' Langevin magnetization behavior.

45. The method of any one of claims 1-44, wherein processing the magnetic response signal comprises removing at least a portion of the magnetic noise from the magnetic response signal.

46. The method of any one of claims 1-45, wherein detecting the magnetic response

comprises detecting a voltage induced in a receiver coil.

47. The method of any one of claims 1 -46, wherein the plurality of superparamagnetic

particles comprise iron, cobalt, zinc, nickel, manganese, silver, gold, carbon, cadmium, or combinations thereof.

48. The method of any one of claims 1 -47, wherein the plurality of superparamagnetic particles comprise an oxide of iron, cobalt, zinc, nickel, manganese, or combinations thereof.

49. The method of any one of claims 1-48, wherein the plurality of superparamagnetic

particles comprise an iron oxide.

50. The method of any one of claims 1 -49, wherein the plurality of superparamagnetic

particles comprise Fe30₄.

51. The method of any one of claims 1 -50, wherein the plurality of superparamagnetic

particles have an average maximum dimension of 3 nm or more.

52. The method of any one of claims 1-51, wherein the plurality of superparamagnetic

particles have an average maximum dimension of from 1 nm to 200 nm.

53. The method of any one of claims 1 -52, wherein the plurality of superparamagnetic

particles is adapted to withstand downhole conditions.

54. The method of any one of claims 1-53, wherein each of the plurality of

superparamagnetic particles further comprises a coating.

55. The method of claim 54, wherein the coating comprises a polymer.

56. The method of claims 54 or claim 55, wherein the coating selectively adsorbs to a target in the subsurface formation.

57. The method of any one of claims 1 -56, wherein the plurality of superparamagnetic

particles is mono-disperse, bi-disperse, tri-disperse, tetra-disperse, or multi-disperse.

58. The method of any one of claims 1-57, wherein the variable magnetic field is generated using an electric coil, an electromagnet, or combinations thereof.

59. The method of any one of claims 1-58, wherein the variable magnetic field is sinusoidal.

60. The method of any one of claims 1-59, wherein the variable magnetic field has a

frequency of from 0.001 Hz to 10 MHz.

61. The method of any one of claims 1-60, wherein the variable magnetic field has a strength of 1 μΤ or more. The method of any one of claims 1-61, wherein the variable magnetic field has a strength of from 1 μΤ to 100 T.

The method of any one of claims 1-62, wherein the subsurface formation is adjacent to a well, an aquifer, a mineral deposit, a contamination site, or combinations thereof.