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Manufacturing Execution Systems Intellectualization:
Oil and Gas Implementation Sample
Stepan Bogdan, Anton Kudinov, and Nikolay Markov
Tomsk Polytechnic University, Lenin Avenue. 30,
634050 Tomsk, Russia
{Bogdan,Kudinovav}@tpu.ru, [email protected]
Abstract. Up-to-date trend in industrial automation is implementation of
Manufacturing Execution Systems (MES) everywhere and in oil and gas
industry. Conception of MES is constantly in progress. Many researches
suppose that analytical features, available for low-end users (engineers,
dispatchers, geologists, etc.) are necessary in manufacturing management, but
today there is no ready-to-use framework applicable to make intelligent
manufacturing systems for oil and gas industry. A model-driven approach of
MES intellectualization and an original iMES framework proposed. iMES
based on functions of the traditional MES (within MESA-11 model), business
intelligence (BI)-methods (On-Line Analytical Processing & Data Mining) and
production markup language (industrial data standard for oil and gas
production). Case study of well tests results validation using iMES framework
is considered.
Keywords: Manufacturing Execution System, data mining in industry,
Manufacturing Process Control, Intellectual Manufacturing Systems.
1 Introduction
Continuous process manufacturing is a very sophisticated object to manage,
especially if it is an oil & gas production industry. To improve controllability of
production management automation and information systems are traditionally
implemented. Computer integrated manufacturing (CIM) conception promulgates a
fundamental strategy of integrating manufacturing facilities and systems in an
enterprise through computers and its peripherals to control the entire production
process [1]. According to Williams [2] there are three main architectures widely
known: the CIMOSA, the reference model GRAI-GIM and the PERA.
Despite selected architecture, according to CIM there are 5 hierarchical levels of
information systems of manufacturing enterprise [2]. Zero and first levels are for
automation such as sensors and measurement elements. Supervisory Control And
Data Acquisition (SCADA), Manufacturing execution system (MES) and Enterprise
Resource Planning (ERP) systems are placed on higher appropriate levels. MES
market is rapidly growing [3] and lots of MES solutions are now implemented
everywhere and in oil & gas production enterprises, but today MES implementation is
still a problem [4].
L. Bellatreche and F. Mota Pinto (Eds.): MEDI 2011, LNCS 6918, pp. 170–177, 2011.
© Springer-Verlag Berlin Heidelberg 2011
Manufacturing Execution Systems Intellectualization: Oil and Gas Implementation Sample 171
MES conception evolves from MESA-11 model to c-MES model [5]. Nowadays,
according to Littlefield [6], MES has outlived its original definition and new
conception of manufacturing operations management (MOM) software is the next
generation of manufacturing systems. Nevertheless, mass of modern MES are made
based on MESA-11 and c-MES models. Effect of MES implementation could be
better when it is primarily focused on operational (main) business process
management (production process management) [7]. In this article by the term
management we mean planning and control plans execution [8].
This article is about enhancing MES by using BI technologies and meta-model based
warehouse to fulfill modern oil and gas production management system requirements.
2 MES Intellectualization
According to Van Dyk [9] software can provide all functions traditionally expected
from MES, but if these functions are not integrated within business process, then such
software cannot be counted as a MES. In oil and gas production companies main
continuous operational process consist of four sequential parts: production, treatment,
transportation and realization of hydrocarbon products. Management of this process
crosses several time cycles (minutes, hours, days, months, years) and usually
managed by process engineers (technologists), geologists and dispatchers by plans
and monitoring systems.
In “minutes/seconds” time zoom, fields dispatch units continuously get information
from field automation and asynchronously from field production units. In “hour” time
zoom a dispatching unit of every field making summary of production indicators to
pass it to central dispatch unit. Every day the central dispatch unit distributes
manufacturing summary to other organizational units including geological unit and
makes daily mission plan to each of field dispatch unit. Geological unit can intervene
in the management process if daily manufacturing summary indicates risk of disorder
and monthly generates well operating practices plan. Process engineer unit acts like a
global supervisor and every year generates a detailed production plan depending on
factual wells operating plan execution.
