National Instrument 51-101 (NI 51-101) Reserves Reconciliation-Part 1
Bibliographic record
Abstract
Introduction Because of the uncertainty in estimating oil and gas reserves, the exact quantity of hydrocarbons to be recovered will never be known until production reaches the economic limit and the reservoir is abandoned. It is, however, necessary from time to time to estimate the reserves to be recovered throughout the producing life of a field, and to verify that the reserves are properly estimated and classified according to the definitions and guidelines specified in the Canadian Oil and Gas Evaluation Handbook, "COGEH." The testing procedure that is used is referred to as a "Reserves Reconciliation" and should be performed at least annually or more often if there are material changes to reserves estimates. A reserves reconciliation is the single most powerful tool in tracking changes in oil and gas reserves estimates. The process keeps track of the flow of reserves into and out of a company's reserves inventory. Reserves can be added to inventory by:Discovery of new reservoirs;Extensions of existing reservoirs;Installation of improved recovery schemes;Infill drilling projects; and,Acquisitions of reserves. Reserves can be deducted from inventory by:Dispositions of reserves; and,Production of reserves. Reserves can fluctuate within inventory because of:Economic changes; and,Technical revisions. Changing economic conditions may cause either additions or reductions to reserves as economic perceptions of the future change. Technical revisions occur due to estimation procedures, resulting from moving reserves from one classification to another, obtaining new information, and, unfortunately, due to poor geological and engineering reserves estimation practices. Technical revisions form the main focus of this paper. Basic Concepts of Reserves Definitions Before a reserves reconciliation can be performed, an evaluator must have a sound knowledge of reserves definitions and their TABLE 1: Reserves classifications and notations. Available In Full Paper. application. As detailed in the COGEH, reserves estimates are classified according to the following criteria:Certainty or uncertainty (Proved, Probable, Possible);Production status (Producing, Non-Producing); and,Development stage (Developed, Undeveloped). These classifications are summarized in Table 1 along with generally accepted notations for each classification. The objective of estimating reserves is to establish the "best estimate" of the reserves to be recovered which, according to the COGEH definitions, is the proved + probable (2P) reserves. The proved (1P) and proved + probable + possible (3P) reserves estimates are conservative and optimistic estimates, respectively, which provide a measure of the central tendency about the 2P reserves estimate. When there is a wide range between the 1P and 3P reserves estimates, this indicates a high degree of uncertainty. When the range is very small, there is a low degree of uncertainty. Generally, a high degree of uncertainty exists in the early stages of development of an entity, and this uncertainty decreases (i.e., certainty increases) with time as more and more information becomes available, as illustrated in Figure 1.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.163 | 0.111 |
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".