Patent Description:
Reservoir quality is largely controlled by reservoir parameters such as porosity, hydrocarbon saturation, permeability, minerology and thermal maturity. Generally, high vertical resolution log measurements and core analysis can provide accurate reservoir parameters for assessing reservoir quality in vertical wells. Similar technologies can be extended to characterize reservoir parameters for assessing reservoir quality in highly-deviated production wells for unconventional plays, including the conveyance of traditional wireline tools, measurements behind the bit, and measurements collected by passing the tool through the bit. However, the length and conditions of these highly-deviated wells makes these measurements challenging and not economically viable, resulting in most lateral wells not being logged or logged only with gamma ray (GR) tools. Instead, multiple vertical wells are placed across the extent of the basin and formation properties, such as bed boundaries, pinch-out points, porosity, mineralogy, and organic-matter content, are determined and assumed to be linearly uniform. Depending on the unconventional play, this indirect method of reservoir characterization may fail to capture the variation in porosity and fluid types, insoluble hydrocarbon (kerogen) concentrations, minerology, and clay-type and clay-volumes that impact reservoir quality laterally. Without a record of the well obtained by logs, little information is available in the event of poor production of a lateral well after drilling and completion.

Completion quality is an engineering assessment of factors that determine the effectiveness of stimulation treatments (particularly hydraulic fracture treatments) in unconventional reservoirs, and includes the ability to initiate and create an induced fracture network, the degree of reservoir contact of the newly created fractures, the level of connection to the natural fracture system of those created fractures and ability of the stimulated reservoir to deliver gas or oil into the well. Important inputs to the derivation of completion quality include the rock's mineralogy, porosity, mechanical properties, compressive strength and tensile strength. In addition, the presence and state of natural fractures, the in-situ stresses and the formation pore pressure can also be provided as inputs to the derivation of completion quality.

Nuclear Magnetic Resonance ("NMR") relaxometry has been gaining ground as a reliable approach to core analysis due to its capabilities to characterize fluids in reservoir rocks and measure them quantitatively. The NMR relaxometry measurements yield fluid types and wettability non-destructively and in a relatively quick fashion. These aid in assessing reservoir quality and reserve estimates and in core-log correlations. The porosities derived from the NMR log have been shown to compare favorably to the measured core porosities and to represent the potentially producible fluid fractions and/or the fluids fractions contained within the rock sample. NMR laboratory measurements are typically made at frequencies similar to the NMR logging tools (~<NUM>), on <NUM>" x <NUM>" rock core samples that are trimmed and surface ground to fit in the NMR probe, resulting in a high filling factor of the radio frequency (RF) probe to achieve optimum signal-to-noise ratios (SNR). The <NUM>H (proton) NMR measurements are mainly used to obtain the volumes of the fluids in the pores (pore volume). When combined with the bulk volumes measured using calipers on regular shaped cores, porosity can be calculated.

Although the value of NMR measurements of unconventional shale rocks has been shown on cores and logs in vertical pilot wells, the absence of routine logs and reliable core measurements from horizontal wells make it challenging to obtain a more complete understanding of unconventional reservoirs.

<CIT> describe a method including estimating the weight fractions of kerogen and inorganic mineral components of at least an interval of a subsurface formation, determining the grain density of kerogen and inorganic mineral components, wherein at least the grain density of kerogen is determined by one or more infrared measurements and calculating the formation matrix density of at least an interval of the subsurface formation from the estimated weight fractions and the determined grain density. <CIT> describes a method for analyzing a formation samples including performing an NMR measurement of the formation sample to obtain NMR data, the NMR measurement detecting NMR signals with echo times of less than or equal to <NUM> microseconds. The NMR data is analyzed to determine a measure of organic hydrogen content of the formation sample, such as (i) total organic hydrogen content, (ii) kerogen content, (iii) bitumen content, and/or (iv) oil content. <CIT> describes a method for determining a mineralogy composition of a formation of interest using core samples or downhole measurements. A dry permittivity is determined for each identified mineral and a volumetric mixing law is employed using the determined mineralogy composition and the determined dry permitivities. An effective matrix permittivity is determined using results from the volumetric mixing law. Dielectric dispersion measurements of the subject formation are acquired using the core samples or the downhole measurements and a dielectric petrophysical model is produced using the dielectric dispersion measurements and the effective matrix permittivity. A water saturation is estimated based on the dielectric petrophysical model. Nuclear magnetic resonance (NMR) T2 measurements having short echo spacings are acquired and a NMR petrophysical model is generated based on the NMR T2 measurements and is used to determine a total porosity.

The present invention resides in a method for characterizing properties of a rock sample obtained from a subterranean formation as defined in claim <NUM>. Preferred embodiments are defined in claims <NUM> to <NUM>.

The discussion below is directed to certain implementations and/or embodiments. It is to be understood that the discussion below may be used for the purpose of enabling a person with ordinary skill in the art to make and use any subject matter defined now or later by the patent "claims" found in any issued patent herein.

It is specifically intended that the claimed combinations of features not be limited to the implementations and illustrations contained herein but include modified forms of those implementations including portions of the implementations and combinations of elements of different implementations as come within the scope of the following claims. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Nothing in this application is considered critical or essential to the claimed invention unless explicitly indicated as being "critical" or "essential.

These terms may be used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the disclosure. The first object or step, and the second object or step, are both objects or steps, respectively, but they are not to be considered a same object or step.

The term "reservoir quality" or "RQ" is defined by a number of petrophysical and hydrocarbon properties (e.g., porosity, permeability, total organic content or TOC, total inorganic content or TIC, maturation, hydrocarbon content and type, gas sorption mechanisms) that define reservoir potential. The term "completion quality" or "CQ" depends on the poromechanical properties of the field and reservoir, which means the conditions that are favorable to the creation, propagation and containment of hydraulic fractures, as well as the placement of proppant and retention of fracture conductivity. It depends mainly on the intrinsic geomechanics properties, i.e., in situ stress field, pore pressure, material properties (elastic, yield or quasi-brittle failure, hardness, rock-fluid sensitivity), their anisotropic nature and their spatial heterogeneities, as well as the presence of discontinuities (such as natural fractures or geological layering) and the orientation of the well.

Further, as a well is being drilled, the rock that is being drilled is cut or otherwise fragmented by the drill bit into small pieces, called "cuttings", that are removed from the bulk of the formation via drilling fluid. The cuttings are carried to the surface by the drilling fluid and can be screened out of the drilling fluid and collected without interfering with the drilling operations. The cuttings are representative of the reservoir rock - although they have been altered by the drilling process, they still provide an understanding of the properties of the reservoir rock. This is often referred to as "mud logging" or "cuttings evaluation. " For effective logging or evaluation as described below, in some embodiments, the cuttings are prepared by removing residual drilling fluids.

The term "unconventional" is used to refer to a formation where the source and reservoir are the same, and stimulation (such as hydraulic fracturing) is required to create production. The term "source" implies that the formation contains appreciable amounts of organic matter, which through maturation or biological processes has generated hydrocarbons (gas or oil, as in Barnett and Eagle Ford, respectively). The term "reservoir" signifies that the hydrocarbons have not been able to escape and are trapped in the same space (or very near) where they were generated. Unconventional formations can have extremely low permeabilities, (mainly in the order of nanodarcies), which explains why stimulation is needed.

The terms "bitumen" and "kerogen" are non-mobile, organic parts of shales. "Bitumen" is defined as the fraction that is soluble in a solvent (typically a polar solvent such as chloroform or a polarizable solvent such as benzene). "Kerogen" is defined as the fraction that is insoluble in the solvent.

The term "rock core" is a reservoir rock sample collected with a special tool that extracts large samples with little exposure to drilling fluids.

The embodiments described herein relate to the field of geomechanics and its application to the oil and gas industry. Geomechanics is an integrated domain linking in situ physical measurements of rock mechanical properties via wellbore logging or wellbore drilling, in situ hydraulic measurements of in situ pore pressure and stress field, surface laboratory measurements on cores to engineering practices for drilling, fracturing and reservoir purposes via the construction of integrated earth models, and modeling tools and workflows.

In embodiments described herein, an NMR measurement can be performed on cuttings. The results of the NMR relaxometry measurements can be used to characterize properties of the cuttings, such as pore volume, pore fluid saturations and pore fluid type. The NMR measurement is performed on the cuttings without cleaning the cuttings with a solvent that removes organic components (such as oil-based components that originate from an oil-based drilling fluid or additives).

Furthermore, the pore volume obtained from the NMR measurement performed on the cuttings is combined with rock properties obtained from a spectroscopy measurement (such as diffuse reflectance infrared Fourier-transform spectroscopy or DRIFTS measurement) performed on the cuttings to characterize porosity of the cuttings (and thus porosity of the drilled formation rock from which they originate) and possibly other useful reservoir parameters.

In examples that do not form part of the present invention, the cuttings can be cleaned. To clean the cuttings for the spectroscopy measurement, the cuttings can be immersed or otherwise exposed to a solvent that removes soluble organic components, such as oil-based drilling fluids and additives, oil and bitumen. After such cleaning, the cuttings can be dried. Thereafter, insoluble organic components (i.e., kerogen) can remain in the cuttings with little or no soluble organic components. Thus, information regarding the inorganic mineral components and the insoluble organic components (i.e., kerogen) of the cuttings remain, while information regarding the soluble organic components and porosity of the cuttings is lost. To accommodate for the loss of such information, the workflow combines the pore volumes obtained from the NMR measurement with rock properties obtained from the spectroscopy measurement to characterize porosity of the cuttings and possibly other useful reservoir parameters.

