Inflow Profile Control in Horizontal Wells in a Fractured Carbonate Using Swellable Elastomers

Welling, Rudolf Wilhelmus; Marketz, Franz; Moosa, Riyadh; Al-Riyami, Noha Said; Follows, Ed; Bruijn, Gerrit de; Hosny, Khalid M.

doi:10.2118/105709-ms

Cited by 10 publications

(2 citation statements)

References 12 publications

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“…Nonetheless, the tools employed to acquire this data are incapable of withstanding jarring loads (Daniels and Williams, 2001 ). To address this challenge, Welling et al ( 2007 ) suggested that data analysis could be conducted upon retrieval to the surface. This analysis can primarily be utilized for troubleshooting any issues that may arise after the completion of the fracking process.…”

Section: Related Workmentioning

confidence: 99%

XGSleeve: detecting sleeve incidents in well completion by using XGBoost classifier

Somi,

Jubair,

Cooper

et al. 2023

Front. Artif. Intell.

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The sliding sleeve holds a pivotal role in regulating fluid flow during hydraulic fracturing within shale oil extraction processes. However, concerns persist surrounding its reliability due to repeated attempts at opening the sleeve, resulting in process inefficiencies. While downhole cameras can verify sleeve states, their high cost poses a limitation. This study proposes an alternative approach, leveraging downhole data analysis for sleeve incident detection in lieu of cameras. This study introduces “XGSleeve,” a novel machine-learning methodology. XGSleeve amalgamates hidden Markov model-based clustering with the XGBoost model, offering robust identification of sleeve incidents. This method serves as an operator-centric tool, addressing the domains of oil and gas, well completion, sliding sleeves, time series classification, signal processing, XGBoost, and hidden Markov models. The XGSleeve model exhibits a commendable 86% precision in detecting sleeve incidents. This outcome significantly curtails the need for multiple sleeve open-close attempts, thereby enhancing operational efficiency and safety. The successful implementation of the XGSleeve model rectifies existing limitations in sleeve incident detection, consequently fostering optimization, safety, and resilience within the oil and gas sector. This innovation further underscores the potential for data-driven decision-making in the industry. The XGSleeve model represents a groundbreaking advancement in sleeve incident detection, demonstrating the potential for broader integration of AI and machine learning in oil and gas operations. As technology advances, such methodologies are poised to optimize processes, minimize environmental impact, and promote sustainable practices. Ultimately, the adoption of XGSleeve contributes to the enduring growth and responsible management of global oil and gas resources.

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Section: Related Workmentioning

confidence: 99%

XGSleeve: detecting sleeve incidents in well completion by using XGBoost classifier

Somi,

Jubair,

Cooper

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…The elastomers are engineered to swell when in contact with formation water at downhole conditions, i.e. they act as "hydrodynamic semiconductors", which admit oil at earlier stages of recovery and block water breaking through the fractures at mature stages of flooding (Marketz et al, 2005;Welling et al, 2007). This non-invasive (with respect to the reservoir rock) technique is called EZIP (Expandable Zonal Isolation Profiler) and applied in several wells of Omani oil fields with water rates reduced by up to 40% and oil gains of up to 45 m 3 /day/well .…”

Section: Introductionmentioning

confidence: 99%