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.
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 [
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 [
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 [
ERP [
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.
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 [
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
entity attributes. The most important are the attributes that define the relationship between the entities
[
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 [
Wattmetrograms and dynamograms can be considered as part of technological time series (TTS)
[
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: <<EXPRESS Type Declaration>> ndt_identifier <<Epicentre Type Declaration>> ndt_position_array <<Epicentre Type Declaration>> ndt_electric_current <<Epicentre Type Declaration>> ndt_load_array <<Epicentre Type Declaration>> ndt_electric_potential <<EXPRESS Type Declaration>> ndt_name <<Epicentre Type Declaration>> ndt_power <<Epicentre Type Declaration>> ndt_integer_element <<Epicentre Type Declaration>> ndt_time_second
Thus, the overall structure of the information model is as follows (Figure 4) [
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 [
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 [
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.
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 highperformance architectural templates that take into account the specifics of the research object and the accumulated experience of industry experts. 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.