Pipeline Route Planning for Multiphase Pipelines
Bibliographic record
Abstract
Pipelines transporting multiphase products (i.e. mixtures of liquids and gas) are common in the upstream oil and gas industry. However, there are numerous flow assurance challenges to the operation of multiphase pipelines, particularly in hilly terrain. For multi-phase pipelines the flow pattern, pressure drop, and associated liquid hold-up within the pipeline is highly dependent on the elevation profile, the gas to liquid ratio, the fluid properties, and the rate of flow. It is desirable to consider how multiphase pipelines can be routed to minimize these operational challenges. Variation from project to project and the complex nature of multi-phase flow can create challenges to developing common rules of thumb to be used in pipeline routing. There are commercially available software programs that model steady-state and transient flow conditions for multiphase flow, but these programs accept only one set of inputs for a particular routing scenario and the process of finding an optimal pipeline profile through the landscape can become tedious. Consequently, in the author’s experience, it is still common practice to develop pipeline routes for multiphase pipelines using traditional pipeline routing methods that are biased towards pipeline construction rather than operational factors. However, operational cost-savings can be realized through the application of multi-phase flow optimization in early pipeline routing and facility siting. This paper proposes an alternate method to routing multiphase pipelines using Geographic Information Systems (GIS) based design tools to simultaneously evaluate route options at a landscape level and model the hydraulic behavior of the multiphase flow to identify optimal pipeline routes that minimize the challenges related to multiphase flow. This method allows for proper consideration of the potential construction and operational challenges of multiphase pipelines to be integrated into the pipeline design and balance against various other factors which includes land use, construction methods, terrain challenges and environmental or social impacts. Considering these challenges early in the conceptual design process will help operators realize capital and operational cost savings while allowing for safer and more reliable pipeline operations. The method uses a multi-criteria approach coupled with a hydraulic model with the ability to balance the influence each factor has on the calculated “least cost path” to route options that improve the flow assurance aspects by strategically navigating the terrain while respecting the host of other factors that contribute or influence the final chosen pipeline route. Using this method, operators can be assured that where opportunities exist to improve the hydraulic performance of a pipeline through route selection, these opportunities will be presented as outputs of the model and expert judgment can be used to determine the final pipeline alignment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".