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Cognitive Reservoir Analytics

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Overview of Watson Cognitive Reservoir Analytics, highlighting how cognitive technologies are ready to address the challenges of the Oil & Gas industry and to transform practices in the industry, in face of data overload, new frontiers, workforce and skills shortage. -Renato Cerqueira, Sr. Research Manager and Technical Team, IBM Research Brazil

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Cognitive Reservoir Analytics

  1. 1. Wake up with Watson Cognitive ReservoirAnalogues Renato Cerqueira November 15, 2016
  2. 2. IBM Research Big Plays in Oil & Gas Capital Projects, Operations, Optimization Resource Characterization and Management Nanotechnology Applied to Enhanced Oil Recovery and Mining Cognitive Computing for Natural Resources (Oil & Gas) HPC Industry Cloud 2
  3. 3. Selected Use Cases 3 UC2: Basin / Reservoir Analogues Adviser UC3: Seismic Interpretation Adviser UC1: Basin / Reservoir Knowledge Base Builder UC5: Machine-learnt Well Log Analytics UC4: Capital Project Management Adviser UC6: IoT-driven Reservoir Digital Twin Decision- Making Operation & Monitoring Data Transformation Living Model Insights Actions, strategies Big Data IoT News and social media Specs and requirement s Rules and Regulations Contracts Reports Lessons Learned Capital Projects Management Adviser Image / video feeds Projects schedules Weather Risk factors TEXTUAL/UNSTRUCTUREDDATA Cognitive Computing Technologies Optimization Simulation Forecasting Look to the past to understand the project’s current situation Look to the future and identify main points of attention What can be done to avoid potential impacts? Define optimal course of action and implement measures Seismic Coherence
  4. 4. Selected Use Cases 4 UC2: Basin / Reservoir Analogues Adviser UC3: Seismic Interpretation Adviser UC1: Basin / Reservoir Knowledge Base Builder UC5: Machine-learnt Well Log Analytics UC4: Capital Project Management Adviser UC6: IoT-driven Reservoir Digital Twin Decision- Making Operation & Monitoring Data Transformation Living Model Insights Actions, strategies Big Data IoT News and social media Specs and requirement s Rules and Regulations Contracts Reports Lessons Learned Capital Projects Management Adviser Image / video feeds Projects schedules Weather Risk factors TEXTUAL/UNSTRUCTUREDDATA Cognitive Computing Technologies Optimization Simulation Forecasting Look to the past to understand the project’s current situation Look to the future and identify main points of attention What can be done to avoid potential impacts? Define optimal course of action and implement measures Seismic Coherence
  5. 5. UC 2: Cognitive Reservoir Analogues Adviser 5
  6. 6. UC 2: Cognitive Reservoir Analogues Adviser 6 Basin/ Reservoir Similarity + DeepQA Data Driven Analytics (Machine learning modeling/prediction) Data Management (Injection, Edition) Unstructured Data Sources (Technical Articles, Reports from OnePetro) RES. ID PERMEAB ILITY BASIN CODE … VISCOSIT Y POROSITY TYPE CODE R1 112.5 2-1-2 … ̶ 8 R2 63.0 2-3 … 6.5 24 R3 ̶ 1-2-4 … 12.0 18 … … … … … … R1200 75.0 2-3 … 0.75 ̶ Structured Data Sources (Known Basin/Reservoir Databases) “What is the average permeability in the F1 field?” Answers both directly based on known facts from data sources as well as extrapolated from analogues Questions about reservoir properties and analogues in natural language “What is the reservoir most similar to R7 in terms of structural properties?” “Average permeability in the F1 field is 22mD with 90% confidence.” “R11 is the reservoir most similar to R7 in terms of structural properties with similarity .78” DeepQA (enhanced to consider similarity reasoning) Knowledge Base Domain Ontology ponents nents Information Extraction Agents Reasoning Engine Knowledge Graph Query Interface
  7. 7. Cognitive Reservoir Analogues – Technical Details 7 Structural Code = 8 Area (Km2) = 20 Porosity (%) = 12 Oil Viscosity (MPa.s) = 1.1 Permeability (mD) = 22 Lithology Code = 410 ID PERMEA BILITY BASIN CODE … VISCOSITY CR1 112.5 2-1-2 … ̶ CR2 63.0 2-3 … 6.5 … … … … … CR1200 75.0 2-3 … 0.75 New Target Reservoir Known Reservoirs Databases Resulting Analogues Rank ID Similarity 1 CR354 .82 2 CR180 .82 3 CR22 .81 … … … Uncertainty Characterization Target Reservoir with predicted parameters Reservoir Analogues Toolset Data Management (injection/edition) + Data Driven Analytics (machine learning modeling/prediction) Structural Code = 8 Area (Km2) = 20 Porosity (%) = 12 Oil Viscosity (MPa.s) = ? Permeability (mD) = ? Lithology Code = ?
  8. 8. Flow Diagram of the RA method 8 Reservoir Analogues Toolset Data Management (injection/edition) + Data Driven Analytics (machine learning modeling/prediction)
  9. 9. Application Example Computing ROI of a set of reservoir targets 9 Reservoir Visual Analytics (RVA) ECONOMIC EVALUATION RESERVOIR SIMULATION FACILITIES DESIGN RESERVOIR MODELING PRODUCTION STRATEGY Asset Uncertainty Characterization Business & Market Information
  10. 10. Prototype Demo – Video (on site at event only) 10 Scenario • How can a Cognitive Reservoir Analogues (CoRA) help Jonatan, a Petroleum Engineer, make critical decision as to whether or not to go ahead with the well exploration in the Sergipe-Alagoas Basin, Brazil? You will see • How CoRA helps Jonatan calculate the oil in place by identifying and estimating important missing parameters • How CoRA points out that Jonatan needs the value of Porosity of the rock to carry out the task. • How CoRA computes and shows evidences in support of its calculations. As a result • Jonatan was able to explore and inspect analogous reservoirs in his company’s DB as well as external unstructured knowledge so as to estimate with high confidence unknown parameters of the prospect reservoir.
  11. 11. Summing up 11 Natural Language Understanding Analytics Multimodal Dialog Machine Learning Visual Analytics Uncertainty Quantification Semantic- based Integration Multimedia Feature Extraction Automatic Reasoning Knowledge Management Predictive Models Parallel Hypotheses Generation  Cognitive Computing is the use of computational learning systems to augment human cognitive capabilities and accelerate, enhance and scale human expertise to solve real world problems.  Cognitive technologies are ready to address the challenges of the Oil & Gas industry and to transform practices in the industry, in face of data overload, new frontiers, workforce and skills shortage.  Cognitive Systems can provide unprecedented gain in productivity in knowledge-intensive processes, such as exploration and production, giving geologists to tap quickly into information and draw relationships at a scale that is almost prohibitive today.  CoRA can support several different business processes, such ROI estimation of a target prospect, seismic interpretation, geological risk assessment, production planning and forecast, among others.
  12. 12. 12 Thank you.
  13. 13. 13 © IBM Corporation 2016 IBM, the IBM logo, ibm.com, and Watson are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. If these and other IBM trademarked terms are marked on their first occurrence in this information with the appropriate symbol (® or ™), these symbols indicate U.S. registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. A current list of IBM trademarks is available on the Web at “Copyright and trademark information.” • SoftLayer is a trademark or registered trademark of SoftLayer, Inc., an IBM Company. • Other company, product, and service names may be trademarks or service marks of others. • References in this publication to IBM products or services do not imply that IBM intends to make them available in all countries in which IBM operates. Trademarks and notes

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