Machine learning based reservoir modeling

Patent metadata

Field	Value
Publication number	US-2023237225-A1
Application number	US-202318100928-A
Country	US
Kind code	A1
Filing date	Jan 24, 2023
Priority date	Jan 24, 2022
Publication date	Jul 27, 2023
Grant date	—

Abstract

Systems and methods for reservoir modeling use reservoir simulation and production data to predict future production for one or more wells. The system receives static data of a reservoir or well, receives dynamic data of the reservoir or well, and processes the static data and the dynamic data to generate a reservoir model. For instance, the static data and dynamic data can be used to generate a Voronoi grid, which is used to create a spatio-temporal dataset representing time steps for a focal well and offset wells. The reservoir model can predict reservoir performance, field development, production metrics, and operation metrics. By using one or more Machine Learning (ML) models, the systems disclosed herein can determined reservoir physics in minutes and replicate the physical properties calculated by more complex and computationally intensive reservoir modeling.

First claim

What is claimed is:

1 . A method for reservoir modeling, the method comprising:
receiving static data of a reservoir from a spatio-temporal database; receiving dynamic data of the reservoir from a spatio-temporal database; processing the static data and the dynamic data to identify centroids of wells in the reservoir; generating polygons based on the centroids of the well, and generating a reservoir model based on at least one of the polygons, static data, or dynamic data, wherein the reservoir model includes reservoir performance, field development, production metrics, and operation metrics.

2 . The method of claim 1 , wherein processing the static data and the dynamic data includes inputting the static data and the dynamic data into a machine learning model.

3 . The method of claim 1 , further comprising:
training a machine learning model based on historical static data and historical dynamic data.

4 . The method of claim 1 , further comprising:
training a machine learning model based on the static data and the dynamic data.

5 . The method of claim 1 , wherein the static data and the dynamic data are stored in the spatio-temporal database, and wherein the spatio-temporal database associates the static data and the dynamic data with a point in time.

6 . The method of claim 1 , wherein the spatio-temporal database includes data of the reservoir from a plurality of points in time.

7 . The method of claim 1 , wherein the spatio-temporal database includes a simulation grid and a polygon grid, wherein the simulation grid is associated with the static and dynamic data, and wherein the polygon grid is associated with at least one of drainage areas or irrigation areas of the reservoir.

8 . The method of claim 1 , wherein the static data and dynamic data include both field data and data obtained in a simulation of the reservoir.

9 . The method of claim 1 , wherein the static data and the dynamic data respectively are consecutive data to historical static data and historical dynamic data.

10 . One or more non-transitory computer readable media storing instructions, the instructions, when executed by a computing system, causing the computing system to:
receive static data of a reservoir from a spatio-temporal database; receive dynamic data of the reservoir from a spatio-temporal database; process the static data and the dynamic data to identify centroids of wells in the reservoir; generate polygons based on the centroids of the wells in the reservoir; and generate a reservoir model based on at least one of the polygons, the static data, or the dynamic data, wherein the reservoir model includes reservoir performance, field development, production metrics, and operation metrics.

11 . The one or more non-transitory computer readable media of claim 10 , processing the static data and the dynamic data includes inputting the static data and the dynamic data into a machine learning model.

12 . The one or more non-transitory computer readable media of claim 10 , wherein the computer readable medium further comprises instructions that, when executed by the computing system, cause the computing system to:
train a machine learning model based on historical static data and historical dynamic data.

13 . The one or more non-transitory computer readable media of claim 10 , wherein the computer readable medium further comprises instructions that, when executed by the computing system, cause the computing system to:
train a machine learning model based on the static data and the dynamic data.

14 . The one or more non-transitory computer readable media of claim 10 , both the static data and the dynamic data are stored in the spatio-temporal database.

15 . The one or more non-transitory computer readable media of claim 14 , the spatio-temporal database includes data of the reservoir from a plurality of points in time.

16 . The one or more non-transitory computer readable media of claim 14 , the spatio-temporal database includes a simulation grid and a polygon grid, the simulation grid is associated with the static and dynamic data, and the polygon grid is associated with drainage areas of the reservoir.

17 . The one or more non-transitory computer readable media of claim 10 , the static data and dynamic data include both field data and data obtained in a simulation of the reservoir.

18 . A system comprising:
a storage configured to store instructions; a processor configured to execute the instructions and cause the processor to:
receive static data of a reservoir from a spatio-temporal database;
receive dynamic data of the reservoir from a spatio-temporal database;
process the static data and the dynamic data to identify centroids of wells in the reservoir;
generate polygons based on the centroids of the wells in the reservoir; and
generate a reservoir model based on at least one of the polygons, the static data, or the dynamic data, wherein the reservoir model includes reservoir performance, field development, production metrics, and operation metrics.

19 . The system of claim 18 , wherein processing the static data and the dynamic data includes inputting the static data and the dynamic data into a machine learning model.

20 . The system of claim 18 , wherein the processor is configured to execute the instructions and cause the processor to:
train a machine learning model based on the static data and the dynamic data.

Assignees

• Conocophillips Co

Inventors

• Huang Chung-Kan
• Chen Qing

Classifications

• G01V99/00

  Subject matter not provided for in other groups of this subclass · CPC title

• G06F30/27 Primary

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

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G06F2111/10

  Numerical modelling · CPC title