OutlineOutline

• Terminology– Typical Expert System– Typical Decision Support System– Techniques Taken From Management

Science and Artificial Intelligence

• Overall Project– Project Evolution -- Synthesis of

Techniques– System Diagram– Ultimate Goal for System Outputs

Outline (continued)Outline (continued)

• Plan for First Prototype of Automated System– Immediate Objective for Prototype Automated

System– System Flow Diagram– A Possible Starting Point

• Basis for Storage Injection/Withdrawal Model Computation

• Monthly Plan for Supply Selection• Rules for Monthly Injection Computation• Rules for Monthly Supply Selection• Rules for Monthly Withdrawal Computation

– Discussion Agenda -- Input from Team of Experts

• Next Steps

Typical Expert SystemTypical Expert System

• Accumulates knowledge, including tricks

• Codifies expert knowledge, often in the form of rules

• Makes the expertise available, even when the expert is not, by emulating the decision-making ability of the human expert

• Performs (at best) as well as the human expert that it emulates, but cannot go beyond the knowledge that was gathered

Typical Decision Support SystemTypical Decision Support System

• Assists managers in their decision processes about semi-structured tasks

• Supports managerial judgment by providing a smorgasbord of analytical tools and models

• Seeks to improve the effectiveness of decision-making and to generate better solutions than are currently in use

• Helps managers respond to novel or unanticipated situations

Techniques Taken from Management Techniques Taken from Management Science and Artificial IntelligenceScience and Artificial Intelligence

• Linear Programming (LP)

• Heuristic Search

• Pattern Recognition

• Machine Learning

As our project evolves, we find As our project evolves, we find that it needs a synthesis of that it needs a synthesis of techniquestechniques

• Gathers knowledge from multiple experts

• Uses rules to simulate the decisions made in managing gas sources for the pipeline

• Tries more possible solutions than are possible to evaluate by hand– Not an exhaustive search– Guided by heuristics from human experts

• Uses machine learning techniques to try to improve the rules

Knowledge-Based Application Development ProjectKnowledge-Based Application Development Project

WeatherIndustrialDemand

SystemFailures

UNCONTROLLED EVENTS

- Supply- Pipeline- Burnertip- Interconnect

Contracts - Take-or-Pay - Recoup - Tests for Deliverability

Regulations - Ratable

Physical System Capacity - Maximum Limits (e.g., MAOP, Well Deliverability, Injection, Withdrawal) - Minimum Required - Transients

CONSTRAINTS

Storage

Well & Pipeline Supply

Transportation Imbalance

Demand Curtailment

High Reliability of Service

Lower Gas Cost

GAS SOURCESOBJECTIVE

Ultimate Goal for System Ultimate Goal for System OutputsOutputs• Create monthly plans for selection of

gas supply that will minimize WACOG, while maintaining a high reliability of service and meeting contractual and regulatory requirements

– Consider the yearly cycle when developing the monthly plans

• Support replanning on a real-time basis in response to changing circumstances during the month

Immediate Objective for Prototype Immediate Objective for Prototype Automated SystemAutomated System

• Create monthly* plans for selection of gas supply that will result in a lower WACOG

* Generate plans for winter heating season only

• Use rules that assure meeting contractual and regulatory requirements

• Present information that allows managers to appraise the level of risk associated with each plan

First Prototype of Automated System First Prototype of Automated System --

System Flow DiagramSystem Flow Diagram

Monthly Plans

Rule-BasedExpertSystem

Budgeted Demand

WeatherProfiles

Weather-Driven

Demand

WACOGModel

Multiple

Scenarios

WACOG/RiskProfile

Basis for Storage Basis for Storage Injection/Withdrawal Model Injection/Withdrawal Model Computation - A Starting PointComputation - A Starting Point

• Weather profile for each calendar month

• Need to add electric generation usage later

• Sample table from academic paper– Shows only December– Based only on estimates, not analysis– Consider this a starting point

