Invasion characterization and formation evaluation in a logging-while-drilling operation

Abstract

A method comprising developing a near-wellbore reservoir model based on a logging-while-drilling (LWD) process in a wellbore, simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data, converting the simulated saturation and salinity distribution data to a resistivity distribution data, simulating azimuthal multi-depth-of-investigation (DOI) resistivity imaging data based on the resistivity distribution data, and performing an inversion on the azimuthal multi-DOI resistivity imaging data to infer invasion parameters.

Description

INVASION CHARACTERIZATION AND FORMATION EVALUATION IN A LOGGING-WHILE-DRILLING OPERATIONCROSS REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to United States Provisional Patent Application 63 / 607,792, filed on December 8, 2023, the entirety of which is incorporated by reference.FIELD OF THE DISCLOSURE

[0002] Aspects of the disclosure relate to geological formation evaluation methods. More specifically, aspects of the disclosure relate to invasion characterization and formation evaluation for logging-while-drilling operations.BACKGROUND

[0003] Wireline measurements, such as array resistivity logging, as well as formation testing and sampling, have been widely used for mud-filtrate invasion characterization and formation evaluation. These approaches; however, are more suitable for vertical or low-angle wells. For deviated wells, logging-while-drilling (LWD) is usually more feasible, and the technique has replicated most wireline measurements to corresponding LWD counterparts.

[0004] As invasion depth is usually shallow during a LWD logging pass, a multi-depth azimuthal resistivity imaging technique was developed to characterize the early invasion profile. Conventionally, interpretation of LWD azimuthal resistivity imaging is either qualitative, or only through a simple one-dimensional inversion. Conventional interpretation of LWD data is often limited when it comes to high-angle or horizontal wells, as the invasion profile in such wells are often asymmetric around the borehole. Gravity segregation and permeability anisotropy of the well make the one-dimensional assumption not accurate enough in many cases. Furthermore, conventional interpretation of LWD azimuthal resistivity imaging neglects the relative permeability and capillary pressures in a LWD operation, and the invasion profile of such operation is typically not verified against LWD azimuthal resistivity measurements.

[0005] It is imperative to provide a quantitative interpretation method for characterizing invasion profile and estimating formation properties from LWD azimuthal resistivity imaging and formation testing and sampling data.

[0006] There is a need to provide a method for characterizing an invasion profile that provides accurate answers and that is not computationally complex.

[0007] There is a further need to provide a method for characterizing an invasion profile that is economically cost effective to perform to provide accurate geological data to engineers.SUMMARY

[0008] So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized below, may be had by reference to embodiments, some of which are illustrated in the drawings. It is to be noted that the drawings illustrate only typical embodiments of this disclosure and are therefore not to be considered limiting of its scope, for the disclosure may admit to other equally effective embodiments without specific recitation. Accordingly, the following summary provides just a few aspects of the description and should not be used to limit the described embodiments to a single concept.

[0009] In one example embodiment, a method for invasion characterization and formation evaluation is described. The method may comprise developing a near-wellbore reservoir model based on a logging-while-drilling process in a wellbore. The method may further comprise simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data. The method may further comprise converting the simulated saturation and salinity distribution data to a resistivity distribution data. The method may further comprise simulating azimuthal multi-depth-of-investigation resistivity imaging data based on the resistivity distribution data. The method may further comprise performing an inversion on the azimuthal multi-depth of investigation resistivityimaging data to infer invasion parameters. The method may further comprise providing a notification to a user that calculations are complete.

[0010] In another example embodiment, a method for invasion characterization and formation evaluation in a logging-while-drilling operation is presented. The method may comprise developing a near-wellbore reservoir model based on a logging-while-drilling process in a wellbore. The method may further comprise simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data. The method may further comprise converting the simulated saturation and salinity distribution data to a resistivity distribution data. The method may further comprise simulating azimuthal multi-depth-of-lnvestlgation resistivity Imaging data based on the resistivity distribution data. The method may further comprise performing a query if the simulated azimuthal multi-depth-of-investigation resistivity imaging data matches field data. The method may further comprise performing an inversion on the azimuthal multi-depth-of- investigation resistivity imaging data to infer invasion parameters when the simulated azimuthal multi-depth-of-investigation resistivity imaging data matches field data and creating a notification to a user. The method may further comprise looping back to the developing the near-wellbore reservoir model when the simulated azimuthal multi-depth-of-investigation resistivity imaging data does not match the field data.

