Improving downhole pump efficiency and well productivity in heavy oil reservoirs utilizing back pressure regulator
Bibliographic record
Abstract
A highly unconsolidated undersaturated reservoir producing heavy oil with an API of 12.1° is located in Lindbergh Field of Elk Point area, Alberta, Canada. A specific well in this reservoir was initially designed to produce oil via a cold heavy oil production with sand (CHOPS) mechanism. However, a large amount of the sand production on a daily basis plugged the progressive cavity pump installed in the well. The cost of well services to unplug the pump on a monthly basis exceeded the revenue from produced oil, and thus, the well was considered uneconomic. Various techniques have been sought to control the sand production and to increase the cumulative oil production and the pump efficiency. Installing screens and meshes in the production interval of the wellbore was analyzed as a solution to the sand production. Installing screens increased the skin factor and resulted in a very low production rate of 0.15 m3/day. The cost of purchasing and installing screens was estimated to be approximately $87,650 with five shut-in days. In addition, the screens also needed further sand clean up, which is an expensive process. Hence, the screens were not recommended for this candidate well. A Back-pressure regulator (BPR) is currently installed on the casing of the well. The initial purpose of installing BPR on the casing was to control the wellbore pressure. The BPR restricts the flow of gas vented through the casing-tubing annulus. This study analyzes the effects of restricting flow of the vented gas such as solution gas reduction, which causes (i) higher settling velocity for the sand grain, (ii) lower Basic sediment and water (BS&W), and (iii) lower in situ oil density. The production data of candidate well obtained from AccuMap (v.18.12) shows that the production hours increased significantly after installing BPR. This is because the number of well services reduced by 90 %. This results in an approximately $34,000 per month increase in profit (assuming $30.00/barrel of oil) for each well. This shows one million dollars savings on a monthly basis when the application of the BPR installation is implemented on 30 similar wells. The cost of the BPR installed on well is $328.00, and there is no operating cost involved since the cost of additional, necessary maintenance and operation is nearly negligible. Moreover, this study provides the field examples of improper BPR operation, which resulted in economic loss. Possible solutions to fix the improper installation of BPR are proposed as well.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".