According to MES conception its functions cover only dispatching functions on
hours/days time cycles of the management process. Production management process
is not only data and document flow. Every stage of it involves complex sub-processes
of complicated analytics. So there is a problem of the implementation such
management process within classical CIM model [1, 2]: well operating practices and
plan for production, treatment and realization of hydrocarbon products phases are off
the borders of ERP (because of geological and process engineer units, their functions
and used data are logically related to MES level) and MES (because of month and
year planning periods and advanced analytics are not typical for MES [10]).
Furthermore there is a problem of data integration because production data is
usually used by different units in different way. There are also various naming styles;
it demands a data integration tools implementation. Such solution associated with lots
of negative effects such as high cost of solution, integration problems, low
maintainability and so on. So general problems are: data integration, naming systems
unification and advanced analytical processing.
172
S. Bogdan, A. Kudinov, and N. Markov
There are two general approaches to solve these problems: enhancing ERP to work
with month and year technological data and enhancing MES by adding BI-systems
are usually used to solve analytical processing problem. Both of these approaches are
generally based on Intellectual Manufacturing System (IMS) idea [11] and allow
using advanced analytics to solve manufacturing tasks, but there is still data
integration and naming systems unification problems unsolved.
Enhancing ERP to include year planning and well operating practices generation
entails transfer of a big part of industrial data to fifth level of CIM. It is contradicts
with general idea of CIM where an industrial data stream narrows while moving up to
next level [2].
Enhancing MES to include whole production process management in single MES
seems as good solution. To use this approach there should be an appropriate
framework. Unfortunately, state-of-art IMS theory does not describe an appropriate
framework to use in oil and gas production management system design [11]. To solve
this problem we propose an industry oriented approach based on MES
intellectualization.
Our thesis is: enhancing MES functions by using OLAP technologies, data mining
techniques (solves analytical processing problems) and meta-model based data marts
(solves data integration and naming system unification problems) can provide
applicable solution for operational process management without unwanted negative
effects listed above. Such enhanced MES we propose to call intellectual MES or
iMES. Traditionally MES are based on Online Transaction Processing (OLTP)
technologies which optimized to bulk load of real time data. Unfortunately OLTPbased MES are shows low performance for analytical processing.
Traditionally business intelligence technologies are used on the higher level of
CIM where ERP systems situated, but there are lots of analytical problems on this
automation level [11]. For oil and gas production company data mining techniques are
very useful especially on two upper levels of production management. Data mining
techniques are used to solve some typical industrial analytical problems, but such
solutions are usually isolated within specific software that does not come in everyday
industrial management practice because of their low integration abilities [12]. iMES
conception allows to easily implement any industrial analytics within single
homogenous information space to reduce production management uncertainty,
decrease human influences, help in finding factors problems and so on.
Intellectualizing MES provides lots of possible profits for traditional MES
functions. For convenience MES functions and data mining goals [13] were
assembled into Table 1.
Let consider some examples of data mining implementation in oil and gas industry
on MES automation level. For oil and gas production companies clustering can be
successfully used in dispatching to determine normal operating practices, anomalies
and possible trends of controlled processes. Regression methods can be successfully
used in maintenance management to find causes of breakdowns. Classification
methods can be widely used all over industry. In oil and gas industry mining methods
can be successfully used for well and reservoir modeling, oil and gas production
transportation, component composition of hydrocarbon production forecasting, well
tests analyzing etc [14].
Manufacturing Execution Systems Intellectualization: Oil and Gas Implementation Sample 173
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Sequence
discovery
Visualization
+
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+
Regression
+
Forecasting
+
Clustering
RAS (Resource Allocation and Status)
ODS (Operations/Detail Scheduling)
DPU (Dispatching Production Units)
DOC (Document Control)
DCA (Data Collection/Acquisition)
LM (Labor Management)
QM (Quality Management)
PM (Process Management)
MM (Maintenance Management)
PTG (Product Tracking and Genealogy)
PA (Performance Analysis)
Classification
MESA-11functions
Association
Table 1. Common data mining tasks in MES applications
+
+
+
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To let oil and gas specialists use the power of data mining in their daily work
within MES context, iMES implementation needed. We propose iMES framework to
make MES intellectualization easier and cheaper than providing solutions for every
analytical problem that can appear in oil and gas production management.
