Next generation diagnosis and optimization workflow for digital oilfield

A. K. Al-Jasmi, H. Al-Zaabi, H. K. Goel, M. Al-Hamer, R. Vellanki, S. Singh, M. Villamizar, G. Moricca

Producción científica: Capítulo del libro/informe/acta de congresoPonencia publicada en las memorias del evento con ISBNrevisión exhaustiva

1 Cita (Scopus)


Each year, oil companies experience declining production rates and face challenges in terms of sustaining production targets, diagnosing well problems, and designing solutions to address such production decline. Identifying problems and opportunities at the correct moment, without losing time, is critical to the success of a digital oil field's (DOFs) intelligent solutions. Traditional industry solutions involve using historical data for surveillance. In DOFs, tools are available to assist engineers with diagnosing fields made up of thousands of wells using instantaneous real-time data. With multiple reservoirs and thousands of wells in a field, it can be extremely challenging to diagnose, identify the opportunity and make right decisions collaboratively to optimize the well without losing time. This paper describes a multidimensional surveillance (MDS) approach using real-time and historical data, which can handle thousands of wells more effectively for problem identification and optimization. This solution is coupled with an action tracking system to assist the Engineers in monitoring the Field implementation and assess the opportunity collaboratively. This paper presents the results of the application of intelligent agents to traditional work procedures to help increase production performance and final oil recovery in the Sabriyah KwIDF (SA KwIDF). SA KwIDF is part of a strategy undertaken by the operator to enhance asset performance using groundbreaking redefined DOF concepts. These concepts involve tightly integrated well instrumentation to provide enhanced data availability, power, and communication infrastructure to help improve field control, a new concept of collaborative centers to enhance asset team cross disciplinary integration across physically separated locations, and, finally, platform and production optimization workflows to increase effectiveness through automating work processes, helping shorten observation-to-action cycle time. The approach can make more effective problem identification and optimization possible. MDS acts as string facilitator when troubleshooting well performance and optimization. MDS allows engineers to track the implementation of suggested actions in the field. MDS approaches also allow engineers to compare wells side by side, to better understand the reservoir behavior, enhance the optimization process, asset awareness, team efficiency, and ultimately provide improvement to short-term production rates. The MDS approach used in the Kuwait integrated DOF SA KwIDF involved 133 wells and the operator has projected to expand the system to an additional 500 wells in 2015. The primary objective of this initiative project was to maximize and sustain oil rates while controlling well decline and honouring safe well operating envelope constraints. This paper describes the use of data mining agents to help enhance the optimization process, asset awareness, team efficiency, and, ultimately, provide improved short-term production rates.

Idioma originalInglés
Título de la publicación alojadaSociety of Petroleum Engineers - SPE Digital Energy Conference and Exhibition 2015
EditorialSociety of Petroleum Engineers
Número de páginas14
ISBN (versión digital)9781510800595
EstadoPublicada - 2015
Publicado de forma externa
EventoSPE Digital Energy Conference and Exhibition 2015 - The Woodlands, Estados Unidos
Duración: 3 mar. 20155 mar. 2015

Serie de la publicación

NombreSociety of Petroleum Engineers - SPE Digital Energy Conference and Exhibition 2015


ConferenciaSPE Digital Energy Conference and Exhibition 2015
País/TerritorioEstados Unidos
CiudadThe Woodlands

Nota bibliográfica

Publisher Copyright:
Copyright 2015, Society of Petroleum Engineers.


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