Halliburton Energy Services, Inc.

Junsheng Hou

Luis Emilio San Martin

Burkay Donderici

Dagang Wu

Michael S Bittar

John Andrew Quirein

Ahmed Elsayed Fouda

Glenn Andrew Wilson

Baris Guner

Li Pan

Jin Ma

YiJing Fan

Natasa Mekic

Ferhat Turker Celepcikay

Ameet B Agnihotri

MingJen Wang

Methods for determining water-filled porosity using a general volumetric mixing law and the measurements of a dielectric tool are described. The water-filled porosity estimates are used to obtain water salinity estimates and the optimal parameters of the volumetric mixing law. These estimates are in turn used to generate novel quality indicators.

Interpretation of dielectric tool measurements using general mixing laws

A method includes setting a value of a formation parameter for a subsurface formation and creating an initial three-dimensional (3D) model of the subsurface formation based on the formation parameter. The method also includes applying a constrained transformation to one or more inversion variables of the initial 3D model to create a variable-constrained 3D model of the subsurface formation and applying an unconstrained minimization operation to the variable-constrained 3D model to generate a first transformed 3D model. The method also includes inverting the first transformed 3D model to generate a first inverted 3D model of the subsurface formation.

3D inversion of deep resistivity measurements with constrained nonlinear transformations

Systems and methods for determining formation properties are disclosed. In some embodiments, a multi-component induction tool is utilized to measure a set of triaxial resistivities at each of a plurality of borehole depths. Differences among the sets of triaxial resistivities are correlated with a compaction-dependent property determined for one or more borehole depths. Depth-dependent laminate resistivity models are generated based on the correlating. A formation property is determined by processing the sets of triaxial resistivities using a formation tensor model that incorporates the depth-dependent laminate resistivity models.

Anisotropic formation modeling

Evaluation of formation and fracture characteristics based on multicomponent induction (MCI) and multipole sonic logging (MSL) data includes automated calculation of inverted biaxial anisotropy (BA) parameters for the formation by performing an iterative BA inversion operation based on the MCI log data and using a BA formation model that accounts for transfers by axial formation anisotropy to resistivity. The inverted BA parameters and the processed MSL data can be used, in combination, to calculate a quantified value for an identification function, to indicate estimated presence or absence of a fracture in the formation.

Fracture interpretation with resistivity and sonic logs in biaxial anisotropic formations

Example embodiments are described for a method and system for logging data processing in determining permeability anisotropy effects. A permeability anisotropy model is used to derive a relationship between formation permeability anisotropy and resistivity anisotropy in both TI and BA formations. Implementations can provide the permeability anisotropy plus the true reservoir (or sand) permeability by using an integrated interpretation of the MCI resistivity anisotropy measurements with conventional permeability logs from other sensors (e.g., NMR or sonic). Biaxial and triaxial permeability components of the permeability anisotropy tensor can be determined for application to synthetic and field log interpretations.

Permeability anisotropy assessment in subsurface anisotropic formations

Numerical and/or semi-analytical methods are leveraged to decouple a complete set of nonzero electromagnetic field tensor components () from detected signal data (). Nine nonzero components can serve as inputs for a three-dimensional inversion process to determine formation properties. A resistivity tool () containing at least one transmitter () and at least one receiver () at tilted angles receives an electromagnetic signal throughout a rotation. A difference in the azimuthal positions of the transmitter(s) and receiver(s) during rotation of the resistivity tool can result in an azimuthal offset between resistivity tool subs. The components () are decoupled from the detected signal data () numerically or semi-analytically according to whether the azimuthal offset angle is known. If the azimuthal offset angle is known, the nine components are determined numerically through curve fitting. If the azimuthal offset angle is unknown, a semi-analytical process is used to solve for the nine components.

Decoupling tensor components without matrix inversion

A method and system can include a sensor positioned in a borehole, characteristics of the earthen formation can be measured and logged by the sensor, and an effective permeability can be determined based on the logged characteristics. Multi-component induction (MCI) data can be measured by a logging tool, 3D resistivity components can be determined by inverting the MCI data, and the 3D resistivity components can be logged. Tri-axial permeability components can be determined based on the effective permeability and the 3D resistivity component logs. A permeability of sand in the earthen formation can be determined based on the tri-axial permeability components, the effective permeability, and a laminated shale volume. The sand permeability can be logged and modifications to operation(s) can be initiated based on the sand permeability.