Basis for Storage Injection/Withdrawal Basis for Storage Injection/Withdrawal Model Computation - A Starting PointModel Computation - A Starting Point

December Weather Profile*

Number of Days Warmer Normal Colder

To Inject (> 60° F) 12 6 2

To Be Shut In(45° F < T < 60° F)

12 8 5

To Withdraw Secondary(35° F < T < 45° F)

7 12 12

To Withdraw Primary &Secondary (< 35° F)

0 5 12

31 31 31

* Sample Taken From Academic Paper

Rules for Monthly Supply Selection - Rules for Monthly Supply Selection - A Starting PointA Starting Point

1 Add casinghead and exempt (C&E)monthly deliverability

If C&E deficit demand then curtail C&Eand stop; else go to Step 2

2 Add service agreement minimums(SAM)

If SAM deficit demand then curtail SAM(per priority list) and stop; else go to Step3

3 Add take-or-pay, ratable, and othergas well gas (TPRO) to 100%deliverability

If TPRO deficit demand then curtailTPRO and stop; else go to Step 4

4 Add firm peaking minimum (FPM)(zero from April through October)

If FPM deficit demand then curtail FPMand stop; else go to Step 5

5 Add Reata (R) to maximumdeliverability

If R deficit demand then curtail R andstop; else go to Step 6

6 Compute withdrawal amountaccording to rules shown on Page 17

Go to Step 7

7 Add new peaking until deficit demandis zero

Go to Step 8

8 After completing all 12 months, findthe minimum new peaking over the 12month plan

If new peaking is > 0 in each of the 12months, then take the smallest amountand shift this amount to new ratable, andreduce new peaking for each month bythat amount

AgendaAgenda

• Consider the architecture of the proposed model

– Granularity of models

• Discuss temperature thresholds

• Discuss translations of weather profiles to Bcf of gas–

Incremental demand by residential and commercial customers

– Storage injection and withdrawal

Next StepsNext Steps

• Further analysis of weather data

• Research historical transportation imbalances and use of storages

• Implement a very simple version of this system in CLIPS

• Compare the Possible Starting Point method to the current Operating Guidelines

Decision Support Model for Gas Expert System ProjectDecision Support Model for Gas Expert System Project

PreliminaryCalculation

Model

Rules&

Contestants

PredictedResponse ofSystem

ManualComparison

to Actual

Actual GasSupply

Deliverability Max. & Contractual Min.

Actual Weather

Validation

Decision Support Model for Gas Expert System ProjectDecision Support Model for Gas Expert System Project

PreliminaryCalculation

Model

Rules&

Contestants

PredictedResponse of

System

Gas SupplyForecast

Deliverability Max. & Contractual Min.

Current Implementation

EvaluationFunction

or “Critic”

Risk Factors &Distribution ofProbable WACOG

WeatherHistory Generate

Scenarios(Monte Carlo

Method)

Wea

ther

Scenarios

Decision Support Model for Gas Expert System ProjectDecision Support Model for Gas Expert System Project

PreliminaryCalculation

Model

PredictedResponse of

System

Gas SupplyForecast

Deliverability Max. & Contractual Min.

Partially Automate the Search for BetterRules by Using A.I. Techniques

EvaluationFunction

or “Critic”

WeatherHistory

GenerateScenarios

Wea

ther

Scenarios

ModifyRules

Rules &Contestants WACOG

& RiskFactors

GasXpert System Design OverviewGasXpert System Design Overview

Genetic Programming

• Selection

• Crossover

• Mutation

Create New

GasXpertPlans as

CLIPS Rules

Create Weather and Demand Scenarios

Expert System Control, Constraint,and Input/Output Rules

GasXpert Plan

(Supply contracts and storage capacitiesare considered fixed in this model)

EvaluatePerformance ofPlan on Given

Scenario

EvaluatePerformance of

Given Plan Across AllScenarios

• Fitness

Evaluate Perfor-

mance ofAll Plansin Popu-

lationAcross

AllScenarios