View in EDS

Machine learning based decline curve analysis for short-term oil production forecast.

Saved in:
Bibliographic Details
Title: Machine learning based decline curve analysis for short-term oil production forecast.
Authors: Tadjer, Amine1 (AUTHOR) amine.tadjer@uis.no, Hong, Aojie1 (AUTHOR), Bratvold, Reidar B1 (AUTHOR)
Source: Energy Exploration & Exploitation. Sep2021, Vol. 39 Issue 5, p1747-1769. 23p.
Subjects: Machine learning, Geological modeling, Artificial intelligence, Time series analysis, Petroleum production, Petroleum
Abstract: Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific models to fit production data for production forecasting. Various decline curve models have been applied for unconventional wells, including the Arps model, stretched exponential model, Duong model, and combined capacitance-resistance model. However, it is not straightforward to determine which model should be used, as multiple models may fit a dataset equally well but provide different forecasts, and hastily selecting a model for probabilistic DCA can underestimate the uncertainty in a production forecast. Data science, machine learning, and artificial intelligence are revolutionizing the oil and gas industry by utilizing computing power more effectively and efficiently. We propose a data-driven approach in this paper to performing short term predictions for unconventional oil production. Two states of the art level models have tested: DeepAR and used Prophet time series analysis on petroleum production data. Compared with the traditional approach using decline curve models, the machine learning approach can be regarded as" model-free" (non-parametric) because the pre-determination of decline curve models is not required. The main goal of this work is to develop and apply neural networks and time series techniques to oil well data without having substantial knowledge regarding the extraction process or physical relationship between the geological and dynamic parameters. For evaluation and verification purpose, The proposed method is applied to a selected well of Midland fields from the USA. By comparing our results, we can infer that both DeepAR and Prophet analysis are useful for gaining a better understanding of the behavior of oil wells, and can mitigate over/underestimates resulting from using a single decline curve model for forecasting. In addition, the proposed approach performs well in spreading model uncertainty to uncertainty in production forecasting; that is, we end up with a forecast which outperforms the standard DCA methods. [ABSTRACT FROM AUTHOR]
Copyright of Energy Exploration & Exploitation is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 152929987
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Machine learning based decline curve analysis for short-term oil production forecast.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Tadjer%2C+Amine%22">Tadjer, Amine</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> amine.tadjer@uis.no</i><br /><searchLink fieldCode="AR" term="%22Hong%2C+Aojie%22">Hong, Aojie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bratvold%2C+Reidar+B%22">Bratvold, Reidar B</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energy+Exploration+%26+Exploitation%22">Energy Exploration & Exploitation</searchLink>. Sep2021, Vol. 39 Issue 5, p1747-1769. 23p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Geological+modeling%22">Geological modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Petroleum+production%22">Petroleum production</searchLink><br /><searchLink fieldCode="DE" term="%22Petroleum%22">Petroleum</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific models to fit production data for production forecasting. Various decline curve models have been applied for unconventional wells, including the Arps model, stretched exponential model, Duong model, and combined capacitance-resistance model. However, it is not straightforward to determine which model should be used, as multiple models may fit a dataset equally well but provide different forecasts, and hastily selecting a model for probabilistic DCA can underestimate the uncertainty in a production forecast. Data science, machine learning, and artificial intelligence are revolutionizing the oil and gas industry by utilizing computing power more effectively and efficiently. We propose a data-driven approach in this paper to performing short term predictions for unconventional oil production. Two states of the art level models have tested: DeepAR and used Prophet time series analysis on petroleum production data. Compared with the traditional approach using decline curve models, the machine learning approach can be regarded as" model-free" (non-parametric) because the pre-determination of decline curve models is not required. The main goal of this work is to develop and apply neural networks and time series techniques to oil well data without having substantial knowledge regarding the extraction process or physical relationship between the geological and dynamic parameters. For evaluation and verification purpose, The proposed method is applied to a selected well of Midland fields from the USA. By comparing our results, we can infer that both DeepAR and Prophet analysis are useful for gaining a better understanding of the behavior of oil wells, and can mitigate over/underestimates resulting from using a single decline curve model for forecasting. In addition, the proposed approach performs well in spreading model uncertainty to uncertainty in production forecasting; that is, we end up with a forecast which outperforms the standard DCA methods. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energy Exploration & Exploitation is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=152929987
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/01445987211011784
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 1747
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Geological modeling
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Time series analysis
        Type: general
      – SubjectFull: Petroleum production
        Type: general
      – SubjectFull: Petroleum
        Type: general
    Titles:
      – TitleFull: Machine learning based decline curve analysis for short-term oil production forecast.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Tadjer, Amine
      – PersonEntity:
          Name:
            NameFull: Hong, Aojie
      – PersonEntity:
          Name:
            NameFull: Bratvold, Reidar B
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2021
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 01445987
          Numbering:
            – Type: volume
              Value: 39
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Energy Exploration & Exploitation
              Type: main
ResultId 1