Determining permeability in subsurface anisotropic formations

The disclosed embodiments include a method to determine formation properties of a downhole formation and a downhole logging system. In one embodiment, the method includes obtaining a first set of measurements of a formation from a multi-component induction logging tool and performing an inversion process of the first set of measurements to determine a first set of values for one or more formation properties of the formation. The method also includes determining a second set of values for the model parameters based on the measurements of a second logging tool. The method further includes comparing values of the first set of values with corresponding values of the second set of values to determine a data quality of the values of the first set of values and accepting the values of the first set of values if the data quality of the values is above a first threshold.

Systems and methods to determine formation properties of high-resistivity formations

Apparatus and techniques are described, such as for obtaining information indicative of a formation resistivity near a casing, such as using an array laterolog apparatus. For example, raw measurements received from a well tool in a borehole near a casing may indicate a resistivity of a geologic formation through which the borehole extends. Any errors or interference in the raw measurements caused by the proximity of the well tool to the casing may be removed to eliminate the casing effect on the raw measurements. In some examples, a signal and formation libraries that include casing specific parameters may be used in conjunction with non-casing optimized signal and formation libraries to perform correction mapping of raw measurements. The corrections and their application to the raw measurements may be based on the position of a well logging tool with respect to a casing termination point.

Electrical log processing near casing point

A method includes acquiring signals generated from operating a multi-component induction tool in a wellbore. The multi-component induction tool has a plurality of receiver arrays. The method includes generating, for each receiver array at a fixed frequency, a combination of components from the acquired signals, wherein the components corresponds to components of an apparent conductivity tensor. The generating of the combination of components includes generating vertical magnetic dipole (VMD) data and horizontal magnetic dipole (HMD) data. The method includes mixing together the combination of VMD data and HMD data measured from the plurality of receiver arrays and generating data, with respect to evaluation of a formation around the wellbore, from the mixing together of combinations.

Apparatus and method of processing multi-component induction data

Various embodiments include apparatus and methods to utilize signals acquired from a multi-component induction tool operating in a wellbore. The acquired signals can be correlated to apparent conductivity of a formation and mapped to components of an apparent conductivity tensor. For each receiver array operated by the multi-component induction tool, a combination of these components from the acquired signals can be generated. Combinations generated from different receivers of the multi-component induction tool can be mixed together. From such combinations, data, with respect to evaluation of formation around the wellbore, can be generated. Additional apparatus, systems, and methods are disclosed.

In some embodiments, an apparatus and a system, as well as a method and article of manufacture, may operate to parameterize a geological formation in terms of initial earth model parameters, and generate predicted data by simulating a tool response using earth model parameters. Further activity may include determining a difference between calibrated data and the predicted data; and simultaneously adjusting calibration parameters and the earth model parameters such that the difference between them satisfies selected convergence criteria, so that a controlled device can be operated according to at least one of updated calibration parameters, updated calibrated data, or updated earth model parameters. Additional apparatus, systems, and methods are disclosed.

Simultaneous error correction and inversion

Disclosed herein are embodiments of systems, methods, and computer program products for assessing the data-quality of the calculated reservoir-rock resistivity Rin laminar formations. For instance, in one embodiment, a computer-implemented method for assessing the data-quality of the calculated reservoir-rock resistivity Rin laminar formations comprises the steps of receiving multi-component induction (MCI) data and other sensor logs data associated with a laminar formation; determining a set of calculated reservoir-rock resistivity Rusing at least one of the multi-component induction (MCI) data and other sensor logs data; and performing data quality assessments of the set of calculated reservoir-rock resistivity R

Data-quality evaluation of calculated true reservoir resistivity

Disclosed herein are embodiments of systems, methods, and computer program products for evaluating sand-resistivity and/or sand-conductivity, as well as saturation in laminated shale-sand formations with biaxial anisotropy (BA) in resistivity. In one embodiment, the method determines sand resistivity (R) of the laminated shale-sand formation directly from triaxial resistivities (R, R, and R) of the laminated shale-sand formation based on a bimodal model with BA anisotropy. In another embodiment, the method determines sand resistivity (R) directly based on a bimodal model with transverse isotropy (TI) anisotropy using the triaxial resistivities, horizontal resistivity, and vertical resistivity (R, R, R, R, and R). In another disclosed embodiment, the above methods may be used to perform quality control on a determined sand-resistivity and saturation of a laminated shale-sand formation.

Sand-resistivity/conductivity and saturation evaluation in laminated formations with biaxial anisotropy

A method to minimize borehole effects upon a multi-component induction tool within a well and borehole with water-based mud includes measuring parameters of the reservoir with the induction tool to create an array of measured components. The method further includes comparing a measured component from the array of measured components with a corresponding model component from an array of model components for a reservoir model with known parameters and no borehole effects, and determining the parameters for the reservoir based upon the comparison of the measured component and the corresponding model component.

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Kingwood, TX, United States of America

Junsheng Hou