Well Integrity Management: Data, Visualization. Mahesh Venugopalan, Prakash Dhake PPDM: 2014 Oklahoma City Data Management Workshop 03 Jun 2014

Transcription

1 Well Integrity Management: Data, Visualization Mahesh Venugopalan, Prakash Dhake PPDM: 2014 Oklahoma City Data Management Workshop 03 Jun 2014

2 Well Integrity Management 2 application of technical, operational, and organizational solutions to reduce risk of uncontrolled release of formation fluids throughout the life cycle of a well NORSOK Standard D-010 Safety and Environment Equipment Integrity: Tubing, Casing, Annulus, Well Head / Tree Integrity Monitoring Recording and Evaluation Failure Scenarios, Risk Monitoring Remedial Measures Continuous Improvement: Assurance and Prevention Regulatory Compliance License to Operate Reputation Well Integrity Related Processes and Procedures Well Integrity Data Management: Availability, Quality, Governance Organizational Alignment and Accountability Asset Revenue and Profitability

3 Well Integrity Management is newsworthy! 3 18% of the production and injection wells suffered from some form of weakened integrity. Seven percent of these wells were shut down as a result of the problem - Petroleum Safety Authority, Norway, Aug 2013 Joint industry project to address well integrity - Energy Global

4 How Well Integrity is typically managed: A familiar scenario? 4 Record Anomaly Identify and record anomaly details, classification 1 2 Manual effort to collate data from multiple sources Repetitive entry of key data for processing: header, design Collate Data Well history, equipment, design, Well work, pressure production, etc. 3 4 Data quality across systems not cross referenced Inadequacy of data in various systems Analyze Data Use data to calculate MOASP vs. design, assess anomalies, history, production vs. capability 5 6 Lack of standard processes / roles leading to inconsistency Missing / over lapping accountability Evaluate and Manage Risks Risk classification, prioritization, decision on next steps 7 8 Risk classification / management is subjective, inconsistent Manual tracking / operations to take risk mitigation action Take Remedial Action Track Wellwork / other action to be done, notify, report 9 10 Unclear picture of the overall WI health for the leadership Significant manual effort for reporting, calculations, recording

5 Increased emphasis on safety is causing companies move towards the right side of the continuum 5 Chaotic Aware Evolving Mature Shared Well Integrity function Reactive approach / Accountability not clear defined Introduction of objectivity Multiple data Repositories Manual gathering of data Well Integrity processes defined Dedicated Well Integrity organization Custom developed system focused on particular aspect of Well Integrity (e.g. anomaly database) Partial integration among related applications Users refer multiple systems for analysis Process driven end to end Well Integrity Management System (WIMS) Evolved risk management Well Integrity process integrated with Well workover process Analytics & Value realization All key data sources integrated with WIMS A staged approach towards addressing various facets of WI will help us progress towards maturity

6 6 While the silver bullet remains elusive, we have seen a few things work well to help the progression 1 Manual effort to collate data from multiple sources Repetitive entry of key data for processing: header, design Data quality across systems not cross referenced Inadequacy of data in various systems Lack of standard processes / roles leading to inconsistency Missing / over lapping accountability Risk classification / management is subjective, inconsistent Manual tracking / operations to take risk mitigation action Unclear picture of the overall WI health for the leadership Significant manual effort for reporting, calculations, recording DATA PROCESS RISK MANAGEMENT VISUALIZATION

7 User should focus on data analysis instead of data collation 7 Data Consolidation Identify the data sources Cross references Automate data consolidation Prioritize Data Quality Assessment Improvement Ongoing model for DQ DQ value Streamlining Data inputs BENEFITS Standard Taxonomy for Anomaly classification Minimize free text entry Tag images at an anomaly level Avoid duplication Consistency Data Quality User Confidence

8 Integration with related processes for end-to-end tracking of anomaly 8 Anomaly Reporting Well Integrity Processes Operations Processes Base Management Processes Well Handover Roles and responsibilities defined by RACI Matrix Well Workover Processes Risk Identification Risk Assessment Risk Mitigation BENEFITS Accountabilities Ability to measure KPIs Standards Tracking to closure

9 Risk Management: Standardized approach will minimize subjectivity 9 Inputs by users Data from related systems Weightages and parameters Risk Algorithm Impact Score Probability Score Custom algorithm to calculate likelihood and consequences score for an anomaly Prioritization based on automatic risk rating/category calculation of an anomaly BENEFITS Objectivity Interrelationship between anomalies Standards Automation

10 Visualizations provide additional insights into data 10 What is the overall Well Integrity status? Through better visualization, we can Which area is at maximum risk due to Well Integrity issues? Which are the Top-10 wells to focus on? How many anomalies are still open? get more understanding and uncover new insights prioritize work better improve efficiency get leadership attention and buy-in How are my high producing wells doing?

11 Asset wise leadership view 11 Color: Darker the color, more is the impact High: Low: Size: Nos. of anomalies in the Asset

12 Distribution of Anomalies across Assets based on Risk Category 12 Color: Risk Category (Category 1 to 4) Size: # of anomalies

13 Ability to drill-down offers additional insights 13 Color: Production Volume (High, Med, Low) Size: 1 small square = 1 Anomaly

14 Ability to drill-down offers additional insights 14 Color: Production Category of the well (High, Med, Low)

15 15 Summary Win themes Low hanging fruits Staged approach Data quality and Data Readiness Visualize better

16 Thank You 2014 Infosys Limited, Bangalore, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change withoutnotice. Infosys acknowledges theproprietary rightsofother companies to thetrademarks, productnames and such other intellectual property rights mentioned in this document. Except asexpresslypermitted, neitherthisdocumentation noranypartofitmaybe reproduced, stored in aretrieval system, ortransmitted in anyformorbyany means, electronic, mechanical, printing, photocopying,recording orotherwise, withouttheprior permission ofinfosyslimitedand/oranynamed intellectualpropertyrightsholdersunderthisdocument.

17 Questions? Contact: Mahesh Venugopalan Associate Partner Business Consulting Prakash Dhake Principal Energy Practice