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Data Validation and Reconciliation - A Promising Technology for Upstream Applications

Professor Boris Kalitventzeff European Computer Aided Process Engineering (CAPE) working party. Founder and Chairman of Belsim s.a. View Supplier Profile

Originally printed in: Exploration & Production: The Oil & Gas Review - 2005

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The Production System

An oilfield is an industrial process system with its inputs and outputs. It is composed of subsystems – the field itself or reservoir that is likely to remain a fuzzy subset, the incoming streams network with its water, gas and (in deep-sea) methanol injection systems, the outgoing streams network including production wells, pipes, manifolds, riser and topside installations for energy delivery and crude oil separation and pre-treatment. The oilfield itself may be subdivided into several production areas. The incoming and outgoing stream networks can cover several field areas (possibly exploited by different oil companies or owners).

Appropriate DVR software tools can be potentially applied to deal with such process systems due to their modular structure. At present it is only partly the case as reported hereafter. To prepare such integrated applications, the challenges will be analysed and the expected obstacles and incentives identified.

The Challenge

What matters is to expand the ‘smart wells’ concept to the ‘smart field’ concept. Each subsystem is to be deeply analysed and modelled for itself – all the subsystems then have to be integrated together to obtain a process representation synthesis.

The challenge is to manage the inflow system, setting the individual water and gas (and methanol) injection flow rates and to manage the outflow system, opening or closing wells or zones to increase integrated oil production and oil recovery during the life-cycle of the exploited field.

The core subsystem is the reservoir itself – it can be treated as a ‘black box’, with arrays of input data and arrays of output data. It has been previously shown that all those data can be validated providing quality information data. Any mathematical model (simple or complex; static or dynamic) represents a functional relationship between individual outputs in response to individual inputs.

Appropriate models result from a compromise – the more complex the model the more numerous its parameters and to determine more numerous parameters there is a need for more high quality information data. In an environment of costly measuring devices, limitations on model complexity can arise.

Smart operations along the field subsystems are the key to steady increment production performance; however, there is a question over whether it should be recognised that smart operations rely on the concept of quality and whether proper actions can be decided on a system where there is a lack of knowledge, or a partially fuzzy understanding of the system due to a lack of consistency of the raw information data. The technology exists to unearth reliable and more precise data interpretation. Such technology can help production managers to use their own skills more proficiently.

Many questions can potentially be answered applying the DVR technology to exploit available measurement data. These questions concern:

Implementation in Upstream Operations

DVR is a proven technology in downstream applications. Payback times have been reported as ‘always less than one year’; however, the question of whether it will be readily implemented in the upstream area now arises.

Several obstacles other than suspicion or reasons of unawareness have been identified. Obstacles encountered and in the downstream include:

• there is already a range of software technology deployed;
• how DVR complements existing simulation tools; and
• DVR tool comparison with competitors.

A second series of obstacles originates from general trends in research and development (R&D) financing:

• the development effort size needed to adapt or to more competantly dedicate software tools to field systems;
• whether this kind of innovative technology is part of the objectives that need to be focused on and whether this answers the specified needs;
• whether the technology can be integrated in current IT systems and whether turnkey implementations, including modelling, can be offered; and
• it is important to know whether the technology has already been applied as companies often do not want to be the first to try the technology. There is also a question over the costs incurred if it fails to meet expectations.
  
  DVR implementation incentives:
  
• Much is written about R&D financing decreases; however, innovation is still recognised as a must.
• The technology has been proven to be highly beneficial in the chemical industry and in the downstream.
• The technology has successfully been applied in offshore facilities in West Africa – it is at field trial stage.
• There is a tendency in several upstream majors to address selected R&D topics to services companies.
• There is a tendency to encourage collaborations between production companies – this may be indicated when the exploitation of big oilfields is shared.
• There is a tendency to outsource noncore functions.
• If an application partly fails, there is no risk of heavy consequences because the technology is an add-in; it operates as a watchdog.
• Lastly, the technology can be used for the design or for retrofit of measuring instrumentation.

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