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

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

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

Our Focus 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? Workflows to be Considered • Screening • Laboratory • Geology • Simulation • Field Trial • Production

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

An Ontology Is Often Just the Beginning Declare Databases structure Ontologies Knowledge bases Provide domain description Domain- independent Software Problem- agents applications solving methods “Ontology Development 101: A Guide to Creating Your First Ontology” by Natalya F. Noy and Deborah L. McGuinness

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

EOR Screening Ontology Pilot

Depth Limitations...

Permeability Guides...

Preferred Oil Viscosity Ranges... Oil Viscosity - Centipoise at Reservoir Conditions 0.1 1 10 100 1000 10000 100000 1000000 EOR Method Hydrocarbon- Very More Good Good Difficult Miscible Nitrogen and More Good Difficult Flue Gas Very More CO 2 Flooding Good Good Difficult Surfactant/ Very Good Fair Not Feasible Polymer Difficult Polymer Good Fair Difficult Not Feasible Very Alkaline Good Fair Not Feasible Difficult May Not Be Fire Flood Good Not Feasible Possible (Can Be Steam Drive Good Waterflooded)

Partial TORIS Data Base

Protégé Depth EOR Methods Reservoir Permeability Individual EOR Individual Methods Reservoirs Oil Viscosity TORIS Protégé Rules Editor Data Base Rules hasEORMethod Protégé Expert System Shell

EOR Screening Ontology Pilot – Summary • Use of SWRL. • Use of Expert System Engine (JESS) • Large numbers of reservoirs screened at once • Relatively simple structure in ontology

Surfactant Selection Workflow

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Workflow Driven Ontologies (WDO) Leonardo Salayandía, University of Texas at El Paso

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

EOR General Ontology with Chemicals

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

EOR Simplified Recovery Calculation Ontology

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Perm eability Guides... Preferred Oil Viscosity Ranges... Depth Lim itations... Oil Viscosity - Centipoise at Reservoir Conditions Oil Viscosity - Centipoise at Reservoir Conditions 0.1 0.1 0.1 1 1 1 10 10 10 100 100 100 1000 1000 1000 10000 10000 10000 100000 100000 100000 1000000 1000000 1000000 EOR Method EOR Method EOR Method Hydrocarbon Hydrocarbon Hydrocarbon Very Very More More Good Good -Miscible -Miscible -Miscible Good Good Difficult Difficult Nitrogen and Nitrogen and Nitrogen and More More Good Good Difficult Difficult Flue Gas Flue Gas Flue Gas Very Very More More CO 2 Flooding CO 2 Flooding CO 2 Flooding Good Good Good Good Difficult Difficult Surfactant/ Surfactant/ Surfactant/ Very Very Good Good Fair Fair Not Feasible Not Feasible Polymer Polymer Polymer Difficult Difficult Polymer Polymer Polymer Good Good Fair Fair Difficult Difficult Not Feasible Not Feasible Very Very Alkaline Alkaline Alkaline Good Good Fair Fair Not Feasible Not Feasible Difficult Difficult May Not Be May Not Be Fire Flood Fire Flood Fire Flood Good Good Not Feasible Not Feasible Possible Possible (Can Be (Can Be Steam Drive Steam Drive Steam Drive Good Good Waterflooded) Waterflooded)

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

Scale-Up Uncertainty Ontology

Physical scale EOR Motivation Uncertainty in Scale up Experimental scale

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

Example of Instances in the Ontology

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

EOR Ontology: Risk Based Decision Making Pilot

Portfolio Decisions Estimate the value of implementing sensors in four different advanced hydrocarbon recovery scenarios. Mature Onshore Deepwater Tight Gas Heavy Oil Unfractured Unfractured Fractured Fractured Side Side Frac Frac Frac Frac Frac Frac Top Top Radial Radial Linear Linear

Decision Tree Initial Prod. Decline Outcome Prob. Mature Reservoir Rate (bbl/D) Rate (%/yr) (MM$/pattern) 5 0.0095 1.33 25 15 0.0005 1.02 Continue WF 5 0.9405 0.120 0.129MM$ 5 15 0.0495 0.058 No Sensor 5 0.25 0.599 0.234 MM$ 15.6 15 0.25 0.405 CO 2 Flood 5 0.475 -0.0306 0.234 MM$ 5.2 0.025 -0.095 15 VoS=0.384-0.234=0.15 MM$ 5 0.04816 1.350 25 15 0.15291 1.039 Continue WF 5 0.7574 0.138 0.332 MM$ 5 15 0.0416 0.0765 Sensor 5 0.3975 0.634 0.384 MM$ 15.6 15 0.30 0.440 CO 2 Flood 5 0.29 -0.0040 0.384 MM$ 5.2 0.0125 -0.061 15

Framework of Classes

Mature Reservoir Instances

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

Some Tentative Visions

Generic Petroleum EOR Workflow Screening Ontology Ontology EOR Polymer •Data Workflow •Method Ontology EOR Surfactant •Product Salinity Workflow Scan Ontology EOR CO2 Flooding Workflow Generic Laboratory EOR Surfactant Ontology Workflow Core Laboratory Workflow Flood Generic Geologic Workflow EOR Surfactant Data IRSS Simulation Mining UTCHEM Generic Simulation Workflow Forecasting Workflow EOR Surfactant Field Trial Generic Field Trial Workflow Workflow Data Base VOI EOR Surfactant Generic Operations Operations Workflow Workflow A Vision for an Ontology-Based EOR Intelligent Decision Support System