Optimizing Field Development with Petrel-14: A Case Study of the Penobscot Field in Nova Scotia, Canada
Bibliographic record
Abstract
An optimized field development strategy at the initial stages of any oil and gas project plays a crucial role in achieving a maximized hydrocarbon recovery at minimal cost. It encompasses a broad range of disciplines, including subsurface studies, well and production technology specialists, surface facilities engineering, and economics of the project. This research aims to sketch an optimum field development strategy for the exploratory Penobscot field at offshore Nova Scotia of Canada through a comprehensive geological and geophysical study of the subsurface. Penobscot is considered in this research as it part of the public dataset provided by the Nova Scotia Offshore Petroleum Board (CNSOPB), which includes 2D/3D seismic data, well logs, interpretations, velocity models that are all freely available for academic and research use. The Repeat Formation Testing (RFT) which estimates the formation pressure, permeability, and obtains fluid samples, along with the Neutron-Porosity (NP) log which determine the porosity of a formation by measuring the hydrogen content within the formation are analysed to determine porosity, permeability, and water saturation of the reservoir. The hydrocarbon in place (HIP) is estimated based on these parameters, and the reserve volume is cross-checked with the results derived from RFT. The reservoir model building and development strategies are carried out using Petrel-14 software. Finally, based on the details of the subsurface studies three field development strategies are simulated by using an optimum number of producers and also, if required converting some producers into the injection wells for enhanced oil recovery (EOR). The three simulated development strategies of the field are ‘two producers (existing L-30 and B-41 wells)’, ‘two producers and one injector’, and ‘three producers and one injector’. It is found that out of all the field development strategy with ‘three producers and one injector’ gives an oil production of 3.335 × 106 m3, gas production of 4.592 × 109 m3, water production of 3.744 × 105 m3 with a recovery rate of 20.84%, in a cumulative period of 5 years. The petroleum profit for this field development strategy is about $ 82784897.2 which is higher than any of the other strategies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".