Note that porosity measurements from cuttings require a reliable measurement of bulk volume. Typically, bulk volume is determined by measuring the sample mass and grain density (which can be measured by helium pycnometer or calculated from mineralogy). But these additional measurements require time-consuming sample cleaning procedures making them improbable for wellsite applications. In embodiments, the spectroscopy measurement performed on the cuttings can be used to determine bulk volume.

The results of the workflow can be provided quickly and efficiently. Furthermore, the results can provide a valuable source of information on the geology of the formation and reservoir quality, specifically providing an accurate indication of reservoir quality of lateral wells in an otherwise data poor environment. Furthermore, the cuttings can be correlated with depth in the wellbore and can help with understanding formation stratigraphy and finding pay zones. Furthermore, the results can provide a quantitative measure of porosity as well as the different fractions (namely kerogen, bitumen and liquid hydrocarbon, free water, and bound water) that occupy the pore space of the reservoir rock, which can be very useful to understand the reservoir and design and optimize completion of the well that traverses the reservoir.

<FIG> illustrates a wellsite system in which the disclosed methods and systems can be employed. The wellsite system can be onshore or offshore. In this exemplary system, a wellbore <NUM> is formed in a subsurface formation <NUM> by directional rotary drilling in a manner that is well known. A drill string <NUM> is suspended within the wellbore <NUM> and has a bottom hole assembly <NUM> which includes a drill bit <NUM> at its lower end. The surface system includes platform and derrick assembly <NUM> positioned over the wellbore <NUM>, the assembly <NUM> including a rotary table <NUM>, kelly <NUM>, hook <NUM> and rotary swivel <NUM>. The drill string <NUM> is rotated by the rotary table <NUM>, energized by means not shown, which engages the kelly <NUM> at the upper end of the drill string <NUM>. The drill string <NUM> is suspended from a hook <NUM>, attached to a traveling block (also not shown), through the kelly <NUM> and a rotary swivel <NUM> which permits rotation of the drill string <NUM> relative to the hook <NUM>. As is well known, a top drive system could alternatively be used.

In the example of this embodiment, the surface system further includes drilling fluid or mud <NUM> stored in a pit <NUM> formed at the wellsite. A pump <NUM> delivers the drilling fluid <NUM> to the interior of the drill string <NUM> via a port in the swivel <NUM>, causing the drilling fluid to flow downwardly through the drill string <NUM> as indicated by the directional arrow <NUM>. The drilling fluid exits the drill string <NUM> via ports in the drill bit <NUM>, and then circulates upwardly through the annulus region between the outside of the drill string and the wall of the wellbore <NUM>, as indicated by the directional arrows <NUM>. In this well-known manner, the drilling fluid lubricates the drill bit <NUM> and carries cuttings up to the surface as the drilling fluid is returned to the pit <NUM> for recirculation.

As is known in the art, sensors may be provided about the wellsite to collect data, preferably in real time, concerning the operation of the wellsite, as well as conditions at the wellsite. For example, such surface sensors may be provided to measure parameters such as standpipe pressure, hook load, depth, surface torque, rotary rpm, among others.

The bottom hole assembly <NUM> can include sensors or modules (such as one or more logging-while drilling modules or one-more more measurement-while-drilling (MWD) modules) and a rotary steerable system that controls the drilling direction of the drill bit <NUM>. For example, one or more LWD modules of the bottom-hole assembly <NUM> can include capabilities for measuring and storing directional electromagnetic response data that is sensitive to resistivity profile of the formation in the vicinity of the bottom hole assembly <NUM>, and one or more MWD modules can include capabilities for measuring, processing, and storing information that characterizes a position and direction of the drill string <NUM> and the drill bit <NUM> as well as other drilling measurements, such as a weight-on-bit, torque, and shock and/or vibration. As used herein, the term "module" as applied to LWD and MWD devices is understood to mean either a single tool or a suite of multiple tools contained in a single modular device.

The bottom hole assembly <NUM> can also include a downhole telemetry subsystem that communicates data signals and control signals between the components of the bottom hole assembly <NUM> and a surface-located logging and control unit <NUM>. The downhole telemetry subsystem can employ a variety of telemetry methods, such as wired telemetry methods (e.g., drill pipe that incorporate telemetry cables or fiber optic cables) and wireless telemetry method (e.g., mud-pulse telemetry methods, electromagnetic telemetry methods, and acoustic telemetry methods). The downhole telemetry subsystem can also supply electrical power supply signals generated by a surface-located power source for supply to the components of the bottom hole assembly <NUM>. The bottom hole assembly <NUM> can also include a power supply transformer/regulator for transforming the electric power supply signals supplied by the surface-located power source to appropriate levels suitable for use by the components of the bottom hole assembly <NUM>. In alternate embodiments, the bottom hole assembly <NUM> can include an apparatus for generating electrical power for supply to the components of the bottom hole assembly <NUM>, such as a mud turbine generator powered by the flow of the drilling fluid. Other power and/or battery systems may be employed.

The surface-located logging and control unit <NUM> (and possibly other computer systems remotely coupled thereto via a data communication network) can cooperate with the rotary steerable system of the bottom hole assembly <NUM> to provide geo-steering control of the drilling direction of the drill bit <NUM>. As shown in <FIG>, the wellbore <NUM> has been directionally drilled to enter an unconventional formation reservoir <NUM> disposed between an upper formation layer <NUM> and a lower formation layer <NUM> in the formation <NUM>. The planned wellbore trajectory is shown by arrow <NUM>.

Rock samples <NUM> from the reservoir <NUM> (such as cuttings collected from the drilling fluid <NUM> that returns from the wellbore <NUM>) can be collected and transported to a surface-located analysis facility <NUM>. Note that the analysis facility <NUM> can be located at the wellsite or it can be located remotely from the wellsite. The analysis facility <NUM> includes an NMR apparatus <NUM>, a spectrometer <NUM>, one or more central processing units <NUM>, storage system <NUM>, a user display <NUM> and a user input system <NUM>. The storage system <NUM> can be in the form of magnetic storage, such as a hard disk, and/or in the form of solid-state memory such as flash memory but is not limited to these two. The NMR apparatus <NUM> can be configured to conduct NMR measurements on the rock samples <NUM> and the spectrometer can be configured to conduct spectroscopy measurements on the rock samples <NUM>. The results of these measurements can be used to characterize properties of the rock samples as described herein.

<FIG> shows a schematic diagram of an exemplary NMR system <NUM> that can be part of the analysis facility <NUM> and configured to conduct NMR measurements on rock samples (e.g., cuttings). The NMR system <NUM> includes a permanent magnet having spaced-apart magnetic pole pieces <NUM>, <NUM>, spacers (e.g., pillars) <NUM> separating the magnetic pole pieces <NUM>, <NUM>, and an RF coil <NUM> which is configured to receive a sample holder <NUM> that contains a rock sample. The arrow <NUM> shows the direction of the magnetic field, B0. Connectors <NUM> provide for electrical connection of the RF coil <NUM> to control circuitry as described below.

The NMR system <NUM> further includes an RF coil controller <NUM> for generating and delivering RF excitation pulses to the RF coil <NUM> for transmission into the space occupied by the rock sample as part of the NMR measurements, and a signal receiver <NUM> for receiving an NMR signal detected by the RF coil <NUM> as part of such NMR measurements. The NMR system <NUM> further includes a data collector/analyzer <NUM> for receiving data from the signal receiver <NUM> and data storage <NUM>. The signal receiver <NUM> generates a signal or data which represents the NMR signal detected by the RF coil <NUM> and supplies such signal to the data collector/analyzer <NUM> for processing.

The NMR measurements can use specially designed data acquisition schemes (called NMR pulse sequences) which describe the timings of transmission and reception of electromagnetic signals. The NMR pulse sequence for the measurement of a T<NUM> relaxation time distribution is called the CPMG echo train and is shown in <FIG>. The CPMG echo train includes an initial idle time or wait time to allow the nuclei in the fluids contained in the rock sample to come to equilibrium with the magnetic field induced by the permanent magnet of the NMR system. Thereafter, a series of radio-frequency pulses are applied to the space occupied by the rock sample using the RF coil <NUM>. The time between the adjacent <NUM>-degree RF pulses is the echo spacing, TE. The initial wait time is often long enough to fully polarize the system. Midway between the <NUM>-degree RF pulses, NMR signals called echoes are detected by the RF coil <NUM>. The amplitude of the echoes decay or attenuate with time. The data collector/analyzer <NUM> (or some other data processor) can be configured to obtain a T<NUM> distribution by fitting the echo amplitudes to a multi-exponential model as follows.

In such an experiment, a train of echo signal is acquired. The signal amplitude, S, is measured as a function of the echo time, techo, which is the time of the echo from the beginning of the first <NUM>-degree pulse and given by: <MAT> where n is the number of echo, and TE is the echo spacing or time between two adjacent <NUM>-degree pulses.

The signal amplitude S(techo) at a given echo time techo then follows an exponential decay form given by: <MAT> for a rock sample with a single T<NUM> component.

For many rock samples where a number of different T<NUM> components are present, the signal amplitude S(techo) at a given echo time techo is a sum of all T<NUM> components, which is given by an integral over a range of T<NUM> values as follows: <MAT> where f(T<NUM>) is the T<NUM> distribution function.

