Halliburton Energy Services, Inc.

Imageprovision Technology Pvt. Ltd.

Sandeep Kulkarni

Sharath Savari

Shadaab Syed Maghrabi

Dale E Jamison

Kushabhau Dagadu Teke

Robert J Murphy

Vikrant Bhavanishankar Wagle

Dhanashree Gajanan Kulkarni

Matthew Lynn Miller

Arunesh Kumar

Prithviraj Jadhav

Anita Gantepla

Saket Kumar

Ankita Saxena

Pooja Kalshetti

Kushabhau D Teke

Disclosed herein is a methodology, including the method and system to enable the same, for detection and classification of microscopic particles by an image analysis approach employing grayscale pixel intensity contour data generated from microphotographs, identifying discrete particles therein by applying user-defined filters, and outputting a statistically-processed report of the outcome thereof.

Method and system for detection and classification of particles based on processing of microphotographic images

An invert emulsion drilling fluid, and a method of drilling with such fluid, having improved rheology at low mud weights and high temperatures. The improved rheology is effected with addition of a rheology additive of the invention comprising fatty dimer diamines or dimer diamines and an organic acid or ester of the acid. A nonlimiting example of such a rheology additive comprises a C36 fatty dimer diamine and adipic acid or dimethyl adipate.

Methods and materials to enhance high temperature rheology in invert emulsions

A method for quantitatively determining dynamic barite sag in drilling fluids includes measuring rheological properties with viscometers and/or rheometers, and introducing the parameters into an equation to obtain the sag rate.

Methods for predicting dynamic sag using viscometer/rheometer data

An apparatus and method for determining a formation/fluid interaction of a target formation and a target drilling fluid is described herein. The method may include training an artificial neural network using a training data set. The training data set may include a formation characteristic of a source formation and a fluid characteristic of a source drilling fluid and experimental data on source formation/fluid interaction. Once the artificial neural network is trained, a formation characteristic of the target formation and fluid characteristic of target drilling fluid may be input. The formation characteristic of the target formation may correspond to the formation characteristic of the source formation. The fluid characteristic of the target drilling fluid may correspond to the fluid characteristic of the source drilling fluid. A formation/fluid interaction of the target formation and the target drilling fluid may be determined using a value output by the artificial neural network.

Methods and apparatuses for modeling shale characteristics in wellbore servicing fluids using an artificial neural network

A method of servicing a wellbore comprises determining a cation exchange capacity of a sample of a shale, determining a swelling characteristic of the shale using the cation exchange capacity in an equation comprising a term of the form:(cation exchange capacity)where Ais a final swelling volume of the shale in the presence of an aqueous fluid having a salt concentration of z %, and x and y are empirical constants, determining a composition of a wellbore servicing fluid based on the determined swelling characteristic, and drilling the wellbore using the wellbore servicing fluid. The swelling characteristic of the shale can be determined using the cation exchange capacity of the shale and a salt concentration in an equation comprising a term of the form:()*()(cation exchange capacity)where Ais a final swelling volume of the shale in contact with an aqueous fluid having a salt concentration of m %, f(m,z) is a function based on the salt concentration of m % relative to salt concentration of z % in the aqueous fluid in contact with the shale, and x and y are empirical constants defining the relation A=x (cation exchange capacity).

Mathematical modeling of shale swelling in water based muds

Of the many compositions and methods provided herein, one method includes providing a drilling fluid comprising a base drilling fluid and a plurality of particulates, wherein the base drilling fluid without the particulates is characterized by Nand wherein the base drilling fluid with the particulates is characterized by N; and adjusting a concentration of the particulates in the drilling fluid by comparing the value of &#x394;Nto &#x394;Nso that &#x394;N&#x2267;&#x394;N, wherein &#x394;N=|N|&#x2212;|N|.

Methods to characterize fracture plugging efficiency for drilling fluids

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Pune, India

Sandeep Kulkarni