BR112019023216B1 - METHOD FOR DETERMINING PROPERTIES OF A LAMINATED FORMATION PASSED BY A WELL OR BOREHOLE, AND DATA PROCESSOR - Google Patents

Abstract

It is a method for determining properties of a laminate formation traversed by a well or borehole that employs sonic data, resistivity data, and measured density data for an interval of interest within the well or borehole. A formation model that describes properties of the laminated formation in the range of interest is derived from the sonic data, resistivity data and measured density data for the range of interest. The formation model represents the laminated formation in the range of interest as the first and second zones of first and second different rock types. The formation model is used to derive sonic data, resistivity data, and simulated density data for the range of interest. The measured sonic data, resistivity data, and density data for the range of interest and the simulated sonic data, resistivity data, and density data for the range of interest are used to refine the formation model and determine properties of the formation in the range. of interest. Formation properties can be a radial profile for porosity, a radial profile for water saturation, a radial profile for saturation in (...).

Description

RELATED ORDERS

[001] This application claims priority over Provisional Application Serial No. 62/502,239 filed on May 5, 2017 and entitled “IMAGING NEAR-WELLBORE PETROPHYSICAL PROPERTIES IN THINLY LAMINATED FORMATIONS BY JOINT INVERSION OF MULTISENSOR WELLBORE LOGGING DATA”, which is incorporated herein by reference in its entirety.

FIELD

[002] This disclosure refers to the well log in oil and gas fields. In particular, this disclosure relates to the determination of reservoir formation properties using well log data.

BACKGROUND

[003] Accurate estimation of formation properties requires that the interpretation of the welllog data account for the effects of drilling-induced near-borehole alteration caused by mud filtrate intrusion or mechanical damage. A method for estimating formation properties close to the wellbore is described in jointly owned Publication No.: WO2016/123014, entitled “METHOD FOR DETERMINING FORMATION PROPERTIES BY INVERSION OF MULTISENSOR WELLBORE LOGGING DATA”, the content of which is incorporated in this document to reference title in its entirety. This method employs inversion of union of sonic, resistivity and density log data acquired in a vertical well using a formation model with thick isotropic layers and radial variation in fluid saturation and pore shape arising from filtrate invasion. and mechanical damage, respectively. For a formation with thin laminations of isotropic sand and transverse isotropic shale (TIV), the formation model with thick isotropic layers may result in underestimation of hydrocarbon saturation from the conductivity measured by uniaxial induction recordings. The thick isotropic layer assumption can also result in errors in the estimated pore shape from shear and compression sonic records.

SUMMARY

[004] The inversion and formation modeling work processes described in this document can facilitate reservoir characterization and analysis of laminated reservoirs of two different rock types. In embodiments, an interval of interest of a laminate reservoir is modeled by a formation model representing first and second zones of different first and second rock types. The formation model can be used to derive sonic data, resistivity data, and simulated density data for the range of interest. The simulated sonic data, resistivity data, and density data for the range of interest can be compared to the sonic data, resistivity data, and measured density data for the range of interest to refine the formation model and determine properties of the rolled formation in the range of interest.

[005] In embodiments, the first rock type of the first zone of the formation model may represent a transverse isotropic rock medium (e.g. shale) and the second rock type of the second zone of the formation model may represent a medium of isotropic rock or HTI (eg sand).

[006] In embodiments, the formation model can describe properties of the first and second zones of the laminated formation in the range of interest. For example, such properties may include a radial profile of at least one parameter representing the pore shape (e.g., pore aspect ratio) for first zone shale micropores, a radial profile of at least one parameter representing the pore shape (e.g., pore aspect ratio) for randomly oriented macropores of sand from the second zone, and a parameter representing a volume modulus for fluid saturating the randomly oriented macropores of sand from the second zone.

[007] In embodiments, the rock (sand) medium of the second zone may include microcracks aligned in a predefined orientation, and the properties of the laminated formation in the range of interest may include a radial profile of at least one parameter representing the shape of pore (e.g., pore aspect ratio) for the second zone microcracks and a volume modulus for fluid saturating the second zone microcracks.

[008] In embodiments, the properties of the laminated formation in the range of interest may additionally include at least one of a radial water saturation profile, a radial gas saturation profile, a radial oil saturation profile and porosity of training.

[009] The inversion and formation modeling work processes may involve determining a radial profile of effective stiffness data for the first and second combined zones of the formation model by determining the first stiffness data for the first zone in a given radial dimension, determining second stiffness data for the second zone in the given radial dimension, combining the first stiffness data and second stiffness data to produce effective stiffness data for the combined first and second zones in the given radial dimension. radial dimension and repeating these operations for a plurality of radial dimensions. The radial profile of the effective stiffness data can be used to derive simulated sonic data and simulated density data for the range of interest.

[010] In embodiments, the first stiffness data may be a first stiffness matrix that is based on information specifying a pore shape of the first zone, the second stiffness data may be a second stiffness matrix that is based on information that specifies a second zone pore shape and the second stiffness matrix can be based on a stiffness matrix for the second zone rock medium that assumes the second zone rock medium is dry and calculations that modify such a stiffness matrix representing the fluid that saturates the pore space of the rock medium of the second zone.

[011] The inversion and formation modeling work processes may also involve determining a radial profile of effective resistivity data for the first and second combined zones of the formation model by determining first resistivity data for the first zone in a given radial dimension, determining second resistivity data for the second zone in the given radial dimension, iii) combining the first resistivity data and second resistivity data to produce effective resistivity data for the combined first and second zones in the given radial dimension and repeating such operations for a plurality of radial dimensions. The radial profile of the effective resistivity data can be used to derive simulated resistivity data for the range of interest.

[012] In embodiments, the first resistivity data may be based on information specifying a pore shape of the first zone and the second resistivity data may be based on information specifying a pore shape of the second zone.

[013] The formation modeling framework can be used to invert the union of sonic data, resistivity data and measured density data for the range of interest to determine properties of the formation in the range of interest. In one embodiment, formation properties may include (i) a radial profile for porosity in the range of interest, (ii) a radial profile for water saturation (Sw) in the range of interest, (iii) a radial profile for saturation in gas (Sg) in the range of interest, (iv) a radial profile of a pore aspect ratio for the first zone (eg micropores in shale) in the range of interest; and (v) a radial profile of a pore aspect ratio for the second zone (eg, macropores in sand) in the range of interest. (vi) a radial profile for oil saturation (So) in the range of interest.

[014] In one embodiment, the formation properties are parameterized by a formation model derived from a union inversion process that uses petrophysical transformants and tool response simulators to derive simulated log data in the range of interest, where the Simulated log data includes simulated resistivity data, sonic data, and simulated density data. The simulated log data for the range of interest is compared to the measured log data for the range of interest in order to refine (fine tune) the formation model.

BRIEF DESCRIPTION OF THE DRAWINGS

[015] Those skilled in the art should more fully ascertain the advantages of various embodiments of the present disclosure from the following "Detailed Description" addressed with reference to the abstract drawings immediately below.

[016] Figure 1 is a schematic diagram of a front shaping structure, in accordance with an embodiment of the present disclosure.

[017] Figure 2 illustrates constituents of a single zone of shale and constituents of a single zone of sand, in which said two zones represent a laminated layer of multiple thin layers of shale and sand, in accordance with an embodiment of the present disclosure . The single shale zone includes flattened spheroidal pores and kerogen inclusions all aligned in parallel with bedding with an aspect ratio (αshale). The only sand zone has randomly oriented pores with an aspect ratio (αsand). A proportion of the randomly oriented pores are mixed water-hydrocarbon laden macropores, and a proportion of the pores are distributed water-laden shale pores. Parameters representing the constituents of the single shale zone of Figure 2 and parameters representing the constituents of the single sand zone of Figure 2 can be used in the forward modeling structure and work process of Figure 1.

[018] Figure 3 shows a working process for estimating an effective stiffness matrix for a laminated layer of shale and sand represented by the constituents of Figure 2, in accordance with an embodiment of the present disclosure. The work process in Figure 3 can be part of the forward modeling framework in Figure 1.

[019] Figure 4 illustrates a flattened spheroidal pore and grain with an aspect ratio α.

