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Copyright 1999, Society of Petroleum Engineers Inc.

This paper was prepared for presentation at the 1999 SPE Asia Pacific Oil and Gas Conferenceand Exhibition held in Jakarta, Indonesia, 2022 April 1999.

This paper was selected for presentation by an SPE Program Committee following review ofinformation contained in an abstract submitted by the author(s). Contents of the paper, aspresented, have not been reviewed by the Society of Petroleum Engineers and are subject tocorrection by the author(s). The material, as presented, does not necessarily reflect any positionof the Society of Petroleum Engineers, its officers, or members. Papers presented at SPEmeetings are subject to publication review by Editorial Committees of the Society of PetroleumEngineers. Electronic reproduction, distribution, or storage of any part of this paper forcommercial purposes without the written consent of the Society of Petroleum Engineers isprohibited. Permission to reproduce in print is restricted to an abstract of not more than 300words; illustrations may not be copied. The abstract must contain conspicuous acknowledgmentof where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836,Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

AbstractTeikoku Oil Co. Ltd. (TOC) and NKK Corp. established a joint

pilot project in 1994 in order to provide pipeline application and

evaluation of NKK's gas hydraulic simulation engine

(GASTRAN) and to co-develop a Demand Forecasting Model

(DFC). When the pilot project finished in March 1997, a

commercial system, called Support Operation and Monitoring

Application of Pipeline Simulator (SMAPS), was installed inTOC's operation center.

The DFC, which is based on an artificial neural network

architecture, has several advantages for sales forecasting

especially as several dozen delivery points that have different

sales patterns are connected to the pipeline network. The results

from DFC can be easily used for scenarios in off-line simulation

to predict future pipeline situations when it is attached to the

SMAPS system. It automatically assists the pipeline operator by

reducing his workload and evaluating operation plans.

Introduction

This paper describes the development and operation of a newgas demand forecasting system and describes and evaluates the

prediction results.

The conventional statistical and analytical method ofdemand forecasting is difficult to maintain because it requires

that several models and parameters for each node (demand

point) be constantly defined and updated.

TOC wanted to develop a pipeline operation support system

based on an on-line simulator that was able to give useful

information for the routine operation of an actual pipelinenetwork and include the ability to predict demand changes that

could be put into the simulator as boundary conditions for ea

node.

The pipeline operation support system which we developincludes a demand forecasting system based on an artific

neural network model.

Pipeline SystemOutline of Pipeline Network. The main pipeline is called tTokyo Line and is about 300km long, crossing Japan fro

Niigata Prefecture on the Sea of Japan to the Pacific Ocean, ju

outside of Tokyo. In all, we have about 850km of pipelin

including the Tokyo Line. There are about 100 gas dema

points along these pipelines where gas is provided to custome

who are mostly local distributing companies (LDC). They b

gas from us and sell it to businesses and households, etc.

In 1992 we built a compressor station in Nagano Prefectu

in the middle of the Tokyo Line to increase the gas transmissi

capacity to meet increasing customer demands. However,

need to increase our capacity again and are building a new lo

pipeline next to the Tokyo Line. It will be called the NTokyo Line. The first phase of construction was completed

the end of l997. It consists of 55km of pipeline between Kubi

Niigata Prefecture and Nojiri, Nagano Prefecture. The seco

phase of construction, about 100km between Nojiri and Oiwak

Nagano Prefecture, is due for completion in 1999. The pipeli

network map is shown in Fig. 1.

Demand Pattern. As most of our customers are gas distributo

the demand greatly fluctuates on a daily basis depending on t

weather and climate conditions. Each customer operates a g

holder to accommodate any demand changes creating a time l

which causes the gas flow rate from our pipeline to be affect

by the operation and capacity of each gas holder. Therefoaccurate demand forecasting for each customer is important

ensure that the pipeline pressure and gas supply are adequate

maintained. Figure 2 shows a typical customer demand patter

Pipeline Operation. Operating conditions of the transmissi

pipeline and gas flow at all delivery points are remote

monitored by the SCADA system in the pipeline operati

center which is located in Kashiwazaki City, also in Niiga

prefecture. Natural gas is mostly supplied from gas produci

plants in the Minami-Nagaoka gas field which is operated

SPE 54348

Short Term Gas Demand Forecasting Based on Artificial Neural NetworkRitsuo Sato, NKK Corporation and Kazuyoshi Miura, Teikoku Oil Company Ltd.

