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    Method to Characterize Fracture Plugging Efficiency for Drilling Fluids
    3.
    发明申请
    Method to Characterize Fracture Plugging Efficiency for Drilling Fluids 有权
    表征钻井液断裂堵塞效率的方法

    公开(公告)号：US20120316088A1

    公开(公告)日：2012-12-13

    申请号：US13591486

    申请日：2012-08-22

    申请人： Sandeep D. Kulkarni ,  Sharath Savari ,  Arunesh Kumar ,  Matthew L. Miller ,  Robert Murphy ,  Dale E. Jamison

    发明人： Sandeep D. Kulkarni ,  Sharath Savari ,  Arunesh Kumar ,  Matthew L. Miller ,  Robert Murphy ,  Dale E. Jamison

    IPC分类号： C09K8/10 ,  C09K8/05 ,  C09K8/36 ,  C09K8/26 ,  C09K8/32

    CPC分类号： C09K8/04 ,  C09K8/32 ,  C09K2208/08 ,  C09K2208/18

    摘要： A method for determining a Plug Normal Stress Difference (ΔN1(P)) may include providing a test base drilling fluid that is characterized by N1(TB); adding a first concentration of a test particulate to the test base drilling fluid; adjusting the concentration of the test particulate in the test base drilling fluid to achieve a minimum concentration of the test particulate in the test base drilling fluid that will substantially plug a tapered slot, wherein the test base drilling fluid with the minimum concentration of the test particulate is characterized by N1(TA); and calculating ΔN1(P)=|N1(TA)|−|N1(TB)| wherein each First Normal Stress Difference is measured by the same procedure.

    摘要翻译： 用于确定插塞正常应力差（＆Dgr; N1（P））的方法可以包括提供以N1（TB）为特征的测试基础钻井液; 将第一浓度的测试颗粒添加到测试基础钻井液中; 调整测试基础钻井液中的测试颗粒的浓度，以实现基本上堵塞锥形槽的测试基础钻井液中的测试颗粒的最小浓度，其中具有最小浓度的测试颗粒的测试基础钻探流体 特征为N1（TA）; 并计算＆Dgr; N1（P）= | N1（TA）| - | N1（TB）| 其中通过相同的程序测量每个第一正常应力差。

    Method to characterize fracture plugging efficiency for drilling fluids
    5.
    发明授权
    Method to characterize fracture plugging efficiency for drilling fluids 有权
    表征钻井液断裂堵塞效率的方法

    公开(公告)号：US08524638B2

    公开(公告)日：2013-09-03

    申请号：US13591486

    申请日：2012-08-22

    申请人： Sandeep D. Kulkarni ,  Sharath Savari ,  Arunesh Kumar ,  Matthew L. Miller ,  Robert Murphy ,  Dale E. Jamison

    发明人： Sandeep D. Kulkarni ,  Sharath Savari ,  Arunesh Kumar ,  Matthew L. Miller ,  Robert Murphy ,  Dale E. Jamison

    IPC分类号： E21B21/00 ,  E21B33/00 ,  E21B33/13 ,  C09K8/32

    CPC分类号： C09K8/04 ,  C09K8/32 ,  C09K2208/08 ,  C09K2208/18

    摘要： A method for determining a Plug Normal Stress Difference (ΔN1(P)) may include providing a test base drilling fluid that is characterized by N1(TB); adding a first concentration of a test particulate to the test base drilling fluid; adjusting the concentration of the test particulate in the test base drilling fluid to achieve a minimum concentration of the test particulate in the test base drilling fluid that will substantially plug a tapered slot, wherein the test base drilling fluid with the minimum concentration of the test particulate is characterized by N1(TA); and calculating ΔN1(P)=|N1(TA)|−|N1(TB)| wherein each First Normal Stress Difference is measured by the same procedure.

