WO2024097170A1 - Interprétation diélectrique pour déduction de mouillabilité - Google Patents

Interprétation diélectrique pour déduction de mouillabilité Download PDF

Info

Publication number
WO2024097170A1
WO2024097170A1 PCT/US2023/036382 US2023036382W WO2024097170A1 WO 2024097170 A1 WO2024097170 A1 WO 2024097170A1 US 2023036382 W US2023036382 W US 2023036382W WO 2024097170 A1 WO2024097170 A1 WO 2024097170A1
Authority
WO
WIPO (PCT)
Prior art keywords
dielectric
wettability
formation
model
signals
Prior art date
Application number
PCT/US2023/036382
Other languages
English (en)
Inventor
Chang-yu HOU
Jiang Qian
Lalitha Venkataramanan
Laurent Mosse
Wael Abdallah
Shouxiang Mark MA
Original Assignee
Schlumberger Technology Corporation
Schlumberger Canada Limited
Services Petroliers Schlumberger
Schlumberger Technology B.V.
Saudi Arabian Oil Company
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Schlumberger Technology Corporation, Schlumberger Canada Limited, Services Petroliers Schlumberger, Schlumberger Technology B.V., Saudi Arabian Oil Company filed Critical Schlumberger Technology Corporation
Publication of WO2024097170A1 publication Critical patent/WO2024097170A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/18—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging
    • G01V3/32—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging operating with electron or nuclear magnetic resonance
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/12—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E21B49/08—Obtaining fluid samples or testing fluids, in boreholes or wells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/14—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with electron or nuclear magnetic resonance

