Towards an Ontology Driven Enhanced Oil Recovery Decision Support - - PowerPoint PPT Presentation

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Towards an Ontology Driven Enhanced Oil Recovery Decision Support System Emilio J. Nunez The University of Texas W3C Workshop on Semantic Web in Oil & Gas Industry, Houston, December 9,10, 2008 Outline Background Our Focus

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Towards an Ontology Driven Enhanced Oil Recovery Decision Support System

Emilio J. Nunez The University of Texas

W3C Workshop on Semantic Web in Oil & Gas Industry, Houston, December 9,10, 2008

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Outline

  • Background
  • Our Focus
  • Our Approach
  • Pilots
  • Some Tentative Visions
  • Next Steps
  • Acknowledgements
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Background

  • UT Expertise in Enhanced Oil Recovery
  • Knowledge in

– Professors and Students – Dissertations and Papers – Laboratory Procedures – Laboratory Data

  • Need for Integrated Approach
  • Industry needs help in Decision-Making
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Our Focus

Workflows to be Considered

  • Screening
  • Laboratory
  • Geology
  • Simulation
  • Field Trial
  • Production

Decision Making Processes in Enhanced Oil Recovery (EOR)

For a given reservoir:

  • 1. Which EOR Methods are most promising?
  • 2. What is the potential for each of the promising EOR Methods?
  • 3. What is the best design for each EOR Method to be applied?

e.g. Best Alkaline, Surfactant, Polymer (ASP) Formulation?

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Our Approach

  • Capture Knowledge
  • Focus on EOR and its Workflows
  • Build Ontology Pilots
  • Create Knowledge Base and Query

System

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An Ontology Is Often Just the Beginning

Ontologies Software agents Problem- solving methods Domain- independent applications Databases

Declare structure

Knowledge bases

Provide domain description

“Ontology Development 101: A Guide to Creating Your First Ontology” by Natalya F. Noy and Deborah L. McGuinness

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Pilots

  • EOR Screening Ontology Pilot
  • Surfactant Selection Workflow

– Expanded to EOR General Ontology with Chemicals

  • EOR Simplified Recovery Calculation Ontology

Pilot

  • Scale-Up Uncertainty in Reservoir

Characterization Pilot

  • Risk Management Ontology Pilot
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EOR Screening Ontology Pilot

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Depth Limitations...

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Permeability Guides...

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Preferred Oil Viscosity Ranges...

Hydrocarbon- Miscible Nitrogen and Flue Gas CO2 Flooding Surfactant/ Polymer Polymer Alkaline Fire Flood Steam Drive EOR Method

Very Good Very Good Good Good Good Good Good Good Good Good Fair Fair Fair More Difficult More Difficult More Difficult Very Difficult Very Difficult Difficult Not Feasible Not Feasible Not Feasible Not Feasible May Not Be Possible (Can Be Waterflooded)

Oil Viscosity - Centipoise at Reservoir Conditions

0.1 1 10 100 1000 10000 100000 1000000

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Partial TORIS Data Base

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EOR Methods Reservoir hasEORMethod Depth Oil Viscosity Permeability Rules Protégé

Protégé Rules Editor Protégé Expert System Shell

Individual EOR Methods Individual Reservoirs

TORIS Data Base

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EOR Screening Ontology Pilot – Summary

  • Use of SWRL.
  • Use of Expert System Engine (JESS)
  • Large numbers of reservoirs screened at
  • nce
  • Relatively simple structure in ontology
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Surfactant Selection Workflow

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START CONTINUE

1 of 3

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START CONTINUE

2 of 3

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3 of 3

START

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Workflow Driven Ontologies (WDO)

Leonardo Salayandía, University of Texas at El Paso

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Contains subclasses that are used to specify workflow actions and control flow. Contains subclasses used to represent primitive data concepts of a domain, as well as classes used to compose complex data constructs that are both consumed by and derived from workflow actions. Actions (Services, algorithms, application functionalities) Contains 2

  • r more

workflows Alternative outputs for a method

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EOR General Ontology with Chemicals

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Surfactant Formulation Workflow and EOR Ontology with Chemicals Pilot – Summary

  • Complex
  • Basis for Decision Support System
  • Organization of Concepts in Domain
  • Workflow-based Ontology
  • Work in progress
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EOR Simplified Recovery Calculation Ontology

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C A B D

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A

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I J

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Depth Lim itations... Preferred Oil Viscosity Ranges...

Hydrocarbon

  • Miscible

Nitrogen and Flue Gas CO2 Flooding Surfactant/ Polymer Polymer Alkaline Fire Flood Steam Drive EOR Method

Very Good Very Good Good Good Good Good Good Good Good Good Fair Fair Fair More Difficult More Difficult More Difficult Very Difficult Very Difficult Difficult Not Feasible Not Feasible Not Feasible Not Feasible May Not Be Possible (Can Be Waterflooded)

Oil Viscosity - Centipoise at Reservoir Conditions

0.1 1 10 100 1000 10000 100000 1000000

Hydrocarbon

  • Miscible

Nitrogen and Flue Gas CO2 Flooding Surfactant/ Polymer Polymer Alkaline Fire Flood Steam Drive EOR Method Hydrocarbon

  • Miscible

Nitrogen and Flue Gas CO2 Flooding Surfactant/ Polymer Polymer Alkaline Fire Flood Steam Drive EOR Method

Very Good Very Good Good Good Good Good Good Good Good Good Fair Fair Fair More Difficult More Difficult More Difficult Very Difficult Very Difficult Difficult Not Feasible Not Feasible Not Feasible Not Feasible May Not Be Possible (Can Be Waterflooded)

Oil Viscosity - Centipoise at Reservoir Conditions

0.1 1 10 100 1000 10000 100000 1000000 0.1 1 10 100 1000 10000 100000 1000000

Perm eability Guides...

