US-20260125972-A1 - METHODS AND APPARATUSES FOR AUTOMATICALLY IMPROVING WELL MANAGEMENT IN SYSTEMS OF WELLS

Abstract

An example method includes managing a number of machine learning models. The method includes tuning the received machine learning models based on a set of metrics to produce a number of tuned machine learning models. The method includes generating an optimization recommendation in response to receiving statistical information. The method includes modifying an operation condition based on the optimization recommendation to improve oil production.

Inventors

• Lingxiao Zhang
• Chong Hyun AHN
• Ge Yuan
• Manjunath R. NALLAPUSALA
• Amr S. El-Bakry
• Emmanouil KARANTINOS
• Neal L. Adair
• Brock B. ARGYLE
• Varun Gupta

Assignees

• ExxonMobil Technology and Engineering Company

Dates

Publication Date

20260507

Application Date

20251029

Claims (20)

1. 1 . An apparatus, comprising: a first sub-system to manage a plurality of machine learning models, wherein the first sub-system is to accept and respond to an external query; a second sub-system to receive a machine learning model from the first sub-system and to tune the machine learning model based on a set of metrics to produce a tuned machine learning model, wherein the second sub-system is to send a model component of the tuned machine learning model to the first sub-system; and a third sub-system to generate an optimization recommendation in response to receiving statistical information from the first sub-system, wherein the optimization recommendation is used to modify operation conditions to improve oil production.
2. 2 . The apparatus of claim 1 , wherein modifying operations comprises adjusting a control parameter of a system of wells.
3. 3 . The apparatus of claim 2 , wherein the control parameter is adjusted by updating a choke set point for a well of the system of wells based on the optimization recommendation.
4. 4 . The apparatus of claim 2 , wherein the control parameter is adjusted by updating a gas lift set point for a well of the system of wells based on the optimization recommendation.
5. 5 . The apparatus of claim 1 , wherein the optimization recommendation comprises performing more frequent or less frequent well tests in field operations based on the recommendation.
6. 6 . The apparatus of claim 1 , wherein the external query comprises current data and, in response to receiving the external query, the first-subsystem is to run the machine learning model with the current data to generate a prediction result comprising the statistical information.
7. 7 . The apparatus of claim 6 , wherein the first-subsystem is to store the machine learning model, model parameters of the machine learning model, model artifacts of the machine learning model, and the prediction result in a database.
8. 8 . The apparatus of claim 6 , wherein the first sub-system is to send the model prediction result comprising uncertainty bands to the third sub-system in response to detecting that a threshold is exceeded.
9. 9 . The apparatus of claim 6 , wherein the statistical information comprises a statistical average.
10. 10 . The apparatus of claim 1 , wherein first sub-system sends artifacts and parameters of the plurality of machine learning models to the second sub-system.
11. 11 . The apparatus of claim 10 , wherein the artifacts and parameters of the plurality of machine learning models are sent simultaneously.
12. 12 . The apparatus of claim 10 , wherein the artifacts and parameters of the plurality of machine learning models are sent sequentially.
13. 13 . The apparatus of claim 10 , wherein the artifacts and parameters of the plurality of machine learning models are sent on a time basis.
14. 14 . The apparatus of claim 10 , wherein the artifacts and parameters of the plurality of machine learning models are sent in response to detecting that a metric exceeds a threshold.
15. 15 . The apparatus of claim 1 , wherein each of the plurality of machine learning models are tuned individually.
16. 16 . The apparatus of claim 1 , wherein the tuning of the machine learning model continues until an acceptance criteria threshold is exceeded.
17. 17 . A method for managing a system of wells, wherein the method is executed via a processor of a computing system, and wherein the method comprises: managing a plurality of machine learning models; tuning a machine learning model of the plurality of machine learning models based on a set of metrics to produce a tuned machine learning model; generating an optimization recommendation in response to receiving statistical information; and modifying an operation condition based on the optimization recommendation to improve oil production.
18. 18 . The method of claim 17 , comprising: receiving updated well test information based on the modified operating condition; and re-tuning at least one of the machine learning models based on the received updated well test information to generate at least one re-tuned machine learning model.
19. 19 . The method of claim 18 , wherein a time that the updated well test information is received is determined based on an uncertainty of at least one of the tuned machine learning models.