Inversion processing can be used to solve for the T<NUM> distribution function f(T<NUM>) that fits the signal amplitude S(techo) measured for the echo times. The T<NUM> values of the T<NUM> distribution function f(T<NUM>) less than a T<NUM>cutoff (corresponding to pores filled with bound water) can be integrated to provide data describing pore volume of the sample. This pore volume corresponds to the pore space of the rock sample that holds producible fluids (e.g., free water, mud filtrates, oil, and gas).

In other embodiments, other types of NMR analysis can be used to determine pore volume. For example, the NMR analysis can solve for a distribution function of spin-lattice T<NUM> relaxation times, and the pore volume can be determined from such distribution function. In another example, diffusion-edited NMR pulse sequences can be used to solve for distribution functions of T<NUM> or T2 relaxation times for different fluid components, and the pore volumes for the different fluid components can be determined from such distribution functions. In this case, the pore volume of the sample can be determined by adding together the pore volumes for the different fluid components.

The 2D T<NUM>T<NUM> NMR measurements can be used to identify and separate bitumen and clay bound water, fluids in organic and inorganic porosity, and free fluids. At higher fields, kerogen can be identified due to the differences in T1 and therefore NMR provides a nondestructive method for fluid typing in rocks.

<FIG> shows a schematic diagram of an exemplary spectrometer <NUM> that can be part of the analysis facility <NUM> and configured to conduct spectroscopy measurements on rock samples (e.g., cuttings). The spectrometer <NUM> employs the general mechanism of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). DRIFTS responds to the modes of molecular vibrations of the components of the rock sample. In a non-limiting example, DRIFTS provides an easy and rapid measurement of cleaned cuttings, which can be analyzed to characterize the inorganic mineral components and kerogen of the cleaned cuttings. As depicted in <FIG>, source (<NUM>) is a source of infrared radiation. Source <NUM> emits a beam of infrared radiation <NUM> that is directed to parabolic or spherical mirror <NUM>, which in turn directs the beam to the rock sample <NUM> (e.g., cuttings). The incident infrared radiation is specularly reflected, diffusely reflected, or transmitted through part of the sample <NUM>. The reflected infrared radiation is captured by the parabolic or spherical mirror <NUM> and directed to the detector <NUM>. The detector <NUM> collects and measures spectrum data that represents the diffusely reflected infrared radiation as output from the rock sample. The measured spectrum data represents the intensity of the diffusely reflected infrared radiation as output from the rock sample as a function of wavenumber (or wavelength). Each fundamental molecular vibration of the rock sample corresponds to a specific absorbance band in wavelength. The data collector/analyzer <NUM> performs a Fourier transformation on the measured spectrum data. The transformed spectrum data can be analyzed to quantity the mass fractions of mineral components of the rock sample (cleaned cuttings), the mass fraction of the kerogen in the rock sample (cleaned cuttings), and the matrix density of the rock sample (cleaned cuttings). Details of exemplary spectroscopy measurements and analysis that provide such data is set forth in co-owned <CIT>, entitled "Reservoir and completion quality assessment in unconventional (shale gas) wells without logs or core" and co-owned <CIT>, entitled "Method and apparatus for simultaneous estimation of quantitative mineralogy, kerogen content and maturity in gas shale and oil-bearing shale"; both filed on April <NUM>, <NUM>.

According to the invention, atrix density of the cuttings is calculated as the sum of the mass fractions of the mineral components and organic-matter (kerogen) determined from the spectroscopy measurement divided by their known densities. For example, the matrix density calculation that accounts for both mineral components and kerogen in the solid matrix can be given as: <MAT> where ρma is the matrix density, Mi is the mass fraction of the given mineral component i, ρgi is the density of the given mineral component i, Mker is the mass fraction of kerogen, and ρker is the density of kerogen. The integral is performed over the mineral components of the cuttings.

Table <NUM> below provides known densities for kerogen, pyrite, and nine mineral components.

Note that the most abundant sedimentary rock-forming minerals have relatively similar densities between <NUM> and <NUM>/cm<NUM>, with a notable exception of pyrite with a density of <NUM>/cm<NUM>. Note that pyrite is not measured directly by DRIFTS spectroscopy but is correlated to TOC using the ratio of organic carbon and sulfur from pyrite in anoxic marine sediments where C/S = <NUM>. The density of kerogen is related to its thermal maturity and falls within the range between <NUM>/cm<NUM> (waxy) and <NUM>/cm<NUM> (graphitic).

<FIG> is a flowchart of an illustrative workflow not forming part of the present invention, that uses an NMR apparatus (for example, the NMR apparatus <NUM> of <FIG>) and a spectrometer (for example, the DRIFT spectrometer <NUM> of <FIG>) of the analysis facility to characterize rock properties of cuttings obtained from a formation (for example, cuttings obtained when drilling the formation <NUM> of <FIG>).

In block <NUM>, cuttings from the formation are collected (or otherwise obtained) and then sorted and split into two lots designated Lot A and lot B for the sake of description. The cuttings of Lot A can be selected by size such that they are appropriate for the NMR measurement as described in blocks <NUM> to <NUM> below. The cuttings of lot A are processed and subjected to the NMR measurement as described in blocks <NUM> to <NUM> below. The cuttings of Lot B are processed and subjected to a spectroscopy measurement as described in blocks <NUM> to <NUM> below. Result data obtained from the two measurements is combined and used to characterize rock properties of the formation in block <NUM> as described below.

In block <NUM>, the cuttings of lot A are saturated with a fluid if not saturated already. The fluid can be a wetting fluid such as water or heavy water that is suitable for hydrogen proton NMR analysis. Importantly, the cuttings of Lot A are not cleaned or dried to remove soluble organic components from the cuttings as is done for the Lot B cuttings as described below.

In block <NUM>, excess fluid can be wiped off the saturated cuttings of Lot A, and the resulting lot A cuttings can be weighed.

In block <NUM>, the NMR apparatus (e.g., the NMR apparatus <NUM> of <FIG>) is configured and operated to perform an NMR measurement on the resulting lot A cuttings. The NMR measurements can be carried out at the Larmor frequency of <NUM> or possibly higher for measuring <NUM>H nuclei. In block <NUM>, a data processor (e.g., the data collector/analyzer <NUM> of <FIG>) is configured and operated to perform analysis of the resulting data of the NMR measurement of <NUM> to determine data that characterizes properties of the lot A cuttings (such as pore volume, pore fluid saturation, fluid type). Details of exemplary NMR measurements and analysis that can be used to determine properties of the Lot A cuttings (such as pore volume, pore fluid saturation, fluid type) are described above.

In block <NUM>, the cuttings of Lot B can be crushed (for example, into small size fragments that are approximately <NUM> microns in size or less) and cleaned and dried to remove certain organic components (other than kerogen) from the cuttings.

In block <NUM>, the spectrometer (e.g., the spectrometer <NUM> of <FIG>) is configured to perform a spectroscopy measurement (e.g., DRIFTS) on the crushed and cleaned lot B cuttings that result from <NUM>. In block <NUM>, a data processor (e.g., the data collector/analyzer <NUM> of <FIG>) is configured and operated to perform analysis of the resultant spectrum data of the spectroscopy measurement of <NUM> to determine data that characterizes properties of the lot B cuttings (such as mass fractions of inorganic mineral components, mass fraction of kerogen, and matrix density). Details of exemplary spectroscopy measurements and analysis that can be used to determine properties of the Lot B cuttings (such as mass fractions of inorganic mineral components, mass fraction of kerogen, and matrix density) are described above.

In block <NUM>, a data processor (such as CPU <NUM>) is configured to process the data of <NUM> together with the data of <NUM> to determine data that characterizes rock properties of the cuttings.

For example, data representing the bulk volume (Vbulk) of the cuttings can be calculated from the pore volume data of <NUM> and the matrix density data of <NUM> as follows: <MAT> where ms is the mass (in grams) of the lot A cuttings measured in <NUM>, Vpore is the fluid pore volume measured by NMR in <NUM>, ρfluid is the fluid density of the fluid that saturates the lot A cuttings, and ρma is the matrix density measured by spectroscopy in <NUM>.

The liquid density ρfluid can be based on liquid densities obtained from density measurements completed on separate fluid samples (associated with the same cuttings) using established techniques or based on model estimates for the liquid densities using composition and established density models from petroleum thermodynamics.

For example, the fluid that saturates the lot A cuttings can contain multiple components (or compounds) and the liquid density ρfluid of such fluid may be calculated using a number of well-established methods by one skilled in the art, including mixing rules, density correlations, corresponding states, and equation of state (with or without volume translation). For example, the density of an ideal mixture of hydrocarbon compounds (i.e. no volume or enthalpy change upon mixing of the compounds) can be determined using a simple mixing rule: <MAT> where ρi is the compound density, φi is the volume fraction of compound i in the mixture and wi is the weight fraction of compound i in the mixture. However, most hydrocarbon mixtures are non-ideal, requiring the use of well-established density calculation methods that account for excess volume effects.

In another example, data representing porosity (φ) of the cuttings can be calculated from the pore volume data of <NUM> and the resulting bulk volume data as follows: <MAT> where Vpore is the fluid pore volume measured by NMR in <NUM>, and Vbulk is the bulk volume given by Eqn.

