IP Library › Granted Patent US 12,613,357

Granted Patent B2

US 12,613,357 · App. 18/256,824 · Granted Apr 28, 2026

Processing subsurface data with uncertainty for modelling and field planning

Inventors: Andrew Round (Abingdon, GB); Liviu Adrian Anton (Abingdon, GB); Pierre Amoudruz (Abingdon, GB); Kwangwon Park (Houston, TX); Sergey Anisimov (Radal, NO); Adam Younger (Oxford, GB); Mark Wakefield (Abingdon, GB)

Assignee: SCHLUMBERGER TECHNOLOGY CORPORATION

G01V20/00 E21B41/00 E21B2200/20

View Patent ↗

Loading inventors, assignments & file history…

Monitor This Case

Get email alerts when status or documents change.

Order Certified Copies

Most orders are placed with the USPTO same day — all within 24 business hours.

Order via The Patent Place →

Pre-filled with this patent's details

Quick Facts

Patent No.: US 12,613,357
App. No.: 18/256,824
Granted: Apr 28, 2026
Kind: B2

External Links

USPTO Patent Center↗ Full Patent PDF↗

Abstract

A method for field development includes receiving input data representing a subterranean volume, generating a multi-domain model of the subterranean volume, statistically sampling one or more of the realizations of the multi-domain model based at least in part on an uncertainty associated therewith, simulating the sampled one or more of the realizations using a field development planning engine, and generating a field development plan based at least in part on the simulated one or more of the realizations.

Claims (75)

1 . A method, comprising:

performing an oilfield operation using a set of sensors to collect input data from a subterranean volume;

wherein the input data specifies a weight on bit parameter associated with the operation, a torque on bit parameter associated with the operation, a pressure parameter associated with the operation, a flow rate parameter associated with the operation, a rotary speed parameter associated with the operation, a well log, a porosity parameter associated with the operation, a fluid composition parameter associated with the operation, and a permeability parameter associated with the operation,

wherein the set of sensors comprises a geophone and a gauge,

wherein the subterranean volume comprises a shale layer, a carbonate layer, and a sand layer, and

wherein the operation is a drilling operation, a wireline operation, or a seismic survey operation;

receiving the input data representing the subterranean volume;

generating a multi-domain model of the subterranean volume using the input data;

statistically sampling one or more realizations of the multi-domain model based at least in part on an uncertainty associated therewith to generate sampled realizations, wherein statistically sampling comprises using machine learning, k-means clustering, probability bands, or a combination thereof to select the one or more realizations from among other, non-selected realizations;

simulating the sampled realizations using a field development planning engine to generate simulated realizations for determining viability and profitability of a location and a well trajectory in the subterranean volume;

generating a field development plan based at least in part on the simulated realizations;

displaying the field development plan and the multi-domain model to a user; and

using the field development plan to manage a hydrocarbon resource in the subterranean volume and to inspect an outlier realization.

2 . The method of claim 1 , wherein generating the multi-domain model comprises:

generating an ensemble of a plurality of realizations of a first model based at least in part on the input data, an uncertainty of the input data, and an uncertainty of the first model;

generating a plurality of second realizations of a second model based at least in part on the ensemble of the plurality of realizations and an uncertainty of the second model; and

including the plurality of second realizations in the ensemble in connection with the realizations of the first model.

3 . The method of claim 2 , wherein generating the ensemble of the plurality of realizations comprises simulating a process using the first model.

4 . The method of claim 2 , wherein generating the multi-domain model comprises generating an uncertainty space in which the ensemble is represented, wherein the realizations of the multi-domain model are distributed in the uncertainty space, and wherein statistically sampling the one or more realizations comprises statistically sampling the one or more realizations from the uncertainty space based on a distribution of the realizations in the uncertainty space.

5 . The method of claim 2 , wherein the first model comprises a model of at least one physical characteristic of the subterranean volume, and wherein the second model comprises a commercial model, an economic model, or a combination thereof.

6 . The method of claim 1 , further comprising:

before generating the multi-domain model:

storing one or more shared files in a central database including file relationship data and locations of bulk files;

extracting one or more simulation models from the one or more shared files; and

evaluating metadata associated with the one or more simulation models to identify one or more simulation models to use in the multi-domain model.

7 . A non-transitory, computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations comprising:

performing an oilfield operation using a set of sensors to collect input data from a subterranean volume;

wherein the set of sensors comprises a geophone and a gauge,

wherein the subterranean volume comprises a shale layer, a carbonate layer, and a sand layer, and

wherein the operation is a drilling operation, a wireline operation, or a seismic survey operation;

receiving the input data representing the subterranean volume;

generating a multi-domain model of the subterranean volume using the input data;

generating a field development plan based at least in part on the simulated realizations; and

displaying the field development plan and the multi-domain model to a user, wherein the field development plan is used to manage a hydrocarbon resource in the subterranean volume and to inspect an outlier realization.

