Bridging the Data Divide;A software solution which eliminates the barriers to data accessibility providing data integration, analysis, and reporting not possible before.
Bibliographic record
Abstract
Abstract The oil and gas industry is rich in complex data types. The data for any organization is often distributed and stored in a variety of formats. It is vital, but almost impossible to share this data between and among systems, teams, functions and companies. To overcome these integration challenges, companies dedicate important resources that could be better focused on core business functions. This data exchange issue extends into data availability. Employees cannot retrieve and analyze the data that they need because it exists in other systems that they cannot access, or are not trained to use. The net result of these data sharing barriers is that decisions are often made using incomplete or incorrect information. Even when the desired data is accessible, the time required for gathering and formatting it may limit the amount of analysis that can be performed before a decision must be made. Chevron Texaco Canada Resources, Northrock Resources, Ltd, and Pioneer Natural Resources Canada Inc. have worked with aclaro softworks, inc. to develop a new web-based reporting application which bridges this data divide. The resulting application, petroLook, provides a common interface to multiple systems in the organization, allowing a richer and more complete source of data to be used for decision making. In addition to providing a window into the organization's information, the application has been designed to facilitate analysis of the data, allowing users to mix data from multiple systems, ‘slice and dice’ the available data, perform ranking between data objects, perform variance analysis between data for different time periods or from different systems, and to perform additional calculations as required. This paper will describe how the petroLook application has increased data availability and decision making ability at three case companies. Pioneer integrated economic and production forecasts, reserves, and field data capture data, which resulted in improved business process that were then re-integrated into the overall process. The application has improved the planning and budgeting cycle at Northrock, while Chevron 's implementation extended the functions of the application to calculate and report forward-looking financials. Introduction THE DATA DIVIDE The oil and gas industry is rich in software solutions for almost every phase and component of the business. Geoscientists, Landmen, Engineers, Accountants, Righands, Marketers, and Computer Scientists all have a wide variety of tools at their disposal which make it easier to do their jobs. Each of these tools has been built to meet the specific needs of a target user group. The data generated by these tools is stored in a variety of applications, databases, and formats, and can also be stored in distributed locations. As vendors add more and more features to these tools, they are generating an everincreasing amount of data. While the applications are generally designed around job function, the data they contain and generate is not so restricted. Actual operating costs for a field, which might be stored as a result in an accounting system, is an important input for an engineer who is trying to build an economic evaluation for a project in that field.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".