[020] Figures 5A, 5B and 5C are plots showing components C33, C44 and C66 of the effective stiffness matrix derived from the work process of Figure 3 as a function of sand porosity Φsand for various pore aspect ratios of sand (αsand = 0.1, 0.25, 0.5 and 1) at a given Φshaie shale porosity of 10% and a αshale shale pore aspect ratio of 0.1.

[021] Figures 6A, 6B and 6C are plots showing components C33, C44 and C66 of the effective stiffness matrix derived from the work process of Figure 3 as a function of Φshale shale porosity for various pore aspect ratios of shale (αshaie = 0.05, 0.1 and 0.3) at a given sand porosity Φsand of 25% and a sand pore aspect ratio αsand of 0.1.

[022] Figure 7 shows a working process for estimating the effective anisotropic resistivity Rh and Rv for a laminated layer of shale and sand represented by the constituents of Figure 2, in accordance with an embodiment of the present disclosure. The work process in Figure 7 can be part of the forward modeling framework in Figure 1.

[023] Figure 8A is a flowchart illustrating an inversion work process that incorporates the forward modeling structure of Figure 1.

[024] Figure 8B is a flowchart illustrating a merge inversion processing embodiment that may be part of the inversion work process of Figure 8A.

[025] Figure 9 illustrates constituents of a single zone of shale and constituents of a single zone of sand, in which said two zones are used to represent a laminated layer of multiple thin layers of shale and sand, according to an embodiment of the present disclosure. The single shale zone includes flattened spheroidal pores and kerogen inclusions all aligned in parallel with bedding with an aspect ratio (αshale). The only sand zone has randomly oriented pores with an aspect ratio (αsand) along with microcracks with an aspect ratio (αcrack). Parameters representing the constituents of the single shale zone of Figure 9 and parameters representing the constituents of the single sand zone of Figure 9 can be used in the forward modeling framework of Figure 1.

[026] Figure 10 shows a working process for estimating an effective stiffness matrix for a laminated layer of shale and sand represented by the constituents of Figure 9, in accordance with an embodiment of the present disclosure. The work process of Figure 10 can be part of the front modeling structure of Figure 1.

[027] Figures 11A, 11B and 11C are plots showing components C33, C44 and C55 of the effective stiffness matrix derived from the work process of Figure 10 for a pure sand formation, with the components shown as a function of porosity of crack Φcrack for various crack pore aspect ratios (αsand = 0.005, 0.01 and 0.05) at a given sand porosity Φsand of 25% and a sand pore aspect ratio αsand of 0.1.

[028] Figures 12A, 12B, 12C and 12D are plots showing components C33, C44, C55 and C66 of the effective stiffness matrix derived from the work process of Figure 10 for a formation of 50% shale, with the components shown as a function of Φshale shale porosity for various shale pore aspect ratios (αshale = 0.05, 0.1 and 0.3) at a given sand porosity Φsand of 25% and a pore aspect ratio of sand αsand of 0.1 and a given crack porosity Φcrack of 10% and a crack pore aspect ratio αcrack of 0.01.

[029] Figures 13A to 13E illustrate training model parameters that are reconstructed using the inversion work process of Figures 8A and 8B; and Figures 13F through 13J illustrate measured and simulated sonic data, resistivity data, and density data generated using the inversion work process of Figures 8A and 8B. Here, data are from an oil-containing formation drilled with oil-based mud and shale conductivities were computed using Archie's law and a known shale anisotropy ratio.

[030] Figures 14A to 14E illustrate training model parameters that are reconstructed using the inversion work process of Figures 8A and 8B; and Figures 14F through 14J illustrate measured and simulated sonic data, resistivity data, and density data generated using the inversion work process of Figures 8A and 8B. Here, data from a gas-containing formation drilled with oil-based mud and shale conductivities were computed using Archie's law and a known shale anisotropy ratio.

[031] Figures 15A to 15E illustrate training model parameters that are reconstructed using the inversion work process of Figures 8A and 8B; and Figures 15F through 15J illustrate measured and simulated sonic data, resistivity data, and density data generated using the inversion work process of Figures 8A and 8B. Here, data from a gas-containing formation drilled with oil-based mud and shale conductivities were computed using a Differential Effective Means (DEM) model.

[032] Figure 16 illustrates an exemplary well interior registration system that is suitable for carrying out the exemplary structures and work processes of Figures 1, 3, 7, 8A, 8B and/or 10.

[033] Figure 17 illustrates an exemplary computing system, in accordance with some embodiments, for realizing the exemplary structures and work processes of Figures 1, 3, 7, 8A, 8B and/or 10.

[034] Figure 18 illustrates an exemplary registration during drilling (LWD) system that is suitable for realizing the exemplary structures and work processes of Figures 1, 3, 7, 8A, 8B and/or 10.

DETAILED DESCRIPTION

[035] The details shown in the present document are by way of example only and for purposes of illustrative discussion of examples of said disclosure and are presented to provide what is believed to be the most useful and readily understood description of the principles and conceptual aspects of said revelation. In this connection, no attempt is made to show structural details in more detail than is necessary, the description taken with the drawings making it apparent to those skilled in the art how the various forms of said disclosure may be incorporated in practice. Furthermore, reference numerals and similar designations throughout the various drawings indicate similar elements.

[036] The present disclosure refers to an inversion-based method that integrates resistivity data, sonic data and density data of a geological formation acquired from inside a well (or borehole) that crosses the geological formation. In each interval of interest inside the well, geological formation properties are parameterized by a formation model that represents the first and second zones of two different rock types. In embodiments, the first rock type of the first zone may represent a transverse isotropic rock medium (e.g. shale) and the second rock type of the second zone of the formation model may represent an isotropic rock medium (e.g. sand ). In one embodiment, the formation model includes (i) a radial profile for porosity in the range of interest, (ii) a radial profile for water saturation (Sw) in the range of interest, (iii) a radial profile for gas saturation (Sg) in the range of interest, (iv) a radial profile of a pore aspect ratio for the first zone (eg micropores in shale) in the range of interest; (v) a radial profile of a pore aspect ratio for the second zone (eg macropores in sand) in the range of interest and (vi) a radial profile for oil saturation (So) in the range of interest. The term “radial” means in a direction extending laterally from the center of the well (or borehole) into the geological formation. Radial changes in fluid saturations represent the effect of mud filtrate invasion, while radial changes in pore shape represent the effect of mechanical damage or stress. The term “interval of interest” means a portion of the well that corresponds to a location (depth) or measurement station within the well. Formation properties are estimated from the data using a work process that exploits complementary physical sensitivities, different directional dependence, and different radial depths of investigation (DOI) of the various measurements. Petrophysical transformants (which are specific to formation lithology) can be used to map formation model parameters into radial profiles of elastic properties and radial profiles of electromagnetic properties. Electromagnetic properties can represent radial profiles of anisotropic resistivity for the first and second combined zones, while elastic properties can represent radial profiles of an effective stiffness matrix for the first and second combined zones. Electromagnetic properties and elastic properties can be input into tool response simulators, which are used as front models in a bond reversal process, discussed in more detail below.

[037] In embodiments, the union inversion process is based on sonic data, resistivity data and density data acquired through wellbore log measurements acquired in a well (or wellbore) that crosses a formation underground geology.

[038] For example, sonic data can be acquired with a logging tool consisting of cross dipole sources, near and far monopole sources, and receiver array. See Pistre, V., Kinoshita, T., Endo, T., Schilling, K., and Pabon, J., “A modular wireline sonic tool for measurements of 3D (azimuthal, radial, and axial) formation acoustic properties”, SPWLA 46th Annual Logging Symposium, New Orleans, Louisiana, USA, June 26-29, 2005. In this case, acoustic sources transmit acoustic energy that interacts with the formation and then is detected by the array of receivers. The sonic data measured may include acoustic waveform attributes such as Time Coherence and Similarity (STC) records for Stoneley waves, shear, and compression that interact with the formation. The data also includes distribution data for the borehole-guided flexural and Stoneley waves, and compression wave head transit times. The vertical resolution of the data, with multishot processing, can be up to 30 centimeters (1 foot), which is the spacing between pairs of adjacent receivers in the array. The flexural and Stoneley wave distribution can be sensitive to the elastic properties of the formation at different radial depths in the formation. Typical radial depths are in the range of 0.5 to 3 borehole diameters radially in the formation. It should be noted that other suitable sonic recording tools can also be used.

