=Paper=
{{Paper
|id=Vol-2212/paper35
|storemode=property
|title=Storage subsystem for intelligent oil wells control system
|pdfUrl=https://ceur-ws.org/Vol-2212/paper35.pdf
|volume=Vol-2212
|authors=Klara Tagirova,Anvar Ramazanov,Alexey Vulfin,Andrey Nikonov
}}
==Storage subsystem for intelligent oil wells control system ==
https://ceur-ws.org/Vol-2212/paper35.pdf
Storage subsystem for intelligent oil wells control system
K F Tagirova1, A R Ramazanov1, A M Vulfin1and A V Nikonov1
1
Ufa State Aviation Technical University, K.Marks St. 12, Ufa, Russia, 450077
Abstract. The purpose of developing data warehouse architecture is improving the storage
subsystem as part of an intelligent oil well control system. To achieve this goal, it is necessary
to develop the structure and information model of the storage subsystem. It is proposed to
expand the exploration and exploitation data model of the Epicentre oil fields by adding new
entities and a hierarchy of links. Highly effective architectural templates, taking into account
the specifics of the research object and the accumulated experience of industry experts, are
used. This will reduce the costs for development of the data warehouse architecture and will
provide an opportunity for integration with existing information systems of a higher level of
control and management – MES, ERP – and with other software and hardware complexes of
the current level.
1. Introduction
Nowadays, the oil industry in Russia has entered to the stage of oil field development and oil
reservoirs and wells exploitation on a scientific basis with fitting out oil fields and wells with modern
equipment. Various “smart” production facilities are being actively implemented – intelligent wells
and intelligent fields that allow reducing capital costs and reducing production costs by saving energy
and improving control efficiency [4]. At the moment, the second generation of smart fields is being
implemented, the main difference of which is realization of the field development remote control [5].
However, the implementation of such systems is impossible without the use of modern measuring
instrumentation as well as control of technological equipment.
On the Russian Federation territory about 50 thousand wells are operated by installations of sucker
rod pumps (SRP), which provide to 20% of all produced oil [1]. However, most of the wells from the
SRP fund – low-yield, so it is economically inefficient to equip them with modern expensive
controllers with complex intelligent control algorithms. It is necessary to use an intelligent control
algorithms realized as additional modules in the control station of the SRP. Therefore, intelligent
monitoring and control systems are proposed to solve the problem of control the SRP equipped wells.
This system allows to solve the following tasks [6]:
1. Control of the technical condition of the well equipment;
2. Calculation of the well production rate to select control mode;
3. Control of the balance of pumping rocking machine.
During operation of such system the problem of processing and analyzing large volumes of
technological data arises. Therefore is becomes an actual problem to organize a data warehouse for
efficient access to the accumulated data and to apply an intelligent algorithms for well control.
Appropriate requirements for such storage are [7]:
1. Integration with existing information systems of higher level of control and monitoring – MES,
ERP [8];
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K F Tagirova, A R Ramazanov, A M Vulfin and A V Nikonov
2. The ability to integrate with other software and hardware complexes of the current control
level, for example, with a variety of equipment monitoring systems;
3. Ensuring the logical integrity and consistency of the stored data as well as the coherence of data
in distributed storage systems.
The purpose of data warehouse development is improving storage subsystem as a part of the
intellectual oil well control system.
To achieve this goal it is necessary to solve the following tasks:
• Develop a structure of the storage subsystem,
• Develop an information model of the storage subsystem.
2. Structure development of a data warehouse subsystem as part of an intelligent oil well control
system
Proposed information storage is a part of the intelligent oil wells control system which structural
diagram is shown in Figure 1.
Figure 1.Structural diagram of the intelligent oil wells control system.
Equipment receives wattmetrograms and dynamograms of SRP operating, which are stored in the
information storage. From this storage, the data enters the calculation module, which determines the
state of equipment, calculates the flow rate and determines the balance of the rocking machine. These
data are returned to the information storage and are also transferred to the decision module for
determining control actions.
3. Information model development of a storage subsystem based on object-oriented
decomposition
In order to use existing high-performance architectural templates for a proposed storage subsystem
that takes into account the specifics of the research object and the experience gained by industry
specialists embodied in existing information systems for processing and storing data of oil producing
enterprises, the exploration and exploitation data model of the Epicentre deposits is taken as the basis.
It is developed by the corporation POSC (Petrotechnical Open Software Corporation). Expanding the
existing POSC model to implement intelligent management and diagnostic functions, it is possible to
build an effective information model of the data warehouse taking into account design features [9, 22,
23, 24, 29].
Epicentre model is created independently of the applications and at a logical level, with no matter
of the type of final data warehouse, what allows integration of existing solutions in case they are built
with respect account the proposed model. This simplifies the task of communication of modules and
systems from different manufacturers and following to the basic principles of open systems.
More than 1,000 existing technical and business objects associated with the oil exploration and
production are defined in the Epicentre data model. In the terminology of POSC data modelling, these
objects are named entities. Characteristics than may be contained in entities, defines in the model as
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K F Tagirova, A R Ramazanov, A M Vulfin and A V Nikonov
entity attributes. The most important are the attributes that define the relationship between the entities
[2].