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Simplified Recovery Calculation Ontology Pilot – Summary

  • Large Complex Calculation
  • Essentially one Property

– “is calculated from”

  • Errors, insights found when ontology and

CMAP created

  • Previously available only to students to

read.

  • Now available to software agents
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Scale-Up Uncertainty Ontology

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Motivation

EOR

Experimental scale

Physical scale

Uncertainty in Scale up

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Workflow

Non-Linearly Averaging – Second Porosity

1.Transform the secondary porosity to another variable space that is linearly additive 2.Normal score transform the second porosity data and compute semi-variograms Construct a licit 3D variogram model with sill standardized to be 1.0. 3.Calculations of representative elementary volume and variance of mean using the 3D point- scale variogram from Step #2. 4.Computation of up-scaled variogram via linear volume averaging. 5.Use of the up-scaled variogram from Step #4 to perform conditional simulation. 6.Backtransform simulated values to secondary porosity units scale up uncertainty

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Example of Instances in the Ontology

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Scale-Up Ontology Pilot – Summary

  • Captured Knowledge of Different Scale-Up

Methods

  • Use SQWRL to answer queries on steps

involved in particular scale-up procedure

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EOR Ontology: Risk Based Decision Making Pilot

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Side Top Unfractured Radial Side Top Unfractured Radial Frac Frac Fractured Linear Frac Frac Frac Frac Fractured Linear

Mature Onshore Deepwater Tight Gas Heavy Oil

Portfolio Decisions

Estimate the value of implementing sensors in four different advanced hydrocarbon recovery scenarios.

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Initial Prod. Rate (bbl/D) Decline Rate (%/yr)

5 15 5 15 5 15 5 15 5 15 5 15 5 15 5 15 1.33 1.02 0.120 0.058 0.599 0.405

  • 0.0306
  • 0.095

1.350 1.039 0.138 0.0765 0.634 0.440

  • 0.0040
  • 0.061

Continue WF CO2 Flood Continue WF CO2 Flood 0.129MM$ 0.234 MM$ 0.332 MM$ 0.384 MM$ Sensor No Sensor 0.234 MM$ 0.384 MM$ 25 5 15.6 5.2 25 5 15.6 5.2 0.0095 0.0005 0.9405 0.0495 0.25 0.25 0.475 0.025 0.04816 0.15291 0.7574 0.0416 0.3975 0.30 0.29 0.0125

Prob. Outcome (MM$/pattern)

Decision Tree Mature Reservoir

VoS=0.384-0.234=0.15 MM$

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Framework of Classes

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Mature Reservoir Instances

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Risk Management Ontology Pilot – Summary

  • General Risk Management Concepts
  • Specific Application
  • Captured all numbers and meanings from

published SPE paper

  • Now available to software agents
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Some Tentative Visions

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Generic Laboratory Workflow Generic Field Trial Workflow Generic Geologic Workflow Generic Simulation Workflow Generic Operations Workflow EOR Polymer Workflow Ontology EOR Surfactant Workflow Ontology Generic Petroleum Workflow Ontology

  • Data
  • Method
  • Product

EOR Screening Ontology

EOR CO2 Flooding Workflow Ontology EOR Surfactant Laboratory Workflow

Data Base Data Mining Salinity Scan Core Flood

IRSS UTCHEM

Forecasting VOI

A Vision for an Ontology-Based EOR Intelligent Decision Support System

EOR Surfactant Simulation Workflow EOR Surfactant Field Trial Workflow EOR Surfactant Operations Workflow

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Surfactants Data Base Reservoir and Oil Properties Solvents Data Base Alkalis Data Base Polymers Data Base

Lab Tests

Chemical Flood Formulation

Field Trial Transition Decision Rules Operations Simulation EOR Project

Operations Data Base Field Trial Results Data Base Simulation Results Data Base Lab Test Results Data Base

Status Forecast VOI Workflow Definition Chemical EOR Master Program Protégé API PROTEGE User Interface

EOR IDSS Architecture Vision

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Possible Queries for Decision Support System

  • What EOR Methods should be considered for this reservoir?
  • How do we calculate the oil recovery vs. time when this EOR Project is implemented?
  • What is the total porosity/permeability of the reservoir and what is their uncertainty?
  • If chemical flooding, what chemicals should be considered as candidates for

surfactants, co-surfactants, alkali, polymers, co-solvents for this particular chemical flooding project?

  • What is a rough estimate of the net present value (NPV) of this EOR Project?
  • How much uncertainty is associated with the prediction of performance in the field?
  • Given that chemicals are available and the NPV is acceptable, what is the chemical

EOR formulation that we should simulate?

  • How do we calculate the value of doing more lab work before going into production

with this EOR method?

  • Should we do a pilot test in the field?
  • How do we decide whether to skip a step in the process to accelerate production?
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Next Steps

  • Use Lessons from Pilots to Design the

Ontology – Based EOR Decision Support System.

  • Prepare Software Development Plan

including Knowledge Capture and Ontology Development

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Thanks to the Co-Authors

  • Larry W. Lake
  • Robert B. Gilbert
  • Sanjay Srinivasan
  • Fan Yang
  • Mark W. Kroncke

ALL from The University of Texas at Austin

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ACKNOWLEDGEMENTS

We Thank For Sponsoring This Work