[039] In another example, resistivity data can be acquired with a triaxial array induction tool. See Rosthal, R., Barber, T., Bonner, S., Chen, K.-C., Davydycheva, S., Hazen, G., Homan, D., Kibbe, C., Schlein, R., Villegas , L., Wang, H., and Zhou, F., “Field test of an experimental fully triaxial induction tool”, SPWLA 44th Annual Logging Symposium, Galveston, Texas, USA, June 22-25, 2003. In this case , resistivity measurements are acquired with a triaxial source array and a triaxial receiver array. The triaxial source array transmits electromagnetic energy that interacts with the formation and is then detected by the triaxial receiver array. The measured resistivity data can be corrected for borehole effects and rotated in the borehole prior to entering the bond reversal. The component of the resistivity data from the axial source and axial receiver is sensitive to horizontal conductivity, while the components of the transverse sources and transverse receivers are sensitive to vertical resistivity. Records for different source and receiver spacing have different depths of radial and vertical investigation. Typical investigation depths are in the range of 30 to 121 centimeters (1 to 4 feet) vertically and 30 to 213 centimeters (1 to 7 feet) radially in the formation. It is noted that other suitable induction type resistivity recording tools that induce a current distribution in the formation and measure its magnitude can also be used.

[040] In yet another example, density data can be acquired using a gamma-gamma density tool. See Allioli, F., Faivre, O., Jammes, L., and Evans, M., “A new approach to computing formation density and Pe free of mudcake effects,” SPWLA 38th Annual Logging Symposium, Houston, Texas, USA. , June 15-18, 1997. In this case, density measurements are acquired with a gamma source that emits gamma radiation that interacts with the formation and is then detected by a gamma detector. Measured density data may be corrected for borehole effects prior to entering the inversion join. Density data can be sensitive in formation volume density up to 5 centimeters (2 inches) vertically and 15 centimeters (6 inches) radially from the borehole. Note that other suitable density recording tools can also be used.

FRONT MODELING

[041] Figure 1 shows an exemplary forward modeling structure to simulate resistivity data, sonic data and density data acquired by one or more logging tools in a borehole (well). In the example shown, the borehole is vertical, and the formation contains multiple laminations (or layers) of an isotropic rock medium (sand) and a TIV rock medium (shale). It is assumed that the properties of the isotropic rock medium (sand) are uniform in all directions with a given layer of sand. The TIV rock medium (shale) has transverse isotropy with a vertical axis of revolving symmetry. In this case, it is assumed that shale properties are uniform horizontally within a given shale layer, however, they can vary vertically within a given shale layer. In the vertical direction, the formation around the borehole is described by a formation model 11 that approximates multiple thin layers of sand and shale with a single zone of shale and a single zone of sand, as shown in Figure 2. The fraction Vshale shale zone volume fraction and Vsand sand zone volume fraction are known or derived from log data. The shale zone consists of pores (micropores) 104 loaded with water as well as kerogen inclusions 105. Shale micropores and organic inclusions are aligned in the horizontal direction. The sand zone contains randomly oriented pores (macropores) 106 loaded with water and hydrocarbon mixture, and randomly oriented shale pores 107. The formation model 11 has an axial extent defined by the maximum vertical resolution of the sonic, resistivity and density. In the radial direction, the formation around the borehole is described by a formation model 12 that discretizes the formation into a number N of radial compartments or pixels. In each pixel (identified ri = 1, 2, ... N), values for water saturation Sw(ri), gas saturation Sg (ri), oil saturation So (ri), porosity Φ(ri), ratio pore aspect ratio αshale(ri) for the shale zone and pore aspect ratio αsand(ri) for the sand zone are specified. The radius refers to the radial distance from the center of the borehole. The wellbore extends radially to radius . An outermost pixel (i=N) is unlimited in radial extent and represents far-field formation. The αshale and αsand pore aspect ratios are modeled as constants at each pixel. Note that radial changes in pore aspect ratios αshale(ri) and αsand(ri) represent effects of mechanical damage or stress, and radial changes in fluid saturations Sw(ri) and Sg(ri) and So(ri) are caused by filtrate invasion. Fluid saturations satisfy the constraint that Sw(ri) + Sg(ri) + So(ri) = :

[042] Formation models 11 and 12 can be used as input in a petrophysical transform 21 (identified rock physics model TIV) that maps the formation parameters of formation models 11 and 12 to corresponding elastic properties (which include values for a matrix of effective stiffness C(ri) and density p(ri) for the two zones combined) at each pixel. These properties (identified 23) are fed into various physics-based tool response simulators (25, 29) to generate simulated sonic data 27 (such as flexural wave distributions, Stoneley wave distributions, shear STC log data , Stoneley STC log data) and simulated sonic data (such as P wave head transit time data and compression STC log data). The properties (identified 23) are also entered into a physics-based tool response simulator 33 to generate simulated density data 33. The sonic data 27 and 31, as well as the density data 33, can be simulated using of the front models described in WO2016/123014.

[043] Distribution data for flexural wave and Stoneley wave can be simulated from the acoustic guidance condition matrix, obtained from the boundary conditions satisfied by a wellbore guided wave at the interface of each pixel (Tang and Cheng , 2004). Compression wave head transit times can be simulated by ray tracing, according to Snell's law as given in Zeroug et al., 2006. The compression slowdown record can be obtained from a linear fit in the transit times. The shear and Stoneley wave slowdown records can be computed from C(ri) in the far-field pixel and are given by for fast shear , for slow shear and for Stoneley horizontal shear. The density data can be computed as a weighted sum over all pixels in the formation model 11. The weights can be precomputed from the normalized sensitivities of the tool response. The weights can be set to decay with radial distance from the borehole so that 50% of the response is 5 centimeters (2 inches) from the formation and 100% of the response is 15 centimeters (6 inches) from the formation.

[044] In embodiments, the petrophysical transform 21 employs an effective medium rock physics model to compute elastic properties of the two-zone formation model, as shown in Figure 3. In this model, the effective elastic properties of the shale zone are computed in blocks 301 to 303 separately from the effective elastic properties of the sand zone computed in blocks 305 to 311. The results from blocks 303 and 311 are then combined in block 313 to produce effective elastic properties for the combined shale and sand zones ( identified 315). Computation is performed on each pixel to produce radial profiles of the effective stiffness matrix

[045] In block 301, a rock physics model for wet shale is used to determine a TIV stiffness matrix for the shale zone (named “Cshale” and identified as block 303). In modalities, the rock physics model of block 301 employs Self-Consistent Approximation (SCA) and Differential Effective Means Model (DEM). The rock physics model for shale can employ inputs such as shale mineralogy, Φshale porosity of the shale zone, and pore shape of grains and pores. For example, shale grains and pores can be defined as flattened spheroids with major axis aligned with the layer boundaries and aspect ratio defined in Figure 4. Organic kerogen inclusions can be added to shale given the inclusion aspect ratio and mineralogy.

[046] In modalities, the rock physics model of block 301 employs Self-Consistent Approximation (SCA) followed by Differential Effective Means Model (DEM). This model supports different aspect ratios for pores, grains and organic inclusions. Here, to reduce the number of free parameters, the aspect ratio of grain, pores and kerogen inclusions can be limited to be identical, denoted by αshale. The 50% porosity shale base can be computed using SCA followed by incremental adjustment of the base porosity in small amounts until the desired porosity Φshale is achieved. SCA helps establish the connectivity of the base array. For example, SCA might involve the following equation: Equation 1 where is the SCA stiffness matrix, is the stiffness matrix for clay mineral and is the stiffness matrix for fluid inclusion. Furthermore, and are the Eshelby terms for clay and fluid inclusions, as given in T. Mura, Micromechanics of defects in solids, 2nd ed., ser. Mechanics of Elastic and Inelastic Solids. Springer Netherlands, 1987.

