Upstream
A Benchmark study Originally prepared foR total Fina s.a. by the data room. Upstream information management is now available for purchase.
Summary and Prospectus
From the Press release
The
Data Room SARL, based near Paris, France was engaged by Total SA to perform a
benchmark study of Information Management (IM) in the upstream sector of the
world-wide oil and gas sector. The study involved 11 units of international
majors in Europe, Canada and the United States which, while operating in
different environments, shared many problems in IM. Patrick Fréchu, Director
of Research and Data Management with Total SA said "We commissioned the
Upstream Information Management Benchmark Study from the Data Room because of
their experience in the exploration and production business as well as their
expertise in data management." Fréchu went on, "The results of The
Data Room's survey have been integrated into our corporate Information
Management strategy, providing a sound basis for our decisions in the fields
of data management tool selection, investment arbitration and corporate
policy".
Digital Trap
Companies
deployed IT-intensive solution successfully in areas of established E&P
activity, but in frontier areas and New Ventures, more reliance was made of
traditional library functions. Of particular note was the position of the
participants in the business process cycle. Those having shed traditional
library management functions during reorganization reported lost business
opportunities as a result. Others, using Asset-focused business units, also
reported problems managing data beyond the life-span of an Asset. Some
companies reported running into a ‘digital trap’ whereby traditional
library functions were disbanded before adequate digital management of data
was assured.
Decentralized tools
Neil
McNaughton, Director of The Data Room commented, "What interested me most
in this study was the fact that some of the simplest ideas had the most
impact, whether this was a weekend spent indexing data from dusty cardboard
boxes prior to a bidding round or capturing a minimalist subset of information
from an asset prior to closure. In a similar vein, one of the partners had
enhanced productivity across the board, by initiating a program of home-based
training in the use of Office Automation products. In the area of New
Ventures, none of the companies deployed ‘big-iron’ IT solutions to the IM
problem. Successful companies were those that have either maintained
traditional library functions, or those in a ‘post BPR’ phase of
development, who were developing relatively light-weight solutions to
recording the corporation’s previous experience.” The success of
decentralized IT tools such as Intranets and Lotus Notes was also a
significant development, as was the extensive deployment of Geographic
Information Systems (GIS).
1
Project background and methods
7
2
Abstract
8
2.1
Management and organisation
8
2.2
The application is king.........
8
2.3
Bespoke development is widespread
8
2.4
Standard data models have had limited impact.......
8
2.5
Commercial data management products - early days
9
2.6
Unstructured data management - the digital "trap"........
9
2.7
Office Automation - inadequate tools?........
9
3
Main Data Storage and Access Technologies Used..........
11
4
Analysis
13
4.1
Organisation and sourcing of data management
13
4.2
Master Repositories
13
4.3
Finder
13
4.4
Other Repositories
14
4.5
Project Database(s)
15
4.6
Data Models....
16
4.7
Data Access technology
16
4.8
IRIS21 visibility from master data browser..
16
4.9
ArcInfo
17
4.10
Data management by data type
17
4.11
Derived and Interpreted data types
17
4.12
Standards and Formats..
17
4.13
Media
17
4.14
Rode Encapsulation
18
4.15
Information/knowledge management technologies
18
4.16
Best Practices
18
4.17
Office Automation
19
4.18
Links to Finance and Administration
19
5
Company A Interview
20
Introduction.................
20
Production
New Ventures
21
5.1
Exploration New Ventures
23
6
Company A Questionnaire
25
6.1
Data Management Function and Organisation
25
6.2
Sourcing of data management function
25
6.3
Data and information repositories employed
25
6.4
Project database
26
6.5
Standards, and formats..
26
6.6
Media
27
6.7
Data access technology
27
6.8
Data Management by data type
28
6.9
Best Practices
29
6.10
Derived and interpreted data types
29
6.11
Office Automation
30
6.12
Links to Finance & Administration
30
7
Company B Interview
31
7.1
Introduction.....
31
8
Company B Questionnaire
35
8.1
Data Management Function and Organisation
35
8.2
Sourcing of data management function
35
8.3
Data and information repositories employed
35
8.4
Project database
36
8.5
Standards, and formats..
36
8.6
Media
37
8.7
Data access technology
37
8.8
Data Management by data type
38
8.9
Best Practices
39
8.10
Derived and interpreted data types
39
8.11
Office Automation
40
8.12
Links to Finance & Administration
40
9
Company D Questionnaire
41
9.1
Data Management Function and Organisation
41
9.2
Sourcing of data management function
41
9.3
Data and information repositories employed
41
9.4
Project database
42
9.5
Standards, and formats..
42
9.6
Media
43
9.7
Data access technology
43
9.8
Data Management by data type
44
9.9
Best Practices
45
9.10
Derived and interpreted data types
45
9.11
Office Automation
46
9.12
Links to Finance & Administration
46
10
Company E Interview
47
11
Company E Questionnaire
50
11.1
Data Management Function and Organisation
50
11.2
Sourcing of data management function
50
11.3
Data and information repositories employed
50
11.4
Project database
51
11.5
Standards, and formats..
51
11.6
Media
52
11.7
Data access technology
52
11.8
Data Management by data type
53
11.9
Best Practices
55
11.10
Derived and interpreted data types
55
11.11
Office Automation
56
11.12
Links to Finance & Administration
56
12
Company F Interview
57
13
Company F Questionnaire
61
13.1
Data Management Function and Organisation
61
13.2
Sourcing of data management function
61
13.3
Data and information repositories employed
61
13.4
Project database
62
13.5
Standards, and formats..
62
13.6
Media
63
13.7
Data access technology
64
13.8
Data Management by data type
65
13.9
Best Practices
67
13.10
Derived and interpreted data types
67
13.11
Office Automation
68
13.12
Links to Finance & Administration
68
14
Company G Interview
69
15
Company G Questionnaire
72
15.1
Data Management Function and Organisation
72
15.2
Sourcing of data management function
72
15.3
Data and information repositories employed
72
15.4
Project database
73
15.5
Standards, and formats..
73
15.6
Media
74
15.7
Data access technology
74
15.8
Data Management by data type
75
15.9
Best Practices
76
15.10
Derived and interpreted data types
76
15.11
Office Automation
77
15.12
Links to Finance & Administration
77
16
Company H Interview
78
17
Company H Questionnaire
81
17.1
Data Management Function and Organisation
81
17.2
Sourcing of data management function
81
17.3
Data and information repositories employed
81
17.4
Project database
82
17.5
Standards, and formats..
82
17.6
Media
83
17.7
Data access technology
83
17.8
Data Management by data type
84
17.9
Best Practices
86
17.10
Derived and interpreted data types
86
17.11
Office Automation
87
17.12
Links to Finance & Administration
87
18
Company I Interview
88
19
Company I Questionnaire
92
19.1
Data Management Function and Organisation
92
19.2
Sourcing of data management function
92
19.3
Data and information repositories employed
92
19.4
Project database
93
19.5
Standards, and formats..
94
19.6
Media
95
19.7
Data access technology
95
19.8
Data Management by data type
97
19.9
Best Practices
98
19.10
Derived and interpreted data types
98
19.11 Office Automation 98