Upstream Data Quality Management at MCA Chevron

Transcription

1 Upstream Data Quality Management at MCA Chevron A DQM Factory Model Delivers Scalable Results Edgar Dias Tech Computing MCA, Chevron PPDM Houston Forum Houston, TX Mar 8th 2011 Chevron USA Inc All Rights Reserved

2 Speaker Bio Edgar Dias, Tech Computing Supervisor Mid-Continent Alaska Business Unit Chevron, USA 25 yrs experience in Upstream Oil and Gas business as a Reservoir Engineer and Field Service Manager. Currently manages Technical Computing services at MCA, Chevron. Previous assignments: o o VP-IT Operations, B2B e-procurement services, hubwoo Business Development Manager, Production Information Services, Schlumberger. Masters in Petroleum Engineering, Stanford University, California Member SPE

3 Agenda Mid-Continent Alaska Business Unit Brief Business drivers and opportunities for Data Quality Management Challenges The DQM Factory Model Solution for Upstream Lessons learned Partner Recognition Questions?

4 Mid-Continent Alaska Business Unit Brief Headquartered in Houston, Texas, with major offices in Midland, Texas, and Anchorage, Alaska, MCA produces approx 300,000 BOED. The business unit operates crude oil and natural gasproducing assets in eight states, and is also involved in numerous non-operated joint ventures throughout the U.S. Reservoir types and production mechanisms vary from artificial lift and CO2 recovery in the Permian, tight gas and oil-shale in Colorado, oil and gas assets in Alaska and Wyoming, and tight oil and coal-bed methane. Historical mergers and acquisitions include Gulf Oil, Texaco, Unocal and PURE and include a variety of reservoir data types and historical data legacies. Several multiple initiatives currently aimed at transforming brownfield operations into the oil and gas fields of the future.

5 Business drivers and Opportunities for DQM Safety and Environmental Stewardship o Timely access to accurate information reduces hazards and promotes accident free operations Operational Efficiency Improvements o Reduces Time Spent by Geoscientists Looking for Information increases productivity Financial Competitiveness o o Variable pricing and tight margins requires effective decisions. Accurate information improves the accuracy of underlying analysis. Organizational Capability o Re-establish a culture of ownership of data quality and foster IT stewardship Leverage Offshore Partnership Strengths

6 Challenges Large Data Volumes stored in diverse decentralized systems Legacy data of varying quality marked by changes in ownership Lengthy projects (investments) needed to consolidate, validate and clean large data volumes Decentralized Operations need fit-for-purpose solutions Structured / Unstructured Data Synchronization Newer Higher frequency and higher density data Integrating new processes with/within older legacy systems Shortage of Subject Matter Experts to Guide Standardization

7 The DQM Factory: Business Process Change Model Logical Process Model Physical Process Model Tools Process Logical Data Model Physical Data Model Data 7

8 The DQM Factory: Design to Production to Support B U S I N E S S Logical Physical Process Model Process Model Logical Physical Data Model Data Model IT Program Data Factory Support Support Model B U S I N E S S Project Manager Business Analyst Data Analyst Data Admin Process Analyst Data Loaders Technologist BU Data Application Custodian, Analyst DBA Systems Analysts SME Project DRB Data DRB BU Owner Asset Mgr 8

9 The DQM Factory: Process Governance Sponsor/DE Senior Manager Sponsor Senior Manager Data Champion IT Support Department Supervisor Data Custodian Process Champion Data 1 Process Champion Data 2 Process Champion Data 3 Process Champion ### Data Factory Data Owners Data Owners Data Owners Data Owners 9

10 The DQM Factory: Data Quality Rules Catalog

11 The DQM Factory: Quality Tracking

12 Lessons Learned Data Quality is NEVER going to be someone else s responsibility to fix. Data Quality Management requires joint effort between Business and IT The Factory Model allows Business to own Data Quality And lets IT manage and steward the Process Establish appropriate quality metrics to help extract business value One size does not fit all. Executive Management commitment helps prioritize efforts Process Governance is a key component in sustaining efforts to maintain quality. Leverage partnerships to execute from low-cost geography offshore. The Factory Model provides excellent scalability of Organizational Capability

13 Partner Recognition Part of Mahindra Group 6 decades of Excellence Revenue of US $7.1 billion 110,000+ Associates Operations in 79 countries Mahindra Satyam Presence in 35 countries 28,000 + Associates 360+ global customers and 75+ Global Fortune 500 customers 13

14 Questions 14