The workflow determines porosity and other properties of the cuttings analyzed by both NMR and IR spectroscopy techniques. These properties are relevant to RQ and CQ. The cuttings can also be analyzed to estimate other quantities relevant to RQ and CQ, including thermal maturity, kerogen content, mineralogy, and surface area. Note that other IR spectroscopy or other forms of spectroscopy can be used in place of DRIFTS in this workflow for the property measurements, including but not limited to x-ray diffraction, x-ray fluorescence, and Raman spectroscopy, or a measurement of matrix density, such as with a pycnometer.

Historically, limited information has been collected by FTIR or visual inspection of formation solids under a microscope, especially cuttings with residual drilling mud solvents. More involved analysis has not been selected because of the accuracy, time and cost for equipment and low likelihood of return of useful information. Moreover, the presence of drilling fluid in the cuttings can distort the FTIR analysis as shown in the plot of <FIG>. Because of these issues, cuttings (such as the lot B cuttings in block <NUM>) can be cleaned by any method to remove soluble hydrocarbons including solvent treatments or heat treatments prior to the FTIR analysis. Some exemplary embodiments are described below.

Historically preparation of cuttings samples often involves collecting material from a shale shaker, additional sorting via a small hand-held sieve, rinsing the material with the drilling fluid base oil, and then exposing the material to hexane. The hexane and other volatile organic material are baked out of the sample in an oven at <NUM>. Soap and water may also be used to remove residual base oil.

Examples that do not form part of the present invention, can employ a cleaning procedure designed to prepare shale cuttings drilled with oil-based drilling fluid for spectroscopy analysis (for example by DRIFTS or other FTIR methods), gas sorption analysis as well as other measurements. With these specifications, the goals of the cleaning procedure are as follows:.

A detailed cleaning procedure for preparing shale cuttings drilled with oil-based drilling fluid is set forth below. The procedure can be carried out at a wellsite or in the laboratory. <FIG> is a flow chart that illustrates the cleaning procedure, which is summarized below.

One option for step <NUM> is to clean the cuttings over a vacuum filter. A vacuum filter is a standard piece of equipment in a chemistry laboratory. It involves a fritted piece of glassware, with a filter membrane resting on it. Because the cuttings have been crushed to <NUM> microns, a filter membrane with a smaller pore size is required (sieves are not an option here because sieves with openings below <NUM> microns are not available). An example filter membrane that is readily available is a <NUM>-micron polycarbonate filter membrane. Below the frit is a volume evacuated by a pump. The cuttings are placed on top of the filter membrane at atmospheric pressure, solvent is added and the vacuum on the other side of the frit forces the solvent to flow through the cuttings. This process efficiently removes residual drilling fluid from the cuttings because of the small particle size. Pentane is the optimum choice of solvent for the same reasons as above. Alternative solvents listed above may be selected for this step. <FIG> lists a step labeled "Method G" which uses a pentane rinse over a <NUM>-micron membrane which is simple, cheap, and takes approximately <NUM> minutes per sample.

Another option for step <NUM> is to clean the cuttings at elevated temperature and pressure. Cleaning at elevated temperature and pressure can be achieved in an instrument such as the SPEED EXTRACTOR™ manufactured by Buchi of Newcastle, DE, which lowers the viscosity, allowing the solvent to invade the particles quickly: high temperature also increases the solvating power, allowing the diesel to be dissolved more easily: high pressure forces the solvent into the cuttings more quickly: high pressure also allows the temperature to be increased beyond the atmospheric-pressure boiling point without vaporizing, allowing further increases in temperature. Combined with the small particle size, this technique cleans the cuttings quickly and effectively. However, this technique is more likely to remove bitumen. If removing bitumen from the cuttings is a goal, this step could be performed with powerful solvents such as toluene that will remove bitumen from the cuttings even more effectively. Example operating conditions include using toluene as a solvent, at <NUM> temperature and <NUM> bar pressure for approximately <NUM> minutes. This technique can be handled in an automated way, requiring only a few minutes of operator time. Taking advantage of the automation, a quick final rinse with a volatile solvent such as pentane can be applied after the toluene rinse to accelerate evaporation. Another advantage of this technique is that these conditions can dissolve drilling fluid additives that are not dissolved in room temperature solvent (save for very long exposure times) thereby removing mud additives beyond those loosely attached to the cuttings. This technique can also be performed on multiple samples at once. <FIG> includes a step labeled "Speed Extractor" which uses a toluene and/or pentane wash at temperatures or pressures higher than the sample temperature which is simple, faster for multiple samples, and automated.

Some examples may benefit from exposing the sample to a second cleaning fluid and using vacuum filtration and/or solvent extraction. In some examples, the extraction occurs at higher temperature and/or higher pressure than the sample temperature and pressure. Some examples may have a final rinse with a volatile solvent.

After completing these steps, the cuttings are sufficiently clean, have the correct particle size, and have retained their kerogen. They are now ready for analysis of thermal maturity, organic content, mineralogy, surface area, pore volume, porosity, etc. by instruments such as IR spectroscopy, gas sorption, among many others. Additional tests may include TOC analysis by acidization, Rock Eval, Fischer Assay, XRD, XRF, WDX, EDX, gas sorption, pyconometry, and porosimetry.

For the purpose of quality assurance, four well-characterized conventional quarry rock samples (two limestone, a dolomite, and a sandstone) and four oil shale samples (instead of actual drilling cuttings samples) were used in a study. For each rock sample, a regularly shaped plug (20x7 mm) was drilled and trimmed to fit the NMR probe dimensions. Simulated cuttings were obtained by breaking the sample into gravel-sized pieces with a mortar and pestle and then sieving to obtain fragments with a particle size between <NUM> and <NUM>. The conventional rock samples were pressure saturated to <NUM> psi with brine and the shale samples were pressure saturated to <NUM> psi with dodecane. Samples were stored in their saturating fluid until they needed to be measured. A representative split (<NUM>) of the unsaturated, cuttings-sized pieces was retained for a DRIFTS spectroscopy measurement.

Prior to the NMR measurement, the core and cuttings samples were wiped of outer fluid with printer paper and weighed. Because the NMR measurements are sensitive to all the <NUM>H nuclei, any fluid that is not in pores could lead to an inaccurate estimation of pore fluid volume, therefore it is important to remove the outside fluid without losing the fluid in the pores. The saturated samples were measured using a <NUM> Niumag permanent magnet benchtop NMR fitted with a <NUM>-mm RF probe. The T<NUM> relaxation times of the saturated rocks were measured using a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence. The echo spacing used was <NUM> microseconds, the number of echoes was <NUM>, and the recycle delay was set to <NUM> seconds. The total acquisition time plus processing took <NUM> minutes per sample. Probe dimensions and coil length were considered when preparing the samples. For an accurate measurement of the pore fluid volume, the samples needed to be centered within the probe and large enough to fill the sensitive area of the RF coil, but also well below the upper limit of the coil dimensions. Due to these restrictions, samples, both core and cuttings amounts, were kept to <NUM> in length. NMR measurements have been shown to be able to not only determine the total fluid volumes but also their types (bitumen versus water versus oil) and the environments they occupy (clay associated water versus bulk water, oil in organic versus inorganic pores).

The cuttings were prepared for the DRIFTS spectroscopy measurement by crushing the split of dry cuttings samples to a fine powder in a swing mill. The shale samples were cleaned with n-pentane to remove soluble hydrocarbons and then dried. A DRIFTS infrared spectroscopy measurement was made using a Bruker Alpha-R spectrometer. The spectrometer measures the intensity of diffusely reflected IR radiation returned to the spectrometer after interacting with the cleaned cuttings. The spectral measurement is made over the mid-IR region between wavenumbers <NUM> and <NUM>-<NUM>. The intensity of diffusely reflected light at each frequency in the measured spectrum is a function of the abundance of chemical bonds in the sample (i.e., chemical bonds in inorganic minerals and organic compounds), which vibrate and absorb IR radiation at characteristic frequencies. The reflected IR spectra is given in Kubelka-Munk (KM) units. The resulting KM spectrum is uniquely described by the type and abundance of molecular vibrations in the sample and so is a direct function of the mineral and organic matter concentrations.

<FIG> is a plot of the bulk volume of the plug samples measured using a caliper (x-axis) versus the bulk volume of the corresponding cuttings obtained using NMR measured pore volume, DRIFTS measured matrix density and the sample weight (y-axis). The caliper measured bulk volume compares well with the NMR-DRIFTS bulk volume to within <NUM> cc on average and shows the successful application of this method for these regularly shaped core samples. The black markers correspond to the conventional rock samples and the grey markers represent the unconventional rock (shale) samples.

<FIG> is a plot of porosity of the plug samples measured using NMR pore volumes and caliper measured bulk volume (x-axis) as compared to porosity of the plug samples using NMR pore volumes and DRIFTS calculated bulk volume (y-axis). The agreement is very good, within less than <NUM> p. on average, which shows that combining DRIFTS and NMR can provide a good estimate of the porosity for regularly shaped saturated samples. Additional measurements on samples from several basins can possibly provide better quantification of the error bars for this measurement and the factors upon which it depends.

Since a benchmark for measuring the bulk volume of the cutting samples is not provided, <FIG> provides a plot of the porosity of the plug sample (x-axis) vs. the porosity of the cuttings (y-axis) where both are calculated using the calculated bulk volume from DRIFTS and NMR. Agreement between the plugs and cuttings are reasonable. Heterogeneity between splits of the whole rock (the measured cuttings were not made from the measured core) could contribute to the small scatter observed in the data.