Definitions

  • Wettability is defined as the preference of a solid to be in contact with one fluid rather than another (Abdallah et al., 2007).
  • the general qualification of a reservoir being preferably water-wet, hydrocarbon (HC)-wet or neutral-wet is not sufficient for accurate production planning, and more quantitative assessment is required.
  • Laboratory core-based measurements using established industry accepted techniques such as contact angle, Amott-Harvey and US Bureau of Mines (USBM) provide information about wettability indices, but they do not necessarily represent actual reservoir-wettability conditions, because wettability of cores measured in the lab may already be altered during coring and later on core processing, before wettability measurement. Downhole formation wettability measurement is both required and desirable.
  • the method may comprise obtaining dielectric dispersion signals of a formation and obtaining a total porosity of the formation.
  • the method may also comprise obtaining a dielectric dispersion model that includes a polarization response of trapped water droplets to indicate a wettability change of the formation; and processing the dielectric dispersion signals and the total porosity of the formation with the model to determine a wettability state of rock in the formation.
  • the software may include instructions for obtaining dielectric dispersion signals of a formation by obtaining a total porosity of the formation, obtaining a dielectric dispersion model that includes a polarization response of trapped water droplets to indicate a wettability change of the formation, and processing the dielectric dispersion signals and the total porosity of the formation with the model to determine a wettability state of rock in the formation.
  • FIG.1 shows a complex dielectric dispersion of a same core in different wettability stages.
  • FIG.2 are schematical plots of three possible first stage constructions by including different shaped water droplets into a rock matrix/hydrocarbon background.
  • FIG.3 shows three schematic plots of three possible constructions at the second stage for the inclusion of matrix/HC/water droplet composite into the conductive background.
  • FIG.4 are graphs of typical relative permittivity and conductivity dispersion in 1 – 1000 MHz for the two-stage BM-BM construction, with varying amounts of ETWF.
  • FIG.5 are graphs of typical relative permittivity and conductivity dispersion in 1 – 1000 MHz for the two stage LN-LN construction, with varying amounts of ETWF.
  • FIG.6 is a set of graphs of joint fit results for dielectric dispersion data shown in FIG.1.
  • FIG.9 is a proposed workflow for delineating the wettability states of a formation.
  • FIG.10 is a graph of inverted ⁇ w (ptw) for six water wet cores from their measured dielectric dispersion following the workflow of FIG.9.
  • FIG. 11 is a graph of inverted ⁇ w (ptw) for four HC-wet cores from measured dielectric dispersion following the workflow of FIG.9.
  • first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, components, region, layer or section from another region, layer or section. Terms such as “first”, “second” and other numerical terms, when used herein, do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed herein could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.
  • a series of new dielectric dispersion models are developed using the trapped/isolated water droplets as a proxy to indicate the hydrocarbon-wetting preference level of the formation. Namely, a higher trapped water fraction is used to indicate a more hydrocarbon-wet formation. Utilizing these newly developed models, a workflow is disclosed to delineate the wettability properties of formations based on the qualitatively different model inversion response for water-wet and for hydrocarbon-wet formations.
  • the multi-frequency complex dielectric downhole logging tools in 10 –1000 MHz range have been developed and used to probe formation petrophysical properties.
  • the multi-frequency complex dielectric signals are used to ATTORNEY DOCKET IS22.0660-WO-PCT DIELECTRIC INTREPETATION FOR WETTABILITY INFERENCE infer water saturation, brine salinity, water phase tortuosity exponent and cation exchange capacity in the presence of clay minerals. It has been observed that the change of formation wettability will affect its complex dielectric properties. However, no concrete workflow is established to enable the inference of the formation wettability properties from multi-frequency complex dielectric signals from existing tools in 1 – 1000 MHz frequency range.
  • the water residing in the pore space of rocks is ⁇ effectively’’ separated into two parts: the trapped (isolated) water droplets with their volume as ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ and the contiguous water phase with its volume as ⁇ ⁇ ⁇ ⁇ (1 ⁇ ⁇ ⁇ ⁇ ).
  • an effective permittivity, ⁇ ⁇ / ⁇ ⁇ is assigned as the combined dielectric property of the matrix/hydrocarbon phase, using the Complex Refraction Index Model (CRIM).
  • all polarization due to the inclusion of any material (either trapped water droplets or any composite) into a background material are assumed to be approximated by the inclusion of two simple shape type: spherical and oblate spheroidal particles.
