Near-Field Exploration: From Failure to Success
Bibliographic record
Abstract
Abstract During the late 1980's and early-mid 1990's both BP and Amoco invested large sums of money exploring close to their core producing fields in traditional ‘heartlands’ such as the North Sea, Alaska and L48. These investments were largely unsuccessful. Amoco coined the term ‘stealth exploration’ to describe the activity carried out by individual assets whose failure costs only later appeared on the corporate balance sheet as exploration write-off. The recent industry focus on short-term production has reawakened interest in near-field exploration. BP is no exception. Despite the overall corporate perception of failure, it became apparent that some business units, notably Canada Gas and Egypt Oil, were making a quiet success of near-field exploration. Therefore the company conducted a study to understand what made these BU's successful. The lessons learned were: – A focus on monetisation and cycle time – Tight integration between exploration and production – Tight control of subsurface technical risk – Focus on certain key plays The company allocated a limited amount of seed capital to test the concept of near-field exploration in five business units where BP has a dominant ownership of the regional infrastructure. This diverse group of upstream businesses has successfully managed a limited exploration programme and demonstrated that near-field exploration can be controlled and can add value through short-term production. The key conclusion to be drawn from this story of ‘corpor; learning’ is that near-field exploration in large companies c make money provided three actions are taken: Do not compromise on subsurface technical risk in the face of pressure from engineers and others attracted by the economics – an investment with a positive EMV and/or high RoR with a high technical risk is still a high-risk investment. Manage the activity as an integrated part of the production asset to minimise cycle time. Set tight performance metrics and manage the portfolio globally.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".