[0011] In another example embodiment, an article of manufacture capable of being read by a computing apparatus is disclosed. The article of manufacture may provide for, the computing apparatus to perform calculations and control equipment based on a list of method instructions, the method instructions comprising developing a near-wellbore reservoir model based on a logging-while-drilling process in a wellbore. The method instructions may further comprise simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data. The method instructions may further comprise converting the simulated saturation and salinity distribution data to a resistivitydistribution data. The method instructions may further comprise simulating azimuthal multi- depth-of-investigation resistivity imaging data based on the resistivity distribution data. The method instructions may further comprise performing a query if the simulated azimuthal multi- depth-of-investigation resistivity imaging data matches field data. The method instructions may further comprise performing an inversion on the azimuthal multi-depth of investigation resistivity imaging data to infer invasion parameters when the simulated azimuthal multi- depth-of-investigation resistivity imaging data matches field data. The method instructions may further comprise looping back to the developing the near-wellbore reservoir model when the simulated azimuthal multi-depth-of-investigation resistivity imaging data does not match the field data.BRIEF DESCRIPTION OF THE DRAWINGS

[0012] So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the drawings. It is to be noted; however, that the appended drawings illustrate only typical embodiments of this disclosure and are therefore not be considered limiting of its scope, for the disclosure may admit to other equally effective embodiments.

[0013] FIG. 1 is a workflow of an embodiment of the method for characterizing invasion profile and estimating formation properties.

[0014] FIG. 2A and FIG..2B are examples of polar plots from LWD azimuthal resistivity imaging at different measured depths.DETAILED DESCRIPTION

[0015] In the following, reference is made to embodiments of the disclosure. It should be understood; however, that the disclosure is not limited to specific described embodiments.Instead, any combination of the following features and elements, whether related to different embodiments or not, is contemplated to implement and practice the disclosure. Furthermore, although embodiments of the disclosure may achieve advantages over other possible solutions and / or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the disclosure. Thus, the following aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the claims except where explicitly recited in a claim. Likewise, reference to “the disclosure” shall not be construed as a generalization of inventive subject matter disclosed herein and should not be considered to be an element or limitation of the claims except where explicitly recited in a claim.

[0016] Although the terms 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.

[0017] When an element or layer is referred to as being “on", “engaged to”, “connected to", or “coupled to” another element or layer, it may be directly on, engaged, connected, coupled to the other element or layer, or interleaving elements or layers may be present. In contrast, when an element is referred to as being “directly on”, "directly engaged to”, "directly connected to", or “directly coupled to" another element or layer, there may be no interleaving elements or layers present. Other words used to describe the relationship between elements should beinterpreted in a like fashion. As used herein, the term “and / or" includes any and all combinations of one or more of the associated listed terms.

[0018] Some embodiments will now be described with reference to the figures. Like elements in the various figures will be referenced with like numbers for consistency. In the following description, numerous details are set forth to provide an understanding of various embodiments and / or features. It will be understood; however, by those skilled in the art, that some embodiments may be practiced without many of these details, and that numerous variations or modifications from the described embodiments are possible. As used herein, the terms “above" and “below", “up" and “down", “upper" and “lower", “upwardly” and “downwardly”, and other like terms indicating relative positions above or below a given point are used in this description to more clearly describe certain embodiments.

[0019] Illustrative examples of the subject matter claimed below will now be disclosed. In the interest of clarity, not all features of an actual implementation are described in this specification. It will be appreciated that in the development of any such actual implementation, numerous implementation-specific decisions may be made to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort, even if complex and time-consuming, would be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.

[0020] Further, as used herein, the article “a” is intended to have its ordinary meaning in the patent arts, namely "one or more.” Moreover, examples herein are intended to be illustrative only and are presented for discussion purposes and not by way of limitation.

[0021] Embodiments of the present disclosure are directed towards a method for invasion characterization and formation evaluation from logging-while-drilling (LWD) azimuthal resistivity imaging and formation testing and sampling data.

