FIELD

The present disclosure relates to methods and systems that can detect clay in a rock sample or other porous media sample and that can also determine data characterizing clay type and volume fraction of clay in the rock sample or other porous media sample.

BACKGROUND

Geological knowledge of formation rock is very critical for resources exploration, field development and production planning. To assess reservoir quality and the amount of hydrocarbons in-place, characterization of the mineralogy of the formation rock is needed in order to accurately calculate reservoir porosity and hydrocarbon saturation from logs. This is particularly true in the event that clay is present in the formation rock. Each hydrocarbon reservoir can contain different types of clays and different amounts of such clay types. Each clay type has its own different characteristics, which can be translated to what is called cation exchange capacity (CEC) in common petrophysical applications.

Clays are hydrous aluminum silicate minerals that are platy in structure and can form by the alteration of silicate minerals like feldspar and amphibole. Clays are commonly lumped or grouped into a number of clay types, including but not limited to smectite, kaolinite, chlorite, illite. Some clays have a tendency to swell when exposed to water, creating a potential drilling hazard when clay-bearing rock formations are exposed to water-base fluids during drilling, possibly reducing the permeability of a good reservoir rock. Some clays are used in drilling fluids to form an impermeable mudcake to isolate a formation from the invasion of drilling fluid. The structural differences amongst the clay types (smectite, kaolinite, chlorite, illite) can determine the surface area exposed to reservoir fluids or stimulating fluids. Clays can be found in pore spaces, as part of the matrix or as grain-cementing material. Authigenic clays, which grow in the pores from minerals in the connate water, can be pore-filling or pore-lining. These clays have considerable surface area exposed in the pore and can be reactive, while detrital clays that are part of the matrix are usually less reactive. Additionally, clays can be cementing, or grain-binding, materials that react with water or acid to disaggregate the formation if they are not protected by quartz overgrowths. The most common clays that create clay problems in hydrocarbon reservoirs are kaolinite, smectite, illite and chlorite.

Clays in formation rock can significantly impact estimation of hydrocarbon reserves and the methods and costs for producing the stored hydrocarbons from formation rock. In evaluating unconventional formation rock such as shale that contains clays, determining clay type and composition is a significant challenge considering the chemical complexity and heterogeneous nature of the unconventional formation rock.

In the downhole environment, gamma ray logs have been traditionally used to estimate formation shale/clay volume based on correlation related to inherent elements such as potassium, uranium and thorium.

Combining density, neutron and spectral natural gamma ray logs, one can determine clay density and neutron response to 100% clay at every depth and also determine clay volume independent of the clay types. Combining such knowledge allows the calculation of CEC and hydrogen index (HI), and from the CEC vs. HI crossplot, clay typing is possible.

In a laboratory setting, there are multiple techniques for elemental and mineralogical analysis for rock samples, most important are X-ray fluorescence (XRF), X-ray diffractometry (XRD), Fourier transform infrared spectroscopy (FTIR), and Diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) in addition to qualitative thin section analysis under polarized transmitted light. In all cases, it is necessary to prepare the required samples to certain standards.

XRF is an electroscopic technique that employs a secondary or fluorescent emission generated by exciting a sample with a source of X-radiation. The energy absorbed by the atoms cause the production of secondary x-ray and fluorescence, emitted by the sample. The intensity of these secondary x-rays is proportional to the concentration of each element in the sample. XRF allows fast identification of elements heavier than Lithium (Z=3) in theory. But in practice, it is often difficult to quantify elements lighter than Sodium (Z=11). Currently, portable fluorescence devices allow real-time measurements, which translate to fast decisions making on the field. They offer the possibility to analyze samples without needing any previous preparation. Despite the advances in portable devices, the accuracy that can be expected from its results is still lower than the one that can be obtained by means of laboratory analytic techniques although it can be enhanced using reference samples. It is also noted that XRF measures elements, converting elements to minerals is an inversion process which may require calibrations and boundary conditions to ensure converging solutions.

In XRD, when an x-ray diffraction beam strikes the surface, the matter absorbs the radiation to a greater or lesser extent, depending on the different mechanisms of interaction that are registered, either fluorescent type or a disperse radiations. The dispersive one is constituted by the fraction of the incident energy that is emitted again without changing its wavelength. X-ray diffraction is a particular case of this type of radiation. The constant distances of each crystalline structure originate a characteristic distribution of maximums (peaks), which allows identifying the crystals qualitatively; the intensity of these peaks is proportional to the number of planes that diffract the incident beams, what happens just with certain angles of incidence. In such a case, a semi-quantitative concentration of a specific structure can be obtained by analyzing the area under the curve. Each peak is assigned to an intensity value and, with this information, the crystal or crystals to which the diffraction pattern investigated belongs are identified. Peaks of some minerals overlap and therefore depending on their concentration, minerals with little concentration can be completely mischaracterized with others. This can create large quantification error in sample mineral analysis which can be above 20% depending on the characterized mineral. In addition, for low angle diffraction lines (peaks) such as clays, the signal to noise ratio is usually poor, and due to the amount of disorders that can occur in clays structures, quantifying clays accurately is often difficult. It is also noted that XRD works only for crystalline, not for amorphous materials.