Proposed framework to enhance MES for oil and gas production is shown on
Figure 1. Automatic control systems continuously obtain industrial data, which comes
into production database by OPC1 protocol. After industrial data processing it loads in
the common warehouse. Based on oil and gas industry data definition standards
(PRODML, WITSML, RESQML2) data marts are based on common warehouse.
These standards define hierarchical data models for most common oil and gas task
groups. They are tightly connected with each other but intended for other purposes.
This problem can be solved by OLAP implementation.
Data mining models and analytic queries use general warehouse and/or data marts
as data sources. Results of data mining can be presented to user by iMES client or
other software. This framework is very useful: it consolidates and standardizes
heterogeneous data needed on every stage of management of operational process; it
allows making analytics of any complicity; it can be source of data for other
applications (this is essential in manufacturing summary data population) etc. To
improve an efficiency of the framework implementation we could highly recommend
developing both OLTP and BI server parts of the iMES using integrated data
management platform (like Oracle BI Suite, Microsoft SQL Server with Analysis
Servises, etc.)
1
2
OLE for Process Control
Production Markup Language, Wellsite Information Transfer Standard Markup Language,
Reservoir Characterization Markup Language (www.energistics.org).
174
S. Bogdan, A. Kudinov, and N. Markov
Fig. 1. iMES framework
Next part of this article is a case study of solving practically significant problem
for oil and gas industry using proposed industry oriented metamodel-driven approach
to MES intellectualization (iMES framework).
3 Case Study: Well Test Validation
As an example of practical usage of iMES well test validation problem was used.
There are several different well test types; only production tests are considered in this
article. There are 3 popular formal methods of data mining: KDD1, SEMMA2 and
CRISP-DM3 [15]. To describe possible solution of this problem industry- and toolneutral data mining process model (CRISP-DM) was implemented. It involves next
sequential phases: Business Understanding, Data Understanding, Data Preparation,
Modeling, Evaluation and Deployment.
Business Understanding, Data Understanding and Data Preparation. Well
dynamics are very complicated. Analytical processing of well tests results is aimed to
make more accurate well operating practices plans as an important phase of oil and
gas production management [14]. There is a problem of classifying well test results as
valid or invalid. This classification depends on lots of factors such as random
distortion, features of field exploitation, production intensification activities,
production management style and others. So in practice geologists use complex expert
estimation to make a decision: to use or not to use well test result data in later
calculations. To support such decision making statistical models can be implemented.
The goal is to create a model which can automatically estimate well test results and
suggest validity of them using their history with predictive accuracy at least 75% [14]
for the most common test results.
To solve such analytical problems iMES framework disposes data-mart level as a
source of data to generate data models. For the well test validation PRODML-based
Manufacturing Execution Systems Intellectualization: Oil and Gas Implementation Sample 175
data mart of iMES framework should be used. Actually PRODML is a hierarchical
object model so to use it for online analysis it must be transformed to
multidimensional model. This model in iMES framework consists of six tightly
connected parts: Installed System (describes wells equipment), Measurements
(contains history of different on-field measures), Product Operation (contains a
history of on-field equipment usage), Product Flow (contains a history of product
flow through nodes), Product Volume (contains a history of product volumes storage),
Well Test (contains a factual information about different types of well tests
performed). The data is collected by MES into production database and then filtered
and validated it comes to common warehouse and goes to PRODML-based data mart.
Well production test data of each test can be described by about 30-50 (depends on
types of fluids) standard attributes such as test date, well head temperature, flowing
pressure, flow line pressure, pOverZ, choke orifice size, gas oil ratio, fluid velocity,
gas potential, gas volume, pressure drawdown, gas rate, gas density etc. As a base
dataset we used gas well production tests history of JSC Vostokgazprom3 for a period
of 1998-2011 years. All data was previously loaded from papers to database and
verified by experts. Whole dataset is 3528 results of well tests. Only 2319 of dataset
were classified by experts as valid and 1209 as invalid. For this dataset missing values
in well tests attributive part are common so model should consider them correctly.
