Near-Field Exploration: From Failure to Success
Bibliographic record
Abstract
Near-Field Exploration: From Failure to Success Tim Marchant; Tim Marchant BP Search for other works by this author on: This Site Google Scholar Hamish Wilson; Hamish Wilson Paras Consulting Search for other works by this author on: This Site Google Scholar David Bamford David Bamford BP Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, September 2001. Paper Number: SPE-71428-MS https://doi.org/10.2118/71428-MS Published: September 30 2001 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Marchant, Tim, Wilson, Hamish, and David Bamford. "Near-Field Exploration: From Failure to Success." Paper presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, September 2001. doi: https://doi.org/10.2118/71428-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractDuring 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 timeTight integration between exploration and productionTight control of subsurface technical riskFocus on certain key playsThe 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 'corporate learning' is that near-field exploration in large companies can 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.IntroductionThis paper describes how BP's successful value driven, near-field exploration programme was designed and set up. The case study shows how the company built on the lessons learned from the historical failure of this type of activity in the three companies that now make up BP. A best practice near-field exploration process was designed which emphasised both capital allocation and cycle time. The process was implemented and the early indications are that it is successful.Exploration is proving to be a lever that materially contributes production rate and therefore is becoming one of the key contributors to short term production growth. This requires the exploration business units to add to production yet stay within BP's traditional tight management of exploration spend and write off - the challenge is to deliver while not allowing a degradation of performance through doing too much. Measures to maintain and improve performance include - collective ownership of portfolio outcomes, tight performance metrics and continued restriction on budget.Within three years the company has moved from a position where risk and size were the key metrics for the exploration portfolio to one in which a significant portion of the budget is now allocated on the basis of value and short term production rate. This change required a shift in the mindset of the exploration community to take part ownership of the production and value that is generated from exploration activity and not just discovered volumes. Keywords: wilson, marchant, exploration, david bamford, cycle time, programme, exploration management, society of petroleum engineers, prediction accuracy, para consulting Subjects: Asset and Portfolio Management This content is only available via PDF. 2001. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".