[047] For the DEM 301 block model, the following common differential equation can be solved: Equation. 2 in what is the stiffness matrix at the end of the porosity increment, is the inclusion rigidity matrix and is the inclusion Eshelby term that depends on the fluid and matrix inclusions, as given in Hornby et al., 1994. Then, the Cshale stiffness matrix can be determined from the final stiffness matrix after N increments dictated by the porosity Φshale and the change in porosity per increment. Alternatively, the Cshale stiffness matrix can be computed just using DEM starting from a uniform clay base.

[048] For the sand zone, block 305 employs a rock physics model for dry sand to produce an isotropic stiffness matrix for dry sand (called “Cdry” and identified as block 307). Inputs into the model of block 305 may include the sand mineralogy, the porosity parameter Φ, and the pore aspect ratio αsand of the randomly oriented dry pores of the sand zone.

[049] In embodiments, block 305 may employ an effective Xu-White rock physics model that has the following inputs: porosity parameter , the pore aspect ratio , and the mineralogy defined by shear modulus , volume module density and volume fractions of each mineral in the range of interest. Note that increasing (or decreasing) the pore aspect ratio αsand to the sand zone has the impact of stiffening (or softening) the rock.

[050] The Xu-White model computes the effective volume modulus and and the effective shear modulus of the dry rock by dividing the porosity into shale porosity distributed and sand macroporosity proportional to the volume fraction of clay minerals and sand. The Kuster-Toksoz model is then applied iteratively for D iterations, where in each iteration, the base medium has effective modulus from the previous iteration and the porosity is . To the equations each in iteration are: on what are the volume and shear modulus of the base medium, are the volume and shear modulus of the pore fluid and are quantities that depend on the matrix, pore aspect ratio and pore fluid, as defined in Annex B of Xu-White (1995).

[051] Equations 3(a) and 3(b) can be applied with fluid volume modulus and fluid shear modulus both set to zero to obtain dry rock modulus. In the above equations, the modulus of the solid base for the first iteration are obtained from the modulus of the component minerals through a mixing law (eg Voigt mean or Voigt-Reuss-Hill mean). For example, the Voight mean can be calculated as follows:

[052] In block 309, a model for fluid replacement (such as the Gassman model) is applied to saturate the macropores of the sand zone with fluid of specified volume modulus and determine an isotropic stiffness matrix for the saturated sand (called “Csand” and identified block 311). For example, Gassman's model can be represented by the following: The parameter Kf in Equation 5(a) can be determined from Woods law as: The parameter Kf in Equation 5(a) can also be determined from Brie's empirical law as: on what There are water, oil and gas volume modules. Equation 5(d)

[053] In block 313, the TIV Cshale stiffness matrix (block 303) can be combined with the isotropic Csand stiffness matrix (block 311) to produce effective stiffness matrix C for the combined shale and sand zones (block 315) . Block 313 may employ Backus averaging with the Vshale shale volume fraction as input. Backus averaging provides the long wavelength limit, effective TIV stiffness for a medium composed of multiple thin layers of a TIV or isotropic medium, as given in Mavko et al., 2009.

[054] The sensitivities of the stiffness elements C33, C44 and C66 of the effective stiffness matrix for the combined shale and sand zones (315) are shown in the plots of Figures 5A to 5C and 6A to 6C. These plots show that the pore to sand zone aspect ratio impacts all stiffness elements of the effective stiffness matrix, while the pore to shale zone primarily impacts element C44 of the effective stiffness matrix. Note that for a TIV formation, the effective stiffness matrix element C55 is equal to the effective stiffness matrix element C44. Thus, element C55 is not shown in Figures 5A to 5C and 6A to 6C.

[055] The radial density profile in the combined shale and sand zones can be computed by assuming the volumetric averages of the density in the shale and sand zones in each pixel, as follows: on what e are the mineral volume fractions and mineral densities in the entire rock matrix (which includes both sand and shale zones), is the effective porosity, is the bound water porosity and are densities of water, oil and gas.

[056] Formation models 11 and 12 can also be used as input in a petrophysical transform 13 (identified TIV resistivity model) that maps the formation parameters of formation models 11 and 12 to corresponding electromagnetic properties (such as horizontal resistivity Rh(ri) and vertical resistivity Rv(ri) of the combined shale and sand zones). These properties (identified 15) are input into a physics-based tool response simulator 17 (identified “EM Solver”) to generate simulated resistivity data 19 (identified “Triaxial Induction Logs”). In embodiments, the physics-based tool response simulator 17 can be based on the forward model described in Wang et al. “Fast simulation of triaxial borehole induction measurements acquired in axially symmetrical and transversely isotropic media”, Geophysics, Vol. 74, No. 6, November to December 2009.

[057] Note that for the two-zone model in Figure 2, the shale zone has intrinsic TIV behavior with anisotropic resistivities . AND , while the sand zone is isotropic with the resistivity . Following the teaching of Clavaud et al., “Field Example of Enhanced Hydrocarbon Estimation in Thinly Laminated Formation with Triaxial Array Induction Tool: A Laminated Sand-Shale Analysis with Anisotropic Shale”, SPWLA 46th Annual Logging Symposium, June 26-29 2005, the anisotropy ratio for the shale zone can be obtained from log data in a pure shale zone. A disadvantage of this approach is that the pure shale zone may not be present in the recorded range, or shale properties may vary with depth.

[058] In an alternative approach shown in Figure 7, petrophysical transformants are used to compute anisotropic resistivity properties of the two-zone formation model. In this approach, the shale zone resistivity properties are computed in blocks 701 to 707 separately from the sand zone resistivity properties computed in blocks 709 to 711. The results from blocks 707 and 711 are then combined in block 713 to produce effective anisotropic resistivity properties for the combined shale and sand zones (block 715). Computations are performed at each pixel to produce radial profiles of the anisotropic resistivity properties for the combined shale and sand zones of the formation.

[059] In block 701, the anisotropic resistivities for the shale zone can be computed with a resistivity model for the shale zone, which can take in information regarding the mineral components of the shale zone, information regarding kerogen inclusions in the shale zone, and information (such as pore shape) with respect to wet micropores aligned in the shale zone. The resistivity model of block 701 produces resistivity data (block 703) that characterize the resistivity of the shale zone. Block 705 extracts or solves TIV components of such resistivity to produce the anisotropic resistivities for the shale zone data (block 707).

[060] In modalities, the resistivity model of block 701 is an approximation model of Differential Effective Medium (DEM), which receives conductivities from the clay matrix of the shale zone, pore aspect ratio of the shale zone , and fluid conductivities as input. The DEM model computes a shale zone conductivity matrix (block 703) by incrementally adding a small amount of porosity from fluid inclusions into the base clay matrix of the shale zone until the desired porosity is achieved. For example, the DEM model can produce a 3x3 conductivity matrix as the solution of a common differential equation: on what is the inclusion conductivity matrix and '? is the Eshelby term that depends on the fluid and matrix inclusions, as defined in Durr et al., 2002. In block 705, the diagonal terms of the conductivity matrix can be extracted to produce values for the horizontal and vertical resistivities and the shale zone (block 707).

[061] In other embodiments, block 701 can use other resistivity models (such as SCA and DEM models) to characterize the resistivity of the shale zone.

[062] In block 709, the isotropic resistivity for the sand zone can be computed with a resistivity model for the sand zone, which can receive information regarding the mineral components of the sand zone, information (such as pore shape) regarding randomly oriented dry macropores in the sand zone. sand zone and information (such as pore shape) regarding randomly oriented wet micropores in the sand zone. The resistivity model of block 707 produces resistivity data (block 711) that characterize the isotropic resistivity to the sand zone.

[063] In modalities, the resistivity model of block 709 may refer β Φ to the isotropic resistivity of the sand zone for porosity and water saturation through Archie's law given the cementing and saturation exponents men as follows: on what is the resistivity of brine formation.

[064] In other embodiments, block 701 can use other resistivity models (such as a DEM model, SCA and DEM models) to characterize the resistivity of the sand zone.

[065] In block 713, the effective horizontal resistivity Rh(ri) and the vertical resistivity Rv(ri) of the combined shale and sand zones (block 715) can be obtained by combining the resistivities of anisotropic shale (block 707) and the resistivity of isotropic sand (block 711). In embodiments, such a combination can be performed using series and parallel watering for resistivity as follows:

[066] Given the radial profiles of Rh(ri) and Rv(ri) of the combined shale and sand zones, the triaxial induction data can be simulated using the numerical mode that corresponds to the method, as described in Wang et al ., 2009.