Modeling. There are several types of classification methods such as clustering, neural
networks, decision trees and others. Comparison of different modeling methods is out
of the scope of this article. The article shows practical benefit of data mining
application in MES level of oil and gas production company. To generate a statistical
model for well test results Microsoft Decision Trees algorithm [16] algorithm was
used. As result we have got a tree-structured model shown on Figure 3.
Fig. 3. Tree-structured model of well test validity
Values in circles mean amount of well tests estimated as valid (in percent). This
model shows that hypothesis about well test estimation can be successfully done
basing on gas rate and pressure drawdown is invalid on current dataset because
3
www.vostokgazprom.ru
176
S. Bogdan, A. Kudinov, and N. Markov
obtained classifiers are annulus pressure, bottom hole temperature pressure drawdown
and stratum type. According to proposed iMES framework tree-structured data model
should be implemented within analytic module on appropriate level.
Evaluation and Deployment. 82% of well test results were correctly classified by
model. It is better than 75% stated as criteria above (other not boosted tree algorithms
did not showed a significantly better accuracy). So this model can be used for preestimation of well test results validity and help geologist to make decision.
From user side workflow of well test validation is very easy: every new well test
substitutes from user interface and shows model based validity estimation. Geologist
can agree or disagree with this recommendation. Each disagreement causes model
recalculation.
Further experience. Shown well test classification generously based on manually
inputted data through user interface, but using iMES conception specialists can easily
mix such manual data and automatically gathered data. A practical example of it is
using iMES to identify features of defective equipment.
From time to time on field automation marks sensor measured value as "bad".
There are lots of possible reasons for this. Using data mining we can find features that
can help experts to solve the problem of "bad" data. Automatically gathered data from
thousands of sensors on fields of Vostokgazprom were processed on data preparation
level of iMES and loaded on warehouse level (Fig. 1). Then were selected data
sources of the most frequent "bad" marked values toward all data (gathering such
statistics is resource-intensive for OLTP systems but easy for OLAP systems). After
that obtained processed data was mixed with static data describing data sources on
data mart level of iMES. Using Microsoft SQL Server Analysis Services data mining
algorithms several mining models were built. As a result of subsequent querying
general features of defective data sources were found. Analysis shown that defective
data source is usually a pressure sensor set on pump of exact type, produced in 19982000 periods by a single manufacturer and installed outdoors. This mined knowledge
saved resources of specialists to find the reason of "bad" data, and gave them useful
analytical data to replace defective equipment. These samples show that iMES
conception can be successfully used in oil and gas and possibly on other industries.
Today most of organizational units in Vostokgazprom are involved in MES
context, so deployment of this model should be made in tight connection with MES
“Magistral-Vostok”4 implemented here. Before deployment of our model MES
“Magistral-Vostok” was intellectualized as describe above. It allowed: to consolidate
needed data for our model, to deploy our model on appropriate level of iMES between
other possible data mining models and analytic functions, to make needed feedback
for continuous model improvement, to easily use results of automatic well tests
estimation at MES client-side etc. MES “Magistral-Vostok” made using Microsoft
technologies (Microsoft SQL Server). Success of this solution has been proven by
practical usage in Vostokgazprom for a several years [17].
4
mes-magistral.ru
Manufacturing Execution Systems Intellectualization: Oil and Gas Implementation Sample 177
4 Conclusion
MES play a huge role in modern industry management. Despite the rapid evolution of
MES conception, classical MESA-11 [12] is a common architecture for such systems.
We stand for future intellectualization of MES. There are samples of IMS
implementation in industry, but there are no actual frameworks which can be easily
applied for oil and gas industry. We propose the meta-model driven approach to MES
intellectualization and framework to design iMES. Shown example of MES
intellectualization for oil and gas production company and its practical benefits let us
assume that analogous approach can be implemented almost everywhere where MES is
suitable.
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