The results presented show that pore volume calculated from NMR and bulk volume from DRIFTS matrix density measurements on saturated samples is a quick and effective way to measure porosity of regularly-shaped saturated core samples and irregularly-shaped saturated cuttings samples in the absence of log data. By combining NMR and DRIFTS measurements, and from the cuttings alone, we can obtain information on the reservoir's mineralogy and porosity allowing for a more conclusive assessment of reservoir quality. The NMR measurements average about a few minutes in duration with an additional couple of minutes for the sample cleaning. If the saturation of the shale cuttings is necessary, that would take additional time and needs to be determined. DRIFTS measurement including sample collection and thermal cleaning is about <NUM> minutes in duration. The cleaning and measurements for both NMR and DRIFTS measurements can also be automated if desired.

In order to prepare cuttings for the NMR measurements described herein, the cuttings can be subject to sieving between greater than <NUM> and less than <NUM>. The sieving above <NUM> can help avoid mud additives and improve the NMR signal. The sieving less than <NUM> can help avoid caving's. After separating the cuttings, the mud and fluid can be removed from the outside of the cuttings (e.g. by wiping with paper) and the sample weighed. The sample preparation for the DRIFTS measurement would remain unchanged. It is understood that cuttings may have lost some of the original pore fluid as they travel to the surface for collection and that the cuttings may be fractured, perturbing the calculated porosity. However, in such cases the relative changes in porosity or the quantities of pore fluids (such as bitumen, oil in organic pores, oil in inorganic pores etc.) based on cuttings analysis may indicate important changes in reservoir conditions even when quantitative information may not otherwise be available.

In an embodiment according to the present invention, , a workflow is provided that combines NMR and IR spectroscopy measurements without the need for efficiently cleaning rock samples. For a native formation sample comprising both matrix (comprising one or more of at least minerals and kerogen) and pore volumes (comprising one or more of at least bitumen, oil, and water), the NMR measurement provides the determination of fluid-filled pore volumes, and the IR spectroscopy measurement provides the determination of matrix volumes which may be complicated by the presence of pore-fluid components (e.g., bitumen, oil) whose spectral response is identical or nearly identical to that of matrix components (i.e., kerogen). The NMR measurement on the same formation sample provides the minimum determination of soluble organic (non-kerogen) components within the pore volume such that their contribution to the measured IR spectrum can be 'corrected' from the contribution of kerogen within the matrix. Such a workflow is shown in the flowchart of <FIG> and described below.

In block <NUM>, a sample of a rock formation is collected or otherwise obtained. The sample may be cuttings, a core plug, or other rock sample type or form. When collected as a function of depth, the prescribed workflow will provide a 'log' of desired property values (e.g., thermal maturity) as a function of depth.

In block <NUM>, optionally, the size of the rock sample can be reduced to a size relevant for the NMR measurement (block <NUM>). Such a method might comprise, for example, reducing inch-size or larger core plugs to centimeter-size chips to fit within the sample holder for the NMR measurement.

In block <NUM>, the rock sample (which has been optionally reduced in size in <NUM>) is weighed to determine the absolute mass MR of the rock sample. In the given notation for MR, the subscript R refers to the rock formation, comprising matrix (e.g., minerals, kerogen) and fluid (e.g., bitumen, oil, water).

In block <NUM>, an NMR apparatus (such as the NMR apparatus <NUM> of <FIG>) is configured and operated to perform an NMR measurement on the rock sample weighed in <NUM>. The NMR measurements can be carried out at the Larmor frequency of about <NUM> to <NUM> for measuring <NUM>H nuclei. The NMR measurement of block <NUM> can be done in the field (e.g., wellsite) or in a laboratory. Frequencies higher than <NUM> can also be suitable for laboratory purposes.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to perform analysis of the result data of the NMR measurement of <NUM> to acquire T<NUM>-T<NUM> distribution of the rock sample. Such computations can employ NMR interpretations well-known to those skilled in the art as described herein.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG> or the CPU <NUM> of <FIG>) can be configured and operated to perform analysis of the data representing the T1-T2 distribution of <NUM> to compute data representing volumes of components of the rock sample (such as volume of kerogen, volumes of soluble organic components (bitumen, oil), and volume of water) and data representing pore volume of the sample. Such computations can employ NMR interpretations well-known to those skilled in the art. The absolute volume for a given component of the rock sample is here given notation Vi where subscript i refers to kerogen VK, bitumen VB, oil VO, etc. The pore volume of the sample can be determined by adding together the volumes for the different fluid components.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG> or the CPU <NUM> of <FIG>) can be configured and operated to compute data representing mass fractions of the soluble organic components of the rock sample based on the volumes of the soluble organic components of <NUM> and the mass of the rock sample of <NUM>. For example, the mass fraction of bitumen mB in the rock sample can be computed from the relationship: <MAT> where ρB is the absolute mass density of bitumen, VB is the volume of bitumen in the rock sample as determined in <NUM>; and MR is the absolute mass of the rock sample as determined in <NUM>.

In another example, the mass fraction of oil mO in the rock sample can be computed from the relationship: <MAT> where pO is the absolute mass density of oil, Vo is the volume of oil in the rock sample as determined in <NUM>; and MR is the absolute mass of the rock sample as determined in <NUM>.

In block <NUM>, optionally, the rock sample (which was weighed in <NUM> and possibly subject to the NMR measurement in <NUM>) can be reduced in size to a size relevant for the IR measurement (block <NUM>).

In block <NUM>, a spectrometer (such as the spectrometer <NUM> of <FIG>) is configured and operated to perform an IR spectroscopy measurement (e.g., DRIFTS) on the rock sample (which was weighed in <NUM> optionally reduced in size in <NUM>) to acquire an IR spectrum of the rock sample in the spectral range from <NUM>-<NUM>-<NUM>. The IR spectroscopy measurement can be done in the field (e.g., wellsite) or in a laboratory. The IR spectroscopy measurement can be done using any mode of IR spectroscopy, such as transmission, diffuse reflectance, attenuated total reflectance, etc., but is here exemplified using diffuse reflectance techniques (DRIFTS). <FIG> illustrate the DRIFT spectra of a formation sample comprising several discrete and identifiable components, including minerals such as quartz (characteristic vibration modes between approximately <NUM> and <NUM>-<NUM>) and kaolinite (characteristic vibration modes between approximately <NUM> and <NUM>-<NUM>), and organic matter such as bitumen and kerogen (characteristic vibration modes between approximately <NUM> and <NUM>-<NUM> and between approximately <NUM> and <NUM>-<NUM>).

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to perform analysis of the IR spectrum of <NUM> to determine data that characterizes mass fractions (mass concentrations) of inorganic mineral components and kerogen of the rock sample. In embodiments, the mass fractions of the inorganic mineral components and kerogen in the sample(s) can be determined from the measured IR spectrum using methods such as a least squares regression well-known to those skilled in the art. The mass fractions of the inorganic mineral components and kerogen and their sum can be referred to by a notation mj and Σ mj where subscript j refers to the individual inorganic mineral component or kerogen (such as Quartz, Illite, Smectite, Kaolinite, Chlorite,. , Kerogen).

In block <NUM>, optionally a data processor (such as the data collector/analyzer <NUM> of <FIG> or the CPU <NUM> of <FIG>) is configured and operated to subtract a contribution of the inorganic mineral components from the IR spectrum of <NUM> based on the mass fractions of the inorganic mineral components of the rock sample of <NUM>. For example, if the rock sample contains a mass fraction of Quartz equal to <NUM>% of the sample(s), the IR spectrum of a pure Quart multiplied by <NUM> can be removed from the measured IR spectrum. Similar operations can be repeated for one or more other inorganic mineral components in the rock sample.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG> or the CPU <NUM> of <FIG>) is configured and operated to obtain a residuum IR spectrum representative of kerogen in the rock sample by subtracting a contribution of organic soluble components (including oil and bitumen) from the IR spectrum of <NUM> or <NUM> based on the mass fractions of the organic soluble components (including oil and bitumen) in the rock sample of <NUM>.