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2 RITSUO SATO, KAZUYOSHI MIURA SPE 543

TOC. Pipeline operators in the operation center keep their eyes

on the balance between supply and demand.

In recent years, because of increased customer demand the

pipeline load has increased so much that the system does not

have enough linepack to accommodate for the swings in demand

in spite of a high (50 or 70 bars) MAOP (Maximum Allowable

Operation Pressure) of the pipeline. This means that not onlydoes production rate of the wells have to be carefully monitored

to meet daily demand changes but also it has become difficult to

maintain the operation pressure within an allowable range

without using hourly production rate adjustment.

In the operation center, the statistical model of daily demand

forecasting has been utilized for ten years but for the past couple

of years the operators have requested development of an hourly

forecast model to realize proper operation.

SMAPS (Support operation and MonitoringApplication of Pipeline Simulation)

Development. In order to assess the need for an off-line pipeline

simulator, we did a basic survey in 1984, developed it on our

own in 1985 when we started to use it in the Production

department for engineering research and planning. In 1990, we

bought a commercial Pipeline Simulator Software and have used

it for planning a compressor station, developing new pipelines,

and researching methods for more effective pipeline operations.

The simulator was an off-line simulator and not used by the

operation center. However, by 1993, on-line pipeline simulators

to assist pipeline operators were becoming a reality in North

America and we began to conduct a survey for it in our own

company.

In July 1994, TOC and NKK Corporation established a jointpilot project. The main purposes of the project were to provide

pipeline application and evaluation of NKKs pipeline simulator

called Gas Hydraulic Simulation Engine (GASTRAN), to co-

develop a demand forecasting model, and to develop an

integrated pipeline operation support system.

We introduced the completed system to the operation center

in February 1997 when we did a final function check and usertraining. From April 1997, we officially started to use the

software, called Support Operation and Monitoring Application

of Pipeline Simulator (SMAPS).

System Configuration. Figure 3 outlines this system, including

SMAPS. This system consists of a UNIX-based computerserver that performs the simulation and NeXT Step-based MMI

machines that display computed results and accept instructions

from operators. Each machine is connected via Ethernet,

accepts on-line input of pressure, flow, and temperature

information from the SCADA system, and accepts and submitsoperating instructions and computed results.

Outline of Functions. Figure 4 shows the relationship between

the subsystems that configure this main system. The functions

of each subsystem are described below.

PLSM creates a simulation model using (CAD-like) mou

operations.

RTS acquires measured data (e.g., pressure, flow) from ea

pipeline via the SCADA system, synchronizes it with t

real time, and executes a dynamic simulation of the ent

pipeline. RTS thus closely monitors the flow conditio

and any hardware failures throughout the entire pipelisystem, and calculates the precise amount of linepack.

PLS provides an off-line pipeline simulation that h

independent pipeline models. It provides planni

support in the case of engineering and planning nepipelines. It also provides daily operational support usi

short-term forecast calculations. The demand for ea

delivery point, the production plan of the gas producti

plant, the operational conditions of compressors, a

control valves which can all be set arbitrarily and are us

as boundary conditions for a simulation scenario. Whendemand is forecast with the present value as the start po

(i.e., operation support information) the PLS can use t

latest simulation results obtained by RTS. D/S DB is a utility that simplifies entry of the scenario in

the PLS. D/S DB can register multiple past result patterand forecast results, and arbitrarily select and use one

these patterns during PLS execution. D/S DB can also

the total demand for each group (e.g., each line) and c

automatically use the registered patterns to crea

scenarios that calculate hourly data.

Outline of Demand Forecasting System

Analyzing Demand Characteristics. Before constructing

model of the demand forecasting system (DFC), we analyz

the demand characteristics of several delivery points.This analysis used hourly demand patterns and actu

temperature data from 17 delivery points. We analyzed t

relationship between the temperature and demand chan

patterns. As a result, the demand patterns of the LDCs to

predicted by this system have the following characteristics:

The basic demand pattern of each LDC is based on t

capacity and control method of the gas holder. Since a gas holder lies between the LDC and the end user

time lag occurs and past temperature data is close

related to the demand from LDCs.

