The Digital Government: How Well Test Data Capture is Changing the Way We Do Business
Bibliographic record
Abstract
Abstract Electronic filing of well test data has been introduced to the Alberta upstream oil and gas industry for over a year now. During that time, industry has undergone massive and sometimes extremely difficult change. However, both industry and government are just beginning to realize the long-term benefits that were initially envisioned. These benefits are extending considerably beyond electronic filing. The inevitable use of the Internet and Public Electronic Data Repositories will continue to dramatically change the face of the petroleum industry and the regulatory environment. Introduction Don Tapscottii wrote several years ago about the coming digital economy and how it would impact people and business. The ‘new“ economy will transform not only business processes but also the delivery of products and services, how the enterprise is structured, the dynamics of competition and the new rules for business success. Recently, it became very clear the new economy was coming about. During 1999, the Well Test Capture project was completed at the Alberta Energy and Utilities Board. Prior to the project's completion, the well test staff spent their time manually keying well tests into a legacy system and storing the original submission on microfiche. The well test staff was constantly fighting volumes of data, catching up on backlogs and looking for lost microfiche. Since the implementation of the system, there are now fewer well test staff, they are no longer keying in data or storing microfiche and there are no backlogs. Instead, the well test staff are evaluating submissions and working directly with industry to ensure government requirements are adequately met. Historical data is electronically recovered and issues are immediately pinpointed. One may not appreciate the relevance of the new role of well test staff, as it appears the change is just the result of a new system. However, there is a major clue to the emergence of the new economy. In addition to public availability, well test data has become digital - pressure, deliverability and fluid analyses are converted to bits in computer storage and exchanged over networks at lightning speed. The authors believe that digital well tests not only have an immediate impact on the upstream oil and gas industry but also will continue to impact the industry in the foreseeable future. The following sections examine how the industry could be impacted. DIGITAL WELL TESTS Although pressure and deliverability tests and fluid analysis have been captured at the well in some electronic format, the format was proprietary to the gauge manufacturer, wireline and well test service company, interpretation consultant or laboratory. The concept of the Well Test Capture system was to retain the original electronic format of the data. The major exception was to have the information available in a non-proprietary format where the standards were available to everyone. When the test was finally submitted to the EUB, the electronic format would automatically update legacy systems and the complete electronic format would be available for public dissemination as opposed to microfiche.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".