  • FIG 1 schematical plots of three possible first stage constructions by including different shaped water droplets into the rock-matrix/hydrocarbon background are shown. Three cases shown from top to bottom are: spherical water droplets, bimodal (spheres and single shaped spheroids) water droplets, and lognormal shape distributed water droplets.
  • the effective dielectric constant, ⁇ 1 ⁇ ⁇ , for the rock- matrix/hydrocarbon/trapped-water-droplets composite is modeled and computed by adding water droplets into the nonconductive rock-matrix/hydrocarbon background, as ATTORNEY DOCKET IS22.0660-WO-PCT DIELECTRIC INTREPETATION FOR WETTABILITY INFERENCE schematically shown in Fig.2.
  • the rock matrix/hydrocarbon mixture is treated as a single phase, nonconductive material as in Eq. (1), and is used as the initial background for DEM approach.
  • Fig.2 Three possible constructions are shown in Fig.2: (1) spherical water droplets, (2) bimodal shaped (spheres and single shaped spheroids) water droplets, and (3) lognormal (LN) shape distributed water droplets.
  • the effective dielectric constant, ⁇ 1 ⁇ ⁇ is used for the rock- matrix/HC/trapped-water composite particles immersed in the contiguous water, as depicted in FIG.3.
  • the DEM approach is used to compute the dielectric constant and yield the predicted complex dielectric dispersion, ⁇ ⁇ , of the formation.
  • both the trapped water droplets in the first stage and the composite grains in the second stage can take any tractable shape distribution, such as the bimodal, lognormal, or even multimodal distributions.
  • the associated texture parameters defining the shapes and distributions of droplets and grains in two stages can in principle be entirely uncorrelated, but that will result in doubling the number of textual parameters.
  • the texture parameters, as defined later, for the first and the second stages of inclusion can be set to be identical.
  • FIG.2 schematic plots of three possible constructions at the second stage for the inclusion of matrix/HC/water droplet composite into the conductive background.
  • Three cases shown from top to bottom are: spherical water droplets with bimodal shapes for the composite, bimodal-bimodal (spheres and single shaped spheroids) construction, and LN-LN shape distribution construction.
  • the inclusion of isolated water into the non-conductive materials at the first stage is intended to model the water pockets cut off by the transition from the water- wet to the oil-wet regime.
  • bimodal-bimodal distribution for both stages (see FIG.9, 914) and (b) LN-LN distribution (see FIG.9, 912) for both stages, are discussed below.
  • BM-BM Bimodal-Bimodal
  • ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ is the relative permittivity and ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ is conductivity from the model prediction.
  • the exponent ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ is referred as contiguous water phase tortuosity exponent.
  • the trapped brine “droplets” are added into a “background” at the first stage with the initial background taken as the rock-matrix/HC mixture with its permittivity ⁇ ⁇ / ⁇ ⁇ .
  • Typical complex dielectric responses of the two-stage LN-LN construction in the frequency range ATTORNEY DOCKET IS22.0660-WO-PCT DIELECTRIC INTREPETATION FOR WETTABILITY INFERENCE of 1 – 1000 MHz are shown in FIG.5.
  • the conductivity reduces accompanying with the less dispersive relative permittivity, which is consistent with the observed dielectric response for a transition from water-wet regime to HC-wet regime.
  • LN-LN model can behave very similarly compared to the BM-BM model, especially for a smaller frequency range.
  • the dielectric dispersion of LN-LN model is normally much smoother as it consists of a broader spectrum of characteristic frequency.
  • the workflow demonstrated below will utilize the LN-LN model.
  • a least square fitting process is employed to fit the experimental dataset shown in FIG.1. Two sets of dielectric dispersion signals are measured from the same core but at different wettability states. Incidentally, the residue water volumes are similar at both water-wet and HC-wet state. Hence, based on the physical picture of our model, the ⁇ significant’’ difference between two sets of dielectric dispersion should come from the trapped water fraction, ⁇ ⁇ ⁇ .
  • dielectric dispersion at both water-wet and HC-wet states are jointly fitted with a common set of modeling parameters: ⁇ ⁇ ⁇ , ⁇ ⁇ ⁇ , and ⁇ ⁇ .
  • the brine salinity and total porosity of the core are known and used as an input in the model fitting process.
  • the fit yields a value of ⁇ ⁇ like that inferred from other measurements.
  • the fitting is also performed using LN-LN model, given brine salinity ⁇ ⁇ ⁇ ⁇ , total porosity ⁇ ⁇ , and fixed values of ⁇ ⁇ ⁇ .
  • directly inverting for the value of ⁇ ⁇ ⁇ and using it as an indicator for the wettability states can be very difficult and unreliable.
  • FIG. 8 (left side), ⁇ ⁇ ( ⁇ ⁇ ⁇ ) generally shows the monotonic increase feature with the increasing values of ⁇ ⁇ ⁇ .
  • Fig.8 (right side), shows much less variation and a non-monotonic response with the increasing values of ⁇ ⁇ ⁇ .
  • Such qualitative different response if surviving with real dielectric dispersion, can be used to delineating rocks’ wettability states.
  • FIG.9 The proposed workflow enabling us to distinguish wettability states of formations from multi-frequency dielectric signals in the frequency range of 1 MHz to a few GHz is illustrated in FIG.9.