INVERSION WORK PROCESS

[067] An embodiment of an inversion work process that employs the forward modeling structure of Figure 1 is shown in Figure 8A. In block 801, for an interval of specific interest in a well (or wellbore), resistivity data, sonic data and density data that characterize formation in the interval of specific interest are obtained from well logging operations within of the well (or borehole). In one embodiment, the resistivity data can be corrected and rotated in the borehole to true north. Measurements are acquired at different source frequencies and at different spacing between source and receiver. Measurements of different spacing and frequencies correspond to different channels (identified by a channel index). Sonic data can be generated by processing the measured waveforms with an extended Prony method to extract distribution data for various wellbore guided wave modes, followed by an identification algorithm to extract the distribution data for the wellbore mode. flexural waveform and Stoneley waveform. Typically, distribution data for the flexural waveform between 1 kHz and 10 kHz is used in the inversion work process. In various embodiments, the sonic data may include the compression wave velocity record and shear wave velocity record obtained through Time Coherence Similarity (STC) processing of the measured waveforms. In several embodiments, sonic data is generated with multishot subarray processing to increase the vertical resolution of the data. In another embodiment, sonic data includes the first arrival transit times of the compression wave head extracted from the measured waveforms at each receiver of the sonic recording tool. Measurements on different receivers correspond to different channels (identified by a channel index). In addition, resistivity data, sonic data, and density data obtained from well logging operations can be made accessible for use in the population of initial formation model parameters, as described in more detail below.

[068] In block 803, an initial formation model for the range of interest is derived based on the resistivity data, sonic data and density data measured in block 801 for the range of interest.

[069] In block 805, petrophysical transformants, as described above, are applied to the modeled values (for example, fluid saturations, pore aspect ratios of sand zones, pore aspect ratios of shale zones and porosity parameters ) to obtain radial vertical and horizontal resistivity profiles for the combined shale and sand zones (for example, the vertical and horizontal resistivity for the combined shale and sand zones of block 715).

[070] In block 807, petrophysical transformants, as described above, are applied to the modeled values (for example, fluid saturations, pore aspect ratios of the sand zone, pore aspect ratios of shale zones and porosity parameters ) to obtain radial profiles of a stiffness matrix (e.g., the effective stiffness matrix for the combined shale and sand zones of block 315) and formation density in the range of interest.

[071] In block 809, the radial profiles of horizontal and vertical resistivities of the combined shale and sand zones in the range of interest (for example, the vertical and horizontal resistivity for the combined shale and sand zones of block 715) can be used as an input to the electromagnetic physics-based tool response simulator, as described above, which derives simulated resistivity data for the range of interest. In addition, the radial stiffness matrix profile (e.g., the effective stiffness matrix for the combined sand and shale of block 315) and the radial density profile of the formation in the range of interest can be used as inputs to one or more sonic physics-based tool-response simulators, and a density physics-based tool-response simulator, as described above, which derives simulated sonic data and simulated density data for the range of interest.

[072] In block 811, the simulated resistivity data from the 809 block, the simulated sonic data from the 809 block, and the simulated density data from the 809 block are compared to the corresponding measured record data to determine whether such data corresponds within a limit default, that is, whether simulated log data and measured log data converge. If the simulated data and measured data do not match (for example, there is no convergence), then the training model is refined with new values and blocks 805 to 811 are repeated until the simulated log data matches the measured log data (for example, there is convergence). If the simulated log data matches the measured log within the boundary (there is convergence, as indicated by YES at block 813), then the resulting training pattern values for the range of interest are stored in computer memory at block 815.

[073] In optional block 817, the resulting training model can be used to flag or identify characteristics of the range of interest. Specifically, a two-dimensional formation model, with both radially and vertically varying porosity, fluid saturations, and sand/shale pore aspect ratios, can be created by stacking the one-dimensional radially varying formation models obtained as output from inversion over a sequence of intervals. Simulated data that includes the two-dimensional effect can then be generated using this two-dimensional formation model created as input to the two-dimensional tool response simulators. The simulated data for the two-dimensional formation model is compared with the simulated data for the inversion. Any intervals where the simulated data for the two-dimensional formation model differ significantly (ie, beyond a predetermined threshold) from the simulated inversion data indicates that two-dimensional effects are significant at those intervals. Consequently, the output of the inversion in those intervals can be signaled needing correction for two-dimensional effects.

[074] In block 819, stored data representing the formation model for one or more intervals of interest can be output for analysis, such as for reservoir analysis and understanding.

[075] Figure 8B shows an embodiment of additional details of the inversion work process of Figure 8A. Specifically, the inversion processing includes a plurality of sub-blocks 857 to 863 that together drive a joint, inversion-based solution of the forming parameters model. In sub-blocks 857 to 863, simulated data is used to fine-tune the formation model for the range of interest based on a comparison between measured log data and simulated log data for the range of interest.

[076] In block 857, the inversion of the union of the density data, resistivity data and measured sonic data (for example, STC records of shear (DTSH), compression (DTCO), Stoneley horizontal shear (DTST)) can be performed to determine an initial estimate for radially constant porosity Φ, Sw, Sg, ashaie, and asand in the range of interest.

[077] In block 859, an inversion of the measured resistivity data can be performed to determine an initial estimate for the radial profile Sw(ri) in the interval of interest.

[078] In block 861, an inversion of the union of the density data and measured sonic data (for example, flexural wave, Stoneley wave and P wave head data) can be performed to determine an initial estimate for radial profiles Sg(ri ), αshale(ri) and αsand(ri) in the range of interest.

[079] In addition, in block 863, an inversion of the union of the density data, resistivity data and measured sonic data can be performed to determine radial profiles Φ(ri), S^(ri), Sg(ri), ashae (ri) and asand(ri) in the range of interest.

[080] Several other formation properties can be determined from the finally determined values of the formation model.

[081] In the inversion processing of Figure 8B, the cost function for the inversion operations can be defined as the L2 square norm of the relative error in the measured and simulated data. Best fit parameters can be defined as the minimum of the cost function in a box domain. A smoothness regularization term can be added to the cost function to reduce non-uniqueness. The best fitting parameters can be estimated using Gauss-Newton optimization with line search, adaptive regularization and nonlinear mapping to box constraints. See T. M. Habashy and A. Abubakar, "A general framework for constraint minimization for the inversion of electromagnetic measurements", Progress In Electromagnetics Research, Vol. 46, 265-312, 2004. To increase robustness, the initial guess for the optimization can be obtained by estimating a subset of parameters and subset of data sensitive to these parameters. For example, the initial guess for porosity can be estimated from the density record that assumes water-loaded pores in the washed zone. The shale and sand pore shape in the far field, and gas saturation in the washed zone can be estimated from Stoneley, shear and compression logs in block 857. The initial radial water saturation profile Sw(ri) can be estimated from resistivity data alone in block 859, which keeps all other parameters fixed, starting from radially homogeneous with 50% water saturation. The radial profile of the αshale(ri), and αsand(ri) pore shapes and gas saturation Sg(ri) can be further refined from the flexural wave distribution, Stoneley wave distribution and compression wave head transit times in the block 861. Finally, starting from the forming parameters of the previous steps, all parameters can be refined simultaneously using all the data in block 863.

[082] Stress effects can induce microcracks in the sand zones of the formation. Such microcracks can be accounted for by adding inclusions or microcracks 108 to the formation model as shown in Figure 9. The microcracks can be aligned vertically (or transversely to bending) as shown or possibly aligned in some other predefined orientation. Microcracks can be described by several model parameters that include a crack porosity Φcrack, a crack aspect ratio αcrack, a crack orientation vector tcrack, and Kcrack volume modulus of fluid that saturates the sand zone microcracks. In this embodiment, the forward modeling workflow in Figure 1 can employ a petrophysical transform to compute the elastic properties of the two zones of the formation model, as shown in Figure 10. In this model, the effective elastic properties of the shale zone are computed in the blocks 1001 to 1003 separately from the effective elastic properties of the sand zone computed in blocks 1005 to 1015. The results from blocks 1003 and 1015 are then combined in block 1017 to produce effective elastic properties for the combined shale and sand zones (identified 1019). Computations are performed at each pixel to derive radial profiles from the various stiffness matrices.

