Aligning Diverse Portfolio and Execution for Capital Efficiency
Bibliographic record
Abstract
Abstract Today oil and gas companies are continuously facing choices encompassing a wide range of opportunities, from unconventional to ultra-deepwater, known basins with deeper and different plays to new frontiers and untested plays in the international arena. A quick look at the market underscores the diversity of deals across the conventional and unconventional sectors, across the globe and across public and private companies. Business is further complicated with large gas resources in areas with limited local gas markets and in frontier or remote locations. Aligning a diverse portfolio with an organization's execution capabilities and capacity is an imperative to achieve capital efficiency and meet shareholder expectations. It stems from a challenge of constructing a portfolio that provides both cash flow and long term growth. There should also be a direct connection of the organization's strategy and its investment proposition; i.e. should companies mitigate risk by diversification across countries, or asset types, or play types, or alternatively, be a focused "pure-play" organization leaving the investment diversification to the investors themselves. Either way, it demands a versatile organization to create and maintain the value, and it requires innovative techniques to find a competitive edge. This manuscript is intended to provide basic background and to set the stage for a panel discussion scheduled for 9.30am- Noon, Wednesday, May 6, 2015 at the conference. After identifying the main industry trends and discussing the basics of capital efficiency, the manuscript investigates key elements of a portfolio that enables the company's pursued strategies with the focus on exploration and development initiatives. Four (4) main plays, namely, deepwater and ultra-deepwater, unconventional, oil sands and gas (that includes liquefied natural gas, coal seam gas, etc.) plays are examined as a premise for the panel discussion area. The panel will discuss how companies are handling these challenges of diversity with financial and organizational strengths while carefully selecting technology to be competitive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".