<FIG> illustrate exemplary measured DRIFT spectra in the region of the IR spectrum between <NUM> and <NUM>-<NUM> wherein are expressed prominent IR absorption bands associated with C-H vibrational modes in organic matter. The absorption bands in the native formation sample (<FIG>) in this region of the IR spectrum are a composite of all organic matter components in the sample, here comprising bitumen (in porosity) and kerogen (in matrix). From the soluble organic (non-kerogen) component mass fractions solved from the NMR measurement in <NUM>, the IR spectrum corresponding to the mass fraction of one or more soluble organic (non-kerogen) components is subtracted from the measured IR spectrum to leave a 'residuum' IR spectrum (<FIG>) containing IR absorption bands associated with C-H vibrational modes only in kerogen. For example, if the rock sample contains a mass fraction of bitumen equal to <NUM>% of the sample(s), the IR spectrum of a pure bitumen multiplied by <NUM> can be removed from the measured IR spectrum. Similar operations can be repeated for other organic (non-kerogen) components in the rock sample, such as oil, identified by the NMR measurement. The residuum IR spectrum (<FIG>) is then representative of a pure kerogen (<FIG>) such that the kerogen-associated IR absorption bands in the residuum spectrum can be used to estimate properties of the kerogen in the rock sample.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG> or the CPU <NUM> of <FIG>) is configured and operated to analyze the residuum IR spectrum of <NUM> to generate data that characterizes properties of kerogen in the rock sample, such as kerogen thermal maturity or kerogen density. In embodiments, such analysis can be performed for the region of the residuum IR spectrum between <NUM> and <NUM>-<NUM>. Methods for the determination of kerogen properties from an IR spectrum are well-known to those skilled in the art and described in co-owned <CIT>, <CIT> and <CIT>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG> or the CPU <NUM> of <FIG>) is configured and operated to determine data representing matrix density of the rock sample. The determination of matrix density can be derived from the sum of the mass fractions of the inorganic mineral components and kerogen in the rock sample as determined from the spectroscopy measurement (block <NUM>) divided by their respective densities as given in Eqn. (<NUM>) above. The kerogen density that is used to determine matrix density can be determined from the analysis of the residuum IR spectrum as provided in block <NUM>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG> or the CPU <NUM> of <FIG>) is configured and operated to determine data representing bulk volume of the rock sample. The determination of bulk volume (Vbulk) of the rock sample can be computed from the pore volume data of <NUM> and the matrix density data of <NUM> as follows: <MAT> where ms is the mass (in grams) of the rock sample as measured in <NUM>, Vpore is the pore volume of the rock sample measured by NMR in <NUM>, ρfluid is the fluid density of the fluid that saturates the rock sample, and ρma is the matrix density of the rock sample measured by spectroscopy in <NUM>.

The liquid density ρfluid can be based on liquid densities obtained from density measurements completed on separate fluid samples (associated with the same rock sample) using established techniques or based on model estimates for the liquid densities using composition and established density models from petroleum thermodynamics. For example, the fluid that saturates the rock sample can contain multiple components (or compounds) and the liquid density ρfluid of such fluid may be calculated using a number of well-established methods by one skilled in the art, including mixing rules (e.g., Eqn. (<NUM>) above), density correlations, corresponding states, and equation of state (with or without volume translation).

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG> or the CPU <NUM> of <FIG>) is configured and operated to determine data representing porosity of the rock sample. The determination of porosity (φ) of the rock sample can be calculated from the pore volume data of <NUM> and the bulk volume data of <NUM> as follows: <MAT> where Vpore is the pore volume of the rock sample measured by NMR in <NUM>, Vbulk is the bulk volume of the rock sample as determined in <NUM>.

An example that does not form part of the present invention includes a workflow for measurement of porosity and saturations of cuttings (or some other rock sample with irregular shaped and/or sized pieces) using a multi-nuclear NMR workflow. Multiple techniques are described and claimed. One example is based on the use of a fluid containing non-hydrogen nuclei (e.g., a fluorine-based fluid) in the multi-nuclear NMR workflow to determine the porosity of the cuttings (or some other rock sample with irregular shaped and/or sized pieces).

Note that NMR is routinely used as a fast and non-destructive method to analyze rock cores to provide information on pore volume, pore fluid saturations, and pore fluid typing in the lab. Generally, the NMR measurements are carried out at a Larmor frequency of <NUM> for measuring <NUM>H nuclei, which is similar to the frequency of NMR logging tools to allow for core-log integration. Furthermore, the rock core that is interrogated by the NMR measurements are uniformly cut cylindrical core plugs about <NUM>" x <NUM>". The regular shape of the core plug gives a reliable bulk volume, measured by caliper, and is used along with the pore volume to obtain porosity. The size of the core plug results in a high filling factor in the NMR probe, thus a high signal-to-noise ratio (SNR). When cuttings are used in place of a cylindrical core plug, the small and irregularly shaped cuttings result in a low SNR for the NMR measurements and makes measurements of bulk volume more challenging.

The NMR measurements of the cuttings (or some other rock sample with irregular shaped and/or sized pieces) can be carried out at a frequency higher than <NUM> (for example, at a frequency of <NUM>) to address the challenge of low SNR from the cuttings (or some other rock sample with irregular shaped and/or sized pieces). The benefit of the higher-frequency measurement is to allow shorter echo spacings, which accommodates short T2 relaxation times that are characteristic of unconventional samples, and to improve the separation of fluids, namely free water, clay associated water, oil, bitumen, and kerogen.

Improvement in SNR can be achieved by going to a higher magnetic field as the SNR is proportional to B<NUM><NUM> ~ <NUM>/<NUM>, where B<NUM> is the nominal field strength of the magnet, depending on the noise source (<NPL>. However, in NMR analysis of conventional formations, pore-scale magnetic field distortions (so-called "internal gradients") caused by the solid/fluid susceptibility contrast can bring about complications; molecular diffusion through these internal gradients introduces an enhanced signal decay, leading to uncertainty in T<NUM> measurements (<NPL>). Since the internal gradients increase with the field strength, low field strength of <NUM> T (corresponding to a resonance frequency of <NUM> for <NUM>H) is considered the industry standard to provide quantitative measurements as well as for well-log calibration. On the other hand, the nanometer-scale pores in shale samples ensure that the spins explore the pore multiple times during a measurement and hence the gradient effects across a pore average out (<NPL>). Under these conditions, an increase in B<NUM> from <NUM> T to <NUM> T and possibly beyond results in only a slight increase in the rate of signal decay due to diffusion in the internal gradients, whilst attaining much better SNR (<NPL>).

The irregular shapes and sizes of the pieces of the rock sample makes measurement of bulk volume very difficult. Additionally, the irregular shapes and sizes of the pieces of the rock sample can have a lower filling factor in the RF probes resulting in lower signal to noise ratios. In embodiments, a methodology for measuring bulk volume of an irregularly sized and/or shaped rock sample (such as cuttings) is provided using <NUM>F NMR measurements. Other solvents that could be used include ones with NMR active nuclei such as heavy water (D<NUM>O), or solvents with other NMR active nuclei such as <NUM>C and <NUM>P. In combination with <NUM>H NMR for the pore volume measurement, this provides a quick porosity measurement for unconventional shale samples. Additionally, the method can utilize a higher frequency NMR system in comparison to traditional core analysis workflows thereby addressing the SNR challenge. While the use of a higher frequency would make these experiments better, it is not a strict requirement for this workflow. The use of a higher field also allows for shorter echo spacing, which is beneficial for detecting short T<NUM> components found in unconventional samples. Additionally, the T<NUM> dependence with frequency of different components in shale (the clay associated water and viscous hydrocarbons), enable their better separation. An accurate porosity measurement combined with the ability for identifying different organic components makes this workflow a valuable analysis tool for unconventional reservoirs. The capability of measuring irregular-shaped samples with minimal instrumentation and supervision also opens up automated cutting analysis at either the laboratory environment (as part of cutting screening methodology,) or the wellsite.

Examples may make use of <NUM>F NMR measurements to find bulk volume with the integration with a <NUM>H NMR measurement to obtain porosity. The method can employ a dual-tuned probe or other configuration that provides consistent sensitivity to volume for the <NUM>F and <NUM>H NMR measurements. Fluorine resonates at a Larmor frequency <NUM>% less than that of <NUM>H, which is within the tuning range of most probes. And, because we are observing subtle changes in volume, system stability and calibration can have a significant impact on the outcome. Higher field NMR systems yield better SNR, which is beneficial to measuring samples with low filling factor. And such systems also generally enable measurements at shorter echo spacings, which are useful for shale rock fluid typing and samples of low porosity.

Calibrations for both the <NUM>F and <NUM>H NMR measurements can be made by multiple methods. One example involves measuring the maximum signal amplitude of two known fluid volumes; water with an NMR measurement at an operating frequency for measuring hydrogen nuclei (which is referred to as a <NUM>H NMR measurement), and a fluorine-based fluid (e.g., fluorocarbon) with an NMR measurement at an operating frequency for measuring fluorine nuclei (which is referred to as an <NUM>F NMR measurement). An additional calibration <NUM>F NMR measurement can be made on a sample holder that is "full" of the fluorine-based fluid, where just enough fluid is added to fill the entire measurable volume (or region) of the coil of the NMR apparatus. A rock sample can then be added to the same full tube of the fluorine-based fluid and the maximum signal amplitude acquired again. The difference in maximum signal amplitude between the fluorine-based fluid only and the fluorine-based fluid plus rock sample is the contribution from the bulk volume occupied by the rock sample. In embodiments, the time-varying signal amplitude of the respective NMR measurements can be derived by mono-exponential fitting of the magnetization decay that is measured by the NMR apparatus for the respective NMR measurements, and the maximum signal amplitudes of the respective NMR measurements can be determined from the maximum of the mono-exponential fit of the magnetization decay for the respective NMR measurements. An example of this methodology that does not form part of the present invention is described below with reference to the flowchart of <FIG>.

In block <NUM>, an NMR apparatus (such as NMR apparatus <NUM> of <FIG>) is configured and operated to perform a calibration <NUM>H NMR measurement on a known volume of a hydrogen-based fluid (e.g., water), if not done so already. The calibration <NUM>H NMR measurement of <NUM> is performed at an operating frequency for measuring hydrogen nuclei. The tube labeled A in <FIG> depicts a known volume of fluid, such as the hydrogen-based fluid (e.g., water), used in <NUM>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to obtain and store data representing maximum signal amplitude of the known volume of hydrogen-based fluid which results from the calibration <NUM>H NMR measurement of <NUM>, if not done so already. The time-varying signal amplitude of the calibration <NUM>H NMR measurement of <NUM> can be derived by mono-exponential fitting of the magnetization decay that is measured by the NMR apparatus in <NUM>, and the maximum signal amplitude of the calibration <NUM>H NMR measurement of <NUM> can be determined from the maximum of the mono-exponential fit of the magnetization decay that is measured by the NMR apparatus in <NUM>.

