The Banff Taxonomy of Asset Valuation Methods: How Upstream Petroleum Asset Valuation has Evolved in the Past and Where it Might go in the Future
Bibliographic record
Abstract
Introduction The managers of a public corporation have a fiduciary responsibility to maximise the value of its assets, as determined by their price in the financial markets of relevance to its investors. There are a wide variety of approaches that attempt to determine which management alternative in any given commercial situation will maximise asset value. Practice in the upstream petroleum industry reflects this diversity and has evolved over time, as organisations have sought processes not only to make better choices from among alternatives for the design and management of real assets, but also to generate a better range of alternatives to consider. An important part of many of these approaches is the estimation of asset value using discounted cash-flow (DCF) methods. There has also been exploration of some other approaches to estimating asset value, some of which are known in the industry as "real options analysis" (ROA) methods. Unfortunately, there has been some confusion over the conceptual underpinnings of the various approaches to value estimation, the relationships among them and the implications of this for standards of best practice. Because of this, a group of industry managers and consultants decided to use the technical forum and workshop process of the Society of Petroleum Engineers (SPE) to organise some pre-competitive investigations of these issues. Technical fora were held in 2000 and 2002. Participants in these fora requested a workshop on topics in "advanced" economic analysis, including probabilistic DCF analysis, decision tree analysis, and real options analysis. In response to this, the SPE asked me to organised a workshop on "The Theory and Art of Asset Valuation: Building a Case of Change - Applying to the Oil and Gas Industry What Finance has Learned". The workshop was held on 15–17 September 2003 in Banff, Canada. I gathered together a steering committee, consisting of:the former CEO of two leading independent oil and gas producers;a senior manager in a major division of a large operating company;people who, as managers, have been responsible for rolling out new evaluation processes in several large operating companies;people who act, or have acted, as internal evaluation consultants at several large operating companies;practice leaders at prominent economics, decision analysis and strategy consulting firms;a former senior manager at a major industry software supplier;a former senior manager at a major industry training organisation; andleading financial economists who have consulted on valuation to the industry. One of the key outputs of the workshop was a taxonomy of valuation methods that has come to be known as "the Banff taxonomy". It was designed to support discussions to clarify some of the confusion about valuation methods that has plagued the industry over the last couple of decades. Workshop participants and others since have found it to be very useful for this purpose. Figure (1) (Available in full paper)
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".