[0022] FIG. 1 shows an embodiment of the inversion workflow. In one or more embodiments, the method 100 may comprise, in block 10, developing a near-wellbore reservoir model based on a logging-while-drilling (LWD) process in a wellbore. Thereafter, in block 20, simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data, and in block 30, converting the simulated saturation and salinity distribution data to resistivity distribution data. The method proceeds, in block 40, with the simulating of azimuthal multi-DOI resistivity imaging data which may be conducted by applying electromagnetic modelling. The method proceeds in block 50 with simulating azimuthal multi-depth-of-investigation (DOI) resistivity imaging data based on the resistivity distribution data. The method then proceeds to a query at block 55 to determine if the simulated data matches measured data. If the simulated data matches the measured data, the inversion process is completed and proceeds in block 60 with a list of outputs from the workflow. If the simulated data does not match the measured data, the method may proceed back to block 10 with updated model parameters. If the data does match, a further notification of matching data may be generated to the user. The notification to a user may be by providing a visual confirmation on a computer screen or by creating a computer record of the calculations and storing these confirmatory calculations in a non-volatile memory.

[0023] In one or more embodiments, the relevant inverted or reconstructed parameters may comprise invasion volume, invasion profile (i.e., the invasion front geometry and saturation variation in the invaded region), permeability, permeability anisotropy, relative permeability, and pseudo-capillary pressure.

[0024] In one or more embodiments, a saturation-resistivity transformation formula may be used to convert the simulated saturation and salinity distribution data to the resistivity distribution data. In one or more embodiments, the saturation-resistivity transformation formula may be Archie's Law as represented below:

[0025] In one or more embodiments, the simulation of mud-filtrate invasion may comprise simulating an injection process with consideration of the salinities of mud-filtrate and formation brine of the wellbore.

[0026] In one or more embodiments, the method 100 may further comprise predicting an invasion fluid volume required to be extracted to access uncontaminated formation fluids from the wellbore based on the invasion parameters.

[0027] In one or more embodiments, the method 100 may further comprise completing a fluid saturation profile of the well in a single well predictive model based on the inferred invasion parameters. The invasion parameters may comprise the relative permeability and pseudo-capillary pressure. The well predictive model may be applied to evaluate the effectiveness of structural geo-steering or well placement by comparing production forecasts, downhole pressure decline, liquid flow rates, and liquid production volumes between different geo-steering trajectories / well profiles.

[0028] In one or more embodiments, LWD formation tester sampling pumping rate and duration can be calculated by using a multiphase porous medium fluid flow simulator based on the invasion volume in block 60 and expected formation mobility.

[0029] In one or more embodiments, a simple step invasion profile assumption with a non- symmetric shape for the invasion volume may be applied for the performing of an inversion on the azimuthal multi-DOI resistivity imaging data. In this embodiment, LWD formation tester sampling pumping rate and duration can be calculated by using a multiphase porous medium fluid flow simulator based on the invasion volume and expected formation mobility.

[0030] Advantageously, the application of method 100 provides a quantitative approach to estimate invasion parameters during a LWD operation based on LWD azimuthal resistivity imaging, which is particularly useful for deviated wells such as horizontal wells or high angle wells.

[0031] FIG. 2A and FIG. 2B show typical polar plots from LWD azimuthal resistivity imaging with multi-depth-of-investigation (DOI) at different specific measured depths in a well, with the lines representing button resistivity at different DOI, and the shadowed area representing the invasion front. Specifically, FIG. 2A and FIG. 2B present a well scenario with saline mudfiltrate invading into an oil or gas-bearing formation, with the well trajectory being horizontal. The multi-DOI resistivity channels in FIG. 2A shows obvious gravity segregation effect and decentering of the invasion front, which can be seen from the teardrop shape represented by the shadowed area. This may be due to extended invasion duration and relatively higher formation permeability in the well formation. Whereas the polar plot in FIG. 2B shows a symmetric distribution of the multi-DOI resistivity channels, which implies shorter invasion duration and relatively lower permeability, hence the invasion front is more centering around the wellbore (indicated by the shadowed area).

