Coalbed Methane Artificial Lift Challenges from Alberta's Mannville Coal
Bibliographic record
Abstract
Abstract In developing the Doris Mannville Coal Bed Methane (CBM) gas field near Fort Assiniboine, Alberta, the Nexen exploitation team encountered many unforeseen artificial lift challenges using Electric Submersible Pumps (ESPs). High failure rates and short pump run times made forecasting of production volumes and reserves difficult, while high operating costs driven primarily by downhole servicing reduced the economic viability of the project. The root cause of the high pump failure rate was attributed in most part to the unanticipated volume of produced coal fines and reservoir solids in the produced water stream. The production of these solids, coupled with lower than expected water production rates, led to a re-evaluation of the field's artificial lift strategy. Following a thorough artificial lift evaluation, a change from an ESP to a reciprocating rod pump (Pump Jack) system was initiated. The largest anticipated benefit of reciprocating rod pumping systems over ESP artificial lift systems were: Increased minimum flow path area through the system, reducing the frequency of solids plugging, leading to longer pump run times. Reduced well servicing costs with reciprocating rod pumps through elimination of services required solely by ESPs. Elimination of pump failures due strictly to electrical shortages by changing from an electrical/mechanical (ESP) to a straight mechanical (Pump Jack) downhole system. Improved operating efficiency by matching artificial lift design with actual water production rates. The change in artificial lift strategy made an immediate impact on pump failure rates in the Doris field. This paper will present the resulting increased pump run times, sustained production rates and drastically lower operating costs that has made reciprocating rod pumping systems the artificial lift method recommended by Nexen to exploit Alberta's Mannville coal for CBM.
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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 | 0.000 | 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) | 0.003 | 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".