  • FIG.9 the illustration of the proposed workflow for delineating the wettability states of the formation is provided.
  • the required inputs for the workflow are: • At 902 the complex dielectric dispersion signals, ⁇ ⁇ ( ⁇ ) and ⁇ ⁇ , between 1 – 1000 MHz: Multi-frequency dielectric signals measured from a typical dielectric logging tools in the frequency range of 20 – 1000 MHz with 4 – 5 frequency selections are sufficient for the workflow.
  • the total porosity, ⁇ ⁇ , of the formation The ⁇ ⁇ can be inferred from various standard methods, such as neutron-density ELAN or Quanti-ELAN analysis.
  • matrix permittivity, ⁇ ⁇ The matrix permittivity can be obtained by standard multi-mineral analysis.
  • the formation brine salinity is a needed input from prior knowledge or obtained from the neutron scattering spectral analysis for the chlorine concentration. Other commonly available parameters, such as formation temperature and pressure, are also needed as inputs. [065] In the next step, we utilize dielectric dispersion model, that include the trapped water fraction, ⁇ ⁇ ⁇ , to capture polarization effect of fluid (re)distribution due to wettability changes from water-wet to HC-wet.
  • such dielectric model is governed by the following parameters: (1) the relative permittivity of rock matrix, ⁇ ⁇ ; (2) the relative permittivity of HC, ⁇ ⁇ ⁇ ; (3) the total porosity of the rock, ⁇ ⁇ ; (4) the brine salinity, ⁇ ⁇ ⁇ ⁇ ; (5) the water saturation fraction, ⁇ ⁇ ; (6) texture parameters, such as ( ⁇ , ⁇ ⁇ ⁇ h ⁇ ) for BM-BM model and ( ⁇ ⁇ ⁇ , ⁇ ⁇ ⁇ ) for LN-LN model (for 912); and (7) the effective trapped water fraction among all water volume, ⁇ ⁇ ⁇ .
  • ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ( ⁇ ⁇ ) are modeled relative permittivity and conductivity at frequency, ⁇ ⁇ , with a set of
  • the weight constants, ⁇ ⁇ , ⁇ and ⁇ ⁇ , ⁇ can be chosen to be the error/uncertainty or other normalization values to make each data points more equal weighted. Variation of cost functions may also be used to achieve more stable fitting/inversion results.
  • FIG.10 shows examples of inverted ⁇ ⁇ ( ⁇ ⁇ ⁇ ) from the measured dielectric signals of six water-wet cores by going through the proposed workflow illustrated in FIG. 9.
  • the inverted ⁇ ⁇ increases monotonically with the increasing value of ⁇ ⁇ ⁇ as shown in 918. is consistent with the pattern shown in FIG.8 (left side), which allows us to unambiguously identify the cores as water-wet.
  • FIG. 11 examples of inverted ⁇ ⁇ ( ⁇ ⁇ ⁇ ) from the measured dielectric signals for four HC-wet cores are shown.
  • FIG.10 illustrates the inverted ⁇ ⁇ ( ⁇ ⁇ ⁇ ) for six water-wet cores from their measured dielectric dispersion following the established workflow.
  • FIG.11 illustrates the inverted ⁇ ⁇ ( ⁇ ⁇ ⁇ ) for four HC-wet cores from their measured dielectric dispersion following the established workflow.
  • the method may comprise obtaining dielectric dispersion signals of a formation and obtaining a total ATTORNEY DOCKET IS22.0660-WO-PCT DIELECTRIC INTREPETATION FOR WETTABILITY INFERENCE porosity of the formation.
  • the method may also comprise obtaining a dielectric dispersion model that includes a polarization response of trapped water droplets to indicate a wettability change of the formation; and processing the dielectric dispersion signals and the total porosity of the formation with the model to determine a wettability state of rock in the formation.
  • the method may be performed wherein the dielectric models are in a frequency range of 1MHZ to 3 GHz.
  • the method may be performed wherein the dielectric dispersion signals are measured from a typical dielectric logging tool. [072] In another example embodiment, the method may be performed wherein the signals are in a frequency range of 20 – 1000 MHz. [073] In another example embodiment, the method may be performed wherein an effective trapped water fraction is used as a proxy for indications of wettability. [074] In another example embodiment, the method may be performed wherein the dielectric dispersion signals are combined with at least one petrophysical measurement to reduce an uncertainty of estimated rock and fluid properties. [075] In another example embodiment, the method may be performed wherein the at least one petrophysical measurement includes at least one of a resistivity and a nuclear magnetic resonance measurement.
  • the method may be performed wherein the at least one petrophysical measurement includes at least one nuclear measurement.
  • the method further comprise obtaining a salinity of a fluid from the formation, wherein the salinity is used by the dielectric dispersion model .
  • the method may be performed wherein an effective trapped water fraction is used as a proxy for indications of wettability.
  • a computer readable storage medium having data stored therein representing software executable by a computer is disclosed.
  • the software may including instructions for obtaining dielectric dispersion signals of a formation, obtaining a total porosity of the formation, obtaining a dielectric dispersion model that includes a polarization response of trapped water droplets to indicate a wettability change of the formation and processing the dielectric dispersion signals and the total porosity of the formation with the model to determine a wettability state of rock in the formation.
  • the method may be performed wherein the instructions include the signals are in a frequency range of 20 – 1000 MHz.