[0032] From FIG. 2A and FIG. 2B, it is evident that conventional interpretation of LWD azimuthal resistivity imaging, typically being a qualitative approach or a simple one-dimension inversion performed for each quadrant or octant of a well individually, would not be useful in a deviated well. This is because depending on the invasion profile and the type of probe used for sampling, as well as the location where the probe is targeted on for the sampling operation, the fluid fraction withdrawn from the sampling tool will be different. The quantitative approach provided by method 100 thus alleviates these issues.

[0033] Furthermore, applying method 100 before obtaining formation sampling data (i.e. FIG. 1 without block 25) helps to determine the invasion volume and enables the evaluation of the required volume of fluid to be pumped to obtain a formation sample with minimum filtrate contamination. Pumping rate and pumping time to obtain the sample may thus be optimized by running a multiphase porous medium fluid flow simulator based on the invasion volume and the expected formation mobility.

[0034] An alternative workflow is to invert multi-DOI LWD azimuthal resistivity imaging data with a simple step invasion profile assumption (but allow a non-symmetric shape for theinvasion volume). LWD formation tester sampling pumping rate and duration can then be calculated by using a multiphase porous medium fluid flow simulator based on the invasion volume and the expected formation mobility.

[0035] As will be understood, method steps for completion may be stored in the random access memory, read only memory, flash memory, computer hard disk drives, compact disks, floppy disks and solid state drives. These method steps, contained in a non-volatile memory, also called an article of manufacture, may be read by computer arrangements to subsequently automate the method steps described. The method steps may include not only computational calculations but also control of equipment in a laboratory or the field. In contemplated embodiments of the disclosure, the method steps may be sent to laboratory and / or field equipment for completion. The sending and receiving of signals may be performed through the internet, cellular telephone networks, mainframe computer networks and similar devices. The article of manufacture may be configured as any of the random-access memory, read only memory, flash memory, computer hard disk drives, compact disks, floppy disks and solid state drives, a universal serial bus device as well as mainframe and / or server equipment.

[0036] Example embodiments of the claims will now be described. The described claims should not be considered limiting of the disclosure. In one example embodiment, a method for invasion characterization and formation evaluation is described. The method may comprise developing a near-wellbore reservoir model based on a logging-while-drilling process in a wellbore. The method may further comprise simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data. The method may further comprise converting the simulated saturation and salinity distribution data to a resistivity distribution data. The method may further comprise simulating azimuthal multi-depth-of-investigation resistivity imaging data based on the resistivity distribution data. The method may further comprise performing an inversion on the azimuthalmulti-depth of investigation resistivity imaging data to infer invasion parameters. The method may further comprise providing a notification to a user that calculations are complete.

[0037] In another example embodiment, the method may be performed wherein the invasion parameters comprise at least one of an invasion volume, invasion profile, permeability, anisotropy, relative permeability, and pseudo-capillary.

[0038] In another example embodiment, the method may further comprise matching the simulated azimuthal multi-depth-of-investigation resistivity imaging data to a measured data obtained from the wellbore after simulating azimuthal multi-depth-of-investigation resistivity imaging data based on the resistivity distribution data.

[0039] In another example embodiment, the method may be performed wherein if the simulated azimuthal multi-depth-of-investigation resistivity imaging data differs from the measured data, then the near-wellbore reservoir model is reconstructed.

[0040] In another example embodiment, the method may be performed wherein a saturation-resistivity transformation formula is applied to convert the simulated saturation and salinity distribution data to the resistivity distribution data.

[0041] In another example embodiment, the method may be performed wherein the saturation-resistivity transformation formula is Archie's Law.

[0042] In another example embodiment, the method may be performed wherein the simulating of mud-filtrate invasion comprises simulating an injection process considering salinities of mud-filtrate and formation brine of the wellbore.

[0043] In another example embodiment, the method may further comprise predicting an invasion fluid volume required to be extracted to access uncontaminated formation fluids from the wellbore based on the invasion parameters.

[0044] In another example embodiment, the method may be performed wherein the simulating of azimuthal multi-DOI resistivity imaging data is conducted by applying electromagnetic modelling.

[0045] In another example embodiment, the method may further comprise completing a fluid saturation profile of the well in a single well predictive model based on the invasion parameters.