When designing data model for operating the technological parameters of intelligent control system
of oil wells stations, it is necessary to enter new entity to expand Epicentre model, namely the entity of
wattmetrogramm (wattmetrogramm), dynamogramm (dynamogramm), pretreated dynamogramms
(prepared_dynamogramm) and classified dynamometer (classified_dynamogramm), which are
necessary for solving the problem of diagnostics of equipment condition according to dynamometer
data and for control tasks [10, 11, 13-21].
Wattmetrograms and dynamograms can be considered as part of technological time series (TTS)
[26, 27]. These entities must be integrated with existing entities by building a hierarchy of links. The
Epicentre model for providing communication instead of repeating the identifier, an attribute
(attribute), pointing to another entity, is added in the logically integral element (entity). Thus, to
organize relations with the rest of data, the entities of watmmetrogramm and dynamogramm contain a
reference to the well (well), and the entities prepared_dynamogramm, classified_dynamogramm
contain a reference to the dynamometer. For example, the entity of wattmetrogramm will be as shown
in figure 2:
Figure 2. Wattmetrogramm entity.
In the Epicentre data model attributes presented by named types, having precise definition (named
defined types), which consist of one or several basic data types. In the Epicentre data model
documentation, named types with a precise definition have their names indicated by the prefix “Ndt”.
Therefore, while creating the data warehouse model, already existing types were used, but for
complete description of the model two own types to storage dynamograms were implemented: plunger
stroke counts array – Ndt_position_array and counts of loads per rod array – Ndt_load_array. Figure 3
shows the basic types used during development of warehouse:
<> <> <>
ndt_identifi er ndt_positi on_array ndt_el ectric_current
<> <> <>
ndt_load_array ndt_el ectric_potenti al ndt_name
<> <> <>
ndt_power ndt_integer_el em ent ndt_tim e_second
Figure 3. Main named types used in the development of warehouse.
Thus, the overall structure of the information model is as follows (Figure 4) [22-25]:
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K F Tagirova, A R Ramazanov, A M Vulfin and A V Nikonov
Figure 4. General structure of warehouse model.
Epicentre data model is described in EXPRESS language, but EXPRESS is not the equivalent of
the data definition language (DDL), such as the Standard Query Language (SQL) DDL [3]. Therefore,
for development of data warehouse it is necessary to convert expressions in EXPRESS language into a
set of expressions on DDL language. This conversion POSC corporation called projection. So the
creation of the physical warehouse is presented in Figure 5.
Figure 5. Creating a data warehouse model of EPICENTRE.
In order to project Epicentre model into a set of expressions on DDL language, POSC Corporation
proposes to use the tool to project Epicentre data model to the four types of Relational Database
Management System (RDBMS) DDL: ANSI, Informix, Oracle, and Sybase.
Based on the Epicentre 3.0 model and the subsets of the Epicentre “AC3 data model: Process
Data”, “DM1: Document Specification”, “DM3: Document Schema”, “DM4: Document Content”, it
is forming an information model of data processed in the intelligent oil well control system – time
series – and in the subsystem for storing the parameters of the intelligent analysis module (Figure 6).
Detailed disclosure of attributes of key entities and basic links is given in the section devoted to the
development of the Intelligent Control System (ICS) PC (Figure 7).
From Epicentre data model it was selected three groups of entities characterizing the different
stages of processing the technological time series as the main data source for implementing the
methods of intellectual analysis, additional elements that store and process the accumulated data.
In [28] the application of intelligent analysis technologies in diagnostics and control tasks is shown:
committees of neural network models and preprocessing algorithms for technological time series
based on the application of wavelet analysis.
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K F Tagirova, A R Ramazanov, A M Vulfin and A V Nikonov
Figure 6. Truncated and augmented subset AC3: Process Data.
Figure 7. Prototype of the logical model of technological time series processing in the ICS.
The process of life cycle (LC) management of the developed software complex, which implements
the modules for intellectual analysis and management of oil producing wells, corresponds to the
PDCA cycle and the basic requirements of the process approach, formulated in MS ISO 9000: 2000,
as a functional model. ISO / IEC 12207: 1995 “Information Technology - Software Life Cycle
Processes” is the main normative document regulating the composition of the software life cycle
processes (Figure 8).
Therefore, an information model of TTS processing as part of an intelligent control system for a
group of oil producing wells was developed as a subset of the POSC Epicentre model, which allows
the use standard interfaces between application programs, database management systems, workstations
and users.
4. Conclusions
Thus, it is proposed to expand the Epicenter model by adding new entities and hierarchy of links in the
task of the information model construction to improve the subsystem of data storage as part of an
intelligent oil well control system, which will allow:
1. Reduce the labour costs for the development of the data warehouse by using existing high-
performance architectural templates that take into account the specifics of the research object
and the accumulated experience of industry experts.
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2. Provide an opportunity for integration with existing information systems of a higher level of
control and management – MES, ERP – and with other software and hardware complexes of the
current control level through the use of the principle of open architecture.
Figure 8. Management of the software development process.
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