[083] In block 1001, a rock physics model for wet shale is used to determine a TIV stiffness matrix for the wet shale zone (named “Cshale” and identified box 1003). In modalities, the rock physics model of block 901 employs Self-Consistent Approximation (SCA) followed by Differential Effective Means Model (DEM). The rock physics model for wet shale can employ inputs such as shale mineralogy, Φshale shale porosity of the shale zone, and pore shape of grains and pores. For example, shale grains and pores can be defined as flattened spheroids with major axis aligned with the layer boundaries and aspect ratio defined in Figure 4. Organic kerogen inclusions can be added to shale given the inclusion aspect ratio and mineralogy. This model supports different aspect ratios for pores, grains and organic inclusions. Here, to reduce the number of free parameters, the aspect ratio of grain, pores and kerogen inclusions can be limited to be identical, denoted by αshale. Such operations may be the same or similar to those of block 301, as described above.

[084] For the sand zone, block 1005 employs a rock physics model for dry sand to produce an isotropic stiffness matrix for dry sand (called “Cdry” and identified as box 1007). Inputs in the block 905 model may include the sand mineralogy, the porosity parameter Φ, and the pore aspect ratio αsand of the randomly oriented pores of the sand zone. Such operations may be the same or similar to those in block 305, as described above.

[085] In block 1009, a rock physics model for dry sand with microcracks is used to produce an HTI stiffness matrix (or isotropic transverse with horizontal axis of symmetry) (named “Ccrack” and identified box 1011). The model first computes an isotropic stiffness matrix for dry sand using Xu-White, followed by Differential Effective Medium (DEM) to add the aligned microcracks to the dry sand base.

[086] In block 1013, a model for fluid replacement (such as the anisotropic extension of Gassman model, known as Brown-Korringa) is applied to saturate the macropores of the sand zone with fluid of specified volume modulus Kmacro and saturate sand zone microcracks with fluid of specified volume modulus Kcrack to determine an HTI stiffness matrix for the saturated sand and clay (named “Csand” and box identified as 1015). Such operations can be similar to those of block 309.

[087] In block 1017, the TIV Cshale stiffness matrix (block 1003) can be combined with the HTI Csand stiffness matrix (block 1015) to produce an effective orthorhombic stiffness matrix C for the combined shale and sand zones (box 1019). Block 1017 can employ an anisotropic extension of the Backus mean, as described in Mavko et al., “The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media”, Cambridge University Press, 2009. Such operations can be similar to those of block 313.

[088] The sensitivities of the stiffness elements C33, C44 and C55 of the effective stiffness matrix for the combined shale and sand zones (919) are shown in the plots of Figures 11A to 11C and 12A to 12D. To elucidate the impact of cracking aspect ratio, Figures 11A to 11C are for a shale-free formation, which shows that the slow shear stiffness element C44 is sensitive to cracking aspect ratio. Figures 12A to 12D are for 50% shale formation, and the behavior of these data is consistent with the TIV shale sand effective stiffness matrices of Figures 5A to 5C and 6A to 6C, where shale pore shape primarily impacts the stiffness elements C44 and C55.

Examples for Synthetic Data

[089] Here are examples that employ the inversion processing of Figures 8A and 8B for noisy synthetic data. For all cases, the Archie parameters for sand zone, borehole diameter, mud properties and mineralogy are known from independent pre-processing and given in Table 1 and 2 below. TABLE 1: PARAMETERS FOR SYNTHETIC EXAMPLES TABLE 2: ELASTIC PROPERTIES FOR DIFFERENT MINERAL AND FLUID COMPONENTS IN SYNTHETIC EXAMPLES

[090] In these examples, the pore shape is constrained to be radially homogeneous. Input data include sonic slowdown records for Stoneley horizontal shear triaxial induction (DTST), shear (DTSH) and compression (DTCO) data for axial and transverse coupling, and density data.

[091] In the first two examples, shale conductivities are computed from Archie's law and known anisotropy ratio for shale. In the third example, shale conductivities are computed from DEM given the clay conductivity matrix as input, and shale microstructure geometry common to elastic and electromagnetic formation models. In all cases, bond inversion does a good job of reconstructing the porosity, fluid saturation, and pore shape of the sand and shale zones. The initial guess does not match the measured data, while the final inversion model matches all the measured data.

[092] Results for an oil-containing formation drilled with oil-based mud, using Archie's law to determine shale conductivity, are shown in Figures 13A through 13J.

[093] Results for a gas-containing formation drilled with oil-based mud, using Archie's law to determine shale conductivity, are shown in Figures 14A through 14J.

[094] Results for a gas-containing formation drilled with oil-based mud, using DEM to determine shale conductivity, are shown in Figures 15A to 15J. In these Figures, the final results of the inversion and the true model show a good correspondence.

[095] Figure 16 shows an exemplary well log system that is suitable for carrying out the work processes of Figures 1, 3, 7, 8A, 8B and/or 10. The system includes one or more interior log instruments wellhead 1601 (one shown) with one or more housings 1603 (one shown) shaped and sealed to be moved along the interior of a well or wellbore 1605 passing through a geological formation 1607. Housing (or housings) 1603 may contain measurement components for measuring resistivity data, sonic data (such as compression velocity and shear velocity) and geological formation density data 1607 as a function of radial direction over a range of interest in the well or borehole 1605.

[096] By way of example, the measurement components contained with the housing (or housings) 1003 may include a monopole and dipole sonic recording module that measures sonic data that is sensitive to the elastic properties of formation at multiple radial depths of investigation ( DOI) in an interval of interest within the well or borehole 1605, with lower frequencies probed deeper into the geological formation 1607 than higher frequencies. The radial DOI of the sonic data measurement in the 1607 geological formation is in the range of half the well/borehole diameter to three times the wellbore/borehole diameter. The vertical resolution of the sonic data measurement depends on the aperture of the receiver array, which is in the range of 182 centimeters (six feet) for the entire array to 30 centimeters (one foot) with subarray multishot processing.

[097] The measurement components contained with the housing (or housings) 1603 may also include a triaxial array induction module that measures resistivity data that is sensitive to formation resistivity at multiple radial depths of investigation over a range of interest within of borehole 1605. High frequency, close spacing measurements probe radially shallower in geological formation 1607 than low frequency, long spacing measurements. The radial DOI of the resistivity data measurement is in the range of 25 centimeters (ten inches) to 228 centimeters (ninety inches) in geological formation 1607. The vertical resolution of the resistivity data measurements is in the range of 30 centimeters (one foot) up to 121 centimeters (four feet), where long-spaced measurements have lower vertical resolution than short-spaced measurements.

[098] The measurement components contained with the housing (or housings) 1603 may also include a gamma-gamma density module that measures density data that is sensitive to formation density at multiple radial depths of investigation over a range of interest within of well or borehole 1605. Radial DOI of density data measurements is in the range of 2.5 centimeters (one inch) to 7.6 centimeters (three inches) in geological formation 1607. Vertical resolution of data measurements density is in the range of 5 centimeters (two inches) to 40 centimeters (sixteen inches).

[099] The housing (or housings) 1603 may also include acquisition and processing electronics 1613 that control the operation of the measurement components, store data produced by the measurement components, process the data and store the results, and couple any desired portion of the data to telemetry components for surface transmission. Data may also be stored in housing (or housings) 1603 and retrieved at the surface upon removal of housing (or housings) 1603 from the well or borehole 1605.

[0100] The housing (or housings) 1603 can be coupled to an armored electrical cable 1615 that can be extended into and retracted from the well or well hole 1605. The well or well hole 1605 may or may not include metal pipe or enclosure therein. Cable 1615 carries electrical power to operate the electrical components of instrument (or instruments) 1601. Cable 1615 may also carry electrical signals between the acquisition and processing electronics 1613 of instrument (or instruments) 1601 and a surface located data processor. 1617. In alternative embodiments, coiled tubing or other transport mechanism may be used in place of cable 1615 to transport instrument (or instruments) 1601 within borehole 1605.