The degree of time lag changes with each gas company.

fact, even in the same company it may vary at ea

delivery point depending on the time of day and toperational method of each gas holder. Since the dema

differs depending on the LCD and the time of day and

closely related to the temperature, the temperatu

information from the previous 24 hours to the present

needed.This close relationship between time lag, temperature, a

operational method of each delivery point makes t

conventional statistical and analytical methods of predicti

very difficult to operate and update. In order to make

accurate forecasting model using the statistical and analyti

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SPE 54348 SHORT TERM GAS DEMAND FORECASTING BASED ON ARTIFICIAL NEURAL NETWORK

method, the parameters and time frame for each delivery point

must be decided, the forecasting model also has to be changed

for each delivery point, and parameters must be redefined and

updated for seasonal and long-term demand changes. This

method requires constant maintenance.

Based on the above analysis results, we researched a new

forecasting method and decided upon a neural network whichwas attracting a great deal of attention at the time.

We concluded that the neural network is suitable for this

system because of the following characteristics:

it is easy to construct a forecasting model

it has a learning function that is able to automatically set

adequate parameters for each delivery point and time

frame

it has a regular learning function that enables it to follow

seasonal and long-term demand changes

Construction of the DFC Model. We considered the following

points when constructing the demand forecasting model (DFC).

The DFC is common to all delivery points, it ismaintenance free and can learn to determine the most

suitable parameters for each point

The DFC system can estimate demand for each delivery

point on an hourly basis and provide an accuracy similar

to the existing forecasting model which was based on

daily demand

The DFC system has a sufficient forecasting range so that it

can request adjustments to the production rate at the

production plants

The DFC collects past temperature data which is important

because of the close relationship between the gas holder

time lag and the current demand

Figure 5 shows the structure of the forecasting model. Theforecasting model consists of an ordinary three-layer networkwhich adopts the back propagation method as a learning

function. The input and output layers of this model are as

follows:

[Input layer]

Hourly demand pattern from 6:00am of the previous day to5:00am of the current day

Hourly temperature pattern from 6:00am of the previous

day to 5:00am of the current day

Hourly forecast temperature pattern from 6:00am one day

to 12:00 midnight the following day

The day of the week (Friday, Saturday, Sunday, Monday,before/after a holiday, holiday, etc.)

[Output layer]

Forecast demand pattern per hour from 6:00am one day

to12:00 midnight the following day

The forecasting model is currently running at 27 delivery

points. It simplifies the learning of basic demand patterns for

each delivery point because it uses a constant 43 hour forecast

range which is updated at 6:00am each day. This ensures that

there is always enough prediction data for practical estimating

and is not too long to be unrealistic. The forecasting mod

therefore, only has to learn the latest data once a day. Howev

it constantly updates the latest actual temperature data. T

helps to lower the effects of a demand forecasting error due to

weather forecast inaccuracy, so the predicting accura

improves. After optimizing the number of the hidden layer a

learning parameters, the learning time for the 27 demand pointakes just about one hour on a Pentium 166 MHz PC system.

System Configuration. Figure 6 shows the configuration of th

system. The system connects SMAPS, SCADA systemmeteorological information service terminals via Ethernet a

can acquire on-line the past demand, current and estimat

temperatures which are necessary for learning and forecastin

The demand forecasting system runs on Windows 95. Th

system which also can be used for all demand points consists

a database that stores actual demand data for each delivepoint, estimated and actual temperature in each area along t

pipeline, and a neural network model that can learn and foreca

. The forecasting procedure is as follows.First, it uses the actual demand and temperature of t

previous month which satisfy the relationship shown in Figureso that the system learns the forecasting models for ea

delivery point. Next, the previous days demand pattern, t

previous days temperature patterns, day of the week a

forecast temperature patterns are input into the learn

forecasting model to obtain a prediction demand pattern for ea

delivery point. The obtained demand forecasting pattern feach point is checked and modified by the DFC, and deliver

as a boundary condition of the SMAPS PLS or D/S DB patte

model.

