The accuracy of reservoir facies and petrophysical property models using multiple information sources through quantile machine learning techniques

Patent metadata

Field	Value
Publication number	US-2025298942-A1
Application number	US-202418615485-A
Country	US
Kind code	A1
Filing date	Mar 25, 2024
Priority date	Mar 25, 2024
Publication date	Sep 25, 2025
Grant date	—

Abstract

Some implementations relate to a method for generating, at least in part by a quantile-trained learning machine, a model of a formation property across one or more subsurface formations of a reservoir using a plurality of external data sources different than the formation property.

First claim

What is claimed is:

1 . A method comprising:
generating, at least in part by a quantile-trained learning machine, a model of a formation property across one or more subsurface formations of a reservoir using a plurality of external data sources different than the formation property.

2 . The method of claim 1 , further comprising:
initiating, via a user-specified variogram, a probability field simulation of the formation property across the one or more subsurface formations.

3 . The method of claim 2 , wherein the user-specified variogram determines a heterogeneity of the probability field simulation.

4 . The method of claim 1 , further comprising:
generating, via multiple-point statistical simulation, the model of the formation property across the one or more subsurface formations of the reservoir, wherein the formation property is a discrete formation property.

5 . The method of claim 1 , wherein the formation property is a continuous formation property.

6 . The method of claim 1 , wherein the plurality of external data sources include location data, trend data, and seismic data of the one or more subsurface formations.

7 . The method of claim 1 , further comprising:
predicting, via the quantile-trained learning machine, an uncertainty range of the formation property at each location in the reservoir; determining, via the quantile-trained learning machine, a probability at each location in the reservoir, wherein the probability includes a quantile value; and assigning, at each location in the reservoir, a value of the formation property based, at least in part, on the uncertainty range of the formation property and the probability at each location in the reservoir.

8 . A system comprising:
a processor; and a computer-readable medium having instructions executable by the processor, the instructions including: instructions to generate, at least in part by a quantile-trained learning machine, a model of a formation property across one or more subsurface formations of a reservoir using a plurality of external data sources different than the formation property.

9 . The system of claim 8 , further comprising:
instructions to initiate, via a user-specified variogram, a probability field simulation of the formation property across the one or more subsurface formations, wherein the user-specified variogram determines a heterogeneity of the probability field simulation.

10 . The system of claim 8 , further comprising:
instructions to generate, via multiple-point statistical simulation, the model of the formation property across the one or more subsurface formations of the reservoir, wherein the formation property is a discrete formation property.

11 . The system of claim 8 , wherein the formation property is a continuous formation property.

12 . The system of claim 8 , wherein the plurality of external data sources include location data, trend data, and seismic data of the one or more subsurface formations.

13 . The system of claim 8 , further comprising:
instructions to predict, via the quantile-trained learning machine, an uncertainty range of the formation property at each location in the reservoir; instructions to determine, via the quantile-trained learning machine, a probability at each location in the reservoir, wherein the probability includes a quantile value; and instructions to assign, at each location in the reservoir, a value of the formation property based, at least in part, on the uncertainty range of the formation property and the probability at each location in the reservoir.

14 . One or more non-transitory machine-readable media including instructions executable by a processor to cause the processor to perform a simulation across a reservoir, the instructions comprising:
instructions to generate, at least in part by a quantile-trained learning machine, a model of a formation property across one or more subsurface formations of the reservoir using a plurality of external data sources different than the formation property.

15 . The machine-readable media of claim 14 , further comprising:
instructions to initiate, via a user-specified variogram, a probability field simulation of the formation property across the one or more subsurface formations, wherein the user-specified variogram determines a heterogeneity of the probability field simulation.

16 . The machine-readable media of claim 14 , further comprising:
instructions to generate, via multiple-point statistical simulation, the model of the formation property across the one or more subsurface formations of the reservoir.

17 . The machine-readable media of claim 16 , wherein the formation property is a discrete formation property.

18 . The machine-readable media of claim 14 , wherein the formation property is a continuous formation property.

19 . The machine-readable media of claim 14 , wherein the plurality of external data sources include location data, trend data, and seismic data of the one or more subsurface formations.

20 . The machine-readable media of claim 14 , further comprising:
instructions to predict, via the quantile-trained learning machine, an uncertainty range of the formation property at each location in the reservoir; instructions to determine, via the quantile-trained learning machine, a probability at each location in the reservoir, wherein the probability includes a quantile value; and instructions to assign, at each location in the reservoir, a value of the formation property based, at least in part, on the uncertainty range of the formation property and the probability at each location in the reservoir.

Assignees

• Landmark Graphics Corp

Inventors

• Strebelle Sebastien
• Srivastav Shreshth

Classifications

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title