[0101] It is noted that data processing operations of Figures 1, 3, 7, 8A, 8B and/or 10 can be performed by the acquisition and processing electronics 1613 arranged within the housing (or housings) 1603, through the system of surface located data processing 1617, or through a combination of acquisition and processing electronics 1613 and surface located data processing system 1617. Acquisition and processing electronics 1613 and/or data processing system 1617 may include one or more digital signal processors, one or more general purpose processors, and/or one or more computers or computer systems, as will be explained below with reference to Figure 17 for analysis of the acquired data, as well as devices for recording the data acquired with respect to position in the wellbore and/or time.

[0102] Figure 17 shows an exemplary computing system 1100, in accordance with some embodiments, for carrying out the exemplary work process, such as those to be explained above with reference to Figures 1, 3, 7, 8A, 8B and/or or 10. Computing system 1100 may be an individual computer system 1101A or an array of distributed computer systems. Computer system 1101A includes one or more analysis modules 1103 (a program of computer-executable instructions and associated data) that may be configured to perform various tasks, in some embodiments, such as the tasks described above. To accomplish these various tasks, an analysis module 1103 runs on one or more processors 1105, which are connected to one or more storage media 1107. Processor (or processors) 1105 may also be connected to a network interface 1109 for allowing computer system 1101A to communicate over a data network 1111 with one or more computer systems and/or additional computing systems, such as 1101B, 1101C and/or 1101D. It is noted that computer systems 1101B, 1101C and/or 1101D may or may not share the same architecture as computer system 1101A, and may be located in different physical locations, for example, computer systems 1101A and 1101B may be on a ship sailing the ocean, in a well logging unit disposed close to a borehole drilling, while in communication with one or more computer systems such as 1101C and/or 1101D that are located in one or more onshore data centers, other ships and /or located in several countries on different continents. Any one or more of the computer systems may be arranged in the well log instrument (either the wire rope as in Figure 16 or the LWD as in Figure 18).

[0103] The processor 1105 may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, digital signal processor (DSP), or other control or computing device.

[0104] Storage media 1107 may be deployed as one or more non-transient computer or machine readable storage media. Note that, although in the embodiment of Figure 17, storage media 1107 are depicted as within computer system 1101A, in some embodiments, storage media 1107 may be distributed within and/or across multiple inner and/or outer shells. of computing system 1101A and/or additional computing systems. Storage media 1107 can include one or more different forms of memory that include semiconductor memory devices, such as dynamic or static random access memories (DRAMs or SRAMs), programmable and erasable read-only memories (EPROMs), read-only memories electrically programmable and erasable (EEPROMs) and flash memories; magnetic disks, such as fixed disks, floppy disks and removable disks; other magnetic media including tape; optical media, such as compact discs (CDs) or digital video discs (DVDs); or other types of storage devices. It is noted that the computer-executable instructions and associated data of the analysis module (or modules) 1103 may be provided on a computer or machine readable storage medium from one of the storage mediums 1107, or alternatively, may be provided on multiple media. machine-readable storage files distributed across a large system that has possibly plural nodes. Such computer or machine readable storage media or media are considered part of an article (or article of manufacture). An article or article of manufacture can refer to any manufactured single component or multiple components. The storage media or media can be located on the machine executing the machine-readable instructions or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

[0105] It should be noted that the computing system 1100 is only an example of a computing system, and that the computing system 1100 may have more or less components than those shown, may combine additional components not represented in the embodiment of Figure 18 , and/or computing system 1100 may have a different configuration or arrangement than the components depicted in Figure 17. The various components shown in Figure 17 may be implemented in hardware, software, or a combination of both hardware and software, which include one or more application-specific integrated circuits and/or signal processing.

[0106] In addition, the operations of the work process described above can be implemented by executing one or more functional modules in information processing apparatus, such as general purpose processors or specific application chips, such as ASICs, FPGAs, PLDs , SOCs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are all within the scope of protection of the invention.

[0107] Figure 18 shows an exemplary registration during drilling (LWD) system that is suitable for carrying out the work processes of Figures 1, 3, 7, 8A, 8B and/or 10. In this system, a platform and crane 1210 are positioned over a borehole 1212 that may be formed in geological formation 1214 by rotary drilling. A drill string 1216 may be suspended within the wellbore 1212 and may include a bit 1218 attached thereto and rotated by a turntable 1220 (powered by means not shown), which engages a drill rod 1222 at the top end of the string. drill rig 1216. Drill string 1216 is typically suspended from a hook 1224 attached to a movable block (not shown). Drill rod 1222 is attachable to hook 1224 via a swivel pivot joint 1226 which allows rotation of drill string 1216 relative to hook 1224. Alternatively, drill string 1216 and bit 1218 can be rotated from the surface by a “top drive” type of drilling rig.

[0108] Drilling fluid or mud 1228 is contained in a mud tank 1230 adjacent to the crane 1210. A pump 1232 pumps the drilling fluid 1228 into the drill string 1216 through a port in the pivot joint 1226 to flow downwards ( as indicated by the flow arrow 1234) through the center of the drill string 1216. The drilling fluid leaves the drill string through ports in the bit 1218 and then circulates upward in the annular space between the outside of the drill string 1216 and the wellbore wall 1212, as indicated by the flow arrows 1236. The drilling fluid 1228 thereby lubricates the bit 1218 and carries formation excavations to the surface. At the surface, drilling fluid 1228 is returned to mud tank 1230 for recirculation. If desired, a directional drill assembly (not shown) could also be employed.

[0109] A bottom hole assembly (“BHA”) 1238 can be mounted inside the drill string 1216, preferably close to the drill 1218. The BHA 1238 can include subassemblies to produce measurement, processing and storage information and to communicate with components located on the surface. Such measurements may correspond to those made using the instrument 1601 as explained above with reference to Figure 16. The BHA 1238 is typically located within several lengths of the drill collar of the bit 1218. In the illustrated BHA 1238, a stabilizer collar section 1240 is shown arranged immediately above the bit 1218, followed in the upward direction by a drill collar section 1242, another stabilizer collar section 1244, and another drill collar section 1246. This arrangement of drill collar sections and stabilizer collar sections is illustrative only, and other component arrangements in any BHA 1238 deployment may be used. The need or desire for stabilizer collars will depend on drilling conditions as well as the demands of the measurement.

[0110] In the arrangement shown in Figure 18, the measurement components of the well log instrument can be located in the drilling collar section 1242 above the stabilizer collar 1240. Such components could, if desired, be located closer or further away from drill 1218, such as, for example, outrigger collar section 1240 or 1244 or drill collar section 1246.

[0111] The BHA 1238 may also include a telemetry subassembly (not shown) for data and control communication with components located on the surface. Such a telemetry subassembly may be of any suitable type, for example a mud pulse telemetry system (pressure or acoustic), wired drill pipe, etc., which receives output signals from the LWD measurement instruments at the BHA 1238) and transmits encoded signals representative of such outputs to the surface where the signals are received and decoded at a receiver subsystem 1248, and provided to a processor 1250 and/or a recorder 1252. A surface transmitter subsystem 1254 may also be provided to establish communication with the BHA 1238.

[0112] Power for the LWD instrumentation of the BHA 1238 can be supplied by battery or, as known in the art, through a turbine generator arranged in the BHA 1238 and powered by the flow of drilling fluid. LWD instrumentation may also include directional sensors (not shown separately) that measure the geomagnetic orientation or geodetic orientation of the BHA 1238 and the gravitational orientation of the BHA 1238, both in a rotational and axial manner.

[0113] Note that data processing operations of Figures 1, 3, 7, 8A, 8B and/or 10 can be performed by the acquisition and processing electronics of the BHA 1238, through the data processor located on the surface 1250, or through a combination of the acquisition and processing electronics of the BHA 1238 and the surface located processor 1250. The acquisition and processing electronics of the BHA 1238 and/or the processor 1250 may include one or more digital signal processors, one or more general purpose processors, and/or one or more computers or computer systems as discussed above with reference to Figure 17 for analyzing the detected signals, as well as devices for recording the signals communicated from the BHA 1238 with respect to position in the borehole and/or time.