    摘要翻译： 用于确定插塞正常应力差（DeltaN1（P））的方法可以包括提供以N1（TB）为特征的测试基础钻井液; 将第一浓度的测试颗粒添加到测试基础钻井液中; 调整测试基础钻井液中的测试颗粒的浓度，以实现基本上堵塞锥形槽的测试基础钻井液中的测试颗粒的最小浓度，其中具有最小浓度的测试颗粒的测试基础钻探流体 特征为N1（TA）; 并计算ΔN1（P）= | N1（TA）| - | N1（TB）| 其中通过相同的程序测量每个第一正常应力差。

    Methods and apparatuses for modeling shale characteristics in wellbore servicing fluids using an artificial neural network
    7.
    发明授权
    Methods and apparatuses for modeling shale characteristics in wellbore servicing fluids using an artificial neural network 有权
    使用人造神经网络对井眼维修流体中的页岩特征进行建模的方法和装置

    公开(公告)号：US09117169B2

    公开(公告)日：2015-08-25

    申请号：US13480245

    申请日：2012-05-24

    申请人： Dale E. Jamison ,  Shadaab S. Maghrabi ,  Dhanashree Gajanan Kulkarni ,  Kushabhau D. Teke ,  Sandeep D. Kulkarni

    发明人： Dale E. Jamison ,  Shadaab S. Maghrabi ,  Dhanashree Gajanan Kulkarni ,  Kushabhau D. Teke ,  Sandeep D. Kulkarni

    IPC分类号： G06N3/02 ,  G06N3/04 ,  G06N3/08

    CPC分类号： G06N3/02 ,  G06N3/04 ,  G06N3/08

    摘要： 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.

    摘要翻译： 本文描述了用于确定目标地层和目标钻井流体的地层/流体相互作用的装置和方法。 该方法可以包括训练使用训练数据集的人造神经网络。 训练数据集可以包括源地层的形成特征和源钻井液的流体特性以及源地层/流体相互作用的实验数据。 一旦人工神经网络被训练，可以输入目标形成的形成特征和目标钻井液的流体特性。 目标地层的形成特征可以对应于源地层的地层特征。 目标钻井液的流体特性可以对应于源钻井液的流体特性。 可以使用人造神经网络输出的值来确定目标地层和目标钻井液的地层/流体相互作用。

    METHODS AND APPARATUSES FOR MODELING SHALE CHARACTERISTICS IN WELLBORE SERVICING FLUIDS USING AN ARTIFICIAL NEURAL NETWORK
    8.
    发明申请
    METHODS AND APPARATUSES FOR MODELING SHALE CHARACTERISTICS IN WELLBORE SERVICING FLUIDS USING AN ARTIFICIAL NEURAL NETWORK 有权
    使用人工神经网络在井筒维修流体中建模沙粒特性的方法和装置

    公开(公告)号：US20130318019A1

    公开(公告)日：2013-11-28

    申请号：US13480245

    申请日：2012-05-24

    申请人： Dale E. Jamison ,  Shadaab S. Maghrabi ,  Dhanashree Gajanan Kulkarni ,  Kushabhau D. Teke ,  Sandeep D. Kulkarni

    发明人： Dale E. Jamison ,  Shadaab S. Maghrabi ,  Dhanashree Gajanan Kulkarni ,  Kushabhau D. Teke ,  Sandeep D. Kulkarni

    IPC分类号： G06F15/18

    CPC分类号： G06N3/02 ,  G06N3/04 ,  G06N3/08

    摘要： 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.

    摘要翻译： 本文描述了用于确定目标地层和目标钻井流体的地层/流体相互作用的装置和方法。 该方法可以包括使用训练数据集来训练人造神经网络。 训练数据集可以包括源地层的形成特征和源钻井液的流体特性以及源地层/流体相互作用的实验数据。 一旦人工神经网络被训练，可以输入目标形成的形成特征和目标钻井液的流体特性。 目标地层的地层特征可以对应于源地层的地层特征。 目标钻井液的流体特性可以对应于源钻井液的流体特性。 可以使用人造神经网络输出的值来确定目标地层和目标钻井液的地层/流体相互作用。