The Importance of Quality Data for Accountable Resources Estimation

Transcription

1 The Importance of Quality Data for Accountable Resources Estimation Improving the confidence and accountability of resource estimates through better data management. Volker Hirsinger, Mike Silva, Samantha Murray, Scott Tidemann - Petrosys Group of companies: Presented at the PPDM Perth Data Management Conference Aug 6-7th, 2014

2 The Importance of Estimated Resources Resource estimates are the ultimate statement of the value of the prospective and contingent exploration assets of an E&P company. Collate subsurface, engineering and production data Use stochastic and deterministic methods Processes vary widely across the E&P industry Sophistication is not necessarily based on size

3 Brief History of Digital Resource Estimation Stochastic resource estimation has a long history: In-house FORTRAN systems in the 1970 s Supercalc and subsequent spreadsheets from the 80 s Commercial systems from the 90 s

4 Defining Resources and Reserves SPE 2007 PRMS Maturity Classification

5 Defining Resources and Reserves - Language P1 P2 P3 P4 Target 1 Drilling Opportunity Identification Prospect Prospect Drilling Opportunity Target 2 Drilling Opportunity Type Type Target Resource Estimate Type Play Interval Alpha Target Resource Mean Target 3 Target 3 P2 DOP 1 Target 1Wildcat Target 2 TARGET 1 Play Interval Beta DOP 1 Wildcat TARGET 2 Identify drill locations and trajectories to capture one or more Targets P4 - TARGET 3 Initial 400 Initial 400 Result DOP 1 Wildcat TARGET 3 Resource Estimate Type P2 DOP 1 Wildcat TARGET 1 Initial 500 Discovery Wildcat Drilled 400 P3 DOP 1 Wildcat TARGET 1 DOP 1 Wildcat TARGET 2 Initial 300 Discovery Wildcat Drilled 300 DOP 1 Wildcat TARGET 3 Initial 900 Discovery Wildcat Drilled TARGET 2 P3 DOP 1 Wildcat TARGET 1 Initial 300 Discovery Wildcat Drilled TARGET 2 Initial 500 Initial 500 P4 - TARGET 3 Resource Mean

6 Defining Resources and Reserves SPE 2007 PRMS Maturity Classification

7 Defining Resources and Reserves Facts SPE 2007 PRMS Maturity Classification Opinions

8 Resource Estimation Relies on Many Factors A range of geological, economic and operational influences affect resource estimates.

9 Improving Quality for Data Management of Opinions To effectively manage data that is based on opinions capture background information record the change in the opinions over time encourage consistent and responsible entry of opinions. provide quality assurance

10 Qualifying Opinions Background Data To qualify opinions based on complex technical assessments, record as much of the background knowledge as is possible The structured opinion The unstructured data that was used to derive it

11 Background Data Variable Correlation Correlation defined between Porosity / Hydrocarbon Saturation Store reference material used to derive correlation.

12 Background data Resource inputs Porosity is set at a flat 20% based on knowledge of the area.

13 Background Data - Prospect General Capture Technical Notes: Mapping summary: capture images used a URL s, basic variety to textual text, of notes file justify justification references, and pool extent, references images faults etc. to qualify the prospect

14 Qualifying Opinions Business Processes Record the business processes that are followed to create an opinion: Allows certification, adds a degree of corporate endorsement Allows uncertified opinions to be excluded from formal estimates Economics Geology Engineering

15 Business Processes Enforcing Rules Once Captured is ticked, screen enforces entry of more information (mandatory entries in blue)

16 Business Processes Certifying Opinions A PEP report must be entered before setting a prospect as assured. Imposed business rule: To assure a PEP Reference: Reference needs to prospect, a reference to the company s be made under the Notes interface internal approval document (a PEP in this case) is mandatory

17 Improving Quality: Consistent Data Entry Consistency is one of the key attributes of data quality. Encourage consistency through Having well described reference values Clear user interfaces Avoiding excessively generic data models Prospect Opportunity Set Opportunity Set Type Opportunity Opportunity Type Play Interval Play Interval Target

18 The Value of Consistency Quantifying resource estimates consistently across all investment opportunities allows Accountable global roll-up of company assets Informed investment choices across geographically and geologically diverse environments?

19 Consistency Currency Completeness How do we encourage currency and completeness??

20 Visibility Encourages Currency + Completeness Visible use of estimates for corporate decisions encourages timely and diligent entry of prospect information.

21 Tracking Versions of Opinions Resource estimates are progressively changed as more data becomes available: Completion of seismic surveys Drilling of wells Alternate opinions through regional geological studies. Version management of a resource estimate is important

22 Levels of Version Control Effective understanding and control of change in resource estimates requires different approaches for different contexts of change. Corporate Asset Workflow Process DBMS Technology

23 Version Control DBMS Technology DB tools eg. transaction logging to fix IT related errors. Damage Control only! Ad hoc timing Corporate Asset Workflow Process DBMS Technology

24 Version Control Workflow Processes Generic change tracking in selected parts of the data model eg. PPDM AUDIT_HISTORY Identify workflow issues Can trigger QC actions Ad hoc timing Corporate Asset Workflow Process DBMS Technology

25 Version Control Asset / Prospect Level Applications specific change management related to individual prospect / play editing Understands prospect data structure - on change in key resource components Corporate Automatic version stamps Fine grained history of resource estimates Asset Workflow Process DBMS Technology

26 Version Control Corporate Portfolio management corporate level asset assessment. Corporate reporting cycle based Enrolls specific versions of individual prospects Locked into corporate records Corporate Corporate Asset Workflow Process DBMS Technology

27 Resource Estimates and Data Models No standards for managing resource estimates in structured databases. Adopting PPDM architectural principles helps Database to be maintainable and extensible Linking to future operational and production data Post-drill analyses Post Drill Analysis

28 Resource Data Models - Aggregation A hierarchical model enables flexible aggregation Prospect Roll up Prospect Play Interval Roll up Play Interval Play Interval Play Roll up Play (Trap Type + Reservoir Type) Play (Trap Type + Reservoir Type) Play (Trap Type + Reservoir Type) Play (Trap Type + Reservoir Type) Target Target

29 Resource Data Models - Aggregation Resource risk and economic factors need to be captured at all levels as is appropriate. $$ Prospect $$ Play Interval Play Interval $$ Play (Trap Type + Reservoir Type) Play (Trap Type + Reservoir Type) Play (Trap Type + Reservoir Type) Play (Trap Type + Reservoir Type) $$ Target Target

30 Resource Data Models : Aggregation Resource management data models should facilitate aggregation By individual play Across a basin Over the whole company

31 Better Data Management for Resource Estimation Resource estimates are opinions: track the opinion and Supporting information used to make the opinion Business processes applied to qualify the opinion Encourage a high quality of knowledge capture: Well documented user interfaces to facilitate consistency Show visible impact of opinions on investment Track changes to opinions and data in a consistent way Use a data model that reflects prospect hierarchy Facilitate linking of resource estimates to facts Drilling outcomes, reserves and production