Evaluation. Before actual implementation, we selected fodifferent types of demand points from among all demand pointo be estimated. We evaluated the forecast results using the p

demand and temperature data. Figure 7 shows the forec

results of a demand point (mainly for consumer use) whose tim

lag due to the gas holder is small. As clarified in Fig. 7, t

forecast results agreed quite well with the actual results. Th

agreed with the actual results even when the peak demachanged dramatically in the morning, which is a characteristic

this demand point. Figure 8 shows the forecast results of

demand point (mainly commercial use) whose time lag due

the gas holders is large. It shows how this system follows t

differences in demand patterns of high and low temperatur

and of the differences between a weekday and the weekend.We also checked the total error range based on data collect

for about three months in the winter. We used mean absolu

percent error (MAPE) which is based the daily demand

explained in Eq. 1.

MAPE

Qd Qd

Qd

nd

forecast actual

actuali

n

(%) =

=

1

(1)

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where

Qdforecast = Total daily forecast demand

Qdactual = Total daily actual demandn = Number of samples

Table 1 shows the forecast results. Since all predicted errorsprove within 2 to 4%, this system provides enough accuracy to

be used as a boundary condition for a PLS computation in

SMAPS in order to adjust the production rate from the

production plant.

Operation of DFCOperation Procedure. Once a day at an appointed time the

operator enters a command for the DFC to routinely update and

examine the relationship between the past air temperature and

demand data and execute the learning steps in order to set

parameters for the next 43 hour prediction pattern. Therefore,

the pipeline operator can make predictions at any time. TheDFC can be operated when disconnected from SMAPS Figure 9

shows the standard operation procedure of the DFC.

State of operation. The forecast system was implemented in

April 1997 after a final evaluation the previous winter. The

following is an evaluation to date of the prediction results during

routine operation.

It should be recognized that there is larger error in the

practical operation than in the final evaluation period because of

the difference between the predicted and actual temperatures.

Figure 10 is the real display image of the hourly prediction

results during one forecast period (43 hours). It shows that the

forecast can agree with the actual demand changes. Figure 11also shows the forecast results which represent the daily value of

the total demand for a long period of time. At the beginning of

January 1997, there was a significant error because of the

Christmas and New Year holiday season. It can also be seen

that error exceeds 10 % especially on the weekend but because

the demand dramatically decreases, the absolute error isnegligent. Table 2 shows the percentage error for each month.

Although the new prediction model is calculated on a hourly

basis the sum total of these hourly errors indicates a better than

average error ratio than the previous statistical model (3%)

which is based on daily demand.

SummaryAs described above, this new forecasting system is running

successfully and is a very effective and practical tool for the

pipeline operator to obtain future demand changes. The main

advantages over conventional models are that it requires nomaintenance to keep the model running properly regardless of

the season and a single model applies to several customers.

However, there are patterns that indicate large errors after a

particular period ( e.g. holiday season) because actual data from

the previous few days influence the forecasting. We need to

modify the duration of the learning period and/or actual inp

learning data in order to rectify this situation.

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SPE 54348 SHORT TERM GAS DEMAND FORECASTING BASED ON ARTIFICIAL NEURAL NETWORK

SCADA system

M a n- M ac h in e I nt er fa c e C a lc u la ti on S er ve r

Demand Forecast PC

(Windows95)

Meteorological Information Server

Actual Demand

Actual Temperature

Temperature Forecast

Neural NetworkTemperature

Database

Demand

Database

Demand Forecast System

Planning Simulation(PLS)D/S DB

Forecast

Resul ts

SMAPS system

Fig. 6 System configuration

0

200

400

600

800

1000

1200

2/28Sun

3/01Mon

3/02Tue

3/03Wed

3/04Thu

3/05Fri

3/06Sat

3/07Sun

Day

Demand

[Nm

3/h]

-10

0

10

20

30

40

50

Temperature[C

]

Actual

Forecast

Temperature

Fig. 7 Forecast results LDC1

0

2000

4000

6000

8000

10000

12000

2/28Sun

3/01Mon

3/02Tue

3/03Wed

3/04Thu

3/05Fri

3/06Sat

3/07Sun

Day

De

mand

[Nm

3/h]

-10

0

10

20

30

40

50

Temperature[C

]

Actual

Forecast

Temperature

Fig. 8 Forecast results LDC2

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