[0046] In another example embodiment, the method may be performed wherein the invasion parameters comprise relative permeability and pseudo-capillary.

[0047] In another example embodiment, the method may be performed wherein a simple step invasion profile assumption with a non-symmetric shape for invasion volume is applied when performing the inversion on the azimuthal multi-DOI resistivity imaging data to infer invasion parameters.

[0048] In another example embodiment, a method for invasion characterization and formation evaluation in a logging-while-drilling operation is presented. The method may comprise developing a near-wellbore reservoir model based on a logging-while-drilling process in a wellbore. The method may further comprise simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data. The method may further comprise converting the simulated saturation and salinity distribution data to a resistivity distribution data. The method may further comprise simulating azimuthal multi-depth-of-investigation resistivity imaging data based on the resistivity distribution data. The method may further comprise performing a query if the simulatedazimuthal multi-depth-of-investigation resistivity imaging data matches field data. The method may further comprise performing an inversion on the azimuthal multi-depth of investigation resistivity imaging data to infer invasion parameters until the simulated azimuthal multi-depth-of-investigation resistivity imaging data matches field data and creating a notification to a user. The method may further comprise looping back to the developing the near-wellbore reservoir model when the simulated azimuthal multi-depth-of-investigation resistivity imaging data does not match the field data.

[0049] In another example embodiment, the method may be performed wherein the inverted or reconstructed parameters comprise at least one of an invasion volume, invasion profile, permeability, permeability anisotropy, relative permeability, and pseudo-capillary.

[0050] In another example embodiment, a repeat pass of azimuthal borehole resistivity image can be carried out and used in the inversion workflow to further constrain the inversion process to determine the inverted parameters with reduced uncertainty.

[0051] In another example embodiment, the method may be performed wherein a saturation-resistivity transformation formula is applied to convert the simulated saturation and salinity distribution data to the resistivity distribution data.

[0052] In another example embodiment, the method may be performed wherein the saturation-resistivity transformation formula is Archie's Law.

[0053] In another example embodiment, an article of manufacture capable of being read by a computing apparatus is disclosed. The article of manufacture may provide for, the computing apparatus to perform calculations and control equipment based on a list of method instructions, the method instructions comprising developing a near-wellbore reservoir model based on a logging-while-drilling process in a wellbore. The method instructions may furthercomprise simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data. The method instructions may further comprise converting the simulated saturation and salinity distribution data to a resistivity distribution data. The method instructions may further comprise simulating azimuthal multi- depth-of-investigation resistivity imaging data based on the resistivity distribution data. The method instructions may further comprise performing a query if the simulated azimuthal multi- depth-of-investigation resistivity imaging data matches field data. The method instructions may further comprise performing an inversion on the azimuthal multi-depth of investigation resistivity imaging data to infer invasion parameters until the simulated azimuthal multi-depth- of-investigation resistivity imaging data matches field data. The method instructions may further comprise looping back the developing the near-wellbore reservoir model when the simulated azimuthal multi-depth-of-investigation resistivity imaging data does not match the field data.

[0054] In another example embodiment, the article of manufacture is of a form of one of a solid-state drive, a compact disk, a read-only memory and a universal serial bus device.

[0055] The foregoing description, for purposes of explanation, used specific nomenclature to provide a thorough understanding of the disclosure. However, it will be apparent to one skilled in the art that the specific details are not required in order to practice the systems and methods described herein. The foregoing descriptions of specific examples are presented for purposes of illustration and description. They are not intended to be exhaustive of or to limit this disclosure to the precise forms described. Obviously, many modifications and variations are possible in view of the above teachings. The examples are shown and described in order to best explain the principles of this disclosure and practical applications, to thereby enable others skilled in the art to best utilize this disclosure and various examples with various modifications as are suited to the particular use contemplated. It is intended that the scope of this disclosure be defined by the claims and their equivalents below.

Claims

CLAIMSWhat is claimed is:

1. A method comprising: developing a near-wellbore reservoir model based on a logging-while-drilling process in a wellbore; simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data; converting the simulated saturation and salinity distribution data to a resistivity distribution data; simulating azimuthal multi-depth-of-investigation resistivity imaging data based on the resistivity distribution data; performing an inversion on the azimuthal multi-depth of investigation resistivity imaging data to infer invasion parameters; and providing a notification to a user that calculations are complete.

