Systems and methods for completion optimization for waterflood assets

Patent metadata

Field	Value
Publication number	US-2023140905-A1
Application number	US-202217982799-A
Country	US
Kind code	A1
Filing date	Nov 8, 2022
Priority date	Nov 8, 2021
Publication date	May 11, 2023
Grant date	—

Abstract

Implementations described and claimed herein provide systems and methods for a framework to achieve completion optimization for waterflood field reservoirs. The proposed methodology leverages adequate data collection, preprocessing, subject matter expert knowledge-based feature engineering for geological, reservoir and completion inputs, and state-of-the-art machine-learning technologies, to indicate important production drivers, provide sensitivity analysis to quantify the impacts of the completion features, and ultimately achieve completion optimization. In this analytical framework, model-less feature ranking based on mutual information concept and model-dependent sensitivity analyses, in which a variety of machine-learning models are trained and validated, provides comprehensive multi-variant analyses that empower subject-matter experts to make a smarter decision in a timely manner.

First claim

What is claimed is:

1 . A method for generating a forecast model of a well field, the method comprising:
combining raw field data from a plurality of wells of the well field with user-based data received from a user interface to generate an input dataset; training, based on the input dataset and utilizing a deep learning computing technique, a plurality of completion forecast models; and generating an optimized production forecast model from the plurality of trained completion forecast models.

2 . The method of claim 1 , further comprising:
interpreting the plurality of completion forecast models based on one or more model-agnostic evaluation techniques.

3 . The method of claim 1 , wherein training of the plurality of completion forecast models comprises executing a plurality of different modeling techniques with the input dataset.

4 . The method of claim 3 , wherein the plurality of different modeling techniques is at least one of a tree-based modeling technique or a deep neural network technique.

5 . The method of claim 1 , further comprising:
expanding the raw field data through one of a scatter plot of the raw field data or a box plot of the raw field data.

6 . The method of claim 1 , further comprising:
displaying, on the user interface, a generated result of the optimized production forecast model based on a well completion dataset

7 . The method of claim 1 , further comprising:
recursively executing the optimized production forecast model to generate a completion prediction of a well of the well field, the optimized production forecast model receiving measured production data from the field data.

8 . One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system, the computer process comprising:
combining raw field data from a plurality of wells of a well field with user-based data received from a user interface to generate an input dataset; training, based on the input dataset and utilizing a deep learning computing technique, a plurality of completion forecast models; and generating an optimized production forecast model from the plurality of trained completion forecast models.

9 . The one or more tangible non-transitory computer-readable storage media of claim 8 , the computer process further comprising:
interpreting the plurality of completion forecast models based on one or more model-agnostic evaluation techniques.

10 . The one or more tangible non-transitory computer-readable storage media of claim 8 , wherein training of the plurality of completion forecast models comprises executing a plurality of different modeling techniques with the input dataset.

11 . The one or more tangible non-transitory computer-readable storage media of claim 10 , wherein the plurality of different modeling techniques is at least one of a tree-based modeling technique or a deep neural network technique.

12 . The one or more tangible non-transitory computer-readable storage media of claim 8 , the computer process further comprising:
expanding the raw field data through one of a scatter plot of the raw field data or a box plot of the raw field data.

13 . The one or more tangible non-transitory computer-readable storage media of claim 8 , the computer process further comprising:
displaying, on the user interface, a generated result of the optimized production forecast model based on a well completion dataset

14 . The one or more tangible non-transitory computer-readable storage media of claim 8 , the computer process further comprising:
recursively executing the optimized production forecast model to generate a completion prediction of a well of the well field, the optimized production forecast model receiving measured production data from the field data.

15 . A system for generating a forecast model of a well field, the system comprising:
a waterflood completion optimization system having at least one processor configured to train a plurality of completion forecast models using a deep learning computing technique and based on an input dataset, the input dataset generated by combining raw field data from a plurality of wells of the well field with user-based data, the waterflood completion optimization system generating an optimized production forecast model from the plurality of trained completion forecast models.

16 . The system of claim 15 , wherein the user-based data is received from a user interface presented by a user device.

17 . The system of claim 16 , wherein the user interface is configured to present a generated result of the optimized production forecast model based on a well completion dataset.

18 . The system of claim 15 , wherein training of the plurality of completion forecast models comprises executing a plurality of different modeling techniques with the input dataset.

19 . The system of claim 15 , wherein waterflood completion optimization system expands the raw field data through one of a scatter plot of the raw field data or a box plot of the raw field data.

20 . The system of claim 15 , wherein waterflood completion optimization system recursively executes the optimized production forecast model to generate a completion prediction of a well of the well field, the optimized production forecast model receiving measured production data from the field data.

Assignees

• Conocophillips Co

Inventors

• Hu Bo
• Chen Qing
• Nejad Amir
• Luo Xin
• Olsen Christopher S
• Burton Robert C
• Zhou Liang
• Gou Xin Jun
• Zhang Liu Chao
• Zhang Junjing
• Shahim Iman
• Schneider Curt E
• Smith David D
• Flowers Andy

Classifications

• E21B2200/22

  Fuzzy logic, artificial intelligence, neural networks or the like · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title