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Mining & Mineral Resources (AREA)
  • Environmental & Geological Engineering (AREA)
  • Remote Sensing (AREA)
  • Geophysics (AREA)
  • General Physics & Mathematics (AREA)
  • Fluid Mechanics (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Les modes de réalisation présentés concernent un procédé d'interprétation de paramètres géologiques. Le procédé peut consister à obtenir des signaux de dispersion diélectrique d'une formation et obtenir une porosité totale de la formation. Le procédé peut également consister à obtenir un modèle de dispersion diélectrique qui comprend une réponse en polarisation de gouttelettes d'eau piégées pour indiquer un changement de mouillabilité de la formation et traiter les signaux de dispersion diélectrique et la porosité totale de la formation avec le modèle pour déterminer un état de mouillabilité d'une roche dans la formation.
PCT/US2023/036382 2022-11-02 2023-10-31 Interprétation diélectrique pour déduction de mouillabilité WO2024097170A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202263381945P 2022-11-02 2022-11-02
US63/381,945 2022-11-02

Publications (1)

Publication Number Publication Date
WO2024097170A1 true WO2024097170A1 (fr) 2024-05-10

Family

ID=90931343

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2023/036382 WO2024097170A1 (fr) 2022-11-02 2023-10-31 Interprétation diélectrique pour déduction de mouillabilité

Country Status (1)

Country Link
WO (1) WO2024097170A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150127264A1 (en) * 2013-11-01 2015-05-07 Schlumberger Technology Corporation Downhole Wettability Estimate Using Multi-Frequency Dielectric Measurements
US20180203151A1 (en) * 2017-01-13 2018-07-19 Baker Hughes Incorporated Measuring petrophysical properties of an earth formation by regularized direct inversion of electromagnetic signals
US20220034222A1 (en) * 2020-07-30 2022-02-03 Baker Hughes Oilfield Operations Llc Determine a formation's textural parameters using advancing logging data

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150127264A1 (en) * 2013-11-01 2015-05-07 Schlumberger Technology Corporation Downhole Wettability Estimate Using Multi-Frequency Dielectric Measurements
US20180203151A1 (en) * 2017-01-13 2018-07-19 Baker Hughes Incorporated Measuring petrophysical properties of an earth formation by regularized direct inversion of electromagnetic signals
US20220034222A1 (en) * 2020-07-30 2022-02-03 Baker Hughes Oilfield Operations Llc Determine a formation's textural parameters using advancing logging data