8 . The medium of claim 7 , wherein generating the multi-domain model comprises:

generating an ensemble of a plurality of realizations of a first model based at least in part on the input data, an uncertainty of the input data, and an uncertainty of the first model;

generating a plurality of second realizations of a second model based at least in part on the ensemble of the plurality of first realizations and an uncertainty of the second model; and

including the plurality of second realizations in the ensemble in connection with the realizations of the first model.

9 . The medium of claim 8 , wherein generating the ensemble of the plurality of first realizations comprises simulating a process using the first model.

10 . The medium of claim 8 , wherein generating the multi-domain model comprises generating an uncertainty space in which the ensemble is represented, wherein the realizations of the multi-domain model are distributed in the uncertainty space, and wherein statistically sampling the one or more realizations comprises statistically sampling the one or more realizations from the uncertainty space based on a distribution of the realizations in the uncertainty space.

11 . The medium of claim 8 , wherein the first model comprises a model of at least one physical characteristic of the subterranean volume, and wherein the second model comprises a commercial model, an economic model, or a combination thereof.

12 . The medium of claim 7 , wherein the operations further comprise:

before generating the multi-domain model:

storing one or more shared files in a central database including file relationship data and locations of bulk files;

extracting one or more simulation models from the one or more shared files; and

evaluating metadata associated with the one or more simulation models to identify one or more simulation models to use in the multi-domain model.

13 . A computing system, comprising:

one or more processors; and

a memory system including one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:

performing an oilfield operation using a set of sensors to collect input data from a subterranean volume;

wherein the set of sensors comprises a geophone and a gauge,

wherein the subterranean volume comprises a shale layer, a carbonate layer, and a sand layer, and

wherein the operation is a drilling operation, a wireline operation, or a seismic survey operation;

receiving the input data representing the subterranean volume;

generating a multi-domain model of the subterranean volume using the input data, wherein generating the multi-domain model comprises:

generating an ensemble of a plurality of realizations of a first model based at least in part on the input data, an uncertainty of the input data, and an uncertainty of the first model;

generating a plurality of second realizations of a second model based at least in part on the ensemble of the plurality of realizations and an uncertainty of the second model; and

including the plurality of second realizations in the ensemble in connection with the realizations of the first model, wherein the first model comprises a model of at least one physical characteristic of the subterranean volume, and wherein the second model comprises a commercial model, an economic model, or a combination thereof;

simulating the sampled one or more of the realizations using a field development planning engine to generate simulated realizations for determining viability and profitability of a location and a well trajectory in the subterranean volume; and

generating a field development plan based at least in part on the simulated realizations; and

14 . The computing system of claim 13 , wherein:

generating the multi-domain model comprises generating an uncertainty space in which the ensemble is represented, wherein the realizations of the multi-domain model are distributed in the uncertainty space, and wherein statistically sampling the one or more realizations comprises statistically sampling the one or more realizations from the uncertainty space based on a distribution of the realizations in the uncertainty space, and

statistically sampling from the uncertainty space comprises identifying one or more areas of the uncertainty space that are underrepresented in the sampling, overrepresented in the sampling, represent one or more outlier realizations, or a combination thereof.

15 . The computing system of claim 14 , wherein generating the ensemble of the plurality of realizations comprises simulating a process using the first model.

16 . The computing system of claim 13 , wherein the operations further comprise:

before generating the multi-domain model:

storing one or more shared files in a central database including file relationship data and locations of bulk files;

extracting one or more simulation models from the one or more shared files; and

evaluating metadata associated with the one or more simulation models to identify one or more simulation models to use in the multi-domain model.

Assignments (1)

ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 15, 2023

From: ROUND, ANDREW; ANTON, LIVIU ADRIAN; AMOUDRUZ, PIERRE; PARK, KWANGWON; ANISIMOV, SERGEY; YOUNGER, ADAM; WAKEFIELD, MARK

To: SCHLUMBERGER TECHNOLOGY CORPORATION

Reel/Frame 063957/0023 →

Continuity (2)

Provisional Application 63199161 · Dec 10, 2020

Related Publication 20240019603A1 · Jan 18, 2024

References Cited (32)

US 8849639B2 · Brown · 2014 [cited by applicant]

US 9228415B2 · Ingham · 2016 [cited by applicant]

