Investigation of an Unexpected Flow Event for a Duvernay Artificial Lift System and Optimization for Future Installation
Bibliographic record
Abstract
Abstract This paper summarizes the work of a client project investigating an "unexpected flow phenomenon" observed for an operational downhole jet pump installed in a deep, high-pressure, light hydrocarbon-condensate well in the Duvernay region of Western Alberta. The jet pump appeared to allow the well to produce multiphase condensate, water, and Non-Condensable Gases (NCGs) without the injection of power fluid. Due to a lack of understanding of the cause of this phenomenon, the well production rate could not be predicted for future installations. The objective of this work was to understand the underlying mechanisms causing this flow occurrence, and subsequently use the findings to optimize the artificial lift pump without the use of the injection system. This study was structured into three tasks: creating a custom fluid model for the Duvernay well; importing the custom model into Computational Fluid Dynamics (CFD) simulations to model the flow through the jet pump; and verifying the accuracy of the simulations with a coupled wellbore analysis of the Duvernay system. The fluid model was created with an in-depth Pressure-Volume-Temperature (PVT) analysis using the chemical composition of the production fluid determined from field samples. The Duvernay fluid model consisted of density, viscosity, and specific heat relationships as a function of the local temperature and pressure. The fluid model was implemented into three-dimensional (3D) multiphase flow CFD simulations of the existing jet pump to characterize the flow. The results showed that the jet pump nozzle created a sharp pressure drop triggering the hydrocarbon mixture to flash from a supercritical fluid phase to a gas-liquid mixture resulting in a gas-lift effect that produced flow to surface. A one-dimensional (1D) radial wellbore analysis was conducted for a large range of production flow rates at the current field wellhead pressure to generate a well outflow curve. The discharge pressure of the CFD results was compared to the wellbore pressure at the pump depth to verify the results of the simulations. Using further CFD simulations, the pump was optimized by changing the nozzle and diffuser designs to reduce downstream turbulence and improve discharge pressure recovery. Lastly, a parametric study was conducted using CFD for multiple mass flow rates and nozzle diameters to create a semi–empirical model to predict production rates for any given pump size. This model was used for a second case study to test feasibility in future Montney region wells, in which optimal pump specifications were sized based on the region's downhole properties. The results of this case study showed that this new optimized pump has the capability to produce flow rates in wells that do not naturally produce condensate, with a predictive model having been developed to choose the ideal pump geometry specifications to maximize the outflow.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".