PETRONAS E&P Technical Data Quality Metrics Initiative Going Green with Data

Transcription

1 PETRONAS E&P Technical Data Quality s Initiative Going Green with Data Hassan B Sabirin Regulatory Compliance & Technical Assurance, Technical Data Petroleum Management Unit (PMU) PETRONAS E&P Digital Energy Conference, Kuala Lumpur: Improving E&P Subsurface Data Management 08 October 2013 Impiana Hotel, Kuala Lumpur 2013 PETROLIAM NASIONAL BERHAD (PETRONAS) Hassan B Sabirin, 8 October 2013, Page 1 All rights reserved. No part of this document may be reproduced, stored in a retrieval system or transmitted in any form or by any means (electronic, mechanical, photocopying, recording or otherwise) without the permission of the copyright owner.

2 Contents Organization Background PETRONAS EP Technical Data The role of data management in value creation What problems are we trying to solve? Data quality metrics defined Example Some basic metrics Data quality metrics high level going green roadmap The data quality metrics and standards connection Concluding remarks Hassan B Sabirin, 8 October 2013, Page 2

3 Technical Data was formed to elevate functional excellence and delivery discipline of data management PETROLEUM MANAGEMENT PETROLEUM RESOURCE EXPLORATION PETROLEUM RESOURCE DEVELOPMENT PETROLEUM OPERATIONS MANAGEMENT STRATEGIC PLANNING FINANCE & ACCOUNTS TECHNICAL DATA PSC SCM GOVERNANCE HSE Knowledge Management & Institutional Capability Planning & Delivery Regulatory Compliance & Technical Assurance Geoscience Data Project Data Petroleum Engineering Data Production & Operation Data Institutional Capability Planning & Performance Regulatory Compliance Geospatial Project Data Production Technology Facility Engineering Knowledge Management Program Excellence Technical Assurance Geophysical & Geological Application & Workflow Petrophysics Well Engineering Business Process Advisory & Solution Data Facility Management Application & Workflow Reservoir Engineering Application & Workflow Process Data Application & Workflow Data Quality s Hassan B Sabirin, 8 October 2013, Page 3

4 Technical Data Department Relationship Model PETRONAS MANAGEMENT PSCs / RSCs 3 rd Parties Government Depts Industry Standards Peer Groups TechnicalApps & Data Vendors External Data Benchmarking & Request TD EP Business Strategy ICT & Infra Data Consultancy Services Host Authority Data Requirement PETRONAS EP Business Units Consulting Services DM Services EP IT Hassan B Sabirin, 8 October 2013, Page 4

5 Background PETRONAS EP Data Dimensions ~ 1,000 2D/3D seismic surveys ~ 12,000 wells - All EP data types >766 active projects ~ 159 ventures in 25 countries ~ Average 100 wells drilled per year Measured Data > RM35 Billion investment on data acquisition Interpreted (Result) Data Subsurface understanding which has direct impact on business decisions > 1,000,000 media > 700,000 technical docs > 3,500 TB disk space >120 data bases Media & Corporate DBs Media, hardcopies and Geosamples Dimensions of PETRONAS EP Data Management >250 specialized technical applications (Multiple disciplines & related application workflows ) Tools (Technical Software Application) > RM1.2 Billion investment End Users > 400 geoscientists and engineers from all technical domains in E&P External Stakeholders Potential investors, PSCs, government agencies, universities Hassan B Sabirin, 8 October 2013, Page 5

6 Role of Data Management in Value Creation Previous studies : Geologists spend up to 60% of their time looking for & quality checking data for their projects Source / feeder databases Workflow and Standardization 2 2 Application Data Quality s Master (Reference) Data Value Loop unique master source of quality checked data 3 Re-use 1 1 Step 1 Data collation Cost management Reduce the 60% to % 1 Reduction of time for data collation and project setup Project set up 2 Project Database 2 The project space is where the big decisions are made Step 2 Publishing Approved & final Re-use Process improvement Standards & workflows Bring to acceptable quality Capture work done for re-use Value Loop Corporate Database Feeding new projects 3 Corporate Data Optimization One version of truth datasets Re-use many times 3 Step 3 Hassan B Sabirin, 8 October 2013, Page 6

7 What Problems Are We Trying to Solve? 1- Mitigate business risks created by data quality issues Financial Confidence/Satisfaction Productivity Compliance/Legal Safety Hassan B Sabirin, 8 October 2013, Page 7

8 What Problems Are We Trying to Solve? 2 - Value leakage in business Lost time due to re-checking of data Not re-using data because of trust issues Lack of awareness & enforcement on standards inconsistent business practice You can t manage what you can t measure quantification of leakage Hassan B Sabirin, 8 October 2013, Page 8

9 What Problems Are We Trying to Solve? 3 - Managing change introduced by new technology New data content may introduce a requirement to improve Data standards/guideline Data integration process Data analytics method Hassan B Sabirin, 8 October 2013, Page 9

10 Data Quality s Defined Data Quality s: A measure of data quality performance Data Quality Dimensions: Perspectives from which data quality is being looked at Business Rules: Rules that must be adhered to in order to keep the business running in good/optimum condition Hassan B Sabirin, 8 October 2013, Page 10