US 10767448B2 · Djikpesse · 2020 [cited by applicant]

US 20190271211A1 · Mustapha · 2019 [cited by examiner]

US 20220372846A1 · Lang · 2022 [cited by applicant]

WO 2015138131A1 · 2015 [cited by applicant]

WO 2024064001A1 · 2024 [cited by applicant]

Al Lawati et al., Agile Field Development Study Leveraging Fast-Tracked Reservoir Modeling and Data Analytics While Incorporating Reservoir Uncertainties, Oct. 31, 2022, ADIPEC Abu Dhabi, Society of Petroleum Engineers … [cited by examiner]

Elharith, M. et al., “Integrated Modeling of a Complex Oil Rim Development Scenario Under Subsurface Uncertainty”, In Journal of Petroleum Exploration and Production Technology, vol. 9, Dec. 2019, pp. 2417-2428. [cited by applicant]

Scheidt, C., et al., “Uncertainty Quantification Using Distances and Kernel Methods —Application to a Deepwater Turbidite Reservoir”, In Stanford Center for Reservoir Forecasting, 2022, 29 Pages. [cited by applicant]

Bolt, H., “Quantifying the Uncertainty of the Depth Where Imaging is”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 3 pages. [cited by applicant]

Yusof, A. I. et al., “Quantifying Depth Uncertainty in Regional Scale—a Probablistic Approach”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 3 pages. [cited by applicant]

Basir, F. et al., “Re-Imaging through Multi-Level, Highly Saturated Gas Bodies in a Carbonate Regine—a Case Study”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 5 pages. [cited by applicant]

Sulaiman, M. S. B. et al., “Minimizing depth error through robust velocity model building—a Case Study”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 3 pages. [cited by applicant]

Sandjivy, L., “Quantifying Uncertainty in Depth Imaging: Case Histories”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 2 pages. [cited by applicant]

Pua, M. H. et al., “Quantify velocity model Uncertainty and its relationship with Geobodies”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 2 pages. [cited by applicant]

Johari, A. S. B. et al., “Quantifying Depth Uncertainties below Complex Overburden of Low Velocity Channels”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 2 pages. [cited by applicant]

Muhamad, A. et al., “Quantifying Depth Imaging Uncertainty of a FWI, Q-KDM and Q-RTM Seismic Volume”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 3 pages. [cited by applicant]

Tarang, A. et al., “Structural Depth Uncertainties in Producing Oil Fields”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 2 pages. [cited by applicant]

Hawari, S. A. A. et al., “Depth Prognosis in a Brown Field: When 6m certainty is inadequate”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 2 pages. [cited by applicant]

Alai, R. et al., “Inherited Uncertainties of Advanced Depth Migrated Images”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 2 pages. [cited by applicant]

Bloor, R. et al., “A Decade of Seismic Uncertainty and the Opportunities of the Next Decade”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 2 pages. [cited by applicant]

Zhai, Y. et al., “Efficiently measuring the uncertainty of FWI models and integrateion with tomography and geology”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 2 pages. [cited by applicant]

Hu, W. et al., “A Robust Self-Supervised Learning System for Low Frequency Seismic Data Extrapolation to Reduce Model Building Uncertainty”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur,… [cited by applicant]

Chautru, J. et al., “Analyzing and Quantifying Uncertainty in Time-Depth Conversion”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 2 pages. [cited by applicant]

Balakrishnan, G., “Quantifying Depth Uncertainty in Exploration to Development Cycle through Evolving Imaging Technology”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 2 pages. [cited by applicant]

Ham, W. Y. et al., “Multi-azimuth Q Inverted Full Waveform Inversion: a Data-Driven Approach for Quantifying Structural Uncertainties Beneath Gas Cloud”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, … [cited by applicant]

Li, J. et al., “A 3D pseudo-spectral method for qP-wave simulation in TTI media and its application in RTM”, Eage Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, 3 pages. [cited by applicant]

Hussenoeder, S. et al., “Imaging Depth Uncertainty from an Interpreters Perspective: Implications from Processing to Assessment”, EAGE Workshop on Quantifying Uncertainty in Depth Imaging, 2021, Kuala Lumpur, Malaysia, … [cited by applicant]

International Search Report and Written Opinion of International Patent Application No. PCT/US2021/062737 dated on Mar. 8, 2022; 9 Pages. [cited by applicant]

Extended European Search Report of European Patent Application No. 21904444.3 dated on Oct. 7, 2024; 10 Pages. [cited by applicant]

Baker, M., “Agile Iterative Reservoir Modelling”, In SIS Forum, Oct. 7, 2019, 20 Pages. [cited by applicant]