Implementation First of a Kind Edge Computing Solution to Increase Production and Reduce Emissions at Bakken and Permian Unconventional Basins
Bibliographic record
Abstract
Abstract There is a need to reduce carbon emissions sources at production facilities, which can be addressed by employing a digital solution that optimizes the facility's operational set-points in real-time. A novel software solution was developed which can operate on a cost-effective controller at a facility. The software uses many process variables already monitored at a facility to accurately model variables that are not measured, such as the Reid Vapor Pressure (RVP). The software also computes optimized set points, such as the minimum oil temperature required to meet the RVP of the final product. The novel digital system was installed at one facility in the Bakken and two facilities in the Permian. At the first facility (Bakken), the temperature was optimized using an air cooler. At the second and third facilities in the Permian, the temperature was optimized using heaters. The temperature was optimized to minimize emissions and maximize oil shrinkage whilst ensuring that the RVP would not exceed a specified value required for custody transfer. It was found that the initial model needed calibration at all three sites. The model was calibrated using oil samples that were processed with an analyzer. The calibration method applied was API MPMS 4.2 Appendix C, whereafter the RVP predictions consistently were within a ± 0.5 psi delta compared to values measured with a sample analyzer. Once the system successfully optimized the oil temperature in the Bakken, the tank vapor combustion and oil shrinkage reduced by 40% and 0.7%, respectively. The oil temperature was successfully optimized using the heater at the second facility (Permian West), resulting in a 72% reduction of heater emissions, 28% reduction of tank vapor combustion and 0.5% reduction of oil shrinkage. At the third facility (Permian East), heater emissions were reduced by 77%, tank vapor combustion reduced with 40% and oil shrinkage reduced by 1.7%. The field trials prove that the system can be implemented successfully without cyber-security issues. The relatively low implementation cost combined with reduced oil shrinkage results in an attractive return on investment and a negative cost of carbon abatement. In other words, implementing the system is profitable whilst carbon emissions are reduced at the same time, yielding an attractive business case.
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".