Field Case Studies of Downhole Electric Heating in Two Horizontal Alberta Heavy Oil Wells
Bibliographic record
Abstract
Abstract Downhole electric heating has historically been unreliable or limited to short, often vertical, well sections. Technology improvements over the past several years now allow for reliable, long length, relatively high powered, downhole electric heating suitable for extended-reach horizontal wells. The application of this downhole electric heating technology in two different horizontal cold-producing heavy oil wells in Alberta is presented. The first field case study discusses the application of electric heating in a mature, depleted field as a secondary recovery method while the second case study examines a virgin heavy oil reservoir, where cold production by artificial lift was economically challenged. The completion, installation, expected and actual results of both cases studies are compared and contrasted. Both field deployments demonstrate the benefits and efficacy of applying downhole electric heating. In the case of the mature depleted field, electric heating resulted in a 4X-5X increase in oil rate, sustained over a period of close to two years. The energy ratio of the heating value of the incremental produced oil to the injected heat was slightly over 7.0. In the virgin heavy oil field, electric heating reduced the viscosity of the oil in the wellbore from time zero, which allows for higher rates of oil production along the complete length of the long horizontal lateral at higher, if desired, bottomhole pressures than in a cold-producing well. This degree of freedom may ultimately allow for an operating policy that suppresses excessive production of dissolved gas, thereby helping conserve reservoir energy. Early production data in this field show 4X-6X higher oil rates form the heated well than from the cold-producing benchmark well in the same reservoir. Numerical simulation models, which include reactions that account for the foamy nature of the produced oil and the downhole injection of heat, have been developed and calibrated against field data. The models can be used to prescribe the range of optimal reservoir and fluid properties to select the most promising targets (fields, wells) for downhole electric heating as a production optimization method, which is crucially important in the current low oil price scenario. The same models can also be used during the execution of the project to explore optimal operating conditions and operating procedures. Downhole electric heating in long horizontal wells is now a commercially available technology that can be reliably applied as a production optimization recovery scheme in heavy oil reservoirs. Understanding the optimum reservoir conditions where the application of downhole electric heating maximizes economic benefits will assist in identifying areas of opportunity to meaningfully increase reserves and production in heavy oil reservoirs in Alberta as well as around the world.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".