Video: Data-Driven Reservoir Management of a Giant Mature Oilfield in the Middle East
- Shahab D. Mohaghegh (West Virginia University) | Y. Al-Mehairi (ADCO) | Razi Gaskari (Intelligent Solutions Inc) | Mohammad Maysami (Intelligent Solutions, Inc.) | Yasaman Khazaeni (Boston University)
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- Society of Petroleum Engineers
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- 2014. Copyright is retained by the author. This document is distributed by SPE with the permission of the author. Contact the author for permission to use material from this document.
- Top-Down Modeling, Reservoir Management, Reservoir Modeling, Artificial Intelligence and Data Mining, Data-Driven Modeling
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A novel approach to reservoir management applied to a mature giant oilfield in the Middle East is presented. This is a prolific brown field producing from multiple horizons with production data going back
to mid-1970s. Periphery water injection in this filed started in mid-1980s. The field includes more than 400 producers and injectors. The production wells are deviated (slanted) or horizontal and have been
completed in multiple formations.
An empirical, full field reservoir management technology, based on a data-driven reservoir model was used for this study. The model was conditioned to all available types of field data (measurements) such
as production and injection history, well configurations, well-head pressure, completion details, well logs, core analysis, time-lapse saturation logs, and well tests. The well tests were used to estimates the static
reservoir pressure as a function of space and time. Time-lapse saturation (Pulse-Neutron) logs were available for a large number of wells indicating the state of water saturation in multiple locations in the
reservoir at different times.
The data-driven, full field model was trained and history matched using machine learning technology based on data from all wells between 1975 and 2001. The history matched model was deployed in
predictive mode to generate (forecast) production from 2002 to 2010 and the results was compared with historical production (Blind History Match). Finally future production from the field (2011 to 2014) was
forecasted. The main challenge in this study was to simultaneously history match static reservoir pressure, water saturation and production rates (constraining well-head pressure) for all the wells in the field.
History matches on a well-by-well basis and for the entire asset is presented. The quality of the matches clearly demonstrates the value that can be added to any given asset using pattern recognition technologies
to build empirical reservoir management tools. This model was used to identify infill locations and water injection schedule in this field.
Reservoir management has been defined as use of financial, technological, and human resources, to minimizing capital investments and operating expenses and to maximize economic recovery of oil and gas
from a reservoir. The purpose of reservoir management is to control operations in order to obtain the maximum possible economic recovery from a reservoir on the basis of facts, information, and knowledge
(Thakur 1996). Historically, tools that have been successfully and effectively used in reservoir management integrate geology, petrophysics, geophysics and petroleum engineering throughout the life cycle of
a hydrocarbon asset. Through the use of technologies such as remote sensors and simulation modeling, reservoir management can improve production rates and increase the total amount of oil and gas recovered
from a field (Chevron 2012).