公开(公告)号：US20200096663A1

公开(公告)日：2020-03-26

申请号：US16612320

申请日：2018-04-23

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Sushil Shetty ,  Qiwei Zhan ,  Lin Liang ,  Austin Boyd ,  Smaine Zeroug ,  Vanessa Simoes ,  Fabio Cesar Canesin

IPC: G01V1/50 ,  E21B49/00 ,  E21B43/02 ,  E21B47/00

Abstract: A method for determining properties of a laminated formation traversed by a well or wellbore employs measured sonic data, resistivity data, and density data for an interval-of-interest within the well or wellbore. A formation model that describe properties of the laminated formation at the interval-of-interest is derived from the measured sonic data, resistivity data, and density data for the interval-of-interest. The formation model represents the laminated formation at the interval-of-interest as first and second zones of different first and second rock types. The formation model is used to derive simulated sonic data, resistivity data, and density data for the interval-of-interest. The measured sonic data, resistivity data, and density data for the interval-of-interest and the simulated sonic data, resistivity data, and density data for the interval-of-interest are used to refine the formation model and determine properties of the formation at the interval-of-interest. The properties of the formation may be a radial profile for porosity, a radial profile for water saturation, a radial profile for gas saturation, radial profile of oil saturation, and radial profiles for pore shapes for the first and second zones (or rock types).

公开(公告)号：US11624849B2

公开(公告)日：2023-04-11

申请号：US15978972

申请日：2018-05-14

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Austin Boyd ,  Vanessa Simoes ,  Bikash Kumar Sinha ,  Smaine Zeroug ,  Anna Paula Lougon Duarte

IPC: G01V1/30

Abstract: A method for estimating all five transversely-isotropic (TI)-elastic constants using borehole sonic data obtained from at least one subterranean borehole in a transversely isotropic formation. In an embodiment, the method includes: solving for a quasi-compressional qP-wave velocity VqP using inversion algorithms based on exact solutions of the Kelvin-Christoffel equations for plane wave velocities in arbitrarily anisotropic formations, where the five TI-elastic constants may include C11, C13, C33, C55, and C66.

公开(公告)号：US11422280B2

公开(公告)日：2022-08-23

申请号：US16612320

申请日：2018-04-23

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Sushil Shetty ,  Qiwei Zhan ,  Lin Liang ,  Austin Boyd ,  Smaine Zeroug ,  Vanessa Simoes ,  Fabio Cesar Canesin

IPC: G01V1/50 ,  E21B43/02 ,  E21B49/00 ,  E21B47/005

Abstract: A method for determining properties of a laminated formation traversed by a well or wellbore employs measured sonic data, resistivity data, and density data for an interval-of-interest within the well or wellbore. A formation model that describe properties of the laminated formation at the interval-of-interest is derived from the measured sonic data, resistivity data, and density data for the interval-of-interest. The formation model represents the laminated formation at the interval-of-interest as first and second zones of different first and second rock types. The formation model is used to derive simulated sonic data, resistivity data, and density data for the interval-of-interest. The measured sonic data, resistivity data, and density data for the interval-of-interest and the simulated sonic data, resistivity data, and density data for the interval-of-interest are used to refine the formation model and determine properties of the formation at the interval-of-interest. The properties of the formation may be a radial profile for porosity, a radial profile for water saturation, a radial profile for gas saturation, radial profile of oil saturation, and radial profiles for pore shapes for the first and second zones (or rock types).