FTIR have been extensively used for mineral identification. Generally, sample preparation is extensive for FTIR. Specifically, potassium bromide (KBr) is mixed with specific mesh size sample powder and then pressed into a pellet and heated at 120° C. to remove water. Data is usually recorded in the range of 4000 to 400 cm−1using a spectral resolution of 4 cm−1. FTIR can provide chemical and mineral information on complex samples such as shales without the need for separating clays. Although FTIR is a powerful technique for mineral characterization, it does have some limitations with respect to clay quantification.

Characterization of shales cannot be suitably performed using exclusively one technique. XRD was able to identify and quantify a number of different clay minerals, whereas FTIR was useful in confirming the presence of certain clays along with providing information about the poorly ordered materials and organic matter.

DRIFTS has recently been introduced as a commercial service for material mineralogy characterization. It is a fast and efficient technique for quantifying mineralogy from OBM-free core and cuttings samples. DRIFTS can be used in a laboratory or at a wellsite. It is not a downhole technique. It measures the vibrational absorbance due to chemical bonds and is capable of resolving illite, smectite, kaolinite, and chlorite. Samples are usually scanned as bulk powders without dilution in reflectance mode and spectra are collected over the mid-IR range from 375 to 4000 cm−1with 4 cm−1resolution.

SUMMARY

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

In accordance with the subject disclosure, methods and systems are provided for clay detection, clay typing, and clay volume quantification using electromagnetic measurements on a porous media sample at a low frequency less than 5000 hertz (Hz). The low frequency electromagnetic measurements are used to determine and store permittivity data that characterizes permittivity of the porous media sample at the low frequency less than 5000 Hz. The low frequency electromagnetic measurements can be performed in a laboratory, at a wellsite or other surface location. The low frequency electromagnetic measurements are nondestructive, and the results indicate that the methods and systems are highly sensitive to the existence of clays.

In embodiments, the methods and systems can employ a computational model that relates a parameter extracted from measurement of permittivity of porous media at a low frequency less than 5000 Hz to data that characterizes at least one clay type and corresponding clay volume fraction. Electromagnetic measurements can be conducted on a porous media sample at a low frequency less than 5000 Hz. The electromagnetic measurements can be used to determine and store permittivity data that characterizes permittivity of the porous media sample at the low frequency less than 5000 Hz. A parameter can be extracted from the permittivity data. The extracted parameter can be used as input to the computational model, wherein the computational model outputs data that characterizes at least one clay type and corresponding clay volume fraction for the porous media sample. The data that characterizes at least one clay type and corresponding clay volume fraction for the formation of interest as provided by the computational model can be stored or output for use.

In embodiments, the computational model can be derived by measuring permittivity of porous media of different known clay types and different clay volume fractions at the low frequency less than 5000 Hz (such as multiple low frequencies less than 5000 Hz) and correlating a parameter extracted from the resultant permittivity to data that characterizes at least one clay type and corresponding clay volume fraction.

In embodiments, the computational model can relate a parameter extracted from measurements of permittivity of porous media at multiple low frequencies less than 5000 Hz to data that characterizes at least one clay type and corresponding clay volume fraction. The permittivity data of the porous media sample as well as the parameter extracted from the permittivity data can be derived from the electromagnetic measurements of permittivity of the porous media sample at the multiple low frequencies less than 5000 Hz.

In embodiments, the multiple low frequencies less than 5000 Hz can comprise at least three frequencies less than or equal to 100 Hz (and possibly a set of at least three frequencies between 100 Hz and 1 Hz).

In embodiments, the parameter of the computational model as well as the parameter extracted from the measurement of permittivity of the porous media sample can be selected from the group consisting of a frequency-specific slope, a frequency-specific permittivity, a critical frequency where the measurement of permittivity of the porous media sample diverges from permittivity of porous media that does not have clay, and combinations thereof.

In embodiments, the at least one clay type can be selected from the group consisting of kaolinite, smectite, illite, chlorite, and combinations thereof.

In embodiments, the porous media sample can be a formation rock sample. In this case, the data that characterizes at least one clay type and corresponding clay volume fraction as output from the computational model can be used to calculate a value of cation exchange capacity (CEC) for the formation rock sample. The data that characterizes at least one clay type and corresponding clay volume fraction as output from the computational model can also be used for evaluation of a hydrocarbon reservoir corresponding to the formation rock sample.

In embodiments, the extracting of the parameter can be performed by a processor. The computational model can also be embodied by a processor. The operations of the method or system or parts thereof can also be controlled by a processor.

In embodiments, the surface location can be a laboratory, a wellsite or other surface location.