2. The method according to claim 1 , wherein the invasion parameters comprise at least one of an invasion volume, invasion profile, permeability, anisotropy, relative permeability, and pseudo-capillary.

3. The method according to claim 1, further comprising: matching the simulated azimuthal multi-depth-of-investigation resistivity imaging data to a measured data obtained from the wellbore after simulating azimuthal multi-depth- of-investigation resistivity imaging data based on the resistivity distribution data.

4. The method according to claim 3, wherein if the simulated azimuthal multi-depth-of- investigation resistivity imaging data differs from the measured data, the near-wellbore reservoir model is reconstructed.

5. The method according to claim 1, wherein a saturation-resistivity transformation formula is applied to convert the simulated saturation and salinity distribution data to the resistivity distribution data.

6. The method according to claim 5, wherein the saturation-resistivity transformation formula is Archie's Law.

7. The method according to claim 1, wherein the simulating of mud-filtrate invasion comprising simulating an injection process considering salinities of mud-filtrate and formation brine of the wellbore.

8. The method according to claim 1, further comprising: predicting an invasion fluid volume required to be extracted to access uncontaminated formation fluids from the wellbore based on the invasion parameters.

9. The method according to claim 1, wherein the simulating of azimuthal multi-DOI resistivity imaging data is conducted by applying electromagnetic modelling.

10. The method according to claim 1, further comprising: completing a fluid saturation profile of the well in a single well predictive model based on the invasion parameters.

11. The method according to claim 10, wherein the invasion parameters comprise relative permeability and pseudo-capillary.

12. The method according to claim 1, wherein a simple step invasion profile assumption with a non-symmetric shape for invasion volume is applied when performing the inversion on the azimuthal multi-DOI resistivity imaging data to infer invasion parameters.

13. A method, comprising:developing a near-wellbore reservoir model based on a logging-while-drilling process in a wellbore; simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data; converting the simulated saturation and salinity distribution data to a resistivity distribution data; simulating azimuthal multi-depth-of-investigation resistivity imaging data based on the resistivity distribution data; performing a query if the simulated azimuthal multi-depth-of-investigation resistivity imaging data matches field data; performing an inversion on the azimuthal multi-depth of investigation resistivity imaging data to infer invasion parameters until the simulated azimuthal multi-depth-of- investigation resistivity imaging data matches field data and creating a notification to a user; and looping back to the developing the near-wellbore reservoir model when the simulated azimuthal multi-depth-of-investigation resistivity imaging data does not match the field data.

14. The method according to claim 13, wherein the invasion parameters comprise at least one of an invasion volume, invasion profile, permeability, anisotropy, relative permeability, and pseudo-capillary.

15. The method according to claim 13, wherein a saturation-resistivity transformation formula is applied to convert the simulated saturation and salinity distribution data to the resistivity distribution data.

16. The method according to claim 15, wherein the saturation-resistivity transformation formula is Archie's Law.

17. An article of manufacture capable of being read by a computing apparatus, the computing apparatus configured to perform calculations and control equipment based on a list of method instructions, the method instructions comprising: developing a near-wellbore reservoir model based on a logging-while-drilling process in a wellbore; simulating mud-filtrate invasion in the near-wellbore reservoir model to obtain a simulated saturation and salinity distribution data; converting the simulated saturation and salinity distribution data to a resistivity distribution data; simulating azimuthal multi-depth-of-investigation resistivity imaging data based on the resistivity distribution data; performing a query if the simulated azimuthal multi-depth-of-investigation resistivity imaging data matches field data; performing an inversion on the azimuthal multi-depth of investigation resistivity imaging data to infer invasion parameters until the simulated azimuthal multi-depth-of- investigation resistivity imaging data matches field data; and looping back the developing the near-wellbore reservoir model when the simulated azimuthal multi-depth-of-investigation resistivity imaging data does not match the field data.

18. The article of manufacture according to claim 17, wherein the article is of a form of one of a solid-state drive, a compact disk, a read-only memory and a universal serial bus device.

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