公开(公告)号：US10365405B2

公开(公告)日：2019-07-30

申请号：US15544187

申请日：2016-01-25

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Sushil Shetty ,  Lin Liang ,  Tarek M. Habashy ,  Vanessa Simoes ,  Austin Boyd ,  Bikash K. Sinha ,  Smaine Zeroug

IPC: G01V99/00 ,  G01V11/00 ,  G01N15/08

Abstract: A computer-implemented method is provided for determining properties of a formation traversed by a well or wellbore. A formation model describing formation properties at an interval-of-interest within the well or wellbore is derived from measured sonic data, resistivity data, and density data for the interval-of-interest. The formation model is used as input to a plurality of petrophysical transforms and corresponding tool response simulators that derive simulated sonic data, resistivity data, and density data for the interval-of-interest. The measured sonic data, resistivity data, and density data for the interval-of-interest and the simulated sonic data, resistivity data, and density data for the interval-of-interest are used by an inversion process to refine the formation model and determine properties of the formation at the interval-of-interest. In embodiments, properties of the formation may be radial profiles for porosity, water saturation, gas or oil saturation, or pore aspect ratio.

公开(公告)号：US12129757B2

公开(公告)日：2024-10-29

申请号：US18706186

申请日：2022-11-04

Applicant: Schlumberger Technology Corporation

Inventor： Vanessa Simoes ,  Hiren Maniar ,  Tao Zhao ,  Aria Abubakar

IPC: E21B47/13 ,  E21B47/12 ,  G01V1/48 ,  G06N20/00

CPC classification number: E21B47/138 ,  G01V1/48 ,  G06N20/00 ,  G01V2210/6169

Abstract: A method includes receiving first training well logs, generating second training well logs by injecting one or more different types of systematic errors, random errors, or both into at least a portion of the first training well logs, training a machine learning model to correct well logs by configuring the machine learning model to reduce a dissimilarity between at least a portion of the first and second training well logs, receiving one or more implementation well logs, and generating one or more corrected well logs by correcting at least a portion of the one or more implementation well logs using the machine learning model that was trained.

公开(公告)号：US20240328309A1

公开(公告)日：2024-10-03

申请号：US18706186

申请日：2022-11-04

Applicant: Schlumberger Technology Corporation

Inventor： Vanessa Simoes ,  Hiren Maniar ,  Tao Zhao ,  Aria Abubakar

IPC: E21B47/12 ,  G01V1/48 ,  G06N20/00

CPC classification number: E21B47/138 ,  G01V1/48 ,  G06N20/00 ,  G01V2210/6169

Abstract: A method includes receiving first training well logs, generating second training well logs by injecting one or more different types of systematic errors, random errors, or both into at least a portion of the first training well logs, training a machine learning model to correct well logs by configuring the machine learning model to reduce a dissimilarity between at least a portion of the first and second training well logs, receiving one or more implementation well logs, and generating one or more corrected well logs by correcting at least a portion of the one or more implementation well logs using the machine learning model that was trained.

公开(公告)号：US20170371072A1

公开(公告)日：2017-12-28

申请号：US15544187

申请日：2016-01-25

Applicant: SCHLUMBERGER TECHNOLOGY CORPORATION

Inventor： Sushil Shetty ,  Lin Liang ,  Tarek M. Habashy ,  Vanessa Simoes ,  Austin Boyd ,  Bikash K. Sinha ,  Smaine Zeroug

IPC: G01V99/00 ,  G01N15/08 ,  G01V11/00

CPC classification number: G01V99/005 ,  G01N15/088 ,  G01V11/00 ,  G01V2210/1429 ,  G01V2210/622

Abstract: A method for determining properties of a formation traversed by a well or wellbore employs measured sonic data, resistivity data, and density data for an interval-of-interest within the well or wellbore. A formation model that describe properties of the formation at the interval-of-interest is derived from the measured sonic data, resistivity data, and density data for the interval-of-interest. The formation model is used to derive simulated sonic data, resistivity data, and density data for the interval-of-interest. The measured sonic data, resistivity data, and density data for the interval-of-interest and the simulated sonic data, resistivity data, and density data for the interval-of-interest are used to refine the formation model and determine properties of the formation at the interval-of-interest. The properties of the formation may be a radial profile for porosity, a radial profile for water saturation, a radial profile for gas saturation, a radial profile of oil saturation, and a radial profile for pore aspect ratio.