In block <NUM>, the NMR apparatus (such as NMR apparatus <NUM> of <FIG>) is configured and operated to perform a calibration <NUM>F NMR measurement on a known volume of a fluorine-based fluid (e.g., a fluorocarbon such as C<NUM>F<NUM>N), if not done so already. The calibration <NUM>F NMR measurement of <NUM> is performed at an operating frequency for measuring fluorine nuclei. The tube labeled B in <FIG> depicts a known volume of fluid, such as the fluorine-based fluid (e.g., fluorocarbon) used in <NUM>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to obtain and store data representing maximum signal amplitude of the known volume of the fluorine-based fluid which results from the <NUM>F NMR measurement of <NUM>, if not done so already. The time-varying signal amplitude of the calibration <NUM>F NMR measurement of <NUM> can be derived by mono-exponential fitting of the magnetization decay that is measured by the NMR apparatus in <NUM>, and the maximum signal amplitude of the calibration <NUM>F NMR measurement of <NUM> can be determined from the maximum of the mono-exponential fit of the magnetization decay that is measured by the NMR apparatus in <NUM>.

In block <NUM>, the NMR apparatus (such as NMR apparatus <NUM> of <FIG>) is configured and operated to perform a calibration <NUM>F NMR measurement on a sample holder (e.g., tube) filled with the fluorine-based fluid, if not done so already. In this case, the sample holder can be filled with just enough fluorine-based fluid to fill the entire measurable volume (or region) of the coil of the NMR apparatus. The calibration <NUM>F NMR measurement of <NUM> is performed at an operating frequency for measuring fluorine nuclei. The tube labeled C in <FIG> depicts a sample holder (e.g., tube) filled with fluorine-based fluid used in <NUM>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to obtain and store data representing maximum signal amplitude of the "full" sample holder of fluorine-based fluid which results from the <NUM>F NMR measurement of <NUM>, if not done so already. The time-varying signal amplitude of the calibration <NUM>F NMR measurement of <NUM> can be derived by mono-exponential fitting of the magnetization decay that is measured by the NMR apparatus in <NUM>, and the maximum signal amplitude of the calibration <NUM>F NMR measurement of <NUM> can be determined from the maximum of the mono-exponential fit of the magnetization decay that is measured by the NMR apparatus in <NUM>.

In block <NUM>, cuttings (or some other irregularly sized and/or shaped rock sample) are collected or otherwise obtained from a formation and used as a rock sample.

In block <NUM>, optionally, the cuttings (or some other irregularly sized and/or shaped rock sample) can be reduced in size to a size suitable for the multi-nucleic NMR measurements (<NUM> and <NUM>).

In block <NUM>, the cuttings (or other irregularly sized and shaped rock sample) are added to the sample holder that is filled with the fluorine-based fluid (e.g., fluorocarbon), which was tested in <NUM>.

In block <NUM>, the NMR apparatus (such as NMR apparatus <NUM> of <FIG>) is configured and operated to perform an <NUM>F NMR measurement on the sample holder of fluorine-based fluid with the cuttings (or other rock sample), which is tube D in <FIG>. The <NUM>F NMR measurement of <NUM> is performed at an operating frequency for measuring fluorine nuclei. Note that the tube labeled D in <FIG> depicts a sample holder (e.g., tube) of fluorine-based fluid and cuttings labeled E as used in <NUM>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to obtain and store data representing maximum signal amplitude of the sample holder of fluorine-based fluid with cuttings (or other rock sample) which results from the <NUM>F NMR measurement of <NUM>. The time-varying signal amplitude of the <NUM>F NMR measurement of <NUM> can be derived by mono-exponential fitting of the magnetization decay that is measured by the NMR apparatus in <NUM>, and the maximum signal amplitude of the <NUM>F NMR measurement of <NUM> can be determined from the maximum of the mono-exponential fit of the magnetization decay that is measured by the NMR apparatus in <NUM>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to generate data representing the difference between the maximum signal amplitude data of <NUM> (for the calibration <NUM>F NMR measurement on the sample holder full of the fluorine-based fluid alone, which is tube C in <FIG>) and the maximum signal amplitude data of <NUM> (for the <NUM>F NMR measurement on the sample holder of fluorine-based fluid with cuttings or other rock sample, which is tube D in <FIG>).

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to calculate data representing bulk volume of the cuttings or other rock sample based on the maximum signal amplitude difference data of <NUM>, the maximum signal amplitude data of <NUM> (for the calibration <NUM>F NMR measurement on the known volume of fluorine-based fluid, which is tube B in <FIG>), and the known volume of the fluorine-based fluid in <NUM>. The maximum signal amplitudes from the multi-nucleic NMR measurements can be notated with the labels for the tubes used in <FIG> such that SC and SD are the maximum signal amplitudes of tubes C and D, which is the maximum signal of the sample space determined in <NUM> and <NUM>, respectively. In this case, the data representing bulk volume (Vbulk) of the cuttings or other rock sample can be given as: <MAT> where VB is the known volume of the fluorine-based fluid in <NUM>, SB is the maximum signal amplitude of the known-volume of fluorine-based fluid in <NUM>, Sc is the maximum signal amplitude of the full tube C of the fluorine-based fluid in <NUM>, and SD is the maximum signal amplitude of the tube D of fluorine-based fluid and cuttings in <NUM>.

In block <NUM>, the NMR apparatus (such as NMR apparatus <NUM> of <FIG>) is configured and operated to perform a <NUM>H NMR measurement on the sample holder of fluorine-based fluid with the cuttings (or other rock sample), which is tube D in <FIG>. The <NUM>H NMR measurement of <NUM> is performed at an operating frequency for measuring hydrogen nuclei. The blocks labeled E immersed in tube D in <FIG> depict cuttings (or other rock sample) used in <NUM>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to obtain and store data representing maximum signal amplitude of the <NUM>H NMR measurement of <NUM>. The time-varying signal amplitude of the <NUM>H NMR measurement of <NUM> can be derived by mono-exponential fitting of the magnetization decay that is measured by the NMR apparatus in <NUM>, and the maximum signal amplitude of the <NUM>H NMR measurement of <NUM> can be determined from the maximum of the mono-exponential fit of the magnetization decay that is measured by the NMR apparatus in <NUM>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to calculate data representing pore volume of the cuttings or other rock sample based on the maximum signal amplitude of <NUM> (for the <NUM>H NMR measurement on the sample holder of fluorine-based fluid with cuttings or other rock sample, which is tube D in <FIG>), the maximum signal amplitude data of <NUM> (for the calibration <NUM>H NMR measurement on the known volume of hydrogen-based fluid, which is tube A in <FIG>), and the known volume of the hydrogen-based fluid in <NUM>. In this case, the data representing pore volume (Vpore) of the cuttings or other rock sample can be given as: <MAT> where VA is the known volume of the hydrogen-based fluid in <NUM>, SE is the maximum signal amplitude of the <NUM>H NMR measurement on the sample holder of fluorine-based fluid with the cuttings (or other rock sample) of <NUM>, and SA is the maximum signal amplitude of the known-volume of hydrogen-based fluid in <NUM>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) is configured and operated to calculate data representing porosity of the cuttings or other rock sample based on the pore volume data of <NUM> and the bulk volume data of <NUM>. Specifically, the porosity (φ) of the cuttings or other rock sample can be calculated from Eqn. (<NUM>) by dividing the pore volume data of <NUM> by the bulk volume data of <NUM>.

In block <NUM>, a data processor (such as the data collector/analyzer <NUM> of <FIG>) can be configured and operated to store and/or output the porosity data of <NUM>.

<FIG> is a plot of exemplary NMR signals acquired by the NMR measurements of the workflow. The NMR signal labeled B is the result of the calibration <NUM>F NMR measurement performed on the known-volume of a fluorine-based fluid (fluorocarbon) and acquired in <NUM>. The NMR signal labeled C is the result of the calibration <NUM>F NMR measurement performed on the sample holder filled with the fluorine-based fluid (fluorocarbon) and acquired in <NUM>. The NMR signal labeled D is the result of the <NUM>F NMR measurement performed on the sample holder filled with the fluorine-based fluid (fluorocarbon) and cuttings and acquired in <NUM>.

<FIG> is a schematic illustration of the volume (labeled C) measured by the calibration <NUM>F NMR measurement of <NUM> performed on the sample holder filled with the fluorine-based fluid alone. <FIG> is a schematic illustration of the volume (labeled D) measured by the <NUM>F NMR measurement of <NUM> performed on the sample holder with the fluorine-based fluid and cuttings together with the volume (labeled E) measured by the <NUM>H NMR measurement of <NUM> performed on the sample holder with the fluorine-based fluid and cuttings. Note that the difference between the volume C of <FIG> and the volume D of <FIG> corresponds to bulk volume of the cuttings as determined in <NUM>, while the volume E of <FIG> corresponds to the pore volume of the cuttings as determined in <NUM>.