20. 20 . The method of claim 18 , wherein re-tuning the at least one of the machine learning models comprises re-tuning hyperparameters of the at least one of the machine learning models.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application claims the benefit of U.S. Provisional Application Ser. No. 63/715,992, entitled “METHODS AND APPARATUSES FOR AUTOMATICALLY IMPROVING WELL MANAGEMENT IN SYSTEMS OF WELLS,” filed Nov. 4, 2024, the disclosure of which is hereby incorporated by reference in its entirety. TECHNICAL FIELD The present application relates generally to the field of hydrocarbon management. Specifically, the disclosure relates to a methodology for improving production in a system of wells. BACKGROUND This section is intended to introduce various aspects of the art, which may be associated with exemplary embodiments of the present disclosure. This discussion is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the present disclosure. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art. Oil and gas (OG) production in unconventional (Uncon) OG fields has become a major component of world OG production. The production level is expected to increase in the next decade. What has qualified as unconventional at any particular time is a complex function of resource characteristics, the available exploration and production technologies, the economic environment, and the scale, frequency and duration of production from the resource. Perceptions of these factors inevitably changes over time and often differ among users of the term. As used herein, the term “unconventional resources” is used in reference to oil and gas resources whose porosity, permeability, fluid trapping mechanism, or other characteristics differ from conventional sandstone and carbonate reservoirs. For example, coalbed methane, gas hydrates, shale gas, fractured reservoirs, and tight gas sands are considered unconventional resources. SUMMARY An embodiment provided herein relates to an apparatus. The apparatus includes a first sub-system to manage a number of machine learning models. The first sub-system is to accept and respond to an external query. The apparatus includes a second sub-system to receive a machine learning model from the first sub-system and to tune the machine learning model based on a set of metrics to produce a tuned machine learning model. The second sub-system is to send a model component of the tuned machine learning model to the first sub-system. The apparatus includes a third sub-system to generate an optimization recommendation in response to receiving statistical information from the first sub-system. The optimization recommendation is used to modify operation conditions to improve oil production. Another embodiment provided herein related to a method for managing a system of wells. The method includes managing a number of machine learning models. The method also includes tuning a machine learning model of the number of machine learning models based on a set of metrics to produce a tuned machine learning model. The method further includes generating an optimization recommendation in response to receiving statistical information. The method also further includes modifying an operation condition based on the optimization recommendation to improve oil production. These and other features and attributes of the disclosed embodiments of the present techniques and their advantageous applications and/or uses will be apparent from the detailed description that follows. BRIEF DESCRIPTION OF THE DRAWINGS The present application is further described in the detailed description which follows, in reference to the noted plurality of drawings by way of non-limiting examples of exemplary implementations, in which like reference numerals represent similar parts throughout the several views of the drawings. In this regard, the appended drawings illustrate only exemplary implementations and are therefore not to be considered limiting of scope, for the disclosure may admit to other equally effective embodiments and applications. FIG. 1 is a schematic view of an exemplary active learning framework, in accordance with the present techniques; FIG. 2 is a block diagram of an exemplary model management system in accordance with the present techniques; FIG. 3 is a block diagram of an exemplary model quality control system, in accordance with the present techniques; FIG. 4 is a block diagram of an exemplary recommendation system, in accordance with the present techniques; FIG. 5 is a process flow diagram of an exemplary method for guiding hydrocarbon production using operating conditions modified based on optimization recommendations, in accordance with the present techniques; FIG. 6 is a block diagram of an exemplary cluster computing system that may be utilized to implement the present techniques; FIG. 7 is a block diagram of an exemplary non-transitory, computer-readable storage medium that may be used for the storage of data and modules of program instructions for implementing the present