The Energy Transition, lessons learned from other heavy industries and the opportunities they present for Oil and Gas Operators demonstrated by associated case studies
Bibliographic record
Abstract
Abstract The Oil and Gas industry today faces the ȢEnergy Trilemma,Ȣ that is satisfying the growing global demand for energy, in conjunction with increasing societal pressure to decarbonise whilst also reducing costs. The decarbonisation of Oil and Gas assets is often perceived to be a capital-intensive process, which will make operations more difficult and impact profitability. Whilst this may be true for the more aggressive/ambitious mitigation schemes, there are solutions that can significantly improve the bottom line. Many of these solutions can be easily implemented, without significant disruption, and result present material GHG reductions. This paper highlights the opportunities for Oil and Gas operators to identify, fund, and execute energy transition projects that have successfully decarbonised assets. The decarbonisation methodology builds on lessons learned in identifying low carbon transition pathways for other high emitting industries. The process begins with a framework and evaluation model to assess a wide set of potential carbon reduction technologies that Oil and Gas companies can use to achieve carbon reduction. The key evaluation and prioritisation tool is the marginal abatement model which incorporates low carbon transition scenario planning with extended functionality aimed at providing insights to successfully achieve the targeted reduction and the potential impact of these scenarios on future financial performance. Following the evaluation and prioritisation methodology, this paper will review two decarbonisation case studies that have identified positive cashflow outcomes. The first is the application of a hybrid energy system installed at a remote onshore site to reduce reliance on diesel. The second considers reductions in the cold venting operations on a complex offshore facility to reduce fugitive emissions. The first case study demonstrates how an energy transition programme resulted in the phased delivery of a complete hybrid energy system which integrated wind power, diesel generation, and several energy storage systems including hydrogen electrolysis, storage and fuels cells, as well as lithium ion batteries and flywheel technology, all managed by a custom microgrid controller to power this remote production site whilst reducing GHG emissions. This case study shows how experience and investment in another industry can be exploited in the Oil and Gas industry. The lessons from the first phase were applied to make the second phase more economic, resulting in significant operating cost savings and the reduction in GHG emissions is 10,530 tCO2-eq per annum. The second case study offers an approach to decarbonisation which can be applied more generally in the context of operational efficiency. The ease with which the project can be executed was also assessed to ensure minimum operational downtime during the implementation phase. Our paper concludes that energy transition initiatives, if approached by combining deep techno-economical expertise, coupled with the experience from a wide range of industries, can provide attractive commercial opportunities for upstream and midstream operators. These projects whist meeting decarbonisation goals also make suitable candidates for emerging energy transition financing initiatives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".