11 Data Quality s Defined Data Type Quality Dimension s Target Deviation Survey Business Rules: Well/wellbores with a completion date 3 months back from today must have a definitive/final deviation survey Completeness Validity Uniqueness Conformity Consistency Integrity Project A Data Quality Database B Data Quality Total Error 1 ( Total Records )% Hassan B Sabirin, 8 October 2013, Page 11

12 Examples of Data Quality s s Well measureddepth must be more or equal than true vertical depth Offshore well must have a water depth information Well Header A well deviation header must have end/bottom depth of the survey defined A well deviation header must have start/top depth of the survey defined A well deviation header must have the survey contractor/company performing the job defined A well deviation header must have the survey tool name defined A well deviation survey point must be identifiable to a deviation survey header A well deviation survey point must have the calculated dog leg defined A well deviation survey point must have the calculated vertical section defined A well deviation survey point must record the measured azimuth A well deviation survey point must record the measured inclination Borehole survey depth range Borehole survey tool identity Borehole survey measurements Hassan B Sabirin, 8 October 2013, Page 12

13 Data Quality s Aggregation Borehole Survey Business Rules Target: Drilling Database Aggregation Survey must have at /Query least azimuth, inclination, MD and TVD /Query Well Deviation Survey Survey dogleg must not be more than 10 degrees /Query /Query Hassan B Sabirin, 8 October 2013, Page 13

14 Queries, Query Sets, Targets & Aggregation Single Multiple Query Sets Query Set Query Set OPENWORKS PROJECT OPENWORKS PROJECT OPENWORKS OPENWORKSPROJECT PROJECT OPENWORKS PROJECT OPENWORKS PROJECT OPENWORKS OPENWORKSPROJECT PROJECT OTHER DataBases OTHER DataBases OTHER DataBases OTHER DataBases OTHER DataBases OTHER DataBases Multiple Target Sets Query Set OPENWORKS PROJECT OPENWORKS PROJECT OPENWORKS OPENWORKSPROJECT PROJECT OTHER DataBases OTHER DataBases OTHER DataBases A G G R E G A T I O N Single Query Set MULTIPLE QUERY SETS MULTIPLE QUERIES PROJECT DATABASE PROJECT DATABASE PROJECT DATABASE MULTIPLE TARGET SETS PROJECT DATABASE MULTIPLE TARGETS OTHER DataBases OTHER DataBases OTHER DataBases Single Hassan B Sabirin, 8 October 2013, Page eg. select well_name from well_master; PROJECT DATABASE TARGET

15 Data Quality s Aggregation to Dashboard The (Enterprise) Dashboard The (Enterprise) Data Quality Trend Busines ss Focus Regions of Interest Assets of Interest A B C D E F etc. Data Focus Databases of Interest Data of Interest Data Element of Interest Hassan B Sabirin, 8 October 2013, Page 15 15

16 Data Quality s Dashboard A B C ( Total Error )% Total Records Poor Medium Good Hassan B Sabirin, 8 October 2013, Page 16

17 Data Quality s Dashboard Hassan B Sabirin, 8 October 2013, Page 17

18 Data Quality s Tool Hassan B Sabirin, 8 October 2013, Page 18

19 Data Quality s Dashboard By Drilling Rig Hassan B Sabirin, 8 October 2013, Page 19

20 The Data Quality s Data Standards Connection Business Rules Data Standards Data Quality s measure adherence to data standards A B C Hassan B Sabirin, 8 October 2013, Page 20

21 Data Quality s- High Level Roadmap Data Quality s Infrastructure Architecture Data Quality Focal Person Top 4 data types Measure Completeness Project Quality s Measure Validity, Conformity Top 8 data types Corrective Action Measure Spatial Layers Data Ownership Roles & Responsibilities Other Areas Business Rules Data Standards Management Support Hassan B Sabirin, 8 October 2013, Page 21

22 Conclusion Data Quality s is an important part of the data management portfolio. A data quality metrics program needs to closely engage business lines Business must play a role to define the business rules that adds value to the data. Data Ownership roles and responsibilities must be clearly defined Proactive participation at the working level, and drive from the top Data Quality s results have to tie to business owners as a measure of performance Hassan B Sabirin, 8 October 2013, Page 22

23 Hassan B Sabirin, 8 October 2013, Page 23 Thank You

24 Paper Abstract People, process & technology are often touted as the 3 important components an organization must have in order to ensure that the data is managed properly and effectively. While that is true to a large degree, the existence of this tripartite in the organization does not necessarily support the sustainability of good data management practices over the longer term. Data quality today still remains as a significant challenge we face in the E&P industry. For the data we use within upstream E&P, data quality metrics, when implemented in the right databases, creates a transparency that cannot be easily achieved by any other means, and provides us with a powerful tool to highlight issues before the problem becomes too big. A proper data quality framework also address data issues in holistic way and combines with data ownerships and accountabilities to ensure the right interventions and corrections get done. This paper describes PETRONAS E&P s data quality metrics initiative that will progressively improve the quality of data used by the business and result in a traffic-light format data quality dashboard that gets more and more green. Philip Hassan Lesslar, B Sabirin, 20 th May 8 October 2013, 2013, Page Page 2 24