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Exploration & Production: The Oil & Gas Review - 2005
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Data Validation and Reconciliation - A Promising Technology for Upstream Applications
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Professor Boris Kalitventzeff
European Computer Aided Process Engineering
(CAPE) working party. Founder and Chairman of
Belsim s.a.
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Originally printed in:
Exploration & Production: The Oil & Gas Review
- 2005
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When the author visited part of the subsea oil exploration and production (E&P) system of Mexican Petroleum (PEMEX) in the Gulf of Mexico he did not imagine that computer aided process engineering (CAPE) technologies could benefit from such upstream process systems.
Indeed, it is questionable as to whether the personnel operating in heavy conditions should need to consider mathematical models and optimisation methods. If the crude oil flow rate of a well is sufficiently depleted, the personnel will bore another well. Several questions arose regarding why well production is weakening, whether it could be sustained to a higher quality level and where to drill another well, although this was more than 15 years ago from the time of press. Computer-assisted oil and gas production is not a futuristic vision anymore; however, it can benefit greatly from a mature technology that is successfully applied in the downstream business – the data validation and reconciliation (DVR) technology.
The objective of this article is to draw upstream experts and decision makers into that technology, describe it briefly, analyse obstacles and incentives to its implementation in oil and gas production and report about the benefits obtained from a first demonstration implementation in a subsea production field in West Africa.
The Technology
The data validation and reconciliation technology consists of a method and its corresponding software tool. Its purpose is to:
- detect and correct deviations and errors of measurement data so that these satisfy all balance constraints;
- exploit the structure and the knowledge of the process system together with the measurement data to compute unmeasured data wherever it is possible, in particular the key performance indicators (KPI); and
- determine the post-processing accuracy of measured and unmeasured data including KPIs.
The method enhances the quality of the information data, which in turn enables sufficient monitoring decisions and knowledge-based actions.
Figure 1* displays recorded raw data and the corresponding validated data. It illustrates one of the assets of the DVR technology, allowing valuable information to be unearthed that would otherwise be hidden in the ‘noise’ of raw measurement data. In this situation for instance, it is only after DVR that a functional relationship between the two displayed variables can be obtained.
Figure 1: Data Reconciliation Unearths Hidden Information
From such simple facts a control expert will understand the complementarities of DVR systems and advanced process control (APC) systems.
Mathematically speaking, the method states and solves a non-linear programming problem. In upto- date DVR software tools the constraints equations (mass and energy balances and thermodynamic equilibrium constraints) are automatically generated when modelling the process at hand using a graphical user interface. The method implements statistical and thermodynamic principles so that it differs from simulation methods. DVR determines the performance parameters of the process system based on measurement redundancy. It therefore does not compete with simulation methods that can benefit from the said consistency data processing.
In a recent preliminary study the potential synergy between DVR and data mining (DM) was analysed.
Figure 2: Soft Sensor Reproducing Validated KPI
Figure 2 is based on approximately 4,000 DVR results on an oil refinery plant. The purple curve displays a validated KPI as a function of time and the green curve presents the corresponding KPI values as predicted by a DM soft sensor during and after the learning period. Such an illustration demonstrates that DVR brings new assets for DM systems.
The DVR tool can be used singly but can also be used in synergy with other tools, as shown in this example. Another important asset in upstream applications is when the measuring equipment is very expensive – it has been experienced in downstream applications that the tool can determine a number of unmeasured data two to three times bigger than the number of measured data. It can therefore be regarded as producing a valuable virtual measurement set.
Category:
Reservoir Engineering
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Professor Boris Kalitventzeff has
been Chairman of the European
Computer Aided Process Engineering
(CAPE) working party for six years.
He the founder and Chairman of
Belsim s.a., a European software
and engineering services company.
Belsim s.a. is an 18 year old spinoff
of the research and development
(R&D) team he created as a
professor at the University of Liege,
Belgium, in the 1970s. That R&D
team specialised in data validation
and reconciliation technology
and in process energy
integration technology.
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