From the bulk volume measured with the <NUM>F NMR measurements and the pore volume measured with the <NUM>H NMR measurement, the porosity of the irregularly sized and/or shaped rock sample can be determined in <NUM> independent of grain density or mass. The sequence of measurements has the flexibility to be done in any order. For example, when the probe is tuned to the correct frequency, the decay of fluorine nuclei will not interfere with decay of hydrogen protons and vice versa so that the rock sample can be included in the sample holder filled with the fluorine-base and be measured by the <NUM>H NMR measurement to detect the decay of hydrogen nuclei alone without difficulty.

Note that the analysis of drill cuttings is a progressive step toward understanding the reservoir and implementing NMR porosity with multi-nuclear measurements at the wellsite which can provide valuable information. Example methods deliver accurate porosity of drill-cuttings and have applications both at the wellsite and in the laboratory. In addition, this new approach to porosity measurements opens the door to further core analysis on hard to obtain or unconsolidated rock samples.

The porosity and other properties of the cuttings or other rock samples as determined by the workflow(s) described herein relate to RQ and/or CQ of a reservoir.

In some embodiments, porosity and other properties of rock samples determined by the workflow(s) described herein can be used as inputs to determine the design of a well completion as well as the operation of downhole equipment and surface equipment that produce hydrocarbons from the reservoir. For example, parts of an unconventional reservoir with relatively low RQ and/or relatively low CQ due to poor porosity and/or poor permeability and other parameters can possibly be bypassed or isolated by stages of a completion, while other parts of the unconventional reservoir with relatively high RQ and/or relatively high CQ due to sufficient porosity and/or sufficient permeability and other parameters can be accessed by stages of the completion that provide for fracturing (and/or other stimulation or treatment) and production of hydrocarbons and possibly other reservoir fluids from the reservoir.

In some embodiments, porosity and other properties of rock samples determined by the workflow(s) described herein can be used as inputs to a reservoir simulator to determine an optimal design of a completion as well as the operation of downhole equipment and surface equipment that produce hydrocarbons from the reservoir.

In other embodiments, geomechanical properties of a formation may be needed for a variety of reasons without the use of a logging while drilling tool or wireline tool. There may be a need to complement tool failure.

In other embodiments, the porosity and other properties of the rock samples determined by the workflow(s) described herein can be used to drill the wellbore without core data or log information. A drilling regime may include multiple lateral wells from one initial wellbore and the costs for core and/or log data may be unreasonably burdensome. Some embodiments may use a drill string with no tools for logging. Some embodiments may be performed on site in near real time without time for data actualization, that is, the drill string may remain in the wellbore as people timely use the information available to them without remote mathematical analysis and without operating time lag. Some embodiments may manipulate the data in time to guide the completion time. Also, some of the techniques to address these issues, such as laboratory measurements and some logs, require post-analysis, and interpretation of the data that cannot be done within the drilling timeframe.

Further, while some vertical pilot wells are logged and evaluated in an unconventional play, stimulated horizontal wells are rarely logged or cored. The cost of acquiring the information and/or the associated rig time needed during acquisition (which means that the rig cannot be used for drilling or stimulation elsewhere) are two main reasons for this trend. The solution must be low cost and efficient in terms of delivery times (i.e., in real-time or near real-time). It must not introduce any inefficiency into the development program (such as extended rig time for data acquisition) and must be based on a simple workflow that can be carried at the wellsite by non-experts.

Also, the hydraulic fracturing stimulation of unconventional organic shale reservoirs is performed today in mostly horizontal wells where heterogeneities of petrophysical and mechanical properties along the well are known to be very significant. Staging requires the identification of sections of the well with both good reservoir quality and good completion quality. Completion quality estimates rely on changes in elastic, rock strength, and stress properties along the well reflect variations (heterogeneity) of mechanical properties along the well.

Some examples as described herein, not forming part of the present invention, relate to methods for recovering hydrocarbons from a formation including collecting a formation sample, forming the sample into particles, exposing the sample to a cleaning fluid, and analyzing the sample. Examples also relate to methods for recovering hydrocarbons from a formation including the steps of collecting a formation sample, first exposing the sample to a cleaning fluid, forming the sample into particles, exposing the sample to a second cleaning fluid and analyzing the sample.

Time and location are important considerations for embodiments of this procedure. The analyzing occurs in less than an hour and/or in less than <NUM> hours in some embodiments. The analyzing occurs before recovering hydrocarbons begins in some embodiments or after producing hydrocarbons begins in some embodiments. The analyzing may occur during reservoir characterization during production. Some embodiments may use equipment within <NUM> meters of a wellbore. In some embodiments, analyzing occurs while drilling the formation.

<FIG> illustrates an example device <NUM>, with a processor <NUM> and memory <NUM> that can be configured to implement various embodiments of methods and system as discussed in this disclosure. Memory <NUM> can also host one or more databases and can include one or more forms of volatile data storage media such as random-access memory (RAM), and/or one or more forms of nonvolatile storage media (such as read-only memory (ROM), flash memory, and so forth).

Device <NUM> is one example of a computing device or programmable device and is not intended to suggest any limitation as to scope of use or functionality of device <NUM> and/or its possible architectures. For example, device <NUM> can comprise one or more computing devices, programmable logic controllers (PLCs), etc..

Further, device <NUM> should not be interpreted as having any dependency relating to one or a combination of components illustrated in device <NUM>. For example, device <NUM> may include one or more computers such as a laptop computer, a desktop computer, a mainframe computer, etc., or any combination or accumulation thereof.

Device <NUM> can also include a bus <NUM> configured to allow various components and devices, such as processors <NUM>, memory <NUM>, and local data storage <NUM>, among other components, to communicate with each other.

Bus <NUM> can include one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. Bus <NUM> can also include wired and/or wireless buses.

Local data storage <NUM> can include fixed media (e.g., RAM, ROM, a fixed hard drive, etc.) as well as removable media (e.g., a flash memory drive, a removable hard drive, optical disks, magnetic disks, and so forth).

One or more input/output (I/O) device(s) <NUM> may also communicate via a user interface (UI) controller <NUM>, which may connect with I/O device(s) <NUM> either directly or through bus <NUM>.

In one possible implementation, a network interface <NUM> may communicate outside of device <NUM> via a connected network.

A media drive/interface <NUM> can accept removable tangible media <NUM>, such as flash drives, optical disks, removable hard drives, software products, etc. In one possible implementation, logic, computing instructions, and/or software programs comprising elements of module <NUM> may reside on removable media <NUM> readable by media drive/interface <NUM>.

In one possible embodiment, input/output device(s) <NUM> can allow a user to enter commands and information to device <NUM> and also allow information to be presented to the user and/or other components or devices. Examples of input device(s) <NUM> include, for example, sensors, a keyboard, a cursor control device (e.g., a mouse), a microphone, a scanner, and any other input devices known in the art. Examples of output devices include a display device (e.g., a monitor or projector), speakers, a printer, a network card, and so on.

Various processes of present disclosure may be described herein in the general context of software or program modules, or the techniques and modules may be implemented in pure computing hardware. Software generally includes routines, programs, objects, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. An implementation of these modules and techniques may be stored on or transmitted across some form of tangible computer-readable media. Computer-readable media can be any available data storage medium or media that is tangible and can be accessed by a computing device. Computer readable media may thus comprise computer storage media. "Computer storage media" designates tangible media, and includes volatile and non-volatile, removable and non-removable tangible media implemented for storage of information such as computer readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, RAM, ROM, 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 tangible medium which can be used to store the desired information, and which can be accessed by a computer.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems and methods and according to various embodiments of the present disclosure. The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting of the disclosure.

Claim 1:
A method for characterizing properties of a rock sample obtained from a subterranean formation, comprising:
i) obtaining (<NUM>) a sample of a rock formation and weighing (<NUM>) the rock sample to determine the absolute mass MR of the rock sample;
(ii) performing (<NUM>) an NMR measurement on the rock sample; and
(iii) performing (<NUM>) an IR spectroscopy measurement on the rock sample to acquire an IR spectrum of the rock sample in the spectral range from <NUM>-<NUM>-<NUM>; in a data processor:
iv) analyzing (<NUM>) results of the NMR measurement to acquire data representing a T<NUM>-T<NUM> distribution of the rock sample and analyzing (<NUM>) the data representing the T1-T2 distribution to compute data representing volumes of soluble organic components of the rock sample and data representing pore volume of the sample;
v) computing data (<NUM>) representing mass fractions of soluble organic components of the rock sample based on the data representing volumes of soluble organic components and the absolute mass MR of the rock sample;
vi) analysing (<NUM>) the IR spectrum to determine data that characterized mass fractions of inorganic material components and kerogen of the rock sample;
vii) obtaining (<NUM>) data representing a residuum IR spectrum by subtracting a contribution of organic soluble components from the acquired IR spectrum based on the computed data representing mass fractions of the organic soluble components in the rock sample and analyzing (<NUM>) the data representing the residuum IR spectrum to generate data that characterizes properties of kerogen in the rock sample, the properties including kerogen density;
viii) determining (<NUM>) data characterizing matrix density of the rock sample based on the sum of mass fractions of inorganic mineral components and kerogen in the rock sample as determined in v) divided by their known densities and the density of kerogen as determined in vii)
ix) determining (<NUM>) data characterizing bulk volume of the rock sample based on the pore volume and the matrix density of the rock sample; and
x) determining (<NUM>) data characterizing porosity of the rock sample based on the pore volume and the bulk volume of the rock sample;