Bibliographic record
Abstract
Abstract The value of oil, natural gas, and by-product reserves varies significantly across the Western Canadian Sedimentary Basin. The differences in composition of crude oil, natural gas, and associated by-products result in differences in the values of the reserves. The variation in the capital and operating costs necessary to develop and maintain production also impacts the values. Contrasting provincial and freehold royalties, compounded by federal and provincial income taxes, further add to the differences. All of these factors must be considered when evaluating reserves -beware of short cuts. This paper summarizes selected evaluations of the values of oil and gas reserves obtained from Sproule Associates Limited's database of its most recent year-end evaluation work. These data are grouped according to province, geological setting, type of reserve, cost of recovery, and operational procedure. The information lends itself to establishing several profitability and benchmarking indices, all of which are presented. These indices should be very valuable to all stakeholders involved in the oil and gas industry. Introduction Every year, particularly during the winter months, engineers and geologists prepare estimates and evaluations of oil and gas reserves. Companies undertake this work internally using their own technical staff and/or externally using independent consulting firms. The evaluations are prepared for all types of entrepreneurs and companies involved in producing oil, gas, and by-products, including mineral owners (freehold, provincial, and federal), oil and gas producers (individual entrepreneurs and minor and major companies), and government agencies (provincial, state, and federal). The evaluations are used for corporate reserves management, acquisition and divestment, equity financing, lending and borrowing, estate settlement, regulatory control, and litigation. The evaluation procedure is universal for establishing the value of upstream oil and gas assets, thus, everyone engaged in oil and gas activities relies on this information. Technical professionals require evaluations for planning and development of oil and gas fields and transportation facilities. Financial officers use them for establishing value and making business decisions. Accountants require them when auditing the financial statements of oil and gas companies. Bankers set their lending value on independent evaluations. Securities Commissions and Stock Exchanges require evaluations to regulate filings in the equity markets. Because of the accelerated business in the oil and gas industry in the past 20 years, the demand for independent evaluation services has increased significantly. As a result, these independents have created large databases of reserve estimates and financial information. This paper presents the results of a study undertaken by several staff members of Sproule Associates Limited to develop unit values of reserves across the Western Canadian Sedimentary Basin using Sproule's database that was created during the 1997 – 1998 evaluation season. In addition to using this information for Sproule's internal uses, this paper makes the data available to the industry. There are many dangers in using this type of financial information, expressed as a unit of reserves basis; but, if the process is understood, it can be very valuable. The advantages and disadvantages of using these data are discussed in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".