Application of Artificial Intelligence in Oilfield Operation and Intervention

Lateef Akanji, Idowu Shalom Ofi

Research output: Contribution to conferencePaper

Abstract

Oilfield operation and production management particularly in extreme geographic location is still a challenge from optimisation and cost-effectiveness points of view. Further, data handling and storage attract huge capital investments with little realisation of ultimate goal of effective and efficient handling and management. In this work, we propose an artificial intelligence technique of oilfield operation monitoring and intervention. Real time data generated from remote and challenging oilfields is used to decipher the performance of operational processes. We integrate the meta-heuristic optimisation searching speed of an enhanced algorithm with the robustness of fuzzy logic reasoning; based on the degree of membership functions rather than exact or extreme conditions, and the learning capability of neural networks, to derive the required intelligence. This allows a predictive and proactive intervention in oilfield operations and management. © Copyright 2016, Society of Petroleum Engineers.
Original languageEnglish
Pages1-10
Number of pages10
DOIs
Publication statusPublished - 6 Sep 2016
EventSPE Intelligent Energy International Conference and Exhibition - AECC, Aberdeen, United Kingdom
Duration: 6 Sep 20168 Sep 2016

Conference

ConferenceSPE Intelligent Energy International Conference and Exhibition
CountryUnited Kingdom
CityAberdeen
Period6/09/168/09/16

Fingerprint

Artificial intelligence
Data handling
Membership functions
Cost effectiveness
Fuzzy logic
Neural networks
Monitoring

Cite this

Akanji, L., & Ofi, I. S. (2016). Application of Artificial Intelligence in Oilfield Operation and Intervention. 1-10. Paper presented at SPE Intelligent Energy International Conference and Exhibition, Aberdeen, United Kingdom. https://doi.org/10.2118/181116

Application of Artificial Intelligence in Oilfield Operation and Intervention. / Akanji, Lateef; Ofi, Idowu Shalom.

2016. 1-10 Paper presented at SPE Intelligent Energy International Conference and Exhibition, Aberdeen, United Kingdom.

Research output: Contribution to conferencePaper

Akanji, L & Ofi, IS 2016, 'Application of Artificial Intelligence in Oilfield Operation and Intervention' Paper presented at SPE Intelligent Energy International Conference and Exhibition, Aberdeen, United Kingdom, 6/09/16 - 8/09/16, pp. 1-10. https://doi.org/10.2118/181116
Akanji L, Ofi IS. Application of Artificial Intelligence in Oilfield Operation and Intervention. 2016. Paper presented at SPE Intelligent Energy International Conference and Exhibition, Aberdeen, United Kingdom. https://doi.org/10.2118/181116
Akanji, Lateef ; Ofi, Idowu Shalom. / Application of Artificial Intelligence in Oilfield Operation and Intervention. Paper presented at SPE Intelligent Energy International Conference and Exhibition, Aberdeen, United Kingdom.10 p.
@conference{53a3e3b8d6c54031967cef12eed128a7,
title = "Application of Artificial Intelligence in Oilfield Operation and Intervention",
abstract = "Oilfield operation and production management particularly in extreme geographic location is still a challenge from optimisation and cost-effectiveness points of view. Further, data handling and storage attract huge capital investments with little realisation of ultimate goal of effective and efficient handling and management. In this work, we propose an artificial intelligence technique of oilfield operation monitoring and intervention. Real time data generated from remote and challenging oilfields is used to decipher the performance of operational processes. We integrate the meta-heuristic optimisation searching speed of an enhanced algorithm with the robustness of fuzzy logic reasoning; based on the degree of membership functions rather than exact or extreme conditions, and the learning capability of neural networks, to derive the required intelligence. This allows a predictive and proactive intervention in oilfield operations and management. {\circledC} Copyright 2016, Society of Petroleum Engineers.",
author = "Lateef Akanji and Ofi, {Idowu Shalom}",
year = "2016",
month = "9",
day = "6",
doi = "10.2118/181116",
language = "English",
pages = "1--10",
note = "SPE Intelligent Energy International Conference and Exhibition ; Conference date: 06-09-2016 Through 08-09-2016",

}

TY - CONF

T1 - Application of Artificial Intelligence in Oilfield Operation and Intervention

AU - Akanji, Lateef

AU - Ofi, Idowu Shalom

PY - 2016/9/6

Y1 - 2016/9/6

N2 - Oilfield operation and production management particularly in extreme geographic location is still a challenge from optimisation and cost-effectiveness points of view. Further, data handling and storage attract huge capital investments with little realisation of ultimate goal of effective and efficient handling and management. In this work, we propose an artificial intelligence technique of oilfield operation monitoring and intervention. Real time data generated from remote and challenging oilfields is used to decipher the performance of operational processes. We integrate the meta-heuristic optimisation searching speed of an enhanced algorithm with the robustness of fuzzy logic reasoning; based on the degree of membership functions rather than exact or extreme conditions, and the learning capability of neural networks, to derive the required intelligence. This allows a predictive and proactive intervention in oilfield operations and management. © Copyright 2016, Society of Petroleum Engineers.

AB - Oilfield operation and production management particularly in extreme geographic location is still a challenge from optimisation and cost-effectiveness points of view. Further, data handling and storage attract huge capital investments with little realisation of ultimate goal of effective and efficient handling and management. In this work, we propose an artificial intelligence technique of oilfield operation monitoring and intervention. Real time data generated from remote and challenging oilfields is used to decipher the performance of operational processes. We integrate the meta-heuristic optimisation searching speed of an enhanced algorithm with the robustness of fuzzy logic reasoning; based on the degree of membership functions rather than exact or extreme conditions, and the learning capability of neural networks, to derive the required intelligence. This allows a predictive and proactive intervention in oilfield operations and management. © Copyright 2016, Society of Petroleum Engineers.

UR - http://www.intelligentenergyevent.com/en/About/

U2 - 10.2118/181116

DO - 10.2118/181116

M3 - Paper

SP - 1

EP - 10

ER -