[0114] Although several exemplary embodiments have been described in detail above, those skilled in the art will readily appreciate that various modifications are possible in the exemplary embodiments without materially departing from the scope of this disclosure. Accordingly, all such modifications are to be within the scope of this disclosure.

Claims (17)

1. Method for determining properties of a laminated formation traversed by a well or borehole, characterized in that it comprises: obtaining sonic data, resistivity data and measured density data for an interval of interest of the laminated formation; derive a formation model that describes properties of the laminated formation in the interval of interest, where the formation model represents the laminated formation in the interval of interest as first and second zones of different first and second rock types, where the first zone comprises a TIV rock medium (transverse isotropic with vertical axis of symmetry); and the second zone comprises an isotropic rock medium or HTI rock medium (transverse isotropic with horizontal axis of symmetry), and wherein the TIV rock medium of the first zone comprises shale, and the isotropic rock medium of the second zone comprises sand with randomly oriented macropores; properties of the laminated formation in the range of interest include a radial profile of at least one parameter representing the pore shape for the first zone shale, a radial profile of at least one parameter representing the pore shape for the randomly oriented macropores of the second zone sand, and a parameter representing a volume modulus for fluid that saturates the randomly oriented macropores of the second zone sand; the at least one parameter representing the pore shape for the first zone shale preferably comprises a pore aspect ratio for the first zone shale; and the at least one parameter representing the pore shape for the randomly oriented macropores of the second zone sand preferably comprises a pore aspect ratio for the randomly oriented macropores of the second zone sand; use the formation model to derive sonic data, resistivity data, and simulated density data for the range of interest; and use the measured sonic data, resistivity data, and measured density data for the range of interest and the simulated sonic data, resistivity data, and simulated density data for the range of interest to refine the formation model and determine properties of the rolled formation in the range of interest. 2. Method according to claim 1, characterized by the fact that: the second zone comprises an HTI rock medium that has microcracks aligned in a predefined orientation; and the properties of the laminated formation in the range of interest include a radial profile of at least one parameter representing the pore shape for the second zone microcracks and a volume modulus for the fluid that saturates the second zone microcracks. 3. Method according to claim 2, characterized in that: the at least one parameter representing the pore format for the microcracks in the second zone comprises a pore aspect ratio for the microcracks in the second zone. 4. Method according to claim 1, characterized in that: the properties of the laminated formation in the range of interest additionally include at least one of a radial profile of saturation in water, a radial profile of saturation in gas, a radial oil saturation profile, and formation porosity. 5. Method according to claim 1, characterized in that it further comprises: determining a radial profile of effective stiffness data for a first and a second combined zone of the formation model i) determining first stiffness data for the first zone in a given radial dimension, ii) determining second stiffness data for the second zone in the given radial dimension, iii) combining the first stiffness data and the second stiffness data to produce effective stiffness data for the first and second zones combined in the given radial dimension, and iv) repeating operations i) to iii) for a plurality of radial dimensions; and using the radial profile of the effective stiffness data to derive simulated sonic data and simulated density data for the range of interest. 6. Method according to claim 5, characterized in that: the first stiffness data comprises a first stiffness matrix which is based on information specifying a pore format of the first zone; the second stiffness data comprises a second stiffness matrix which is based on information specifying a pore shape of the second zone; and the second stiffness matrix for the second zone is preferably based on a stiffness matrix for the second zone rock medium which assumes that the second zone rock medium is dry and calculations which modify such stiffness matrix which represents the fluid that saturates the pore space of the rock medium of the second zone. 7. Method according to claim 1, characterized in that it further comprises: determining a radial profile of effective resistivity data for a first and a second combined zone of the formation model i) determining the first resistivity data for the first zone in a given radial dimension, ii) determining the second resistivity data for the second zone in the given radial dimension, iii) combining the first resistivity data and the second resistivity data to produce stiffness data effective for the first and second combined zones in the given radial dimension, and iv) repeating operations i) to iii) for a plurality of radial dimensions; and using the radial profile of the effective resistivity data to derive simulated resistivity data for the range of interest. 8. Method, according to claim 7, characterized by the fact that: the first resistivity data are based on information that specify a pore format of the first zone; and the second resistivity data is based on information specifying a pore shape of the second zone. 9. Method according to claim 1, characterized in that it further comprises: using a first petrophysical transformant to derive a radial profile of an effective anisotropic resistivity for the first and second combined zones; use a simulator response tool to derive simulated resistivity data for the range of interest based on the radial effective anisotropic resistivity profile for the range of interest; using a second petrophysical transform to derive a radial profile of an effective stiffness matrix for a first and second combined zones and a radial density profile of the first and second combined zones; and using at least one tool-response simulator to derive simulated sonic data for the range of interest based on the radial profile of the effective stiffness matrix and the radial density profile for the range of interest. 10. Method, according to claim 9, characterized by the fact that: the second petrophysical transformant is based on a physics model of a rock. 11. Method according to claim 9, characterized in that it further comprises: using a tool response simulator to derive simulated density data for the range of interest based on the radial density profile for the range of interest . 12. Method according to claim 11, characterized by the fact that: the second petrophysical transformant is based on a rock physics model or a density model. 13. Method, according to claim 2, characterized in that it additionally comprises: (i) determining initial estimates for the parameters of the formation model that represent the formation porosity, the pore format of the first zone, the shape of second zone pore, water saturation and gas saturation for the range of interest based on inversion of the union of measured sonic data and measured density data for the range of interest; (ii) determine an initial estimate for the radial water saturation profile for the interval of interest based on the parameters of the formation model determined in i) in combination with the inversion of the measured resistivity data for the interval of interest; (iii) determine initial estimates for a gas saturation radial profile, a first zone radial pore shape profile, and a second zone radial pore shape profile for the interval of interest based on the parameters of the formation model determined in ii) in combination with the inversion of the union of the measured sonic data and the measured density data for the interval of interest; and (iv) determine a final radial profile of formation porosity, a final radial profile of water saturation, a final radial profile of gas saturation, a final radial profile of pore shape of the first zone, and a final radial profile of pore shape of the second zone for the range of interest based on the parameters of the formation model determined in iii) in combination with the inversion of union of the measured resistivity data, the measured sonic data and the measured density data for the range of interest. 14. Method according to claim 1, characterized in that it further comprises: using a downhole tool to derive sonic data, resistivity data and measured density data for the range of interest. 15. Method according to claim 14, characterized by the fact that: the downhole tool is one of a steel cable tool and a data recording tool during drilling. 16. Data processor characterized by the fact that it is programmed to determine properties of a laminated formation crossed by a well or borehole: obtaining sonic data, resistivity data and measured density data for an interval of interest of the laminated formation ; deriving a formation model that describes properties of the laminated formation in the interval of interest, where the formation model represents the laminated formation in the interval of interest as first and second zones of different first and second rock types, where the first zone comprises a rock medium TIV (transverse isotropic with vertical axis of symmetry); and the second zone comprises an isotropic rock medium or HTI rock medium (transverse isotropic with horizontal axis of symmetry), and wherein the TIV rock medium of the first zone comprises shale, and the isotropic rock medium of the second zone comprises sand with randomly oriented macropores; properties of the laminated formation in the range of interest include a radial profile of at least one parameter representing the pore shape for the first zone shale, a radial profile of at least one parameter representing the pore shape for the randomly oriented macropores of the second zone sand, and a parameter representing a volume modulus for fluid that saturates the randomly oriented macropores of the second zone sand; the at least one parameter representing the pore shape for the first zone shale preferably comprises a pore aspect ratio for the first zone shale; and the at least one parameter representing the pore shape for the randomly oriented macropores of the second zone sand preferably comprises a pore aspect ratio for the randomly oriented macropores of the second zone sand; using the formation model to derive simulated sonic data, resistivity data, and density data for the range of interest; and using the measured sonic data, resistivity data, and measured density data for the range of interest and the simulated sonic data, resistivity data, and simulated density data for the range of interest to refine the formation model and determine formation properties laminated in the range of interest. 17. Data processor, according to claim 16, characterized in that: the rock medium of the second zone additionally comprises microcracks aligned in a predefined orientation; and the properties of the laminated formation in the range of interest include a radial profile of at least one parameter representing the pore shape for the second zone microcracks and a volume modulus for the fluid that saturates the second zone microcracks.