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
AL OFI SALAH, DYSHLYUK EVGENY, SAUERER BASTIAN, VALORI ANDREA, ALI FARHAN, ABDALLAH WAEL: "Correlating Dielectric Dispersion Data and Wettability Index of a Carbonate Rock", SPE KINGDOM OF SAUDI ARABIA ANNUAL TECHNICAL SYMPOSIUM AND EXHIBITION, SOCIETY OF PETROLEUM ENGINEERS, vol. 23, no. 192224, 23 April 2018 (2018-04-23) - 26 April 2018 (2018-04-26), pages 1 - 20, XP009554899, ISBN: 978-1-61399-620-1, DOI: 10.2118/192224-MS *
CHANG-YU HOU, JIANG QIAN, LALITHA VENKATARAMANAN, WAEL ABDALLAH: "Dielectric Dispersion Model for Qualitative Interpretation of Wettability", SPWLA 64TH ANNUAL LOGGING SYMPOSIUM; LAKE CONROE, TEXAS, USA; JUNE 10–14, 2023, vol. 10, no. 2023, 10 June 2023 (2023-06-10) - 14 June 2023 (2023-06-14), pages 1 - 17, XP009554902, DOI: 10.30632/SPWLA-2023-0092 *
JIN GUODONG, MA SHOUXIANG, ANTLE RYAN, AL OFI SALAH: "Reservoir Characterization for Isolated Porosity from Multi-Frequency Dielectric Measurements", INTERNATIONAL PETROLEUM TECHNOLOGY CONFERENCE; RIYADH, SAUDI ARABIA; FEBRUARY 21–23, 2022, vol. 21, no. 22424, 21 February 2022 (2022-02-21) - 23 February 2022 (2022-02-23), pages 1 - 9, XP009554901, ISBN: 978-1-61399-833-5, DOI: 10.2523/IPTC-22424-MS *

Similar Documents

Publication Publication Date Title
AU2011213261B2 (en) Multilevel percolation aggregation solver for petroleum reservoir simulations
US20180120468A1 (en) Evaluation of formation utilizing wideband electromagnetic measurements
US20160097876A1 (en) Method of Determining CEC and Other Properties from Multi-Frequency Dielectric Measurements
Zhan et al. Pore-scale modeling of electrical and fluid transport in Berea sandstone
US11719849B2 (en) Modeling textural parameters of a formation with downhole measurements
US10472936B2 (en) Total gas in place estimate
Lauer et al. A new technique for measuring broadband dielectric spectra of undisturbed soil samples
US10330819B2 (en) Downhole wettability estimate using multi-frequency dielectric measurements
Josh et al. Broadband electrical properties of clays and shales: Comparative investigations of remolded and preserved samples
Bertermann et al. Laboratory device to analyse the impact of soil properties on electrical and thermal conductivity
Kumar et al. Mapping 3D pore scale fluid distributions: how rock resistivity is influenced by wettability and saturation history
Nashawi et al. Improved electrofacies characterization and permeability predictions in sandstone reservoirs using a data mining and expert system approach
US20050030019A1 (en) Method for determining the resistivity index, as a function of the water saturation, of certain rocks of complex porosity
Al-Ofi et al. Correlating dielectric dispersion data and wettability index of a carbonate rock
WO2024097170A1 (fr) Interprétation diélectrique pour déduction de mouillabilité
Zhu et al. Characterization of pore electrical conductivity in porous media by weakly conductive and nonconductive pores
Bała Evaluation of electric parameters of anisotropic sandy-shaly Miocene formations on the basis of resistivity logs
Boadu A support vector regression approach to predict geotechnical properties of soils from electrical spectra based on Jonscher parameterization
Gkortsas et al. Indication of wettability from dielectric measurements on partially saturated rocks using effective medium models
Toumelin Pore-scale petrophysical models for the simulation and combined interpretation of nuclear magnetic resonance and wide-band electromagnetic measurements of saturated rocks
Posenato Garcia et al. Multifrequency analytical forward modeling of dielectric permittivity dispersion in mixed-wet sedimentary rocks
Hou et al. Dielectric Dispersion Model for Qualitative Interpretation of Wettability
Rafiee et al. A new cementation factor correlation in carbonate parts of oil fields in south-west Iran
Almarzooq et al. Characterization of shale gas rocks using dielectric dispersion and nuclear magnetic resonance
Kumar et al. Mapping fluid distributions in 3D at the pore scale: Quantifying the influence of wettability and saturation history on rock resistivity

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23886606

Country of ref document: EP

Kind code of ref document: A1